Multi-algorithm fusion-based state of energy assessment of retired lithium-ion batteries

The widespread use of lithium-ion batteries has somewhat alleviated the fossil energy crisis. However, when the state of health (SOH) of a lithium-ion battery drops to 80 %, it usually needs to be taken out of service from its original workplace. Accurately evaluating the state of energy (SOE) of retired batteries can effectively avoid unnecessary losses due to accidental failures of retired lithium-ion batteries in other application scenarios. Therefore, this paper proposes to come to the SOE estimation model of retired lithium batteries based on reinforcement learning Q-learning. Firstly, according to the unstable characteristics of retired Li-ion batteries, Q-learning is used to optimize the weight parameters of three algorithms, namely, back-propagation neural network (BPNN), long and short-term memory network (LSTM) and support vector regression (SVR), to establish the Q-BPNN-LSTM-SVR (QBLS) integration algorithm. Then, considering that the three factors of temperature, voltage, and current affect the state of energy of retired lithium-ion batteries, they are selected as inputs to the QBLS algorithm. Finally, the QBLS algorithm estimates the SOE of individual retired batteries and retired battery packs at different temperatures and operating conditions. The results demonstrate that the method has high precision in estimating the SOE of retired Li-ion batteries, with a Max AE less of than 3 %, and the maximum values of MAE and RMSE do not exceed 0.70 % and 0.80 %, respectively. The method above offers a dependable foundation for using and administering retired lithium-ion batteries. •The effects of real-time current, voltage and temperature on the SOE characteristics of Li-ion batteries are considered.•Combining LSTM, BPNN and SVR algorithms using Q-learning•The test conditions are designed to meet the actual working conditions of retired lithium batteries.•The maximum error of the method does not exceed 3 %.