Abstract
State of charge(SOC) and state of health(SOH) estimation are two critical aspects for the secure operation and echelon utilisation of lithium-ion batteries. Considering the two states are interrelated, combined estimation methods are desired in practice. However, previous machine learning-based or traditional filter-based techniques have high computational requirements or suffer from convergence issues. To this end, the combined SOC and SOH estimation method of lithium-ion batteries using the novel correlation between SOC and SOH is proposed in this study. Specifically, a correlation between the capacity ratio for constant-current charging process and constant-voltage charging process and capacity loss is established. Extended Kalman filter is adopted to track SOC values at the end of constant current charging process instead of relying on the entire charging process during online application and SOH estimation can be accomplished in only one step based on the established correlation. The results show that the proposed combined SOC and SOH estimation method enables precise SOC estimation during both charging and discharging processes and achieves reliable estimation results during battery degradation. Additionally, the proposed method offers a swift means of estimating SOH with guaranteed estimation accuracy.
Published Version
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