Next Article in Journal
From Local Actions to Global Impact: Overcoming Hurdles and Showcasing Sustainability Achievements in the Implementation of SDG12
Previous Article in Journal
Harnessing an Algae–Bacteria Symbiosis System: Innovative Strategies for Enhancing Complex Wastewater Matrices Treatment
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation

by
Runze Liu
,
Jianming Cai
,
Lipeng Hu
,
Benxiao Lou
and
Jinjun Tang
*
School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 7105; https://doi.org/10.3390/su17157105
Submission received: 29 June 2025 / Revised: 1 August 2025 / Accepted: 1 August 2025 / Published: 5 August 2025
(This article belongs to the Section Sustainable Transportation)

Abstract

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior.
Keywords: energy consumption; stacking model; SHAP theorem; battery system; electric vehicle energy consumption; stacking model; SHAP theorem; battery system; electric vehicle

Share and Cite

MDPI and ACS Style

Liu, R.; Cai, J.; Hu, L.; Lou, B.; Tang, J. Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation. Sustainability 2025, 17, 7105. https://doi.org/10.3390/su17157105

AMA Style

Liu R, Cai J, Hu L, Lou B, Tang J. Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation. Sustainability. 2025; 17(15):7105. https://doi.org/10.3390/su17157105

Chicago/Turabian Style

Liu, Runze, Jianming Cai, Lipeng Hu, Benxiao Lou, and Jinjun Tang. 2025. "Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation" Sustainability 17, no. 15: 7105. https://doi.org/10.3390/su17157105

APA Style

Liu, R., Cai, J., Hu, L., Lou, B., & Tang, J. (2025). Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation. Sustainability, 17(15), 7105. https://doi.org/10.3390/su17157105

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop