State of health assessment of lithium battery based on Bayesian optimization-convolution neural network-bi-directional long short term memory neural network
YI Sitong1, LIU Yanong2, MA Yaoyi1, LI Wenjie3, KONG Hang3
1. School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, Liaoning 116028; 2. School of Rolling Stock Engineering, Dalian Jiaotong University, Dalian, Liaoning 116028; 3. School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, Liaoning 116028
Abstract：Accurate estimation of battery state of health (SOH) is the key to the stable operation of the device. In order to solve the problems in the current SOH research, such as the difficulty to measure the volume directly and the time required to adjust the model parameters, a prediction model based on the multi-health features of Bayesian optimization (BO) optimized convolution neural network (CNN) and bi-directional long short term memory (BiLSTM) neural network is proposed. Based on NASA’s publicly available lithium battery data, three health characteristics are extracted. The combination of CNN and BiLSTM improves the processing ability of time series data, and adds BO algorithm to automatically search the optimal parameter set, which avoids the combination network model falling into the local optimal and reduces the estimation time. The results show that the proposed method has the highest prediction accuracy and can aeffectively estimate the SOH of lithium batteries. The mean absolute error and root mean square error are both within 1%.
衣思彤, 刘雅浓, 马耀浥, 李文婕, 孔航. 基于贝叶斯优化-卷积神经网络-双向长短期记忆神经网络的锂电池健康状态评估[J]. 电气技术, 2024, 25(5): 1-10.
YI Sitong, LIU Yanong, MA Yaoyi, LI Wenjie, KONG Hang. State of health assessment of lithium battery based on Bayesian optimization-convolution neural network-bi-directional long short term memory neural network. Electrical Engineering, 2024, 25(5): 1-10.
2024, Vol. 25 
