Modeling and Simulation of a Condition- Based Battery Dispensing Strategy for EV Swapping Stations

As electric vehicles (EVs) are becoming more widely adopted, battery swapping stations (BSS) have been introduced to remove charging bottlenecks and minimize grid impact. Unfortunately, typical first-in-first-out (FIFO) or random dispensing results in uneven inventory aging and inefficient resource use. We propose a robust, condition-based battery dispensing strategy that has been designed and tested using MATLAB/Simulink for maximizing operation lives in swapping stations. The proposed framework combines a stochastic noise-injection module and signal conditioning layer to factor in real-world sensor errors during the diagnostic stage of the deposited battery. Utilizing the embedded parallel architectures found in the capabilities of the custom inventory node and dynamic fitness-scoring algorithms, the system performs the optimum allocation schedule for the battery it receives. Deterministic simulation results show that the algorithm effectively minimized the over-allocation of high-health assets. In the described case study, by selectively skipping aged assets (source battery with a marked State of Health (SOH) of 51.0 percent) and skipping over less suitable assets, the system efficiently distributed station lifecycle between assets and isolated a winning candidate with an SOH of 55.0 percent (Battery 8) for a high match score of 96.0 out of 100. Simultaneously, the selection criterion effectively satisfied multi-variable physical constraints on the final chosen cell, such as full unit operation at 100 percent State of Charge (SOC), and unencumbered surface thermal and voltage areas. The results bode well for the practical viability of the model to maximize long-term asset availabilities while maintaining a long-term multi-user parity capacity balance across the system.