基于多域混合模型的锂电池多耦合状态联合估计算法

doi:10.19595/j.cnki.1000-6753.tces.251202

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Abstract Aiming at the requirements of application scenarios for EV, this paper focused on the accurately expressing problem of the bidirectional mapping of the electro-thermal complex coupling relationship (E/T-CCR) within the battery, with the ultimate goal of improving the accuracy of battery state estimation. Taking 18650 lithium batteries as the research object, a multi-coupled states joint estimation algorithm for (including SOC, SOP, SOT, and capacity states) lithium batteries based on multi-domain hybrid model (MDHM) was proposed. The proposed MDHM had better accuracy and dynamic tracking performance under complex operating conditions. The proposed multi-coupled states joint estimation algorithm enabled high-accuracy joint estimation of 4 key battery states.
The general execution process of the algorithm is as follows:Firstly, in the offline part, the coupling domain of MDHM based on long short-term memory (LSTM) was constructed to initially realize the bidirectional mapping expression of E/T-CCR for the batteries. Secondly, in the online part, the constructed LSTM, autoregressive equivalent circuit model (AR-ECM), and single state lumped thermal model (SSTM) together formed MDHM. They jointly describe the complex electro-thermal dynamic characteristics for batteries. Among them, the E/T-CCR of the electrical and thermal domains was determined by the coupling domain based on real-time battery operating data, and was corrected by the coupling domain corrector based on adaptive square root unscented Kalman filter (ASRUKF) before finally outputting to the state estimator. Finally, the state estimator achieved real-time joint estimation of SOC, SOT, and capacity based on the obtained dynamic E/T-CCR. SOP estimator implemented online estimation of SOP based on multi-constraints (Such as current I, terminal voltage U_d, SOC and SOT).
By comparing with 2 typical multi-state joint estimation methods based on electro-thermal coupling model (ETCM) in 12 temperature scenarios under 3 dynamic operating conditions including aging for 2 types of batteries, the experimental results show that: Under 2 extreme temperatures of 50℃ and -10℃, the proposed MDHM has good accuracy when facing DST and mixed operating conditions. The MDHM can achieve an average improvement of 2.589 mV in electrical model accuracy and an average improvement of 0.027℃ in thermal model accuracy. The multi-coupled states joint estimation algorithm proposed has more stable RMSE over a wide temperature range of [10, 50]℃. It can improve the SOC, SOT and capacity accuracies by an average of 0.094%, 0.026℃, and 0.002 A·h, respectively. In addition, by comparing with MDHM without coupling domain corrector, the MDHM with coupled domain corrector can achieve an average improvement of 1.389 mV in electrical model accuracy and 0.019℃ in thermal model accuracy. This indicates that the stability of the coupling domain can be effectively compensated by the proposed coupling domain corrector based on ASRUKF.
The following conclusions can be drawn from the experimental analysis: (1) The coupling domain in MDHM can more accurately express battery E/T-CCR in scenarios such as EV characterized by wide temperature, wide power, and wide discharge depth operating conditions. (2)The coupling domain corrector can enhance the stability, generalization ability, and reducing errors caused by differences in battery consistency and aging. (3) The proposed SOP estimation that considers SOT constraint is quite necessary. Firstly, the SOT constraint always plays a dominant role in the battery charging/discharging process. Secondly, as the continuous charging/discharging time increases, SOP estimation considering SOT constraint is more critical. In addition, in low-temperature scenarios, SOP estimation considering SOT constraint can avoid battery damage. Finally, in aging scenarios, due to the increase in battery internal resistance and faster temperature rise inside the battery, SOP estimation considering SOT constraint is more important for the battery safety.

Key words： Electro-thermal coupling model long short-term memory (LSTM) multi-coupled states adaptive square root unscented Kalman filter (ASRUKF)

Received: 08 July 2025

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TM912

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	Liu Fang
	Liu Jiayi
	Su Weixing
	Sun Liankun

Cite this article:

Liu Fang,Liu Jiayi,Su Weixing等. Multi-Coupled States Joint Estimation Algorithm for Lithium Battery Based on Multi Domain Hybrid Model[J]. Transactions of China Electrotechnical Society, 2026, 41(15): 5359-5374.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251202 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I15/5359

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