Abstract
<div class="section abstract"><div class="htmlview paragraph">In the emerging economies, there is a growing adoption of electric vehicles into fleet vehicles. With the steady increase in this business area, there is a demand for the innovation in the battery charging methodologies. The swappable charging method is one such charging method that is gaining prominence. Battery swapping involves replacing an EV’s depleted battery with a fully charged one. This approach can significantly reduce wait times for drivers, as swapping batteries typically takes only few minutes, similar to the time it takes to refuel an ICE vehicle. With battery swapping, EV owners can avoid concerns related to battery degradation, since they receive a fully charged, well-maintained battery during each swap. Research is being done either to reduce the cost of operation of Battery Swapping station (BSS), or to reduce the waiting time for the users by charging fast. But focusing on the cost reduction, BSS may not be able to meet the demand of the users and by focusing only on the fast charging, the health of the battery will be under stake. The objective of the present work is to optimize the charging process in the BSS to reduce the waiting time for the users along with prolonging the battery life. This paper addresses the above-mentioned issues by tailoring the charging profile specific to the internal health state of the battery. These internal health states are obtained from a hybrid health model of battery, which is combination of physics based aging model and machine learning correction model. This paper starts with the time estimation for the complete charging of the battery which includes precooling, charging and post-cooling. Using a digital twin of the battery, efficient time estimation is achieved considering different average currents as the input. Then it provides insight on the various charging patterns along with their advantages and disadvantages, which is necessary for selecting the charging profile for the battery pack. Considering the state of health of the battery and the internal states of the battery pack, the charging profile is further optimized. The BSS uses this optimized charging profile. This work is developed in MATLAB/Simulink.</div></div>