Browsing by Author "Fowler, Michael"

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Applications of Mathematical Models for Lithium-Ion Battery Management Systems
(University of Waterloo, 2024-12-12) Ahmed, Moin; Yu, Aiping; Fowler, Michael
Lithium-ion batteries (LIBs) are the most widely used electrochemical storage technology in contemporary electrified vehicles (such as electric vehicles (EVs)), portable consumer products, and renewable energy generation applications. While industries pertaining to LIB manufacturing are now mature, LIB also benefits from enhanced performance in terms of high energy (~300-500 WhrL-1 depending on the battery cell geometry) and power densities, long cycling life (> 1000 cycles), low memory effects, and low discharge rates. The battery cells of LIBs are commercially available in various form factors, including cylindrical, pouch, and prismatic form factors. In the above-mentioned applications, these battery cells are electrically connected in series and parallel combinations to output the desired energy and power requirements. This battery pack in term is connected to the battery management system (BMS) which serves various purposes, including (1) sensing for voltage, current, and temperature, (2) protection against extreme conditions such as excessive currents, under and high voltage limits, etc., (3) interface with the user on useful information such as charge control and range estimation and (4) Battery state estimation for performance management and diagnostics. In this thesis, research works for range estimations, early fault detection, and battery state estimations have been included. Accurate range estimates for EVs are deemed an attractive alternative to conventional internal combustion engines due to their low carbon footprint, low running cost, and higher energy efficiency. However, currently, they suffer from a lower range than conventional vehicles, which induces range anxiety for consumers. First, the EV parameters that strongly impact its range are determined using data-driven techniques. A detailed dataset of commercial EV models manufactured from 2008 to 2021 was collected through web mining. A strong correlation between battery capacity, top speed, curb weight, and acceleration with range was observed. Furthermore, machine learning algorithms were trained and tested on this dataset, with the lowest root-mean-squared error of 19.5 miles. Additionally, a simple linear relationship between the EV range and EV model, battery, and performance parameters was determined to be convenient for EV consumers. Second, the EV system-level vehicle dynamics model and a physics-based battery cell model were utilized to estimate the range for specific vehicles on custom user-specific drive cycles. For this purpose, two types of commercially available battery cells that are utilized in various EV models were selected. While most of the battery cell’s electrochemical parameters from prior literature were used, some of these parameters were estimated using a genetic algorithm (GA). Meanwhile, the vehicle dynamics model was used to determine the battery pack energy and current requirements for various EV models. The drive cycles used in the mentioned model were the highway fuel economy test cycle (HWFET) and urban dynamometer driving schedule (UDDS). These requirements were then inputted into the battery cell models for range estimation. The estimated range was compared to the ranges disclosed by the United States Environmental Protection Agency (EPA). The BMS utilizes the sensor readings from the current, voltage, and temperature readings to estimate the battery diagnostics and ensure user safety. Under extreme conditions, LIBs can undergo thermal runaway leading to battery pack fires and explosions, severely jeopardizing user safety. Hence, early fault detection of an EV battery pack can be a critical asset for EV user safety and battery pack longevity. In this work, an autoencoder was trained and applied to a real-world dataset consisting of 100 EVs. Furthermore, the voltage and temperature time series were compared as input for the fault detection. Temperature-based autoencoder was successfully able to detect a faulty battery pack from normal functioning ones in EVs. Lastly, a process flow of battery pack fault detection, with autoencoder, for large-scale EV applications is discussed. Finally, the last set of research works concerns the state estimations, specifically the state-of-charge (SOC), of LIB. The complex and non-linear electrochemical behavior of the LIBs poses a significant academic and commercial challenge for its state estimations. While cell-level simpler equivalent circuit models (ECM) are commonly used by battery management systems (BMS) hardware, continuum-scale electrochemical models (EM) are attractive due to their higher accuracy, higher fidelity, and ease of integration with thermal and degradation models. However, compared to ECMs, EMs can have higher solution times and their numerical schemes require more extensive mathematical and computational expertise. Various reduced-order EM battery models, specifically the single particle model (SPM), enhanced single particle model (ESPM), reduced order pseudo-two-dimensional (P2D), and their computationally efficient numerical schemes have been proposed in the literature. The computational performance of some of these battery models and solvers has been compared under different programming languages (Python and C++) and computational hardware specifications (hardware specifications representative of embedded, personal computing, and cloud systems). C++ programming language displayed at least a 10-fold reduction in solver and battery model solution times with the exact figure dependent on the cycling steps. Meanwhile, the embedded systems were able to perform the simulations using reduced-order battery solvers and battery models even with the slower-performing Python programming language, making them a reasonable candidate for embedded systems.
Calendar Aging and Lifetimes of LiFePO4 Batteries and Considerations for Repurposing
(University of Waterloo, 2017-08-23) Catton, John; Fowler, Michael
In response to rising petroleum prices, the demand for lower emissions standards for vehicles, and better vehicle fuel economy, the market for hybrid and electric vehicles is expanding. These systems incorporate advanced battery systems which store and provide energy in the vehicle. Over time, though, cells degrade and lose capacity in accordance with two different aging phenomena: cycling and calendar aging. It is imperative to understand how these degradation phenomena occur as the loss in capacity results in a loss in vehicle range. Through understanding how these phenomena occur, mitigation efforts can be designed to prevent or lessen their effects. This thesis will focus primarily on studying the effects of calendar aging on commercial LiFePO4 cells. Cells are aged at varying temperatures and states of charge (SOC) to determine the extent of capacity fade and degradation. Additional testing methods are then utilized to attempt to determine which aging phenomena are promoting the losses within the cell. Capacity loss in cells stored at high temperature and fully charged conditions resulted in faster degradation rates. Temperature had the most significant role in the degradation of the cell and then the cell’s SOC. Comparing capacity losses between cells stored at the same temperature, but with different SOCs, found that the cells with higher SOC experienced increased rates of degradation in comparison to their fully discharged counterparts. In addition, storage at high SOC and high temperatures promoted such severe losses that the cells in question were unable to recapture capacity that they had lost reversibly. The primary degradation mode for the cells was the loss of cyclable lithium, and was found to occur under all of the storage conditions. Cells stored at much more severe conditions, though, also demonstrated a loss of active material at the anode. The extent of the loss of the active material was largely predicated on whether or not the cell was stored at fully charged or discharged conditions. Storage of lithium-ion batteries at high temperatures has a dramatic effect on the continual usage of the cells after storage conditions have changed. Despite shifting temperatures or states-of-charge to a lower value, the initial storage conditions leads to increased degradation rates throughout the cell life. Thus, the history of storage for the cell must be also be taken into account when considering losses in capacity.
Design and Simulation of Novel Sabatier Reactors for the Thermocatalytic Conversion of CO2 into Renewable Natural Gas
(University of Waterloo, 2019-04-22) Currie, Robert; Simakov, David; Fowler, Michael
Producing synthetic chemicals and fuels using CO2 as a feedstock through the thermocatalytic hydrogenation of CO2 via the Sabatier reaction to produce synthetic CH4 is both a CO2 emissions reduction strategy and an intermittent energy storage solution. A simulation-based study of novel Sabatier reactor configurations was performed to study the effect a distributed H2 supply would have on Ni-based catalyst deactivation and to optimize the production of CH4 for the purposes of evaluating the economic feasibility of a renewable natural gas production facility. First, a heat-exchanger type, molten salt-cooled membrane reactor is analyzed using a transient mathematical model that accounts for dynamic catalyst deactivation. The simulation results showed significantly lower catalyst deactivation rates in the membrane reactor due to the distributed H2 supply that results in more uniform temperature distribution. The model predicts that, with a proper selection of operating parameters, it is possible to achieve CO2 conversions over 95% over extended periods of operation (10,000 h). Next, a heat-exchanger type, actively cooled Sabatier reactor is analyzed using a transient mathematical model to assess its techno-economic feasibility. Effect of cooling fluid, space velocity, and cooling rate on reactor performance was investigated. Simulation results show that with a proper selection of operating parameters, it is possible to achieve CO2 conversions more than 90% with 100% CH4 selectivity over extended periods of operation for a renewable natural gas production cost of $15/GJ with electricity at $0.05/kWh.
