Optimal Design and Operation Control of Electrical Vehicle Battery Swapping Station incorporating Hybrid Renewable Energy and Grid Power Systems

Abstract

Transportation is a major contributor to air pollution and one of the largest consumers of fossil fuels. As a sustainable alternative, electric vehicles have gained widespread adoption due to advances in technology, the expansion of public charging networks, and improvements in battery materials and charging infras- tructure. However, limitations in the charger power capacity and battery characteristics of electric vehicles often result in longer charging times compared to refueling traditional vehicles, leading to range anxiety and hindering broader adoption. To address this challenge, battery swapping stations have emerged as an innovative solution, allowing drivers to exchange depleted batteries for fully charged ones, thus minimizing downtime and enhancing the feasibility of electric vehicles for modern transportation systems. Recent research has explored the concept of battery swapping stations, presenting various optimization approaches and operational systems. Battery swapping station providers have successfully implemented swapping services at both private and commercial stations. This research study focuses on the optimal design approach and operation management strategy of battery swapping stations, with a particular em- phasis on leveraging renewable energy sources, such as wind turbines and photovoltaic systems, as primary resources for charging the depleted electric vehicle battery. The aim is to develop solutions that minimize energy consumption and operational costs while enhancing charging performance by reducing the swapping service time. For this purpose, first, an optimal design for a battery swapping station integrated with a grid-connected hybrid power supply system is developed and evaluated. The optimization problem is formulated to minimize the overall life cycle cost and reduce electricity costs purchased from the utility grid while maximizing its reliability. This model provides continuous, cost-effective power support and implements a scheduling strategy that consistently meets charging demand. Mixed integer linear programming is used to determine key decision variables, such as the power drawn from the utility grid and the number of wind turbines and solar photovoltaic panels. Simulation results demonstrate that the optimal configuration, including wind turbines and solar panels, yields significant energy cost savings over its lifecycle. In addition, an economic analysis is performed to assess the payback period, an essential metric for investors. Secondly, an optimal operational control strategy is developed and evaluated for grid-connected wind-solar power generation with a storage system for the battery swapping station. The strategy minimizes the total operational costs associated with the cost of electrical energy purchased from the utility grid and the wear cost of the hybrid system due to the frequent charging and discharging of the battery energy storage system. At the same time, it maximizes the self-consumption of locally generated renewable energy. This optimization model ensures a cost-effective power supply that efficiently meets charging demands while selling excess energy back to the utility grid. The simulation results from four scenarios show that the model optimizes energy use, reduces costs, and increases hybrid power feedback to the utility grid. The main outcome is improved energy efficiency, cost savings, and additional revenue from surplus power sales. vi The impact of this outcome is enhanced financial viability, demonstrated through an economic analysis that evaluates the investment feasibility and payback period of the proposed system. Thirdly, a model predictive control approach is designed and evaluated to determine the optimal opera- tional strategy for a battery swapping station integrated with a grid-connected wind-solar power generation system and a battery energy storage system. The primary objective is to minimize the operational costs of the proposed system. By implementing model predictive control (MPC), the system dynamically optimizes power flow in real-time to account for uncertainties and disturbances. A comparative analysis is conducted between open-loop control and MPC-based closed-loop control under various scenarios, with and without disturbances. The simulation results demonstrate that the closed-loop MPC approach consistently out- performs the open-loop model, exhibiting superior robustness and adaptability in handling uncertainties. By optimizing power flow in real-time, MPC ensures efficient power distribution, reduces reliance on the utility grid, and maximizes renewable energy utilization. Despite its computational complexity, model predictive control offers significant advantages in industrial applications, enhancing efficiency, reliability, and cost-effectiveness through real-time optimization. The findings of this research underscore the effectiveness of integrating battery swapping stations with demand-side management strategies. This integration plays a crucial role in advancing sustainable urban transportation, enhancing the efficiency of electric vehicle infrastructure, and improving the reliability and stability of the utility grid.