Electric fleet charging management considering battery degradation and nonlinear charging profile

• Published in: Energy (Oxford) Vol. 283; p. 129094
• Main Authors: Shi, Junzhe, Zeng, Teng, Moura, Scott
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2023
• Subjects: Battery degradation modeling; Fleet management; Li-ion battery; Mixed integer linear programming; Nonlinear charging profile; Optimal charging; Battery degradation modeling; Optimal charging; Mixed integer linear programming; Fleet management; Li-ion battery; Nonlinear charging profile
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2023.129094

The populations of commercial electric vehicles (EVs) and electric robots (ERs) have been growing rapidly in recent years. Yet, the availabilities and incoordination of the charging infrastructure still constrain the operations of all EVs/ERs, resulting in wasted waiting time and, thus, decreased total profits. Coordinating these electric machines as a fleet and identifying the optimal operation and charging schedules to maximize total profit is essential. On the other hand, the charging process usually consists of two charging stages, constant current (CC) and constant voltage (CV), which lead to a nonlinear charging profile. Other factors, such as the high charging current, may significantly accelerate battery degradation and lead to capacity fade. However, the high nonlinearities make the battery charging profile and the degradation model computationally difficult to be integrated into optimization problems. In this study, we propose an innovative fleet management strategy that maximizes the operation revenue and minimizes the cost of electricity and battery degradation while addressing the aforementioned nonlinear charging profile. By proposing two linearization methods to replace the nonlinear parts, we formulated a Mixed-Integer Linear Program (MILP). Furthermore, stemming from the numerical case study, two managerial insights, the impact of the battery SOH on fleet management and the selection of fast charging vs. normal charging modes, are outlined. •Used MILP for optimizing fleet charging management, considering battery degradation and nonlinear Charging Profile.•Simplified optimization with a derived linear battery degradation model.•Integrated a nonlinear battery charging profile using linearization.•Found that mid-life batteries in EV/ERs enhance economic returns.•Showed fast charging to be more economical, freeing up time for valuable tasks.