Design of a Solid-State Electrochemical Methane Sensor Based on Laser-Induced Graphene
(University of Waterloo, 2018-08-17) Dosi, Manan; Fowler, Michael
Methane is a potent greenhouse gas with significant, yet largely unknown, emissions occurring across gas distribution networks and mining/extraction infrastructure. The development of low-cost, low-power electrochemical sensors could provide an inexpensive means to carry out distributed and easy sensing over the entire network and to identify leaks for rapid mitigation. In this work, a simple and cost-effective approach is proposed for developing electrochemical methane sensors which operate at room temperature with the highest reported sensitivity and response time. Laser-induced graphene (LIG) technology, which selectively carbonizes commercial polyimide films using a low-cost CO₂ laser cutting and patterning system is utilized. Interdigitated LIG electrodes are infiltrated with a dilute palladium (Pd) nanoparticle dispersion which distributes within and coats the high surface area LIG electrode. A pseudo-solid state electrolyte ionic liquid (IL)/polyvinylidene fluoride was painted onto the flexible cell resulting in a porous electrolyte structure which allows for rapid gas transport and improved three-phase contact between methane, IL and Pd. By subjecting the resulting sensors to methane in a gas flow cell, with off-gas analysis analyzed by Fourier transform infrared spectroscopy, the performance of the sensor over a wide range of operating conditions can be determined and the methane oxidation mechanism can be investigated. The optimized system provides a rapid response (less than 50 s) and high sensitivity (0.55 μA/ppm/cm²) enabling a ppb-level detection limit.
Design of Novel Titanium Dioxide-Based Multifunctional Electrochemical Cells
(University of Waterloo, 2018-08-07) Lui, Gregory; Yu, Aiping; Fowler, Michael
Increasing energy demands as well as the depletion of traditional resources has led to the development and improvement of energy conversion and storage technologies. Today, two major concerns are development of renewable sources of energy and establishing reliable sources of potable water to the meet continually growing demands of our society. One technology that has the potential to address both of these issues is the photocatalyst: a material that is able to use natural light irradiation to drive useful chemical reactions and create electrical power. Photocatalytic materials have already been commercially demonstrated to purify waste water and air pollution under ultraviolet irradiation. However, in a photoelectrochemical cell, photocatalytic applications extended beyond waste water remediation to the production of electricity and alternative fuels such as hydrogen gas. This thesis aims to explore the potential multifunctionality of TiO₂ materials and point to applications that extend beyond traditional photocatalysis. Specifically, this work covers the study and design of titanium oxide (TiO₂)-based multifunctional electrochemical cells that can simultaneously purify waste water and provide energy in the form of electrical power. First, this thesis explored potential applications for three-dimensional macroporous (3DOM) TiO₂ morphologies for electrochemical applications. Although the material was a suitable photocatalyst it was found to be an effective freestanding TiO₂ anode on carbon cloth for Li-ion battery applications. The 3DOM TiO₂-based anode provided a flexible, free-standing, binder-free, active material with superior power density when compared to other electrode materials in a similar configuration. This work also provided the first instance of the successful application of 3DOM materials on non-planar surfaces. Commercial TiO₂ (P25) was also employed as a photoanode in a flexible photocatalytic fuel cell as a proof-of-concept device that generates electrical power from the photo-degradation of waste sources such as human sweat (as a wearable device) and dye waste (as a flexible water treatment device). This work established the potential application of photocatalysis in wearable technologies and in flexible photocatalytic devices in general. Finally, a burr-like Ag-TiO₂ photocatalyst was developed as a photoanode material in a flow-photocatalytic fuel cell for the continuous power generation and photo-degradation of brewery effluent under solar-simulated light. This work explored the effect of Ag loading on photoelectrochemical performance, and demonstrated unprecedented power density of a flow-photocatalytic fuel cell in a continuous flow configuration.
Design of Self-Aggregating and Recyclable Quasi-Solid-State Electrolyte towards Dendrite-Free Zn Anode
(University of Waterloo, 2025-01-07) Wang, Tong; Yu, Aiping; Fowler, Michael
Zn-based electrochemistries have received ever-increasing attention given their non-toxicity, easy recycling, biocompatibility, and abundant natural resources, which can solve the fundamental challenges of Li-based batteries and make promises in the application of grid-scale energy storage in the future. In particular, Zn-ion batteries coupled with quasi-solid-state electrolytes such as hydrogel electrolytes rather than conventional liquid electrolytes can bring less dendrite formation, thermal runaways, and electrolyte volatilization along with higher energy density. Herein, my current research work reports a gelatin-based hydrogel electrolyte using commercially available Jell-O powder, which is able to regulate the cycling behavior of Zn anodes by inhibiting the growth of Zn dendrite formation. Numerous characterizations and electrochemical techniques prove that gelatin chains can twin into helical structure and self-assemble to form a 3D triple matrix during sol-gel transition, which promotes better electrochemical performance than single molecules. In the meantime, the Jell-O QSSE can promote the binding of anions in the Zn2+ solvation sheath while reducing the content of free anions, thereby decreasing their contribution to the current density and inhibiting the formation of Zn dendrites. The as-assembled Zn//Zn symmetric cells using Jell-O QSSE can sustain long-term cycling of 2000 h, 1500 h, 250 h, and 120 h at 1, 10, 20, and 50 mA cm-2 with an areal capacity of 1 mAh cm-2, respectively. Moreover, Zn//Cu coin cells show excellent reversibility with a high average CE of 98.61% for more than 100 stable cycles. To validate the practicality of Jell-O QSSE, Zn//MnO2 full cells are assembled, showing an ultrahigh capacity retention of 88.7% after 1000 cycles with a high average CE of 99.64%. Different cathode materials such as sodium vanadate (β-Na0.33V2O5, NVO) was applied as well, which similarly show excellent cycling stability and capacity retention, indicating good compatibility of Jell-O QSSE to various cathode materials. Finally, the recycling of used Jell-O QSSE was demonstrated via a suction filtration method. The filtered Jell-O QSSE was applied again in the assembly of Zn//Zn symmetric cells, showing an excellent cycling stability of more than 1200 h, making Jell-O QSSE much more promising than conventional ZnSO4 electrolyte in the application of aqueous Zn-ion batteries.
