Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Abstract

Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

Keywords

• Equivalent internal resistance
• lithium-ion battery
• real-time
• SoH estimation
• support vector regression