Abstract
The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating per-formance and safety.The coupled SOC and SOH are dif-ficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estima-tion and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolu-tional network with accurate OCVs in time-sliding win-dows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from var-ious sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15 ℃ dynamic stress test and 25 ℃ constant current cycling was less than 0.9％and 0.8％,respectively.Notably,the pro-posed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.
维普
万方数据