Developing Models Using Game Theory for Analyzing the Interaction of Various Stakeholders in Energy Systems
(University of Waterloo, 2020-01-16) Haghi, Ehsan; Fowler, Michael
Air pollution, global warming, climate change, and economic development are all reasons for governments around the world to incentivize the development of renewable energy generation technologies and plan for a transition toward a low-carbon economy. The development of renewable energy projects as well as the liberation in electricity systems has led to the emergence of multiple stakeholders in energy systems. While the research focused on investigating the objective of a single stakeholder in an energy system is abundant in the literature, considering the objectives of all stakeholders in a multi-stakeholder model is a gap in the research. This thesis is aimed at developing a multilevel framework for modeling and analyzing the interaction of various stakeholders in energy systems. The models developed in this thesis are focused on investigating two areas: 1. The role of energy storage systems in Ontario and how they can be used to reduce GHG emissions in the province, and 2. Analyzing the interaction of the heat and electricity supply systems in Great Britain. The contribution of this thesis is presented through four studies. The objective of the first study is to investigate the effect and cost-efficiency of different renewable energy incentives and potential for wind and hydrogen energy systems to the perceived viability of a microgrid project from the prospective of different stakeholders, i.e., government, energy hub operator and energy consumer in the province of Ontario, Canada. Hourly simulation of a microgrid in which wind and/or hydrogen are produced is used for the analysis. Results show that using underground seasonal storage leads to the government paying less incentive per kg of CO2 emission reduction as it lowers the levelized cost of hydrogen and provides a higher carbon emission reduction potential. Results of the first study also show that for the same incentive policy, incentivizing hydrogen production with grid electricity or a blend of wind power and grid electricity and producing hydrogen using wind power with underground hydrogen storage are more cost-efficient options for government than incentivizing wind power production. Regarding the renewable energy incentives, a combination of capital grant and FIT is shown to be a more cost-efficient incentive program for the government than FIT only programs. However, FIT programs are more effective for promoting the development of renewable energy technologies. In the second study, the advantages of energy incentives for all the stakeholders in an energy system were analyzed in the context of a microgrid using a more comprehensive approach. In the second study, the effect of health impacts from fossil fuel consumption and taxes collected from the energy hub operator and energy consumer are considered in the model. The stakeholders considered in the second study include the government, the energy hub operator, and the energy consumer. Two streams of energy incentives were compared in the second study: incentives for renewable energy generation technologies and incentives for energy storage technologies. The first stream aims to increase the share of renewable energies in the electricity system while the second stream aims the development of systems which use clean electricity to replace fossil fuels in other sectors of an energy system such as the transportation, residential and industrial sectors. The results of the analysis in the second study show that replacing fossil fuel-based electricity generation with wind and solar power is a less expensive way for the energy consumer to reduce GHG emissions (60 and 92 CAD per tonne of CO2e for wind and solar, respectively) compared to investing on energy storage technologies (225 and 317 CAD per tonne of CO2e for Power-to-Gas and battery-powered forklifts, respectively). However, considering the current Ontario's electricity mix, incentives for the Power-to-Gas and battery-powered technologies are less expensive ways to reduce emissions compared to replacing the grid with wind and solar power technologies (1479 and 2418 CAD per tonne of CO2e for wind and solar, respectively). The analysis in the second study also shows that battery storage and hydrogen storage are complementary technologies for reducing GHG emissions in Ontario. This third study aims at developing a game theory model for assessing the potential of fuel cell-powered and battery-powered forklifts for reducing GHG emissions in the province of Ontario, Canada. Two stakeholders are considered in the developed model: government and energy consumer, which is an industrial facility operating forklifts. The energy consumer, which is assumed to be an industrial facility, operates 150 diesel forklifts but has the option of replacing them with fuel cell-powered and battery-powered forklifts. The government can encourage this replacement by allocating a percentage of Ontario's surplus power to the energy consumer at a discounted price. The discount is assumed to be in the form of exempting the energy consumer from paying the global adjustment. As a result, the energy consumer only pays the hourly Ontario electricity price when discounted power is available. Discounted electricity will decrease the cost of operating battery-powered and fuel cell-powered forklifts for the energy consumer and will encourage the use of those technologies instead of diesel forklifts. The government has an incentive to pursue such policy as the replacement of diesel forklifts with fuel cell-powered and battery-powered forklifts will reduce GHG emissions and subsequently, the social cost of carbon in the province. The results of the third study show that when the government does not allocate discounted power to the energy consumer, energy consumer does not reduce emissions and keeps using the 150 diesel forklifts. However, when the government provides 0.1% of Ontario's surplus power at each hour to the energy consumer at a discounted price, the energy consumer replaces 31 of diesel forklifts with battery-powered forklifts. When the percentage of discounted power is 0.6% of Ontario's surplus power at each hour, energy consumer replaces 91 of diesel forklifts with battery-powered forklifts and 54 of diesel forklifts with fuel cell-powered forklifts. A policy of discounting surplus power to encourage replacing diesel forklifts with battery-powered and fuel cell-powered forklifts is shown to benefit both stakeholders in the system. The third study also shows that the deployment of both fuel-cell powered and battery-powered forklifts is effective in reducing GHG emissions in Ontario when surplus clean power is available. Battery-powered forklifts are more cost-effective when lower levels of discounted power are available; however, with an increase in the level of available discounted power, fuel cell-powered forklifts become more cost-effective technologies compared to battery-powered forklifts. The same methodology is also used for analyzing the potential of clean surplus power in Ontario to reduce GHG emissions in the residential sector. In the fourth study, an iterative optimization model is developed to analyze the interaction of heat and electricity sectors at a national level in Great Britain. Independent mathematical models for optimizing the selection of technologies in heat and electricity supply systems are developed in the fourth study. The optimal mix of technologies for supplying electricity and heat were then calculated iteratively to take into account the interactions between the electricity and heat systems and their fragmented planning strategies. The capacity and operation of various technologies for electricity generation were optimized to supply electricity demand with a minimum annual cost. Then, the heat supply options were determined through minimization of the annualized cost of the heat supply system. Iterative optimization of electricity and heat was continued until an equilibrium was achieved. The results of the iterative approach were compared with a centralized optimization model in which heat and electricity problems are solved simultaneously.
Development and Evaluation of Nickel and Cobalt-Based Mixed Metal Oxide Catalysts for Anion Exchange Membrane Water Electrolysis
(University of Waterloo, 2024-12-12) Ahmed, Khaja Wahab; Fowler, Michael; Yu, Aiping
Hydrogen production using an Anion exchange membrane (AEM) electrolyzer allows the use of non-platinum group metal (PGM) catalysts for oxygen evolution reaction (OER). Nickel and Cobalt-based oxides are active in an alkaline environment for OER and are relatively inexpensive compared to IrO2 catalysts used in Polymer electrolyte membrane (PEM) electrolysis. This study explores the catalytic performance and stability of nickel and cobalt-based oxides, particularly NiFeOx, NiFe2O4, and NiFeCoOx, in OER and AEM water electrolysis. In the first study, mixed metal oxide catalysts NiFeOx and NiFeCoOx catalysts were synthesized by coprecipitation method using NaOH. The catalysts were characterized through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The NiFeCoOx catalysts demonstrated superior performance in AEM water electrolysis compared to NiFeOx and NiO, achieving the highest current density of 802 mA cm−2 at 2 V and 70°C using 1M KOH as the electrolyte. Electrochemical Impedance Spectroscopy (EIS) and equivalent circuit fitting were used to assess ohmic and activation resistances. Results indicated a reduction in both ohmic and activation resistances with increasing electrolyte concentration. Additionally, the performance of commercially available AEMs, Fumasep FAA-3-50, and Sustainion X-37-50 grade T, was evaluated under similar conditions. EIS results showed that the X-37-50 membrane had lower ohmic resistance compared to the FAA-3-50 membrane. The investigation of catalytic activity and performance of nickel cobalt oxide (NiCoOx) catalysts in AEM water electrolysis for hydrogen production was performed in the second study. The catalysts were synthesized with different ratios of Ni to Co and applied to a nickel foam gas diffusion layer (GDL) at the anode. Scanning electron microscopy (SEM) revealed a distinct flaky structure of the NiCoOx catalyst, while X-ray diffraction (XRD) confirmed the presence of a NiCo2O4 spinel crystal structure. Linear sweep voltammetry (LSV) measurements for the Oxygen Evolution Reaction (OER) in a three-electrode system indicated that NiCoOx (1:3) had the highest catalytic activity, with a current density of 238 mA cm-2 at 1.8 V. Tafel analysis showed that NiCoOx (1:3) had the lowest Tafel slope, indicating faster reaction kinetics and a lower overpotential for higher current densities. Chronoamperometry tests demonstrated the stability of the catalysts at different current densities, with long-term stability testing of NiCoOx (1:3) over 500 hours showing minimal voltage increase during OER, confirming its stability in prolonged operation. NiCoOx (1:3) displayed the highest activity among the tested catalysts at different temperatures, achieving current densities of 1700 mA cm-2 at 2.2 V and 70°C in AEM electrolysis. Nyquist plots and equivalent circuit analysis revealed that NiCoOx (1:3) had lower activation resistance compared to other catalyst compositions for AEM electrolysis. Temperature-dependent measurements showed decreased resistances (ohmic, activation, and membrane) with increasing temperature, indicating improved reaction kinetics and ion conductivity. Long-term durability tests confirmed the stable operation of the catalyst, while short-term tests verified its effectiveness at higher current densities in single-cell AEM electrolyzer operation. In the third study, NiCoOx catalysts were modified using Fe in different proportions ranging from 2.5 to 12.5wt.