基于IViT的锂离子电池健康状态估计

Lithium-ion battery state of health estimation based on IViT

准确预测锂离子电池的健康状态(SOH)至关重要。针对电池单个周期的不同阶段退化机制存在差异和实际运用场景下数据获取不完整等挑战,提出一种基于Involution-Vision Transformer(IViT)的锂离子电池SOH估计方法。从电压时间曲线中自动提取能有效表征锂离子电池退化信息的特征,使用Involution模块在不同位置上自适应地分配权重,利用Vision Transformer学习不同阶段的高级特征表示并捕获全局依赖关系。实验结果表明,IVIT的预测误差在0.5%左右,且当整体数据缺失50%的情况下误差仅为2%左右,证明了所提方法的有效性和稳定性。

It is essential to accurately predict the state of health(SOH)of lithium-ion batteries.Aiming at challenges such as differences in degradation mechanisms at different stages of a single battery cycle and incomplete data acquisition in practical utilization scenarios,a lithium-ion battery SOH estimation method based on Involution-Vision Transformer(IViT)is proposed.Features that can effectively characterize the degradation information of lithium-ion batteries are automatically extracted from the voltage-time profile,weights are adaptively assigned at different positions using the Involution module,and Vision Transformer is used to learn the high-level feature representations at different stages and capture the global dependencies.The experimental results show that the prediction error of IVIT is around 0.5%,and the error is only around 2%when the overall data is missing 50%,proving the effectiveness and stability of the proposed method.