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Reduced order model-based observer design for online temperature distribution estimation in lithium-ion batteries

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Abstract

Time/space separation-based modeling methods have been widely researched for estimating lithium-ion battery (LIB) thermal dynamics. However, these methods have been developed in an offline environment and may not perform well in real-time application since the battery systems in electric vehicles (EVs) are usually subject to external disturbances. Furthermore, the onboard measurements of temperature are often corrupted by significant error. To address these problems, we present a reduced model-based observer design for online temperature distribution estimation in LIBs. First, an extreme learning machine (ELM)-based offline spatiotemporal model is constructed to approximate the thermal dynamics of LIB. Second, an adaptive reduced order observer is designed based on the offline model developed in the previous step. The offline model is then updated with the estimation results of the observer. As the performance of the estimator is highly related to the placement of sensors, a genetic algorithm (GA)-based integrated optimization strategy is also developed to determine the optimal sensor location for online estimation. Finally, the whole temperature distribution is estimated in real time using the observer, the measured voltage, current and the limited available temperature data. Two experiments on different batteries with different input currents verify the effectiveness of this developed model.

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Data Availability

All data generated during the study are available from the corresponding author by request.

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Funding

This project was supported by the National Natural Science Foundation of China (Grant Nos. 51905109, 71701136), and by the Natural Science Foundation of Guangdong Province (Grant Nos. 2021A1515011971, 2022A1515011009).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Bi Fan & Yufen Zhuang

  2. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Kangkang Xu

  3. China International Fund Management Co. Ltd., Shanghai, 200120, China

    Zhen Liu

  4. College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

    Min Gan

  5. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Min Gan

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  1. Bi Fan
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Correspondence to Kangkang Xu.

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Fan, B., Zhuang, Y., Liu, Z. et al. Reduced order model-based observer design for online temperature distribution estimation in lithium-ion batteries. Nonlinear Dyn 111, 3327–3344 (2023). https://doi.org/10.1007/s11071-022-08025-x

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