Hybrid PSO-MPC-based dynamic tuning of battery management parameters for enhanced lithium-ion battery performance in electric vehicles

The growing adoption of electric vehicles (EVs) has intensified the need for reliable and efficient Battery Management Systems (BMS), critically enabling safe operation and prolonged battery lifespan. Conventional BMS designs often suffer from static parameter settings, leading to suboptimal charging/discharging control, thermal instabilities, and increased fault occurrences, especially under dynamic driving and load conditions. Moreover, existing methods lack adaptability in tuning protection limits and control gains, which are essential for managing battery health, efficiency, and fault tolerance in real time. To overcome these limitations, this paper proposes a hybrid intelligent control framework that integrates Particle Swarm Optimization (PSO) and Model Predictive Control (MPC) to enable dynamic and adaptive regulation of battery charge–discharge operations. A multi-objective PSO algorithm is used to optimize the maximum discharge current, charge current limit, initial State of Charge (SoC), and charging voltage, minimizing power losses and fault occurrences. Subsequently, MPC is incorporated to regulate current and thermal dynamics using predictive constraints on SoC, voltage, and temperature, enabling smooth and safe control action. The proposed framework is implemented on a high-fidelity, model-based lithium-ion battery system, benchmarked against conventional and single-optimization schemes. Extensive results from the proposed work demonstrate superior performance with up to 30% reduction in converter and interconnect energy dissipation (defined as cumulative P loss normalized to total delivered energy), enhanced efficiency, improved SoC stability, and complete elimination of over-voltage and over-current fault events. These outcomes validate the effectiveness of the proposed hybrid PSO-MPC approach for real-time battery control, offering a scalable and adaptive solution for next-generation EV battery management. The proposed work has been implemented and simulated in MATLAB R2024b software environment. • Design and development of an efficient BMS using battery parameters for fault diagnosis and thermal safety • A multi-objective PSO-tuned BMS is designed to reduce power loss and battery faults • Model Predictive Control strategy adopted for charge–discharge regulation under SoC limits • Hybrid PSO–MPC strategy is deployed for enhanced SoC stability, thermal safety and fault reduction