Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators

Abstract

Accurately estimating the battery State of Health (SOH) is crucial for the safe and reliable operation of electric vehicles. Based on the actual operating data of electric buses, this article proposes a battery SOH estimation method that can be applied to multiple operating conditions and indicators. Specifically, the complex operating conditions are simplified into charging and driving conditions through data preprocessing. Under charging conditions, combined with Coulomb counting and incremental capacity analysis methods, a battery SOH estimation model of capacity indicators based on the Bayesian optimization bidirectional gated recursive unit model (BO-BiGRU) is established. Under driving conditions, the adaptive forgetting factor recursive least squares method considering the influence of current is used to identify the battery internal resistance feature. In addition, two separate battery SOH estimation models are established: one for internal resistance indicators based on BO-BiGRU and another for power indicators derived from the actual operational data feature. Finally, a joint battery SOH estimation method considering temperature and different operating conditions is proposed based on the SOH estimation results of the three battery indicators. The verification results show that the average error of the battery SOH estimation method proposed in this article is less than 2%, which has better accuracy for actual vehicles.