A method for quick evaluating the capacity of lithium ion batteries under working conditions
The battery capacity is a key parameter for evaluating the state of health of lithium-ion battery.Accurate capacity estimation can prevent the battery system from working in an unhealthy state,which is crucial for extending the battery life.In this work,a quick capacity estimation approach is proposed by using a model based on an artificial neural network.Two commercial LiFePO4(LFP)battery cells are taken as the object of study here.The features derived from the charging and discharging voltage curves are used as inputs of a three-layer back propagation neural network.The results show that after training by 200 cycles data,a root-mean-square error(RMSE)of less than 0.5％can be achieved.The proposed capacity estimation method can obtain high prediction accuracy with a short detection time cost.Moreover,the algorithm weakly relies on continuous historical data,making it suitable for EMS.The features in the model can be directly calculated by the local data processing module and then transmitted to the upper computer or cloud platform,which greatly reduces the pressure of data transmission and improves the efficiency of the system.
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