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A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation

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  • Zhang, Shuzhi
  • Zhang, Chen
  • Jiang, Shiyong
  • Zhang, Xiongwen

Abstract

To achieve more precise and reliable lithium-ion battery state-of-charge (SOC) estimation, this paper performs a comparative study of different adaptive extended Kalman filters (AEKFs)/adaptive unscented Kalman filters (AUKFs). Firstly, three scenarios are artificially established to evaluate different AEKFs/AUKFs' estimation accuracy, sensitivity to uncertainty existing in open-circuit-voltage (OCV)–SOC relationship and robustness ability against different forms of disturbances, respectively. Meanwhile, various AEKFs/AUKFs' difficulty of parameters tuning is also evaluated according to our experience. Subsequently, eight indexes that can reflect algorithms' comprehensive estimation performance are further extracted. On this basis, a novel multi-objective analysis decision method by fusion of analytic hierarchy process and entropy weight is adopted to allocate weights for extracted indexes and further compare various AEKFs/AUKFs’ comprehensive estimation performance, whose results are shown as scores. The algorithm with highest score demonstrates that it has the optimal comprehensive estimation performance and is also recommended to be used in real application. The most remarkable contribution of this work lies in the suggestions and guidance for researchers when choosing AEKFs/AUKFs for online SOC estimation.

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  • Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222003267
    DOI: 10.1016/j.energy.2022.123423
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    References listed on IDEAS

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    Cited by:

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    2. Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Bage, Alhamdu Nuhu & Bobobee, Etse Dablu & Appiah, Emmanuel & Huang, Qi, 2024. "Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data," Applied Energy, Elsevier, vol. 363(C).
    3. Li, Kuo & Gao, Xiao & Liu, Caixia & Chang, Chun & Li, Xiaoyu, 2023. "A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method," Energy, Elsevier, vol. 269(C).
    4. Zhang, Shuzhi & Jiang, Shiyong & Wang, Hongxia & Zhang, Xiongwen, 2022. "A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack," Applied Energy, Elsevier, vol. 322(C).
    5. Xu, Maoshu & Zhang, E. & Wang, Sheng & Shen, Yi & Zou, Binchen & Li, Haomiao & Wan, Yiming & Wang, Kangli & Jiang, Kai, 2024. "Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures," Applied Energy, Elsevier, vol. 355(C).
    6. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    7. Zhang, Shuzhi & Zhang, Qiang & Liu, Dayong & Dai, Xian & Zhang, Xiongwen, 2022. "State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically," Energy, Elsevier, vol. 257(C).
    8. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.

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