International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
PREDICTING STATE OF HEALTH (SOH) OF EV BATTERIES USING MACHINE LEARNING Mr. V. Murugan1, G. Anjali2, Ch. Kruthika3, A. Pallavi Reddy4, Ch. Sudheer5 12345Department of Information Technology, TKR College of Engineering and Technology, Telangana, India
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Abstract - Electric Vehicles (EVs) are becoming a key
original state. Accurate SOH estimation helps in detecting early degradation, planning preventive maintenance, and reducing the risk of sudden battery failure. Traditional methods of battery testing rely on physical inspections and electrochemical analysis, and multi-physics based modelling approaches, which are often time-consuming, costly, computationally intensive, and not feasible for real-time applications. Therefore, machine learning-based methods are gaining attention due to their ability to learn complex battery behaviour from data and generate accurate predictions.
solution in the transition toward sustainable transportation. One of the most critical components of an EV is its battery, and its longevity significantly affects vehicle performance and user trust. This study presents a machine learning-based system for estimating the State of Health (SOH) of EV batteries using predictive modeling techniques. The system incorporates a user registration and authentication module, secure password reset using OTP-based verification, and a robust ML pipeline that processes an extended EV battery dataset to predict SOH. Key models trained include XGBoost Regressor, LightGBM Regressor, and Random Forest Regressor with evaluation metrics such as MAE, MSE, R², and RMSE. The system also generates interpretable visualizations like correlation heatmap, evaluation metric bar graphs, actual vs. predicted SOH plots, and feature importance graphs. Based on the predicted SOH, the system provides contextual feedback such as battery health status, estimated time to replacement, and health maintenance recommendations. The implementation, developed using Django and Python, offers a user-friendly web interface for battery health inference, making it applicable for battery management systems in modern electric vehicles.
This project proposes a machine learning-based EV battery SOH prediction system integrated into a Django web application. The system trains regression models such as XG Boost, Light GBM, and Random Forest using an extended EV battery dataset and predicts SOH based on user-input battery parameters. The system also provides battery health status, estimated replacement time, and recommendations along with visualization graphs to support better decisionmaking.
1.1 Importance of EV Battery SOH Prediction
Key Words: Electric Vehicles (EVs), Battery State of Health (SOH), Machine Learning, Battery Management System (BMS), XGBoost Regressor, LightGBM Regressor, Random Forest Regressor, Feature Importance Analysis, Django Framework.
Battery health monitoring is essential for improving EV reliability and ensuring safe operation. Accurate SOH prediction supports early detection of battery degradation and helps users and manufacturers take timely actions. It also reduces maintenance costs, improves battery lifespan, and enhances driving performance. Machine learning models can provide faster and more scalable solutions compared to manual testing, making them suitable for real-world battery management systems.
1. INTRODUCTION Electric Vehicles (EVs) are rapidly becoming one of the most important solutions for reducing air pollution, fossil fuel dependency, and carbon emissions. The shift from conventional internal combustion engine vehicles to EVs has increased the demand for efficient and reliable battery systems. In an electric vehicle, the battery is the core component that determines the driving range, charging performance, safety, and overall user satisfaction. However, lithium-ion batteries degrade over time due to continuous charging and discharging cycles, temperature variations, and usage patterns. This degradation affects the battery’s capacity, efficiency, and reliability, making battery health monitoring a critical requirement in modern EV systems.
1.2 Motivation and Problem Overview Electric Vehicles (EVs) are rapidly gaining popularity as a sustainable alternative to conventional fuel-based transportation. However, the performance, reliability, and user trust in EVs are highly dependent on the battery, which is the most expensive and critical component of the vehicle. Over time, lithium-ion batteries degrade due to repeated charging and discharging cycles, temperature variations, and operating conditions. This degradation reduces driving range, increases charging time, and may lead to unexpected failures, resulting in high maintenance and replacement costs. Traditional battery health evaluation methods rely on
State of Health (SOH) is one of the key parameters used to measure the condition of a battery compared to its
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