Evaluation of Fast-Charging Effects on Battery Packs Using a Battery-State-Driven Approach

Abstract

The paper proposes a detailed analysis of electric vehicle (EV) battery modelling, state estimation, and charging infrastructure in an integrated MATLAB Simscape framework. As EV penetration is increasing at a rapid rate, sound battery management and coordination of charging are the primary challenges. The analysis combines sophisticated battery modelling, Extended Kalman Filter (EKF)-based estimation, and high-speed DC charging in a single framework. At the pack level, a precise 96s46p pack arrangement with Molicel INR-21700 P45B cells was simulated to embody electro-thermal behavior, resistance characteristics, and degradation patterns. EKF algorithms were implemented for State of Charge (SOC) and State of Health (SOH) estimation with an average error margin less than 1.8%, exhibiting high accuracy, nonlinear stability, and steady thermal profiles at 45 °C. At the charging system level, an ultra-fast DC charging/discharging mode supports both grid-to-vehicle (G2V) and V2G modes. Simulation results confirmed stable performance for different conditions, proving the proposed control strategy. Unlike isolated analyses of existing work, this study integrates modelling, estimation, and charging control. The work aligns with the United Nations Sustainable Development Goals on affordable clean energy, sustainable cities, and climate action. The results encourage both industrial practice and research, and this study is a major contribution towards sustainable and intelligent EV systems.