基于不同工况下的锂离子电池可用容量预测模型

Lithium-ion batteries (LIBs) are the main power source of many devices; hence, the accurate prediction of their usable capacity under different operating conditions is crucial. In response to the limitations of the current Peukert equation, which can only be applied to predict the available capacity of LIBs under constant temperature and current discharge conditions, this study proposes an optimization model for predicting the available capacity of LIBs under different operating conditions. The accurate prediction of the available capacity of LIBs under variable temperature and rate conditions is realized by improving the Peukert equation and providing a reasonable coefficient generation method. The discharge performance of LIBs at different temperatures and discharge rates is tested through experiments. The curve between the battery capacity retention rate and the average battery temperature is then fitted. The Arrhenius equation is used for the analysis, and the equation parameters are determined using the least squares method. The calculations conducted under various discharge conditions based on the predicted optimization model verify that the proposed equivalent capacity method accurately predicts the battery's actual discharge capacity. Finally, the predictive optimization model and experiments are used to confirm the effect of temperature on the battery capacity. The impact of the battery capacity on the discharge rate is found to be relatively small when the ambient temperature is above 25 ℃ . The ambient temperature below 25 ℃ significantly affects the battery capacity, showing a decreasing to increasing trend with the discharge rate increase. The results indicate that the average temperature significantly affects the battery capacity. Moreover, the high temperature has a smaller effect than the low temperature. Therefore, a temperature compensation coefficient k must be introduced to consider the effect of the average temperature on the battery capacity.