Batteries are ubiquitous today, with applications ranging from smartphones, watches, and laptops to electric cars, drones, and electric aircraft. Lithium-ion batteries are widely used in these applications due to their high energy density, rechargeability, and low lifecycle cost. Understanding the lifetime of lithium-ion batteries is essential for their effective utilization across many domains. In this study, data-driven modeling approaches are explored to predict the lifetime of lithium-ion batteries using various measurable battery parameters. A battery dataset from NASA's electric aircraft experiments was used, which included 17 predictor variables and remaining flight time as the response variable representing battery lifetime. The dataset contained more than 4,000,000 rows. However, the original dataset provided limited directly useful information about battery utilization over time; therefore, feature engineering was performed to generate more informative variables. Additionally, dimensionality reduction using principal component analysis (PCA) was applied to reduce computational cost and model complexity by selecting a smaller number of principal components as predictors for model development. Random forest and neural network models were explored for battery lifetime prediction using the engineered features. Multiple neural network configurations were evaluated, including single- and double-hidden-layer architectures with varying numbers of nodes. Mean squared error (MSE) on the test dataset was used as the performance metric for model comparison. The results indicate that data-driven modeling approaches are effective for battery lifetime prediction, with neural network models outperforming other models based on the MSE metric. Furthermore, neural networks demonstrate robustness in handling high-dimensional battery data.

翻译：如今，电池已无处不在，其应用范围涵盖智能手机、手表、笔记本电脑乃至电动汽车、无人机和电动飞机。锂离子电池因其高能量密度、可充电性及低生命周期成本而在这些应用中广泛使用。理解锂离子电池的寿命对于其在众多领域的有效利用至关重要。本研究探索了数据驱动的建模方法，利用多种可测量的电池参数来预测锂离子电池的寿命。研究采用了来自NASA电动飞机实验的电池数据集，该数据集包含17个预测变量以及作为电池寿命表征的响应变量——剩余飞行时间。数据集包含超过4,000,000行数据。然而，原始数据集提供的关于电池随时间使用情况的有用信息有限；因此，通过特征工程生成了信息量更丰富的变量。此外，应用主成分分析（PCA）进行降维，通过选择较少的主成分作为模型开发的预测变量，以降低计算成本和模型复杂度。研究探索了随机森林和神经网络模型，利用工程化特征进行电池寿命预测。评估了多种神经网络配置，包括具有不同节点数的单隐藏层和双隐藏层架构。使用测试数据集上的均方误差（MSE）作为模型比较的性能指标。结果表明，数据驱动建模方法能有效预测电池寿命，且基于MSE指标，神经网络模型的表现优于其他模型。此外，神经网络在处理高维电池数据时展现出良好的鲁棒性。