The validation of battery aging models in automotive applications requires reliable aging data to compare the accuracy of each proposed model. Using a sample of 704 vehicles aged up to eight years under diverse nominal conditions two aging estimation models are proposed. By analyzing relevant automobile battery data a more relevant fit of a semi-empirical holistic model is provided with an Arrhenius temperature dependence and pseudo-Tafel voltage dependence. As a comparison, a neural network capturing the aging behavior using the most correlated variables available in the data-set was also developed. Over 110,000 measurements from seven relevant indicators are available as aging predictors, as well as, highly-accurate capacity measurements which is used as the ground truth capacity targets to train and validate the proposed models. Against these points the Semi-Empirical and Neural Network models achieved a root mean squared error of 3.4%-SOH and 3.0%-SOH, respectively.

汽车应用中电池老化模型的验证需要可靠的老化数据来比较每个建议模型的准确性。使用 704 辆汽车在不同标称条件下使用长达 8 年的样本，提出了两种老化估计模型。通过分析相关的汽车电池数据，提供了具有 Arrhenius 温度相关性和伪 Tafel 电压相关性的半经验整体模型的更相关拟合。作为比较，还开发了使用数据集中可用的最相关变量来捕获老化行为的神经网络。来自七个相关指标的超过 110,000 个测量值可用作老化预测器，以及用作训练和验证所提出模型的真实容量目标的高精度容量测量值。