Despite the rapid expansion of electric vehicle (EV) charging networks, questions remain about their efficiency in meeting the growing needs of EV drivers. Previous simulation-based approaches, which rely on static behavioural rules, have struggled to capture the adaptive behaviours of human drivers. Although reinforcement learning has been introduced in EV simulation studies, its application has primarily focused on optimising fleet operations rather than modelling private drivers who make independent charging decisions. Additionally, long-distance travel remains a primary concern for EV drivers. However, existing simulation studies rarely explore charging behaviour over large geographical scales. To address these gaps, we propose a multi-stage reinforcement learning framework that simulates EV charging demand across large geographical areas. We validate the model against real-world data, and identify the training stage that most closely reflects actual driver behaviour, which captures both the adaptive behaviours and bounded rationality of private drivers. Based on the simulation results, we also identify critical 'charging deserts' where EV drivers consistently have low state of charge. Our findings also highlight recent policy shifts toward expanding rapid charging hubs along motorway corridors and city boundaries to meet the demand from long-distance trips.

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology - allowing EVs to also discharge energy back to the grid - new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximization problem where EV drivers must select customer requests or orders while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighborhood search (LNS). Experiments on real-world data show our methods can double driver profits compared to baselines, while maintaining near-optimal performance on small instances and excellent scalability on larger ones. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

Uber held its second Go-Get Zero event on Tuesday to highlight some of the company's sustainability efforts. First and foremost, it says that there are now enough EV drivers using the service to make an EV-only option available (the current Uber Green includes hybrids). The all-electric Uber Green option, which will cost about the same as an UberX, will initially be available in 40 cities and the company plans to expand this over time. At the jump, US Uber users will be able to select an EV-only option in New York City, Los Angeles, New Jersey, Philadelphia, San Francisco, Denver, Phoenix, San Diego, Orange County, Sacramento, Las Vegas and Palm Springs. The option will soon be available in every city in France where Uber operates, as well as locales in Australia and New Zealand.

Many countries like Singapore are planning to introduce Electric Vehicles (EVs) to replace traditional vehicles to reduce air pollution and improve energy efficiency. The rapid development of EVs calls for efficient deployment of charging stations both for the convenience of EVs and maintaining the efficiency of the road network. Unfortunately, existing work makes unrealistic assumption on EV drivers' charging behaviors and focus on the limited mobility of EVs. This paper studies the Charging Station PLacement (CSPL) problem, and takes into consideration 1) EV drivers' strategic behaviors to minimize their charging cost, and 2) the mutual impact of EV drivers' strategies on the traffic conditions of the road network and service quality of charging stations. We first formulate the CSPL problem as a bilevel optimization problem, which is subsequently converted to a single-level optimization problem by exploiting structures of the EV charging game played by EV drivers. Properties of CSPL problem are analyzed and an algorithm called OCEAN is proposed to compute the optimal allocation of charging stations. We further propose a heuristic algorithm OCEAN-C to speed up OCEAN. Experimental results show that the proposed algorithms significantly outperform baseline methods.

In our last blog post, we've described how crucial charging decisions are to make driving an electric vehicle (EV) truly efficient, sustainable and convenient. Where, when and how to charge decides if the driver finds a vacant charge point, if the charging electricity is based on renewable energy production and if the overall costs are low. But humans sometimes struggle with making the right charging decision, which creates a frustrating charging experience and thus blocks our path to CO2-neutral transportation. We believe that artificial intelligence (AI) can play an important role in supporting humans and that such charging solutions embracing this technology will win the most EV drivers. With this blog post, we want to look deeper into the potential of AI by considering how the end-user experience in public charging could be improved.

Many countries like Singapore are planning to introduce Electric Vehicles (EVs) to replace traditional vehicles to reduce air pollution and improve energy efficiency. The rapid development of EVs calls for efficient deployment of charging stations both for the convenience of EVs and maintaining the efficiency of the road network. Unfortunately, existing work makes unrealistic assumption on EV drivers' charging behaviors and focus on the limited mobility of EVs. This paper studies the Charging Station PLacement (CSPL) problem, and takes into consideration 1) EV drivers' strategic behaviors to minimize their charging cost, and 2) the mutual impact of EV drivers' strategies on the traffic conditions of the road network and service quality of charging stations. We first formulate the CSPL problem as a bilevel optimization problem, which is subsequently converted to a single-level optimization problem by exploiting structures of the EV charging game played by EV drivers. Properties of CSPL problem are analyzed and an algorithm called OCEAN is proposed to compute the optimal allocation of charging stations. We further propose a heuristic algorithm OCEAN-C to speed up OCEAN. Experimental results show that the proposed algorithms significantly outperform baseline methods.