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An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation

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  • Shu, Xing
  • Li, Guang
  • Shen, Jiangwei
  • Lei, Zhenzhen
  • Chen, Zheng
  • Liu, Yonggang

Abstract

Accurate estimation of inner status is vital for safe reliable operation of lithium-ion batteries. In this study, a temperature compensation-based adaptive algorithm is proposed to simultaneously estimate the multi-state of lithium-ion batteries including state of charge, state of health and state of power. In the proposed co-estimation algorithm, the state of health is identified by the open circuit voltage-based feature point method. On the basis of accurate capacity prediction, the state of charge is estimated by the adaptive extended Kalman filter with a forgetting factor considering temperature correction. The state of power is determined according to the multi constraints subject to state of charge, operating temperature and maximum current duration. The substantial experimental validations in terms of different current profiles, aging status and time-varying temperature operating conditions highlight that the proposed algorithm furnishes preferable estimation precision with certain robustness, compared with the traditional extended Kalman filter and the adaptive extended Kalman filter. Moreover, the battery pack validation is performed to further justify the feasibility of proposed algorithm when employed in a product battery management system.

Suggested Citation

  • Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313694
    DOI: 10.1016/j.energy.2020.118262
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    5. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    6. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    7. Li, Qingbo & Zhong, Jun & Du, Jinqiao & Yi, Yong & Tian, Jie & Li, Yan & Lai, Chunyan & Lu, Taolin & Xie, Jingying, 2024. "Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits," Energy, Elsevier, vol. 294(C).
    8. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    9. Gao, Yizhao & Zhu, Chong & Zhang, Xi & Guo, Bangjun, 2021. "Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system," Energy, Elsevier, vol. 221(C).
    10. Xu, Bowen & Lu, Xinjiang & Bai, Yunxu & Xu, Jie, 2024. "A convolution and memory network-based spatiotemporal model for thermal dynamics of multiple heat sources and its application in serial-connected lithium batteries," Energy, Elsevier, vol. 293(C).
    11. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    12. Wang, Chao & Zhang, Xin & Yun, Xiang & Meng, Xiangfei & Fan, Xingming, 2023. "Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm," Energy, Elsevier, vol. 285(C).
    13. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    14. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    15. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).

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