%. and keeping the Ni to Co ratio to 2:1. Evaluation of the catalytic activity of NiFeCoOx catalysts was conducted by linear sweep voltammetry (LSV) and chronoamperometry (CA) experiments for the oxygen evolution reaction (OER). The catalyst containing 5% Fe exhibited the highest catalytic activity, achieving an overpotential of 228 mV at a current density of 10 mA cm-2, with activity declining with further increases in Fe content. Long-term testing for OER at 50 mA cm-2 demonstrated stable electrolysis operation for 100 hours. Further analysis in an AEM water electrolyzer test revealed that the NiFeCoOx catalyst with 5% Fe at the anode demonstrated the highest current densities of 1516 mA cm−2 and 1620 mA cm−2 at 55°C and 70°C at 2.1 V, with a maximum current density of 1880 mA cm−2 achieved at 2.2 V and 70°C. Nyquist plot analysis of electrolysis at 55°C indicated that the NiFeCoOx catalyst with 5% Fe exhibited lower activation resistance compared to other Fe loadings, suggesting enhanced performance. This study compared the performance of NiFeCo(OH)x and NiFeCoOx catalysts for AEM water electrolysis, revealing that NiFeCoOx demonstrated significantly higher current densities at various temperatures compared to NiFeCo(OH)x. The durability test conducted for 8 hours demonstrated stable AEM water electrolysis with minimal degradation, achieving an overall cell efficiency of 70.5% during operation at a higher current density of 0.8A cm-2. The final study investigated the catalytic activity and performance of NiFeOx catalysts in OER and AEM water electrolyzers. These catalysts were synthesized with varying iron content weight percentages and at the stoichiometric ratio for nickel ferrite (NiFe2O4). The stability of NiFe2O4 catalyst over a 600-hour period at 50 mA cm-2 was demonstrated for OER, with a degradation rate of 15 μV/h. In AEM electrolysis using the X-37 T membrane, NiFe2O4 catalyst exhibited high activity, achieving a current density of 1100 mA cm-2 at 45°C, increasing to 1503 mA cm-2 at 55°C. The performance of various membranes was assessed, with Fumatech FAA-3-50 and FAS-50 membranes showing the highest performance, indicating a strong correlation between membrane performance and conductivity. Analysis of Nyquist plots and equivalent circuit analysis revealed that ohmic resistance decreased with increasing temperature, indicating a positive effect on AEM electrolysis. FAA-3-50 and FAS-50 membranes offered lower activation and ohmic resistances, suggesting higher conductivity and faster membrane charge transfer. NiFe2O4 in an AEM water electrolyzer demonstrated strong stability, with a voltage degradation rate of 0.833 mV/h over a 12-hour durability test.
Economic, Environmental, and Health Impact Analysis of Developing Hydrogen Economy in Canada
(University of Waterloo, 2022-01-21) Shamsi, Hamidreza; Fowler, Michael
The greatest challenge to the development of a cleaner energy system is economic issues. However, if the environmental and health externalities of the current energy system is considered, other energy alternatives become economically competitive. Therefore, hydrogen can become an option in different energy sectors. As an energy vector, hydrogen can be represented as the missing link between clean energy sources and energy consumers. The real cost of an energy system includes environmental and health-related hidden costs. The current energy system imposes lots of critical damages to the environment and human lives. All these damages are avoidable if governments follow the prevention policy instead of the cure policy. In other words, governments can support developing clean energy solutions by incentivizing them. In this regard, the government should be aware of the hidden costs of energy for both fossil-fuel-based and hydrogen-based energy systems. Therefore, in this work, a comprehensive cost calculation is conducted for using hydrogen in different energy sectors in this work. The result from this work shows that the idea of Hydrogen Economy is economically competitive with the current energy system, if the hidden costs of environmental and health effects are taken into account. The first study is focused on developing a five-year mathematical model for finding the optimal sizing of renewable energy technologies for achieving specific CO2 emission reduction targets. An industrial manufacturing facility that uses CHP for electricity generation and natural gas for heating is considered the base case in this work. The CHP capacity is 4500 kW and the furnace is operated 8 AM to 4 PM with a natural gas consumption of 4000 m3/h. Different renewable energy technologies are assumed to be developed each year to achieve a 4.53% annual CO2 emission reduction target. The results of this study show that wind power is the most cost-effective technology for reducing emissions in the first and second years, with a cost of 44 and 69 CAD per tonne of CO2, respectively. On the other hand, hydrogen is more cost-effective than wind power in reducing CO2 emissions from the third year onward. The cost of CO2 emission reduction with hydrogen doesn't change drastically from the first year to the fifth year (107 and 130 CAD per tonne of CO2). Solar power is a more expensive technology than wind power for reducing CO2 emissions in all years due to lower capacity factor (in Ontario), more intermittency (requiring mores storage capacity), and higher investment cost. A hybrid wind/battery/hydrogen energy system has the lowest emission reduction cost over five years. The emission reduction cost of such a hybrid system increases from 44 CAD per tonne of CO2 in the first year to 156 CAD per tonne of CO2 in the fifth year. The developed model can be used for long-term planning of energy systems to achieve GHG emission targets in regions/countries with fossil fuel-based electricity and heat generation infrastructure. The second study develops a multi-objective model to determine the optimal sizes and locations of the hydrogen infrastructure needed to generate and distribute hydrogen for the critical Highway Corridor (HWY 401) in Ontario. The model is used to aid the early-stage transition plan for converting conventional vehicles to FCEVs in Ontario by proposing a feasible solution to the infrastructure dilemma posed by the initial adoption of hydrogen as fuel in the general market. The health benefit from the pollution reduction is also determined to show the potential social and economic incentives of using FCEVs. The results show that hydrogen production and delivery cost can reduce from $22.7/kg H2 in a 0.1% market share scenario to $14.7/kg H2 in a 1% market share scenario. The environmental and health benefit of developing hydrogen refueling infrastructure for heavy-duty vehicles is 1.63 million dollar per year and 1.45 million dollars per year, respectively. Also, every kilogram of H2 can avoid 11.09 kg CO2 from entering the atmosphere. In a 1% market share scenario, the proposed hydrogen network avoids more than 37,000 tonnes of CO2 per year. The third study aims to determine the economic burden of environmental and health impacts caused by Highway 401 traffic. Due to the high volume of vehicles driving on the Toronto Highway 401 corridor, there is an annual release of 3771 tonnes of carbon dioxide equivalent (CO2e). These emissions are mainly emitted onsite through the combustion of gasoline and diesel fuel. The integration of electric and hydrogen vehicles shows maximum reductions of 405–476 g CO2e per vehicle kilometer. Besides these carbon dioxide emissions, there is also a large number of hazardous air pollutants. The mass and concentrations of criteria pollutants of PM2.5 and NOx emitted by passenger vehicles and commercial trucks on Highway 401 were determined using the MOVES2014b software to examine the impact of air pollution on human health. Then, an air dispersion model (AERMOD) was used to find the concentration of different pollutants at the receptor’s location. The increased risk of health issues was calculated using hazard ratios from literature. Finally, the health cost of air pollution from Highway 401 traffic was estimated to be CAD 416 million per year using the value of statistical life, which is significantly higher than the climate change costs of CAD 55 million per year due to air pollution. The fourth study discovers the health benefit of reducing fossil-fuel vehicle market share and utilizing more Zero-Emission Vehicles (ZEVs). A historical dataset from 2015-2017 is used to learn a Long Short-Term Memory (LSTM) model that can predict future NOx concentration based on traffic volume, weather condition, time, and past NOx concentration. The developed model is used in a modified manner to predict NOx concentration in the long term. Then, the developed model is utilized to predict annual average NOx reduction in four different scenarios. Interpolation methods are used to predict pollution reduction in all Dissemination Areas (DA) of Toronto. Finally, a health cost assessment is conducted to estimate the health benefit from different scenarios. The results show that the western areas of Toronto experience more NOx concentration reduction in all scenarios, which is the result of a stronger correlation between traffic volume and pollution in those areas. Also, by 10% reduction in fossil-fuel traffic volume, 70 deaths can be prevented annually, equivalent to CAD 560 million health benefit per year. There are plenty of opportunities for future work in this area to make more robust energy models which can take all aspects of implementing the idea of Hydrogen Economy. First, the impact of using different types of hydrogen storage can be investigated in terms of cost. Also, a comprehensive hydrogen-based energy model can be optimized if the cost-benefit analysis is conducted in all energy sectors. Finally, different objective functions such as energy, environmental, health, and social costs can be optimized to reach an optimal sustainable energy system for Ontario.
