Rollout-Based Charging Strategy for Electric Trucks With Hours-of-Service Regulations

Abstract

Freight drivers of electric trucks need to design charging strategies for where and how long to recharge the truck in order to complete delivery missions on time. Moreover, the charging strategies should be aligned with drivers' driving and rest time regulations, known as hours-of-service (HoS) regulations. This letter studies the optimal charging problems of electric trucks with delivery deadlines under HoS constraints. We assume that a collection of charging and rest stations is given along a pre-planned route with known detours and that the problem data are deterministic. The goal is to minimize the total cost associated with the charging and rest decisions during the entire trip. This problem is formulated as a mixed integer program with bilinear constraints, resulting in a high computational load when applying exact solution approaches. To obtain real-time solutions, we develop a rollout-based approximate scheme, which scales linearly with the number of stations while offering solid performance guarantees. We perform simulation studies over the Swedish road network based on realistic truck data. The results show that our rollout-based approach provides near-optimal solutions to the problem in various conditions while cutting the computational time drastically.

Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

“…Also, due to the allowed break split of 15 and 30 min, the driving time model gives room for exactly such optimizations, which are not considered in previous work. Our method supports an online strategy analogous to that of Bai et al [31]. We integrate the charging strategy into a holistic framework for simulating long-haul transportation scenarios with BET.…”

“…To the best of our knowledge, only Bai et al [31] show an approach for optimal charging stop planning specifically for BET and the related consideration of mandatory rest times. They present a powerful algorithm for the online calculation of charging strategies but neglect the possibility of dividing the rest into 15 and 30 min.…”

“…Following this, no realistic battery-charging behavior is modeled due to the formulation of a linear optimization problem. Bai et al [31] base their investigation on a charging infrastructure network in Sweden and corresponding precalculated routes. Our works differ both in the implemented algorithm to overcome the simplifications assumed by Bai et al [31] and in the scope.…”

“…Bai et al [31] base their investigation on a charging infrastructure network in Sweden and corresponding precalculated routes. Our works differ both in the implemented algorithm to overcome the simplifications assumed by Bai et al [31] and in the scope. We specify our contribution in the next section.…”

Event-Triggered Distributed Model Predictive Control for Platoon Coordination at Hubs in a Transport System

“…It enables real-time solutions with cheap hardware at little loss of solution quality. The problem may also be solved exactly by a dedicated mixed-integer solver after performing a linearization procedure discussed in [21,Appendix F].…”