Holistic thermal management and charge stop optimization using model-based fast-charging strategies

Abstract

The growing need to decarbonize the transport sector can be addressed through wide-scale electrification, which is currently hampered by concerns regarding range anxiety and insufficient charging speeds. Therefore, it is critical to provide methodologies that ensure fast-charging capability regardless of route or ambient conditions. Model-based fast-charging and preconditioning strategies have been shown to offer a robust approach to achieve short charging times without endangering battery safety or longevity. However, they must be scaled to the vehicle application while considering factors such as route infrastructure and energy constraints. In this study, we utilize a dynamic programming approach to optimize a charge stop and preconditioning strategy for long-distance journeys. The methodology is validated by performing long-distance travel experiments on a route of 850 km using a Tesla Model 3 Standard Range, revealing that charging time can be reduced by 24 min while simultaneously consuming less thermal management energy compared to the onboard route planning algorithm. A simulation study with a hypothetical high-power cell using an anode potential control charging protocol to prevent lithium plating shows that the inherent self-heating behavior could be leveraged to achieve a charge time reduction of 50 min compared to the reference, while requiring almost no active preconditioning. Optimizing the vehicle speed between charging stations additionally allows total travel duration and energy consumption to be adjusted based on charging constraints and individual preferences regarding the value of time and energy costs.