Comprehensive optimization of electrical heavy-duty truck battery swap stations with a SOC-dependent charge scheduling method

Battery swapping presents a compelling approach for replenishing energy in electric vehicles, showcasing advantages such as reduced refueling time, heightened operational efficiency, and cost savings, especially within the framework of time-of-use electricity pricing. Despite these advantages, the economic viability of battery swap stations faces challenges attributed to substantial investment costs and suboptimal operational strategies. This research systematically addresses these challenges by delving into charge scheduling methodologies specifically tailored for battery swap stations, presenting optimization and configuration strategies, and seamlessly integrating local photovoltaic systems to enhance sustainability and cost-effectiveness. A pivotal aspect is the introduction of a state-of-charge-dependent scheduling method, which entails real-time regulation of charging output based on the station's combined battery levels. To assess the effectiveness of these methodologies, a simulation is devised to collect operation data and analyze economic results. Furthermore, a two-layered model for comprehensive optimization of the station's configuration and charge schedule is developed for economic performance. This optimization problem is solved by a multi-stage heuristic algorithm based on particle swarm optimization. Case studies demonstrate that the utilization of our optimization model and state-of-charge-dependent scheduling method result in an approximate 10.8 % increase in economic performance compared to typical BSS designs.