摘要
基于优化后的反向传播神经网络,提出了一种新能源汽车锂电池SOH(状态健康)预测模型。该模型利用历史电池数据和当前电池参数作为输入,预测电池的SOH。为了优化模型性能,使用遗传算法对模型进行训练和优化,提高了预测精度和鲁棒性。试验结果表明,该模型能够在不同工况下准确预测锂电池的SOH,并且相对于传统方法具有更好的性能。基于优化BP神经网络的SOH预测模型具有广泛的应用前景,可以为新能源汽车锂电池的健康管理提供有力的支持。
A SOH(state of health)prediction model for new energy vehicle lithium batteries is proposed based on an optimized back propagation neural network.The model uses historical battery data and current battery parameters as inputs to predict the SOH of the battery.to optimize the model performance,the model is trained and optimized using a genetic algorithm to improve the prediction accuracy and robustness.The experimental results show that the model can accurately predict the SOH of lithium batteries under different operating conditions and has better performance compared to the conventional method.SOH prediction model based on optimized BP neural network has wide application prospects and can provide powerful support for the health management of Li-ion batteries in new energy vehicles.
作者
郭志庭
GUO Zhiting(Sichuan Institute of Industrial Technology,Deyang 618500,China)
出处
《农机使用与维修》
2023年第8期36-38,共3页
Agricultural Machinery Using & Maintenance
基金
德阳市新能源专用车辆工程技术研究中心2022年度科研项目(XNYCL2212)。
关键词
神经网络
新能源汽车
锂电池
SOH
预测模型
neural network
new energy vehicle
lithium battery
SOH
prediction model