Extend the EV Range through Dynamic Scheduling of Battery: Present and Future Techniques

Abstract

The pursuit of extending the driving range and improving the energy efficiency of electric vehicles (EVs) is a critical objective in advancing sustainable transportation. Central to this pursuit is the battery management system (BMS), which ensures the operational integrity and optimal performance of the EV's battery. Traditional BMSs have largely been conservative, relying on static parameters and predefined rules, which often do not fully exploit the battery's capacity or adapt to the dynamic nature of driving conditions. This has resulted in EVs that do not optimize their range, either underutilizing their energy or risking premature depletion. This paper introduces a dynamic scheduling approach for battery usage in EVs, a paradigm shifts from traditional BMS algorithms that are deterministic and linear, to one that is adaptable and predictive. The proposed dynamic scheduling method utilizes data analytics, machine learning, and real-time monitoring to anticipate and adapt to varying driving conditions, traffic patterns, driver behavior, and route topography. The objective is not just to respond to the battery's current state but to manage the energy distribution proactively, optimizing the use of the stored energy and enhancing the vehicle's range on a single charge. The paper explores the technological advancements enabling dynamic scheduling, the benefits of such a system, and the challenges it may encounter. It is posited that dynamic scheduling represents a necessary evolution in battery management, capable of significantly boosting the range and desirability of EVs. Finally, the paper proposes a novel system and method that leverage real-time data and machine learning to implement an effective energy management strategy for dynamic scheduling, which could markedly improve the range of an EV. The implications of this approach suggest a future where EVs can meet and exceed the range expectations of consumers, thereby accelerating the transition to electric mobility.