用机器学习分析电池配置如何影响电动车加速性能
The Impact of Battery Cell Configuration on Electric Vehicle Performance: An XGBoost-Based Classification with SHAP Interpretability
- 用XGBoost模型分类电动车加速表现，准确率达87.5%
- 发现电池数量增加初期提升动力，但过量会因重量和复杂度拖累性能
- 结合SHAP解释性工具，适合关注电动车电池设计的工程师
随着电动汽车市场对动态性能和快速充电的需求持续增长，电池配置迅速演变。然而，现有文献常忽视电池配置与车辆性能之间的复杂非线性关系。为此，本研究提出一种机器学习框架，将电动车加速性能分为高（≤4.0秒）、中（4.0–7.0秒）和低（>7.0秒）三类。基于276个样本的预处理数据集，采用极端梯度提升（XGBoost）分类器，取得87.5%的预测准确率、0.968的ROC-AUC值和0.812的马修斯相关系数（MCC）。为确保工程可解释性，引入SHapley Additive exPlanations（SHAP）方法。分析结果表明，电池单体数量增加虽初期提升功率输出，但其带来的质量与系统复杂度最终削弱性能增益。因此，电动车电池配置需在系统复杂性与结构设计间取得平衡，以实现并维持最佳整车性能。
原文摘要 · Abstract (English)
As the electric vehicle (EV) market continues to prioritize dynamic performance and rapid charging, battery configuration has rapidly evolved. Despite this, current literature has often overlooked the complex, non-linear relationship between battery configuration and electric vehicle performance. To address this gap, this study proposes a machine learning framework which categorizes the EV acceleration performance into High (<= 4.0 seconds), Mid (4.0 - 7.0 seconds), and Low (> 7.0 seconds). Utilizing a preprocessed dataset consisting of 276 EV samples, an Extreme Gradient Boosting (XGBoost) classifier was utilized, achieving 87.5% predictive accuracy, a 0.968 ROC-AUC, and a 0.812 MCC. In order to ensure engineering transparency SHapley Additive exPlanations (SHAP) were employed. Results of analysis shows that an increase in battery cell count initially boosts power delivery, but its mass and complexity diminished performance gains eventually. As such, these findings indicate that battery configuration in EVs must balance system complexity and architectural configuration in order to receive and retain optimal vehicle performance.
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