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- W3138344186 abstract "An accurate driving cycle prediction is a vital function of an onboard energy management strategy (EMS) for a battery/ultracapacitor hybrid energy storage system (HESS) in electric vehicles. In this paper, we address the requirements to achieve better EMS performances for a HESS. First, a long short-term memory-based method is proposed to predict driving cycles under the framework of a model predictive control (MPC) algorithm. Secondly, the performances of three EMSs based on fuzzy logic, MPC, and dynamic programming are systematically evaluated and analyzed. For online implementation, the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS. Thirdly, the impact of battery aging on EMS performances is explored; it indicates that battery aging consciousness can slightly extend battery life. Finally, a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles." @default.
- W3138344186 created "2021-03-29" @default.
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- W3138344186 date "2020-01-01" @default.
- W3138344186 modified "2023-09-23" @default.
- W3138344186 title "Model predictive control based real-time energy management for a hybrid energy storage system" @default.
- W3138344186 doi "https://doi.org/10.17775/cseejpes.2020.02180" @default.
- W3138344186 hasPublicationYear "2020" @default.
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