Electrode Design for Durable and Energy-Dense Rechargeable Zinc-Air Batteries
(University of Waterloo, 2020-01-21) Cano, Zachary Paul; Fowler, Michael
Zinc-air batteries have been proposed as a low-cost and energy-dense candidate to replace or supplement lithium-ion batteries in electric vehicles. This thesis explores the viability of zinc-air batteries in electric vehicles and experimentally investigates the use of nickel-based air electrodes. The failure mechanism of these electrodes is uncovered, and a new nickel-based air electrode having both improved cycle life and substantially lower mass and volume than previous designs is presented.
Energy Storage for Grid Power: Policy Arguments Based on Technical, Economic, and Environmental Analysis
(University of Waterloo, 2016-08-10) van Lanen, Daniel; Fowler, Michael
Renewable energy has been increasing as the demand for cleaner energy increases. The introduction of renewable energy into the power grid has however introduced supply and demand discontinuities due to the intermittency of renewables. Energy storage implemented alongside renewables aids in the management of energy as it allows for load shifting of the intermittent energy to optimize its use and better match the supply and demand profiles. Energy storage can come in many forms such as batteries or Power-to-gas systems. Batteries offer small scale solutions which can be cost effective if repurposed electric vehicle batteries are used. Hydrogen can also be produced using excess electricity which can then be stored or injected into the natural gas grid. Modeling through a MATLAB model of different scales of storage for both batteries and hydrogen demonstrate the economic viability of these projects as well as the environmental impact. Policies are also examined and recommendations made including: ending the Feed-in Tariff program, providing preferential electricity pricing to energy storage projects, and providing an equivalent ethanol subsidy to hydrogen of 79.5 cents per kilogram.
Extended Range Electric Vehicle Powertrain Simulation and Comparison with Consideration of Fuel Cell and Metal-air Battery
(University of Waterloo, 2016-04-14) Wang, Caixia; Fowler, Michael
The automotive industry has been in a period of energy transformation from fossil fuels to a clean energy economy due to the economic pressures resulting from the energy crisis and the need for stricter environmental protection policies. Among various clean energy systems are electric vehicles, with lithium-ion batteries have the largest market share because of their stable performance and they are a relatively mature technology. However, two disadvantages limit the development of electric vehicles: charging time and energy density. In order to mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range, including the Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), and Extended Range Electric Vehicle (EREV). In this project, two advanced EREV powertrains have been modeled and simulated by using a lithium-ion battery as the primary energy source, with the combination of a fuel cell (FCV) or zinc-air battery as the range extenders. These two technologies were chosen as potential range extenders because of their high energy density and low life cycle emissions. The objective of this project is to compare the combined energy system (zinc-air and lithium-ion battery, fuel cell and lithium-ion battery) powered vehicles with gasoline powered vehicles (baseline vehicle, ICE engine extended range electric vehicle) and battery electric vehicles (BEV) in dimensions of energy consumption, range, emissions, cost, and customer acceptance. In order to achieve this goal, a unique zinc-air battery model was developed in this work with consideration of research data and current market status, and a control logic of the dual energy systems powertrain was created in the vehicle modeling software. A 2015 Chevrolet Camaro had been chosen as the vehicle architecture platform, with modelling of the five vehicle powertrains being built within Autonomie. This vehicle modeling software, developed by Argonne National Laboratory, runs with MATLAB/Simulink, and contains embedded drive cycles and analysis tools needed to perform the necessary simulations. Since the emission analysis in the Autonomie model only considers the vehicle in energy consumption and tailpipe emissions, therefore a Well-to-Wheel analysis method is introduced to evaluate the energy life cycle. This method takes into account the emissions from the energy production and considers the vehicle tailpipe emission. After finished all the simulations, a decision matrix was developed to compare these five powertrains from the metrics of energy consumption, emissions, customer acceptance, and life cycle cost. Three substantial conclusions were obtained from the comparison: The powertrains without use engine and gasoline as the power source have the lower tailpipe emissions and greenhouse gas emissions. The powertrains based on battery power alone, i.e. metal air extended range electric vehicle (MA-EREV) and battery electric vehicle (BEV) are not able to achieve the total range target, likely because of the relative high vehicle mass caused by the weight of the battery pack. However MA-EREV got the highest marks compared to other powertrains. However, metal-air battery is a new technology, and there are no prototypes of the technology, thus full commercialization is expected to take some time.
A Hydrogen Hybrid Powertrain for the Union-Pearson Railway
(University of Waterloo, 2019-09-10) Haji Akhoundzadeh, Mehran; Fowler, Michael
Canadian legislation attempts to regulate particle emissions released from the rail transportation sector. Assessment of the impact of rolling stock is the key to perform such regulations. Different strategies have been proposed to evaluate the health risks of mobile emission sources. Popular methods in measuring health assessment of rolling stock were reviewed in this study. Hydrail was proposed as an alternative option helping Canadian legislation to regulate emission generated from this mode of transportation. The feasibility of developing Hydrail technology is investigated. As a case study, the drive cycle of the DMUs working on the Air-Rail link’s tracks of Great Toronto Area (GTA) was extracted. A theoretical model was implemented to estimate the duty cycle of the train as it was not possible to access the DMU’s throttle data. According to the duty cycle estimator subsystem, the annual emission released from the track is calculated. To assess the health risk on people, 32 places which are located near the track were collected, and the locations were extracted using Google Earth. These places include hospitals, schools, and social community centers. The concentration of three types of pollutants was locally approximated in the 32 places, using Gaussian air dispersion modeling method. To implement the model, commercial software, AERMOD, was used. To contemplate the health effect of the trains, the estimated pollution concentrations were compared with the air quality standards. The Hydrail was introduced as an alternative technology to reduce the health impact of the rail sector. The benefits and drawbacks of the technology were introduced in detail. Finally, a hydrogen powertrain is designed in this study with respect to the estimated duty demand. This should be considered as the first subsystem of an end-to-end Hydrail design platform for this 25 Km long rail route. A frequency-based power management scenario was applied to the developed powertrain to control the power flow between energy sources. A sensitivity analysis was performed to approximate the system dynamics. The proposed power management scenario is capable to optimally keep the system working in its optimal working region whenever it will become integrated with a real-time high-level global optimization subsystem.
Hydrogen storage for mixed wind–nuclear power plants in the context of a Hydrogen Economy
(Elsevier, 2008-08-15) Taljan, Gregor; Fowler, Michael; Cañizares, Claudio; Verbič, Gregor
A novel methodology for the economic evaluation of hydrogen production and storage for a mixed wind–nuclear power plant considering some new aspects such as residual heat and oxygen utilization is applied in this work. This analysis is completed in the context of a Hydrogen Economy and competitive electricity markets. The simulation of the operation of a combined nuclear–wind–hydrogen system is discussed first, where the selling and buying of electricity, the selling of excess hydrogen and oxygen, and the selling of heat are optimized to maximize profit to the energy producer. The simulation is performed in two phases: in a pre-dispatch phase, the system model is optimized to obtain optimal hydrogen charge levels for the given operational horizons. In the second phase, a real-time dispatch is carried out on an hourly basis to optimize the operation of the system as to maximize profits, following the hydrogen storage levels of the pre-dispatch phase. Based on the operation planning and dispatch results, an economic evaluation is performed to determine the feasibility of the proposed scheme for investment purposes; this evaluation is based on calculations of modified internal rates of return and net present values for a realistic scenario. The results of the present studies demonstrate the feasibility of a hydrogen storage and production system with oxygen and heat utilization for existent nuclear and wind power generation facilities.
