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Open AccessArticle

An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

1
Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China
2
Anhui Intelligent Vehicle Engineering Laboratory, Hefei 230009, China
3
Department of Electrical Engineering, Sichuan University, Chengdu 610065, China
4
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
*
Authors to whom correspondence should be addressed.
Energies 2020, 13(2), 478; https://doi.org/10.3390/en13020478
Received: 12 December 2019 / Revised: 12 January 2020 / Accepted: 14 January 2020 / Published: 18 January 2020
(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)
In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP. View Full-Text
Keywords: lithium-ion battery; state estimation; state of charge; genetic particle filter; state of power lithium-ion battery; state estimation; state of charge; genetic particle filter; state of power
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Liu, X.; Zheng, C.; Wu, J.; Meng, J.; Stroe, D.-I.; Chen, J. An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries. Energies 2020, 13, 478.

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