A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method

Abstract

State-of-health (SOH) is crucial to the maintenance of various kinds of energy storage systems, including power batteries. Relevant research articles are mostly based on battery external information, such as current, voltage, and temperature, which are susceptible to fluctuation and ultimately affects the SOH estimation accuracy. In this article, to solve these problems, a fast impedance calculation-based battery SOH estimation method for lithium-ion battery is proposed from the perspective of electrochemical impedance spectroscopy (EIS). The relationship between EIS and state of charge and that between EIS and degraded capacity is first studied by experimental tests. Some impedance features called health factors effectively indicating battery aging states are selected. Second, an improved fast Fourier transform (FFT) utilizing the conversion relationship between the real and complex signals is proposed to realize online fast EIS acquisition. Compared with ordinary FFT, such treatments reduce computational complexity. Then, the SOH evaluation model is built by the extreme learning machine with regularization mechanism, further reducing the computational burden. The relationship between the health factors and aging capacity of batteries is established. Finally, an experimental bench is established. The results indicate that the estimated SOH can be obtained within 35 s for a four-cell series-connected battery pack and the estimation errors are less than 2%.