健康状况
利用
粒子群优化
电池(电)
计算机科学
行驶循环
荷电状态
电动汽车
数据挖掘
功率(物理)
可靠性工程
人工智能
工程类
机器学习
物理
计算机安全
量子力学
作者
Li Xu,Peng Wang,Jian-Chun Wang,Fangzhao Xiu,Yuhang Xia
标识
DOI:10.1016/j.est.2023.108247
摘要
With the rapid development of new energy vehicle industry, power battery is an important power source for new energy vehicles. Effective estimation and prediction of power battery health state (SOH) can help companies to effectively estimate and predict the health state of power battery, so as to ensure the safe operation of new energy vehicles. In this paper, we propose a SOH estimation and prediction method based on a long short-term memory network (LSTM) with time series model, and this method uses multi-source features. We extract potential health features from three perspectives and design the LSTM network model to construct a nonlinear mapping relationship between health features and SOH. To better exploit the battery time series information for SOH prediction, we built a time series prediction model containing trend, cycle and holiday models, and used particle swarm algorithm for multi-model optimization. In order to fully exploit the driver usage behavior and time and other information contained in different charge/discharge cycles, where the cycle model is built to include year, month, week, day, etc., SOH prediction can be performed for each future day without changing the original trend of the feature. The final model validation is performed on two vehicle validation datasets. The experimental results show that the model built in this paper outperforms traditional LSTM, GRU, BP and other network models in terms of accuracy of SOH evaluation and prediction.
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