Data-driven state of health estimation in retired battery based on low and medium-frequency electrochemical impedance spectroscopy

Electrochemical impedance spectroscopy (EIS) is one of the critical techniques to characterize the state of health (SoH) of lithium-ion batteries, which has different performances under different SoH and dynamic working conditions. However, the identification parameters of full-frequency EIS will consume too much computing time. Considering that low and medium-frequency EIS plays a dominant role in battery aging, the present paper constructs a simplified equivalent circuit (SECM) based on low and medium-frequency EIS. Based on the fitted SECM parameters, three SECM parameters are selected as features by grey correlation analysis. Combined with the ambient temperature and SoC under dynamic conditions, the Gated recurrent unit neural network estimates the SoH of lithium-ion batteries. The proposed method has good accuracy and robustness for different SoC states and ambient temperature variations. This method's error values are less than 2 %, which proves the critical value of low and medium-frequency EIS signals in battery management systems.

Automated summary

Electrochemical impedance spectroscopy (EIS) is a critical technique for evaluating the state of health (SoH) of lithium-ion batteries, but it consumes a lot of computing time. This paper proposes a simplified equivalent circuit (SECM) based on low and medium-frequency EIS, which plays a dominant role in battery aging. By using grey correlation analysis, three SECM parameters are selected as features, and the Gated recurrent unit neural network is used to estimate the SoH of lithium-ion batteries under dynamic conditions with ambient temperature and state of charge (SoC). The proposed method has good accuracy and robustness with an error less than 2%, highlighting the importance of low and medium-frequency EIS signals in battery management systems.