The safety of lithium-ion batteries under mechanical abuse has become one of the major obstacles affecting the development of electric vehicles. In this paper, the lithium-ion battery safety model under mechanical abu...The safety of lithium-ion batteries under mechanical abuse has become one of the major obstacles affecting the development of electric vehicles. In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network(BP-ANN) optimized by the Genetic Algorithm(GA). By experimental and simulation results, the proposed method can effectively predict battery mechanical properties. The corresponding correlation coefficient is greater than 0.99, the failure warning model has more safety margin, and the average security margin is greater than 29%. The multi-source warning weight shows that the mechanical soft short-circuit has the greatest warning margin, followed by that of the soft short-circuit of the electrical signal. The thermal soft short-circuit has the lowest warning margin because of the low thermal conductivity. The qualitative simulation results of the battery module reveal that, when electric vehicles are subjected to mechanical abuse conditions, the rapid reduction in the state-of-charge(SOC) of the rear batteries can effectively increase the reliability of the battery module. The proposed safety model is important to protect the safety and stability of lithium-ion batteries, which is conducive to promoting new energy vehicles and protecting the environment.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52072039)the Key R&D Program of Beijing(Grant No.Z181100004518005)。
文摘The safety of lithium-ion batteries under mechanical abuse has become one of the major obstacles affecting the development of electric vehicles. In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network(BP-ANN) optimized by the Genetic Algorithm(GA). By experimental and simulation results, the proposed method can effectively predict battery mechanical properties. The corresponding correlation coefficient is greater than 0.99, the failure warning model has more safety margin, and the average security margin is greater than 29%. The multi-source warning weight shows that the mechanical soft short-circuit has the greatest warning margin, followed by that of the soft short-circuit of the electrical signal. The thermal soft short-circuit has the lowest warning margin because of the low thermal conductivity. The qualitative simulation results of the battery module reveal that, when electric vehicles are subjected to mechanical abuse conditions, the rapid reduction in the state-of-charge(SOC) of the rear batteries can effectively increase the reliability of the battery module. The proposed safety model is important to protect the safety and stability of lithium-ion batteries, which is conducive to promoting new energy vehicles and protecting the environment.
