Simulation-Based Battery Capacity Optimization for Electric Vehicle Hybrid Energy Storage Applications

Abstract

This paper presents a simulation-based optimization of Electric Vehicle (EV) traction network with respect to storage capacity. The main challenge in EV design is determining an optimal storage capacity that balances energy availability, voltage stability, system scalability and operational reliability. This work proposes an EV system that integrates a photovoltaic (PV) array, an incremental conductance (INC)-based maximum power point tracking (MPPT) controller, a boost converter, a bidirectional DC-DC converter, a lithium-ion battery energy storage system and a Brushless DC (BLDC) motor drive powertrain. The entire system was designed in MATLAB/Simulink and Genetic Algorithm (GA)-based optimization approach was implemented with battery capacity being the main parameter. The optimization criterion includes DC bus voltage regulation, state of charge (SOC) constraints, and sizing considerations. The optimization results converged to an improved battery capacity of 110.2 Ah under the defined operating conditions. The comparison performance between the baseline and optimized system shows improvements in term of voltage regulation, better buffer ability of the system, current transitions, and stable SOC evolution. Overall, this work establishes preliminary capacity boundaries that can be used for sizing voltage balancing circuits and tuning multi-source Hybrid Energy Storage System (HESS) management algorithms for future work.