Abstract
In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm’s capability to take recharging decisions and produce desired energy feasible paths.
Highlights
The importance of electric vehicles (EVs) has increased steadily over the past few years with growing concerns about climate change, volatile prices of fossil fuels and energy dependencies between countries
This paper aims to address highlighted drawbacks in the EVspecific route planning by proposing a novel problem formulation suitable for solving by reinforcement learning (RL) techniques
Each node is characterized by its geographical coordinates: latitude, longitude, and elevation
Summary
The importance of electric vehicles (EVs) has increased steadily over the past few years with growing concerns about climate change, volatile prices of fossil fuels and energy dependencies between countries. The EV deployment grows fast around the world (+40% in 2019) with Europe accounting for 24% of the global fleet, specific barriers for a massive uptake of EVs still exist (International Energy Agency, 2020). Researchers in (Noel et al, 2020) identify technical, economic, social and political barriers of EVs’ broad adoption with limited cruising range and sparse charging infrastructure prevailing at present. These barriers are in the essence of the “range anxiety problem” defined as a fear that an EV will not have sufficient charge to reach its destination. Optimal EV route planning together with higher-range EVs entering the market can mitigate this problem
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