The invention particularly relates to a battery life prediction method for an electric vehicle. The method comprises the following steps: acquiring charging process data of a battery; inputting the charging process data into a curve prediction model constructed based on a generative adversarial network to obtain a predicted charging voltage curve image; extracting corresponding charging voltage characteristic data from the predicted charging voltage curve image; inputting the extracted charging voltage characteristic data into a life prediction model constructed based on a deep learning model to obtain a battery life value; and taking the predicted battery life value output by the life prediction model as a battery life prediction result of the electric vehicle, and feeding back the result to a user of the electric vehicle, so that the user knows the battery state of the electric vehicle. According to the method, the high-quality charging voltage characteristic data is efficiently and accurately extracted through the generative adversarial network, the service life of the battery is comprehensively, accurately and efficiently predicted through the deep learning model, and a user is assisted to effectively pre-judge charging, maintenance and replacement of the battery.
本发明具体涉及一种用于电动汽车的电池寿命预测方法,包括:获取电池的充电过程数据;将充电过程数据输入基于生成对抗网络构建的曲线预测模型中,得到预测充电电压曲线图像;从预测充电电压曲线图像中提取对应的充电电压特征数据;将提取的充电电压特征数据输入基于深度学习模型构建的寿命预测模型中,得到电池寿命值;将寿命预测模型输出的预测电池寿命值作为电动汽车的电池寿命预测结果,并反馈至电动汽车的使用者,使其知晓电动汽车的电池状态。本发明通过生成对抗网络高效且准确的提取高质量的充电电压特征数据,同时通过深度学习模型全面、精准且高效的预测电池寿命,并且辅助使用者有效地对电池的充电、维保、更换做出预判。
Battery life prediction method for electric vehicle
一种用于电动汽车的电池寿命预测方法
2023-11-17
Patent
Electronic Resource
Chinese
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