Dynamic Tuning of BMS Parameters for Enhanced LiFePO4 Battery Performance in Electric Vehicles

Neelima Dudhe; Z. J. Khan; Satyanarayana Chanagala

doi:10.48084/etasr.13090

Authors

Neelima Dudhe Department of Electrical Engineering, G. H. Raisoni College of Engineering (GHRCE), Nagpur, India
Z. J. Khan Department of Electrical Engineering, Ballarpur Institute of Technology (BIT), Ballarpur, India
Satyanarayana Chanagala Department of Electrical Engineering, Ballarpur Institute of Technology (BIT), Ballarpur, India

Volume: 16 | Issue: 2 | Pages: 33964-33970 | April 2026 | https://doi.org/10.48084/etasr.13090

Received: 4 July 2025 | Revised: 1 August 2025 | Accepted: 15 August 2025 | Online: 2 March 2026

Corresponding author: Neelima Dudhe

Abstract

The increasing deployment of Electric Vehicles (EVs) necessitates the development of robust and adaptive Battery Management Systems (BMSs) to ensure operational safety, thermal reliability, and prolonged battery life. This paper proposes an efficient dynamic optimization framework for the parameters of a BMS to enhance the performance and operational reliability of LiFePO4 batteries in EVs. An efficient method is employed, which combines Particle Swarm Optimization (PSO) and Model Predictive Control (MPC) to optimize major BMS parameters such as charging and discharging current limits, thermal limits, and voltage limits automatically. The PSO algorithm efficiently explores the parameter space to minimize objective functions related to battery thermal stress, current ripple, and State-of-Charge (SoC) oscillation. Meanwhile, MPC regulates real-time charge-discharge dynamics by forecasting system behavior and applying optimized control actions. The simulation results confirm substantial improvements in battery efficiency, thermal stability, and fault reduction over non-optimized configurations. Particularly, the optimized framework suppresses instantaneous current spikes, ensures stable SoC levels, and damps the temperature rise, thereby extending battery life and guaranteeing safe operation. This paper validates the potential for combining intelligent optimization and predictive control techniques for next-generation BSMs in EVs.

Keywords:

Battery Management System (BMS), lithium-ion battery, lithium iron phosphate battery, Electric Vehicle (EV), Particle Swarm Optimization (PSO), Model Predictive Control (MPC), current limit optimization

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