The Impact of Battery Cell Configuration on Electric Vehicle Performance: An XGBoost-Based Classification with SHAP Interpretability

2026-03-01

AI Summary

This study addresses the insufficient modeling of the nonlinear relationship between battery cell configuration and electric vehicle (EV) acceleration performance in existing literature. Leveraging a dataset of 276 EVs, this work proposes a novel approach that integrates an XGBoost classification model with SHAP interpretability analysis to categorize acceleration performance into high, medium, and low tiers, thereby systematically elucidating the influence mechanism of battery configurations. The model achieves strong predictive performance with 87.5% accuracy, a ROC-AUC of 0.968, and a Matthews Correlation Coefficient (MCC) of 0.812. Key findings reveal a performance gain inflection point associated with the number of battery cells, quantifying the trade-off between enhanced acceleration and increased system complexity. These insights offer data-driven guidance for optimizing battery system design in electric vehicles.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Evaluation and Analysis
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• battery cell configuration
• electric vehicle performance
• non-linear relationship
• acceleration performance
• system complexity

Innovation

• XGBoost
• SHAP interpretability
• battery cell configuration
• electric vehicle performance
• machine learning