基于多元线性回归模型的锂电池充电SOC预测

Lithium battery SOC prediction based on multiple linear regression model

摘要:

在诸多汽车电池中，锂电池因为性能稳定、寿命长、承受力强等优势，成为了电动汽车动力电池的绝佳选择。为了对锂电池进行高效管理，防止过充、过放的情况发生，保证锂电池使用的安全性以及性能，需要对锂电池的荷电状态（state of charge,SOC）进行准确预测。实验基于锂电池充电过程中的实际数据，使用Pyhton语言编程，建立多元线性回归模型，通过模型预测出锂电池开始充电到结束充电过程中准确的SOC值。研究结果表明，锂电池充电SOC的变化过程具有一定的线性规律，多元线性回归模型预测SOC值的误差都能控制得很小，决定系数都高于99%，具有很好的预测效果且有一定的通用性。除此之外，多元线性回归模型参数较少，结构简单，易于实现，更容易在实际应用中推广。

Abstract:

Among many automotive batteries, lithium batteries have become an excellent choice for power batteries for electric vehicles due to their stable performance, long life, and strong endurance. In order to efficiently manage the lithium battery, prevent overcharging and overdischarge, and ensure the safety and performance of the lithium battery, it is necessary to accurately predict the state of charge (SOC) of the lithium battery. The experiment is based on the actual data of the lithium battery charging process, using Pyhton language programming to establish a multiple linear regression model, through the model to predict the accurate SOC value of the lithium battery from the beginning of charging to the end of the charging process. The research results show that the change process of SOC of lithium battery has a certain linear law. The error of multivariate linear regression model to predict SOC value can be controlled to be small, the coefficient of determination is higher than 99%, and has good prediction effect and certain the versatility. In addition, the multiple linear regression model has fewer parameters, is simple in structure, easy to implement, and is more easily popularized in practical applications.