Modeling of Lithium-ion Battery Performance and Thermal Behavior in Electrified Vehicles
Seyed Ehsan, Samadani
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Electric vehicles (EVs) have received significant attention over the past few years as a sustainable and efficient green transportation alternative. However, severe challenges, such as range anxiety, battery cost, and safety, hinder EV market expansion. A practical means to reduce these barriers is to improve the design of the battery management system (BMS) to accurately estimate the battery state of charge (SOC) and state of health (SOH) in addition to communicating with other powertrain components. Along with a robust estimation strategy, a critical requirement in developing an efficient BMS is a high fidelity battery model to predict the battery voltage, SOC, and heat generation profile at various temperature and power demands. Such a model should also be able to capture battery degradation, which is a path-dependent parameter that affects the battery performance in terms of output voltage, power capability and heat generation. In this thesis, the Li-ion battery, a proven technology for electrified vehicles, is studied under different operation scenarios on a plug-in hybrid vehicle (PHEV). The following steps have been accomplished: 1- Development of a data-driven battery thermal model: A set of thermal characterization tests are conducted on Li-ion cells. Heat generation profiles of each battery are driven for a set of operating points including various ambient temperatures, states of charge (SOCs) and load profiles. A regression model is developed accordingly which is able to accurately predict the battery temperature during a driving or charging event. The model shows an average error of 4% in temperature predictions. 2- Development of a data-driven battery performance model for real-time on-board applications: An equivalent circuit model is developed based on the electrochemical impedance spectroscopy (EIS) tests. This model can precisely predict the battery operating voltage under various operating conditions. An overall 6% improvement is observed in voltage prediction compared to common models in the literature. Results also show, depending on the powertrain designer expected accuracy, that this model can be used to predict the battery internal resistance obtained from hybrid pulse power characterization (HPPC) tests. 3- Battery degradation studies through field tests: An electrified Ford Escape vehicle is tested through random and controlled driving and charging events and battery data is collected and analyzed to identify trends of degradation including capacity fade and power fade. A battery life model is recalibrated based on the measured battery capacities over the field test period. Although, data shortage and technical issues prevented this study from meeting its targeted scope, the presented analysis provides a pathway for future research. 4- Battery lifetime modeling: fuel consumption, all-electric range and battery capacity loss are simulated under various scenarios including different climate control loads, ambient conditions, powertrain architectures and battery preconditioning. To simulate the climate control loads impact, a vehicle cabin thermal model is developed that incorporates the ambient conditions to predict the temperature profile of the cabin and the cooling/heating load required to regulate the temperature. Accordingly, this load is translated into additional load on the battery, which enables assessment of its impacts on the battery life, fuel consumption and vehicle range.