Improved Electric Vehicle Powertrain Incorporating a Lithium-Ion Battery and a Range Extender Zinc-Air Battery, plus Associated Health and Economic Benefits
(University of Waterloo, 2019-11-26) Sherman, Steven; Fowler, Michael
As the world confronts the serious challenge posed by anthropogenic climate change, electric vehicles have emerged as a serious candidate to displace gasoline-burning vehicles. In spite of the environmental and operational advantages of electric vehicles, however, and in spite of billions in investment, electric vehicles have not attained meaningful market share in the main national vehicle markets. This is a serious problem not only for climate change mitigation but also for air pollution mitigation, given the substantial air pollution generated by vehicles. The inability of electric vehicles to attain market share may be due to the inadequacies of the lithium-ion batteries which power electric vehicles, and which are heavy and expensive. In this work an electric vehicle with a novel powertrain is designed, optimized and modelled. The novel powertrain uses a lithium-ion battery as the primary energy storage system and a lighter and cheaper zinc-air battery as a range extender. The first objective of this work is to compare this novel powertrain to a conventional electric vehicle powertrain and quantify the benefits. The optimized two-battery electric vehicle achieves 400 km of range, over 12 years of zinc-air battery life and an MSRP of $26,300 – over $5000 lower than that of the conventional electric vehicle. As part of this work, it is necessary to create a zinc-air cell model based on academic literature, since there are no commercially available rechargeable zinc-air cells that are suitable for use in vehicles. The cell model achieved 10% greater specific energy to the lithium-ion cell at a much lower price. An improved cell model achieved even greater specific energy – 65% greater than the lithium-ion cell. The second objective of this work is to analyze the air pollution impacts of electric vehicles in a local context. Specifically, the air pollution impact of increasing levels of electric vehicles on Highway 401 is simulated. Using Ontario Ministry of Transportation data for traffic flows on Highway 401, pollution modelling software and Transport Canada guidance it is estimated that pollution from Highway 401 costs $18.5M per year, and that replacing all the light passenger vehicles with electric vehicles could reduce these costs by 45.6%. The modelling demonstrates that NOx and PM2.5 are the costliest pollutants, and that PM2.5 experiences the least relative reduction in emissions with increased electric vehicle penetration.
Improving Lithium-Ion Battery Management Systems Using Equivalent Circuit Models, Cloud Platforms, and Machine Learning Estimation Techniques
(University of Waterloo, 2024-08-28) Tran, Manh Kien; Fowler, Michael
Building a future that preserves the environment and reduces dependence on fossil fuels is an imperative undertaking, and it greatly hinges on the global transition to renewable energy. Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion (Li-ion) batteries are an excellent solution for energy storage due to their properties including high energy density, high power density, long cycle life, low self-discharge rate, no memory effects, and low environmental pollution. In order to ensure the safety and efficient operation of Li-ion battery systems, battery management systems (BMSs) are required. However, the current design and functionality of the BMS suffer from a few critical drawbacks including its low computational capability and limited data storage. The work in this thesis focuses on improving the BMS by investigating and improving the equivalent circuit model (ECM) which is often the core battery model used in practical BMS, researching and developing a smart BMS utilizing the cloud platform, and proposing potential applications of the cloud-based smart BMS such as cell replacement or SOH estimation using machine learning. One of the main focuses of this work is on the critical role of accurate battery modeling for safe and effective operation. The first contribution of this research is an investigation into the performance of three ECMs—1RC, 2RC, and 1RC with hysteresis—across four common Li-ion chemistries: LFP, NMC, LMO, and NCA. Experimental results demonstrate that all three models can be applied to these chemistries with low error rates, particularly under dynamic current profiles. The findings indicate that the 1RC with hysteresis ECM performs best for LFP and NCA chemistries, while the 1RC ECM is most suitable for NMC and LMO chemistries. These insights are crucial for optimizing BMS applications in real-world scenarios, highlighting the need to tailor ECM selection to specific battery chemistries. This work also seeks to add further to the improvement of the ECM used in the BMS, as another novel contribution. Its next research delves into the effects of state of charge (SOC), temperature, and state of health (SOH) on the parameters of the ECM, particularly the Thevenin model. While SOC and temperature are well-integrated into ECMs, the impact of SOH has been less explored. Through a series of experiments, it was found that as SOH decreases, both the ohmic and polarization resistances increase, while the polarization capacitance decreases. An empirical model was developed to represent the combined effects of SOH, SOC, and temperature on ECM parameters. This model was validated experimentally and showed significant improvements in accuracy without increasing complexity. The proposed model offers a practical solution for real-world BMS applications, enhancing the precision of battery monitoring and control. As part of the next steps in the research, the concept and development of cloud-based smart BMSs were discussed in detail. Traditional BMSs are limited by computational capacity and data storage, which can hinder the development of advanced battery management algorithms. A cloud-based BMS can address these issues by offloading computation and storage to the cloud, enabling more sophisticated and reliable battery algorithms. The study discusses the design, functionality, and benefits of cloud-based BMSs, including improved reliability and performance of Li-ion battery systems. It also explores the division of tasks between local and cloud functions, emphasizing the potential for significant advancements in battery management through cloud integration. This innovation is expected to play a pivotal role in advancing renewable energy technologies. Once the development of the cloud-based smart BMS has been discussed, the practical applications of such innovation are then examined. As the optimization of Li-ion battery pack usage becomes increasingly more necessary and given the inevitable degradation of Li-ion batteries, recent research has focused on maximizing the utilization of battery cells within packs. Another study, which is the next novel contribution of this work, investigates the feasibility and benefits of cell replacement within battery packs, using a simulation framework based on cell voltage and degradation models. The cloud-based BMS, with more data storage, shall be able to advance the cell replacement application by providing significantly more battery historical data. The study, conducted using MATLAB, simulates the life cycles of battery packs with varied cell configurations. Results show that cell replacement can significantly extend the lifespan of battery packs and is economically advantageous compared to a full pack replacement. For practical implementation, the design criteria include individual cell monitoring and easy accessibility for cell replacement, underscoring the potential for more efficient battery usage strategies. Another practical application of the cloud-based BMS with improved computational and memory capability is battery state estimation, specifically complex algorithms such as SOH estimation. A part of this work, its final novel contribution, develops a novel SOH estimation approach, which is crucial for the effective operation of Li-ion battery systems. Existing methods have limitations in adaptability and real-time application. This study introduces a machine learning-based approach for online SOH estimation during fast charging cycles. Using a dataset of 124 cells, with various machine learning algorithms tested, the neural network algorithm demonstrated superior accuracy with an RMSE of 9.50mAh and a MAPE of 0.69%. The methodology, which utilizes partial charge metrics without needing historical data, is highly suitable for real-time BMS integration. This approach enhances the reliability and performance of Li-ion battery systems, contributing to the broader adoption of electric vehicles (EVs) and renewable energy technologies. Overall, this thesis presents significant advancements in the field of battery management systems, particularly through the improvement of the ECM, the introduction of cloud-based smart BMSs, and the development of innovative cell replacement and SOH estimation methods. The cloud-based BMS would be able to solve the problems of limited computational capability and data storage. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. The cloud-based smart BMS is expected to improve the reliability and overall performance of Li-ion battery systems, contributing to the mass adoption of EVs and renewable energy.
