The Electric Vehicle Routing Problem (EVRP) is a key optimization challenge in autonomous and electric transportation. Unlike traditional routing, EVRP must consider battery constraints such as limited capacity and charging needs. Although routing methods have advanced, the integration of battery aging using realistic models remains underdeveloped. Addressing these dynamics is essential for improving long-term fleet efficiency. In this paper, we present a real-time, reconfigurable battery model that captures aging effects by updating key internal parameters based on the battery’s current State of Charge (SoC) and State of Health (SoH). Using this model, we formulate a multi-objective optimization problem and develop a genetic algorithm that balances energy efficiency, battery lifespan, and quality of service. Results show that incorporating aging-aware battery dynamics significantly extends battery life and reduces operational costs.The Electric Vehicle Routing Problem (EVRP) is a key optimization challenge in autonomous and electric transportation. Unlike traditional routing, EVRP must consider battery constraints such as limited capacity and charging needs. Although routing methods have advanced, the integration of battery aging using realistic models remains underdeveloped. Addressing these dynamics is essential for improving long-term fleet efficiency. In this paper, we present a real-time, reconfigurable battery model that captures aging effects by updating key internal parameters based on the battery’s current State of Charge (SoC) and State of Health (SoH). Using this model, we formulate a multi-objective optimization problem and develop a genetic algorithm that balances energy efficiency, battery lifespan, and quality of service. Results show that incorporating aging-aware battery dynamics significantly extends battery life and reduces operational costs.