Online estimation of lithium-ion batteries state of health during discharge

In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed-loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium-ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship.

Automated summary

This study proposed an SOH estimation framework that can automatically correct errors caused by battery consistency, providing accurate estimation of battery health status during electric vehicle charging and discharging. By introducing an equivalent circuit based on the AR model, the complexity of the method was reduced while maintaining estimation accuracy. Comparing with traditional external feature relationship methods, this framework achieves better practicality and higher estimation accuracy in estimating lithium-ion battery SOH during discharge.