Integration of Hydrogen Technology into Large Scale Industrial Manufacturing in Ontario
(University of Waterloo, 2022-01-12) Preston, Nicholas; Fowler, Michael; Elkamel, Ali
Power-to-Gas is particularly applicable in Ontario’s energy market, due to the abundance of curtailed renewable energy. During off peak hours this results in not only low carbon, but low-cost electricity making hydrogen generation a highly profitable and environmentally friendly venture. Despite the benefits listed above, there has yet to be a full-scale adoption of Power-to-Gas technology both globally and in the local market. This eliminate this hesitation there is a requirement for diverse, profitable proof of concept installations and a public uncertainty regarding the inherent safety of the technology. It is the objective of this thesis to address these concerns by demonstrating the versatility of hydrogen in different energy system configurations, to show how layered revenue streams can produce profits in the face of policy uncertainty and by outlining the risks and control methods available to mitigate the safety concerns associated with Hydrogen. The first paper presented in this thesis will address the question of whether a business case with strong financial returns is possible for a finished goods manufacture. Here we demonstrate the potential to capitalize on multiple revenue streams under a single investment and highlight some of the ancillary assets including reduction in air pollution and balance of the electrical grid. This design was developed for an automotive manufacturer requiring a total capital investment of $2,620,448 and resulting in a payback period of 2.8 years. Based on a sensitivity analysis, the annual revenue for selling hydrogen at $1.5 to $12 per kgH2 can sum to $54,741 to $437,928. In the modelled carbon tax program, CO2 allowances can be sold at $18 to $30 per tonne CO2 and the model predicts a CO2 offset of 2359.7 tonnes. The second paper develops a case study that further expands on the use of a single pathway, the is the use of hydrogen enriched natural gas. This paper analyzes the integration of an electrolyzer unit into a manufacturer’s CHP microgrid and both explores the impact a carbon tax has on its feasibility and carries out a failure mode and effects analysis to highlight the safe nature of the technology. Currently realizable capital incentives can see IRRs as high as 13.76% with net present values of approximately $750,000. To realize financial feasibility, the carbon price in Ontario must achieve or exceed a minimum of 60$/ton CO2e. In all economically feasible, cases the system operating under an optimal storage coefficient and operational limit produced an emission offset greater than 3000-ton CO2 per year.
Mathematical Model and Calendar Aging Study of Commercial Blended-Cathode Li-ion Batteries
(University of Waterloo, 2016-08-26) Mao, Zhiyu; Fowler, Michael; Chen, Zhongwei
Commercial blended-cathode Li-ion battery (LIB) systems has been dominating the burgeoning market for portable energy, ranging from consumer electronics to automotive applications. In order to successively improve the energy-power density and usage life of blended-cathode cells, an understanding in terms of the electrode design, electrochemical performance, and cell aging are necessary. A mathematical model based research approach is effective to quantitatively estimate all factors in the complicated system has been developed in this work, which will be beneficial for research and development of Lithium ion battery technology. In this thesis, a model based composition prediction technology for the of unknown commercial blended Li-ion battery cathodes is developed. It includes three steps of combined experimental and modeling methods. The electrochemically active constituents of the electrode are first determined by coupling information from low-rate galvanostatic lithiation data, and correlated with Scanning Electron Microscope (SEM) with Energy Dispersive X-Ray Analysis (EDX) analyses of the electrode. In the second step, the electrode composition is estimated using a physics based mathematical model of the electrode. The accuracy of this model based approach has been assessed by comparison of this electrode composition with the value obtained from an independent, non-electrochemical experimental technique involving the deconvolution of X-ray powder diffraction (XRD) spectra. Based on the prediction technology, the commercial LIB with the composition of LiNixMnyCo1-x-yO2 - LiMn2O4 (NMC-LMO=70:30 wt%) cathode was accurately delineated. Then, a physics based mathematical model, including the two dimensions of single particle and electrode levels, is developed to describe the electrochemical performance of the NMC-LMO blended cathode. The model features multiple particle sizes of the different active materials and incorporates three particle-size distributions: one size for the LMO particles, one size for the NMC primary and one size for NMC secondary particles which presumably are agglomerates of NMC primary particles. The good match between the simulated and experimental galvanostatic discharge and differential-capacity curves demonstrates that the assumption of secondary particles being nonporous (i.e., solid-state transport) is reasonable under the operating conditions of interest in this case up to 2C applied current. In the modeling, a thermodynamic expression for diffusive flux and some parameters such as the effective electronic conductivity have been described and measured. A sensitivity of the fitted model parameters including kinetic rate constants and solid-state diffusivities has been analyzed. Using the multi-particle model, the different Galvanostatic Intermittent Titration Technology (GITT) experiments with varying pulse currents and relaxation periods for a NMC-LMO blended lithium-ion electrode have been described. The good agreement between the simulated and experimental potential-time curves shows that the model is applicable for all GITT conditions considered, but is found to be more accurate for the case of small current pulse discharges with long relaxation times. Analysis of the current contribution and the solid-state surface concentration of each active component in the blended electrode shows a dynamic lithiation/delithiation interaction between the two components and between micron and submicron NMC particles during the relaxation periods in the GITT experiments. The interaction is attributed to the difference in the equilibrium potentials of the two components at any given stoichiometry which redistributes the lithium among LMO and NMC particles until a common equilibrium potential is reached. Moreover, the model can also be used to fit the galvanostatic charge curves from the rate of C/25 to 2C by adjusting model parameters. Through the comparative study with galvanostatic discharge experiment, the asymmetry of capacity contribution of each component during both charge and discharge, i.e., LMO contribution increases during discharging but decreases during charging when the C-rate is raised. Dynamic analysis of the blended cathode shows that this asymmetric charge/discharge behavior of the blended electrode can be attributed to the difference in the equilibrium potentials of the two components depending on Li concentration and electrode composition and to the difference in the rate of solid-state diffusion of Li and kinetics limitations in LMO and NMC. At last, a calendar life under various aging conditions has been studied, including analysis at various states of charge (SOC) i.e., 35℃-0% SOC, 58℃-0% SOC, 35℃-100% SOC and 58℃-100% SOC, for a commercial NMC-LMO/graphite blended lithium-ion battery. Through the analysis of post-mortem for the 280 days aged cell at 58℃-100% SOC with the remaining capacity of 55%, the loss of cycleable lithium is the predominant reason of capacity loss, which can lead to a passivation layer formation on the surface of graphite and gas generation. The fitting result of ‘open circuit voltage (OCV)-model’ indicates the about 40% active materials have not been utilized due to the lack of cycleable lithium and gas generation in the aged pouch cell. A non-destructive pressure-loading experiment has been implemented, which demonstrated a recovery of the capacity of the aged cell by 21%, and the reason of redistribution of gas bubbles under pressure inside the pouch cell has been described in detail.
