As societies worldwide grapple with the urgent need to mitigate carbon emissions, one domain where substantial strides can be made is heavy-duty road freight transport. Electrification has emerged as a practical and technologically mature solution to address this challenge. However, the widespread adoption of electrification in this sector is met with formidable obstacles. These obstacles encompass the inadequacy of charging infrastructure, restrictions in driving range on a single charge, the demand for robust and high-capacity onboard batteries, and the looming specter of potential battery shortages.
Within the European context, discussions revolve around sustainable solutions that can pave the way for a cleaner and greener future. Among the contenders in this realm, the electric road system (ERS) has risen to prominence. ERS introduces a groundbreaking concept where trucks can recharge their batteries while in motion on highways, promising an array of ecological and economic benefits. However, the journey toward the implementation of ERS infrastructure is not without its intricacies. It necessitates the installation of specialized charging infrastructure, which can take the form of overhead catenaries accessed by a pantograph or embedded road equipment. Moreover, there is a substantial financial commitment required to equip entire truck fleets with the necessary batteries, adding to the complexity of the endeavor.
The central challenge in this landscape revolves around the meticulous design of an optimal ERS network that adeptly balances infrastructure costs with battery expenses. This research aims to address this multifaceted challenge by posing a fundamental question: How to determine the optimal ERS network, given the trade-off between infrastructure and battery costs?
To tackle this question head-on, this paper introduces a sophisticated multi-objective optimization model. This model is a computational framework that concurrently minimizes the costs associated with infrastructure investment, encompassing the installation and maintenance of ERS components, and the total transport expenses. These total transport costs encompass a range of factors, including the procurement of batteries, energy consumption, and toll charges. This comprehensive approach takes into account the diverse perspectives and interests of both investors and logistics companies, providing a holistic view of the intricate challenges associated with ERS adoption.
One pivotal advantage of ERS becomes evident in its capacity to extend the lifespan of batteries by reducing wear and tear during typical driving conditions. The model thoughtfully incorporates this aspect, factoring in battery purchase costs that hinge on projected lifespans. These projected lifespans, in turn, are influenced by the chosen route's electrification rate (ERS implementation).
To validate the model's effectiveness and practicality, it is subjected to a rigorous real-world case study. This case study delves into the intricacies of road freight transport in Germany, the Netherlands, Belgium, and Luxembourg. Additionally, this research introduces an enhanced Genetic algorithm, complemented by an Elitism strategy. These enhancements are designed to optimize solutions effectively within the confines of this practical context.
The findings derived from this rigorous analysis reveal a diverse Pareto set. This set showcases the delicate equilibrium between infrastructure investment and total annual transport costs. Notably, when budget constraints are absent, investing in ERS consistently proves advantageous. The total reductions in transport costs demonstrably surpass the initial ERS investment. For instance, the comprehensive electrification of 27,114 kilometers of highway results in a remarkable 30% reduction in total transport costs...