Research on battery SOH estimation method based on electrochemical impedance spectroscopy

Lithium-ion batteries are widely used in electric vehicles, energy storage systems and portable electronic devices due to their high energy density, high power density and long service life. Battery state-of-health (SOH) has become a focus of current research due to the difficulty of accurately obtaining it in real time. Most of the existing SOH estimation methods are unable to establish the intrinsic connection between their intrinsic aging mechanisms, thus affecting the SOH estimation accuracy. In this paper, a method for SOH estimation of battery based on electrochemical impedance spectroscopy (EIS) is proposed. Firstly, the relationship between EIS, state of charge (SOC) and SOH is discussed through experimental analysis, and 7 feature factors were selected to characterize the aging state of the battery. Then, the extreme learning machine algorithm is used to train the network with selected feature factors to achieve SOH estimation. Finally, an experimental platform is set up to verify the proposed method. The results show that the maximum relative error of SOH estimation is 2.06%, and the maximum relative error of another battery in the same batch is 2.23%.