变温度工况下锂离子电池健康状态退化预测

doi:10.3901/JME.260214

摘要/Abstract

摘要： 变温度工况下锂离子电池的健康状态(State of health，SOH)退化预测对其健康管理和安全可靠运行至关重要。现有参数化方法由于对退化轨迹的强先验假设，不适用于在不同温度下具有多样退化模式的锂离子电池。为了准确预测变温度下的SOH退化，首先提出SOH退化轨迹和变换系数的非参数估计方法，在避免参数化模型强假设前提下建模不同温度下的SOH退化规律。其次，提出了温度-变换系数关系的自适应多项式拟合方法，其综合损失函数保证了拟合的准确性、鲁棒性和合理性，可以实现任意恒定温度下的SOH退化预测。然后，提出变温度工况下SOH退化的离线预测方法，其中的等效循环数能将累积退化量对未来SOH退化的影响考虑在内。最后，提出变温度工况下SOH退化预测的在线更新方法，通过变温度交互系数将历史温度对SOH退化的影响纳入预测结果的更新修正。通过试验数据验证了所提方法的有效性和相比其他常用方法的优越性。恒定温度下SOH预测的平均绝对误差至少比其他方法低32%；变温度下的平均绝对误差最低可达0.002 6。本研究能为实际复杂变温度工况下锂离子电池的健康状态退化和寿命提供准确预测，有效保障其健康管理和安全可靠运行。

关键词: 变温度工况, 锂离子电池, 健康状态, 退化预测, 非参数化方法

Abstract: State of health(SOH) degradation prediction of lithium-ion batteries under variable temperatures is crucial for their health management, safety, and reliability. Existing parametric methods with strong prior assumptions on degradation trajectories are not suitable for batteries with diverse degradation modes at different temperatures. To accurately predict SOH degradation, a non-parametric estimation method for SOH degradation trajectory and its transformation coefficient is proposed, which models the SOH degradation at different temperatures without strong assumptions. Secondly, an adaptive polynomial fitting method for temperature and transformation coefficient is proposed, whose comprehensive loss function ensures the fitting accuracy, robustness, and rationality, and can achieve SOH degradation prediction at any constant temperature. Then, an offline SOH degradation prediction method under variable temperatures is proposed, where the equivalent cycle number can consider the impact of cumulative degradation on future SOH degradation. Finally, an online update method is proposed, which incorporates the influence of historical temperature into the updated prediction through the variable temperature interaction coefficient. The effectiveness of the proposed method and its superiority over commonly used methods are verified by experimental data. The mean absolute error of SOH prediction at constant temperature is at least 32% lower than other methods, and the minimum mean absolute error under variable temperature can reach 0.002 6. This study can provide accurate predictions for the SOH degradation and lifetime of lithium-ion batteries under complex and variable temperature conditions, effectively ensuring their health management and safe and reliable operation.

Key words: variable temperature conditions, lithium-ion battery, state of health, degradation prediction, non-parametric method

中图分类号:

TM912

王聪, 陈云霞, 王烽, 梁华. 变温度工况下锂离子电池健康状态退化预测[J]. 机械工程学报, 2026, 62(10): 273-285.

WANG Cong, CHEN Yunxia, WANG Feng, LIANG Hua. State of Health Degradation Prediction of Lithium-ion Batteries under Variable Temperature Conditions[J]. Journal of Mechanical Engineering, 2026, 62(10): 273-285.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260214

http://www.cjmenet.com.cn/CN/Y2026/V62/I10/273

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