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

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... 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. INDEX TERMS Equivalent internal resistance, lithium-ion battery, real-time, SoH estimation, support vector regression. NOMENCLATURE ABBREVIATIONS DI ZHAN received the B.E. degree from Sun Yat-sen University, in 2018, where she is currently pursuing the M.E. degree in transportation engineering with the School of Intelligent Systems Engineering. Her research interests include battery energy management and control strategy of electric vehicle. ZE YU received the B.E. and M.E. degrees in automotive engineering from Chang'an University, China, in 2012 and 2016, respectively. He is currently pursuing the Ph.D. degree with the School of Intelligent Systems Engineering, Sun Yat-sen University. His research interests include artificial intelligence, deep learning, and intelligent vehicles.