Energy-Efficient Routing for Electric Vehicles under Acceleration and Load Effects

TL;DR

The paper introduces ALD-EVRP, a time-dependent electric vehicle routing problem that incorporates acceleration and real-time load into energy consumption via a piecewise-linear speed model. It formulates a MINLP and validates it with BonMin, while proposing a scalable LNS-SPP meta-heuristic that combines Large Neighborhood Search, Local Search, and Set Partitioning to solve large instances. Experiments on 4–15 customer problems show the LNS-LS method matches or surpasses BonMin with far lower runtimes; large-scale tests on 47 instances (30–114 customers) demonstrate that real-time load modeling yields more accurate energy estimates and that the LNS-SPP approach solves all instances efficiently. The results underscore the practical importance of integrating acceleration and load effects for energy-efficient routing of EVs under realistic traffic dynamics, with Singapore data supporting applicability to real-world networks.

Abstract

This paper proposes an Acceleration and Load-Dependent Electric Vehicle Routing Problem (ALD-EVRP), to optimize the energy consumption (EC) while capturing the effects of changing traffic conditions between peak and off-peak periods. We generalize the time-dependent speed model by replacing step functions with piecewise linear functions. The EC of each vehicle is influenced by its speed, acceleration, and real-time load. A mathematical model is developed and solved using BonMin, and a custom meta-heuristic algorithm is proposed for large-scale problems, yielding the same results as BonMin on small problems and performing better on larger ones. This is validated with real data from Singapore.

Figures (3)

• Figure 1: Calculation of $W^k$ for $m\in\Phi_1,\Phi_2$ (acceleration and cruising)
• Figure 2: Calculation of $W^k$ for $m\in\Phi_3,\Phi_4$ (deceleration and idling)
• Figure 3: The entire scheme of our LNS-SPP method