Modeling and Characterization of Lithium Iron Phosphate Battery Electrodes
(University of Waterloo, 2016-09-29) Farkhondeh Borazjani, Mohammad; Fowler, Michael; Pritzker, Mark
A detailed understanding of lithiation/delithiation dynamics of battery active materials is crucial both for optimizing the existing technologies and for developing new materials. Among all, LiFePO4 (LFP) has been subject to intensive fundamental research due to its intriguing phase-transformation dynamics which, unexpectedly, yields an outstanding rate capability and a long cycle life for electrodes made of this insertion material. In this thesis mathematical models are used as cheap and simple tools to investigate the electrochemical performance of LFP electrodes. The thesis begins with the investigation of the solid-state transport (bulk effects) and electronic conductivity (surface effects) of LFP by means of variable solid-state diffusivity (VSSD) and resistive-reactant (RR) models, respectively. Both models are effectively validated against experimental galvanostatic discharge data over a wide range of applied currents. However, a very small solid-state diffusion coefficient (~〖10〗^(-19) "m" ^2 "s" ^(-1)) is required for both models to fit the experimental data. VSSD model features a particle-size distribution (PSD) which is estimated via model-experiment comparison. The fitted PSD, which is a geometric property and essentially invariable, requires to be different at different rates for the model to match experimental data; it is shifted towards smaller particles in order to accurately predict the electrode performance during galvanostatic discharge at higher applied currents. A contact-resistance distribution (CRD) replaces the PSD in the RR model. The fitted CRD turns out to be extremely broad spanning from ~1" to " ~〖10〗^2 "Ω" "m" ^2. Next, following recent observations of ultra fast lithiation/delithiation of LFP, a simple mesoscopic model is developed which, in contrast to the first part of this research, completely disregards solid-state diffusion limitations. Instead, the model accounts for the inherent inhomogeneity of physico-chemical properties and bi-stable nature of phase-change insertion materials such as LFP with no consideration of any geometric detail of the active material. The entire active material domain is discretized into meso-scale units featuring basic thermodynamic (non-monotonic equilibrium potential as a function of composition) and kinetic (insertion/de-insertion resistance) properties. With only these two factors incorporated, the model is able to simultaneously explain a number of unique features associated with lithium iron phosphate electrochemical performance including the quasi-static potential hysteresis, high rate capability, cycle-path dependence, mismatch in electrode polarization during GITT when compared with continuous cycling at the same current, bell-shaped current response in PITT and the most recently observed memory effect. Detailed analysis of the electrode dynamics suggests that a necessary condition for the memory effect to appear in an LFP electrode is the existence of a non-zero residual capacity at the onset of memory-release charging which may originate either from a non-zero initial SOC or from an imbalanced writing cycle. A memory effect should therefore not be observed in an electrode that has been preconditioned at extremely low currents (i.e., zero initial SOC) and has undergone an extremely slow memory-writing cycle (i.e., approaching a balanced cycle). In the next step, the mesoscopic model developed at the unit level is incorporated into porous-electrode theory and validated by comparing the simulation results with experimental data from continuous and intermittent galvanostatic discharge of a commercial LiFePO_4 electrode at various operating conditions. A bimodal lognormal resistance distribution is assumed to account for disparity of insertion dynamics among elementary units. Good agreement between the model and experimental data confirms the fidelity of the model. Investigation of three different GITT experiments suggests that the slow evolution of electrode polarization during each current pulse and the subsequent relaxation period is contributed by the inter-unit rather than intra-unit Li transport in LiFePO_4 electrodes. As such, GITT experiments once formulated for the determination of diffusion coefficient of inserted species in solid-solution systems may also be used to estimate the single-unit equilibrium potential (i.e., thermodynamic properties) as well as the dynamic properties (e.g., resistance distribution) of phase-change insertion materials. Further analysis of the GITT experiments suggest that, depending on the overall depth-of-discharge of the electrode and the incremental depth-of-discharge of each GITT pulse, the solid-solution capacity available in the Li-rich end-member may be able to accommodate Li insertion entirely without the need for active (closed-circuit) phase transformation. Instead, redistribution of Li among units during relaxation equilibrates the solid-solution composition by transforming a few Li-poor units to Li-rich ones. Despite rigorous research in the literature, this thesis presents the first attempt to quantitatively explain the above-mentioned irregularities simultaneously using a single unifying model and pinpoint the dominant contributing factors under various operating conditions. A realistic account of porous-electrode effects in the experimental validation of the mesoscopic model requires accurate estimation of the electrolyte transport properties. In addition to the modeling of phase-change electrodes, this thesis work presents a novel four-electrode-cell method to determine transport properties and the thermodynamic factor of concentrated binary electrolytes. The cell consists of two reference electrodes (i.e., potential sensors) in addition to the working and counter electrodes. The sensors measure the closed-circuit as well as open-circuit potential in response to an input current across the working and counter electrodes. The new method requires the application of only a single galvanostatic polarization pulse and appropriate concentration-cell experiments. By fitting a suitable model to the data obtained from these experiments, the three independent transport properties of a concentrated binary electrolyte, namely, ionic conductivity, diffusion coefficient and transference number as well as the thermodynamic factor can be determined. In particular, the measurement of the closed-circuit potential using this cell provides a simpler and essentially more accurate means to estimate the transference number than the conventional semi-infinite diffusion method.
Modeling and State Estimation of Lithium-Ion Battery Packs for Application in Battery Management Systems
(University of Waterloo, 2018-05-10) Mathew, Manoj, 1989-; Fowler, Michael
As lithium-ion (Li-Ion) battery packs grow in popularity, so do the concerns of its safety, reliability, and cost. An efficient and robust battery management system (BMS) can help ease these concerns. By measuring the voltage, temperature, and current for each cell, the BMS can balance the battery pack, and ensure it is operating within the safety limits. In addition, these measurements can be used to estimate the remaining charge in the battery (state-of-charge (SOC)) and determine the health of the battery (state-of-health (SOH)). Accurate estimation of these battery and system variables can help improve the safety and reliability of the energy storage system (ESS). This research aims to develop high-fidelity battery models and robust SOC and SOH algorithms that have low computational cost and require minimal training data. More specifically, this work will focus on SOC and SOH estimation at the pack-level, as well as modeling and simulation of a battery pack. An accurate and computationally efficient Li-Ion battery model can be highly beneficial when developing SOC and SOH algorithms on the BMS. These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effect of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out a large number of experiments, which can be too costly for many BMS manufacturers. Therefore, the first contribution of this research is to develop a comprehensive battery model, where the ECM parameter surface is generated using a set of carefully designed experiments. This technique is compared with existing approaches from literature, and it is shown that by using the proposed method, the same degree of accuracy can be obtained while requiring significantly less experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out a large number of experiments. Once a comprehensive model has been developed for SIL and HIL testing, research was carried out in advancing SOH and SOC algorithms. With respect to SOH, research was conducted in developing a steady and reliable SOH metric that can be determined at the cell level and is stable at different battery operating conditions. To meet these requirements, a moving window direct resistance estimation (DRE) algorithm is utilized, where the resistance is estimated only when the battery experiences rapid current transients. The DRE approach is then compared with more advanced resistance estimation techniques such as extended Kalman filter (EKF) and recursive least squares (RLS). It is shown that by using the proposed algorithm, the same degree of accuracy can be achieved as the more advanced methods. The DRE algorithm does, however, have a much lower computational complexity and therefore, can be implemented on a battery pack composed of hundreds of cells. Research has also been conducted in converting these raw resistance values into a stable SOH metric. First, an outlier removal technique is proposed for removing any outliers in the resistance estimates; specifically, outliers that are an artifact of the sampling rate. The technique involves using an adaptive control chart, where the bounds on the control chart change as the internal resistance of the battery varies during operation. An exponentially weighted moving average (EWMA) is then applied to filter out the noise present in the raw estimates. Finally, the resistance values are filtered once more based on temperature and battery SOC. This additional filtering ensures that the SOH value is independent of the battery operating conditions. The proposed SOH framework was validated over a 27-day period for a lithium iron phosphate (LFP) battery. The results show an accurate estimation of battery resistance over time with a mean error of 1.1% as well as a stable SOH metric. The findings are significant for BMS developers who have limited computational resources but still require a robust and reliable SOH algorithm. Concerning SOC, most publications in literature examine SOC estimation at the cell level. Determining the SOC for a battery pack can be challenging, especially an estimate that behaves logically to the battery user. This work proposes a three-level approach, where the final output from the algorithm is a well-behaved pack SOC estimate. The first level utilizes an EKF for estimating SOC while an RLS approach is used to adapt the model parameters. To reduce computational time, both algorithms will be executed on two specific cells: the first cell to charge to full and the first cell to discharge to empty. The second level consists of using the SOC estimates from these two cells and estimating a pack SOC value. Finally, a novel adaptive coulomb counting approach is proposed to ensure the pack SOC estimate behaves logically. The accuracy of the algorithm is tested using a 40 Ah Li-Ion battery. The results show that the algorithm produces accurate and stable SOC estimates. Finally, this work extends the developed comprehensive battery model to examine the effect of replacing damaged cells in a battery pack with new ones. The cells within the battery pack vary stochastically, and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the SOH of the pack can be maintained indefinitely above a specific threshold value. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework, as well as the results from this paper, can be utilized to better optimize Li-ion battery pack design in electric vehicles (EVs) and make long-term deployment of EVs more economically feasible.