Electric and Hybrid Vehicle Technologies
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An optimization method for online energy management based on Long Short-term Memory (LSTM) driving condition recognition is proposed and showed that the proposed strategy reduces fuel consumption to 3.12 L/100 km, while the strategy without driving condition recognition consumes 3.45 L/100 km, achieving a 9.57% reduction in overall fuel consumption.
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To solve the problem of poor real-time performance and adaptability in existing minimum equivalent fuel consumption strategies for Plug-in Hybrid Electric Vehicles (PHEVs), an optimization method for online energy management based on Long Short-term Memory (LSTM) driving condition recognition is proposed. Firstly, an equivalent energy consumption model is established by fitting the engine and motor’s instantaneous energy consumption curves, and the optimal torque distribution is solved to improve the Equivalent Consumption Minimization Strategy (ECMS) optimization process. Secondly, an online energy management strategy is developed based on LSTM method, which is used to enhance adaptability of this strategy to driving conditions. Thirdly, the proposed energy management strategy is tested based on a random driving condition. The result showed that the proposed strategy reduces fuel consumption to 3.12 L/100 km, while the strategy without driving condition recognition consumes 3.45 L/100 km, achieving a 9.57% reduction in overall fuel consumption.
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@article{WANG2026Research,
title = {Research on online energy management strategy for plug-in hybrid electric vehicles based on condition recognition},
author = {Dongyang WANG and Xiang Hui Lu and Guanlong Sun and Ke Ma},
journal = {Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering},
year = {2026},
doi = {10.1177/09544070261491952},
url = {https://doi.org/10.1177/09544070261491952}
}FAQ
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