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International Journal of Automotive Technology > Volume 23(2); 2022 > Article
International Journal of Automotive Technology 2022;23(2): 503-510.
doi: https://doi.org/10.1007/s12239-022-0046-6
UNSCENTED PARTICLE FILTER BASED STATE OF ENERGY ESTIMATION FOR LiFePO4 BATTERIES USING AN ONLINE UPDATED MODEL
Xie Wei1, Chen Jun2, Gu Yu3, Ma Jiachen1, Chang Jiaqing2
1School of Information Science and Engineering
2School of Naval Architecture and Ocean Engineering
3System Engineering Research Institute of CSSC
PDF Links Corresponding Author.  Xie Wei , Email. weixie@hit.edu.cn
ABSTRACT
Range issue has become the concern focus in the field of electric vehicles. In contrast to the generally used State-of-Charge (SoC), battery State-of-Energy (SoE) is regarded more appropriate in representing the remnant driving range by taking account of the voltage decline across the discharging process. In this paper, a SoE estimator is constructed using a pseudo power definition upon battery open-circuit-voltage (OCV) to exclude the energy loss on internal resistance; simultaneously, by combining with an equivalent circuit model (ECM), the unscented particle filter (UPF) is exploited to deal with problems of model nonlinearities, internal interferences, sensor noises and accumulated errors. Further, to adapt to battery time-variant features, the ECM parameters are on-line identified resorting to the recursive least square with forgetting factor algorithm. Finally, SoE estimation experiments using the proposed estimator on a LiFePO4 battery show superior performance regarding robustness and accuracy against high-dynamic loads and various temperatures.
Key Words: LiFePO4 battery, State of energy, Unscented particle filter, Recursive least square with forgetting factor
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