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基于实车数据的电动汽车电池剩余使用寿命预测
引用本文:胡杰,何陈,朱雪玲,杨光宇.基于实车数据的电动汽车电池剩余使用寿命预测[J].交通运输系统工程与信息,2022,22(1):292-300.
作者姓名:胡杰  何陈  朱雪玲  杨光宇
作者单位:武汉理工大学,a. 现代汽车零部件技术湖北省重点实验室;b. 汽车零部件技术湖北省协同创新中心; c. 湖北省新能源与智能网联车工程技术研究中心,武汉 430070
摘    要:电动汽车电池剩余使用寿命预测是当下电池研究领域的热点内容,现有电池剩余使用寿命预测模型大多基于单一预测指标,预测精度较低,模型的泛化性能较差。本文通过实车数据构建了GM-LSTM的Stacking融合模型,实现电动汽车电池剩余使用寿命的准确预测。首先根据电池剩余使用寿命影响因素,提取车辆真实的运行参数和环境参数,基于随机森林算法筛选最优特征集合作为模型输入,其次选择差分整合移动平均自回归算法对所选特征进行惯性延伸,克服数据时间维度上的限制,最后基于数据特点,分别建立灰色预测模型和长短时记忆神经网络模型实现电池剩余使用寿命预测,并通过Stacking模型融合进一步降低预测误差。结果表明:模型融合 后平均相对误差为1.6%,平均绝对误差为0.013,能够稳定可靠的实现电动汽车电池剩余使用寿命预测。

关 键 词:城市交通  剩余使用寿命预测  数据驱动  电动汽车  模型融合  
收稿时间:2021-08-07

Predicting Remaining Useful Life of Electric Vehicle Battery Based on Real Vehicle Data
HU Jie,HE Chen,ZHU Xue-ling,YANG Guang-yu.Predicting Remaining Useful Life of Electric Vehicle Battery Based on Real Vehicle Data[J].Transportation Systems Engineering and Information,2022,22(1):292-300.
Authors:HU Jie  HE Chen  ZHU Xue-ling  YANG Guang-yu
Institution:a. Hubei Key Laboratory of Advanced Technology for Automotive Components; b. Hubei Collaborative Innovation Center for Automotive Components Technology; c. Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China
Abstract:Predicting the battery remaining useful life (RUL) of electric vehicle (EV) is a hot topic in the field of battery research. Most of the existing RUL prediction models are based on a single prediction index, with low prediction accuracy and poor generalization. In this paper, a Stacking model of Gray Prediction model and Long-term Memory Neural Network model was developed to predict the RUL of electric vehicle with high accuracy based on real vehicle operating data. First, the movement and environmental parameters of the vehicle were extracted according to the influencing factors of the RUL of the battery, and the optimal features was selected as the model input based on the Random Forest Algorithm. Then, the study used the Auto Regressive Integrated Moving Average model to extend the selected features to overcome the limitation of time dimension. Based on the data characteristics, the Gray Prediction model and Long-term Memory Neural Network model were proposed to predict the battery RUL, and the prediction error was further reduced by the Stacking model fusion. The results show that the average relative error of the fusion model is 1.6%, and the average absolute error is 0.013, which proves a stable and reliable prediction of the RUL with the proposed model.
Keywords:urban traffic  prediction of remaining useful life  data driven  electric vehicle  model fusion  
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