Prediction of battery charging time and range detection in electric vehicle using machine learning algorithms

Priya, K. M and Krishnan. S, Murali and Mohit, K (2025) Prediction of battery charging time and range detection in electric vehicle using machine learning algorithms. Open Access Research Journal of Engineering and Technology, 8 (1). 044-056. ISSN 2783-0128

As a key pillar of smart transportation in smart city applications, Electric Vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. The lack of charging infrastructure and range detection is one of the most significant barriers to Electric Vehicle adoption. To address the problem of EV charging and range detection, employing Machine Learning (ML) algorithms to predict charging analysis, which is beneficial to drivers. ML algorithms in Electric Vehicle (EV) refers to the application of computational algorithms and statistical models that enable EVs and their supporting systems. To train the model own data set was created using hardware setup. This hardware setup consists of Battery, ESP Controller, Sensors, and Arduino IDE. Here two type of machine learning algorithms where we used labelled dataset to train the model or algorithms. one is the Support Vector Regression (SVR), other one is Random Forest Algorithm (RFA). It predicts charging patterns, recommend optimal charging times, and estimate charging times based on factors like battery health, current charge level, and environmental conditions. This leads to more efficient charging and better energy management and also going to predict the driving range of EVs based on performance metric like RMSE (Root Mean Squared Error), MAE (Mean Absolute Percentage Error), R Square, SMAPE (Symmetrical Mean Absolute Percentage Error). This information aids drivers in planning routes and making informed decisions. ML in the Electric Vehicle industry transforms data into actionable insights that enhance vehicle performance, optimize energy consumption, and improve user experiences.