Intelligent Multi-Vehicle Planning

The rising need for green transport drives the imperative to refine electric vehicle (EV) operations, prioritizing both single charge range and battery lifespan. A comprehensive approach is presented to enhance the range of EVs and battery life by optimizing energy consumption and charging/discharging behavior. The problem focuses on the selection of energy-efficient routes, customer allocation, and driving speed for a multi-EV system. Constraints include: depth and rate of charging/discharging, EVs’ capacity and quantity, customers’ availability and demand, speed and acceleration limits, bounds on input torque and its rate of change, and traffic signals, while uncertainties cover slippery road surface and grade. Dijkstra’s algorithm, mixed integer linear programming (MILP) algorithm, and model predictive control (MPC) algorithm are used to perform the task. Energy consumption improved by 6.6%, when compared with integrated routing and driving technique using Clarke and Wright (C&W) and Pontryagin’s minimum principle (PMP), respectively. Battery effective capacity and lifespan increase from 27 to 31.2 kWh and 7.12 to 8.85 years, respectively. The improvement in range per charge and over lifespan compared with commercially available data of Dacia Spring Electric 45 and Leapmotor T03 further strengthens the claim. Almost the same execution time is obtained for the proposed integrated strategy when compared with the standalone routing and driving techniques.