Enhancing Electric Vehicle Battery Lifetime - Ageing Influences and Degradation Forecasts

Gokul CHANDRABHANU NAMBIAR

Master's thesis

Abstract:

The broadening shift towards Electric Vehicle (EV) is essential for reducing global carbon emissions, yet the longevity and reliability of High Voltage Battery (HVB) that drives the vehicle, remain pivotal challenges hindering wider EV adoption. This thesis works on a deep dive analysis focused on identifying and quantifying the impact of various stress factors on the State of Health (SoH) of EV batteries. Starting off this research is the deployment of machine learning models, specifically Gradient Boosting Regressor (GBR) and Artifcial Neural Network (ANN), to forecast the degradation in SoH based on a rich dataset encapsulating real-world operational parameters of EV batteries. The collected features which encompasses battery pack temperature, voltage, vehicle mileage, along with detailed charging and discharging information, form the foundation of the analysis. Utilizing SHapley Additive exPlanations (SHAP) for interpretability, the study elaborates on the significance and influence of each of the features on battery SoH, offering a detailed understanding of extent of battery degradation. The findings not only highlight the paramount importance of some key charging and operational behavior parameters, but also pave the way for formulating actionable recommendations to reduce battery wear and possibly extend lifespan. By providing empirical insights into battery stress factors and predictive modeling of battery SoH degradation, this thesis contributes valuable knowledge towards the enhancement of Battery Management Systems (BMS) and promotes more sustainable EV usage patterns.

Abstract:

The broadening shift towards Electric Vehicle (EV) is essential for reducing global carbon emissions, yet the longevity and reliability of High Voltage Battery (HVB) that drives the vehicle, remain pivotal challenges hindering wider EV adoption. This thesis works on a deep dive analysis focused on identifying and quantifying the impact of various stress factors on the State of Health (SoH) of EV batteries. Starting off this research is the deployment of machine learning models, specifically Gradient Boosting Regressor (GBR) and Artifcial Neural Network (ANN), to forecast the degradation in SoH based on a rich dataset encapsulating real-world operational parameters of EV batteries. The collected features which encompasses battery pack temperature, voltage, vehicle mileage, along with detailed charging and discharging information, form the foundation of the analysis. Utilizing SHapley Additive exPlanations (SHAP) for interpretability, the study elaborates on the significance and influence of each of the features on battery SoH, offering a detailed understanding of extent of battery degradation. The findings not only highlight the paramount importance of some key charging and operational behavior parameters, but also pave the way for formulating actionable recommendations to reduce battery wear and possibly extend lifespan. By providing empirical insights into battery stress factors and predictive modeling of battery SoH degradation, this thesis contributes valuable knowledge towards the enhancement of Battery Management Systems (BMS) and promotes more sustainable EV usage patterns.

Keywords

Battery Health Degradation, Health Degradation Forecast, Random Forest, Gradient Boosting Regression, Artificial Neural Network, SHAP Analysis.

Language used: English

Date on which the thesis was submitted / produced: 11. 2. 2025

Thesis defence

• Supervisor: Markus Eider, M.Sc.

UNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE

Faculty of Science

Master programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science