Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle
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Date
2016-11-15
Authors
Wang, Hong
Huang, Yanjun
Khajepour, Amir
Song, Qiang
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
The series hybrid electric tracked bulldozer (HETB)’s fuel economy heavily depends on its energy management strategy. This paper presents a model predictive controller (MPC) to solve the energy management problem in an HETB for the first time. A real typical working condition of the HETB is utilized to develop the MPC. The results are compared to two other strategies: a rule-based strategy and a dynamic programming (DP) based one. The latter is a global optimization approach used as a benchmark. The effect of the MPC’s parameters (e.g. length of prediction horizon) is also studied. The comparison results demonstrate that the proposed approach has approximately a 6% improvement in fuel economy over the rule-based one, and it can achieve over 98% of the fuel optimality of DP in typical working conditions. To show the advantage of the proposed MPC and its robustness under large disturbances, 40% white noise has been added to the typical working condition. Simulation results show that an 8% improvement in fuel economy is obtained by the proposed approach compared to the rule-based one.
Description
The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.apenergy.2016.08.085 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Series hybrid electric tracked bulldozer, Energy management strategy, Model predictive control, Rule-based, Dynamic programming, Robustness