Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle. (15th June 2022) - Zhou, Wei; Cai, Xuan; Chen, Yaoqi; Li, Junqiu; Peng, Xiaoyan
Highlights: An analytical optimal energy management policy is derived for a series plug-in hybrid electric vehicle. Four aggregated optimal charge depletion behaviors are revealed through rigorous theoretical analysis. The intrinsic and extrinsic mechanisms behind the optimal charge depletion behaviors are explained. An energy-domain SoC planning approach that intelligently exploits driving data statistics based on identified charge depletion behaviors are proposed. Abstract: A critical issue for designing predictive energy management (PEM) strategy of Plug-in Hybrid Electric Vehicles is the planning of optimal global charge trajectory. Existing planning methods have flaws in terms of optimality or computational efficiency due to their lack of in-depth consideration about optimal charge depletion behaviors. To address this issue, rigorous theoretical analysis on the aggregated local and global optimal charge depletion behaviors in energy domain is conducted by combining Pontryagin's Minimum Principle-based analytical derivations and some qualitative reasoning. Fundamental understanding on how the optimal charge depletion rates behave in different driving conditions and why they exhibit such behaviors is provided. The theoretical analysis is further validated through model-in-the-loop tests using an experimentally validated high-fidelity vehicle simulator. The insights gained from the analysis of this paper establish a fundamental knowledge foundation and may pave a new path for more scientific PEM design in the future.