An Adaptive Fast Charging Strategy Considering the Variation of DC Internal Resistance

In this article, an adaptive fast charging strategy for lithium-ion batteries considering the variation of dc internal resistance (DCIR) is proposed, which applies to mobile applications. The experiments prove that the DCIR obeys the Gaussian distribution during the normal charging stage. However, the DCIR no longer obeys Gaussian distribution once the battery is fully charged. The proposed strategy employs the maximum-likelihood method to estimate the expectation and variance of the Gaussian distribution. The charging mode switches from constant current (CC) to constant voltage (CV) immediately when the DCIR deviates significantly from the expectation. Under the premise of satisfying the high accuracy for the parameter estimates, a constrained optimization problem is established to determine the DCIR minimum number of measurements ( n min ), and the constraints are transformed into hypothesis testing for solving. The CC operating time is maximized based on n min and the adaptive control of the transition point. The experiments verify the validity of the proposed method. The results show that compared with the CC-CV method, the proposed strategy saves 52.2%, 41.57%, and 29.72% of the charging time at 1C, 0.75C, and 0.5C, respectively, which effectively improves the charging speed and prevents overcharging.