Machine Learning Based Hybrid State-of-Charge Estimation and Other Battery Parameter Prediction of Commercial EV-Batteries

Abstract

Electrical Vehicles (EVs) are gaining huge attention from researchers due to their importance in environmental sustainability. Accurate and precise EV State-of-Charge (SoC) estimation is the primary challenge for commercial E V Batteries. To address the issue, the researchers have proposed many methods. However, there are a few drawbacks in the existing methods which can be resolved using hybridization of the variants of existing methods. In the previously reported work, the Kalman filter was used for the SoC estimation of EV batteries, which is suitable for linear systems. In most practical cases, the SoC of the EV-Batteries system shows nonlinear behavior. Although, there are many other methods, e.g., Extended Kalman and Modified Extended Kalman reported by the researchers but there are some other drawbacks with the existing methods.  To resolve the issues, the multi-variable optimization approach can be used to improve the accuracy of the SoC. The present work uses the hybridization of machine learning method to predict and estimate the SoC for commercial EV batteries. Machine learning methods precisely tunes the parameters and optimizes the estimation process by iteratively searching for the optimal solution within a defined parameter space. The performance of the proposed method is analyzed using Jupyter Notebook Platform (Scikit Learn Library). The results prove the superiority of the proposed method.