Linear programming MPC optimal V2G scheduling of electric vehicles considering battery degradation

Purpose With rising electric vehicles (EV) penetration, industrial parks and microgrids face increased load volatility and peak stress. This study aims to develop a coordinated EV charging/discharging strategy that simultaneously reduces load fluctuations, ensures state of charge (SOC) targets and protects battery lifetime. Design/methodology/approach The authors propose a linear programming model predictive control (LP-MPC) that, at every time step, linearizes SOC dynamics, charging/discharging constraints and battery-degradation cost to solve a multi-objective optimization for peak shaving, SOC target fulfillment and user benefit maximization. The method is evaluated in large-scale EV coordination simulations and compared with Greedy and predictive Greedy benchmarks. Findings LP-MPC markedly lowers load variance, raises SOC completion rates and substantially mitigates cumulative battery degradation compared with the benchmark strategies, demonstrating improved trade-offs among grid stability, charging performance and battery health. Originality/value The paper introduces a practical LP-MPC formulation that explicitly embeds lifetime-aware cost into real-time multi-objective EV scheduling; this provides a computationally tractable, scalable approach for large-scale EV coordination in industrial parks and microgrids.