Illustration of algorithmic connections between EV charging stations.

Are EV Charging Stations Secretly Colluding? The Shocking Truth About Algorithmic Pricing

Lena Kashyap in Tech & Innovation June 2025 • 4 min read.

"New research reveals how AI could be leading to 'tacit collusion' among EV charging networks, potentially driving up prices for consumers. Is your electric fill-up costing you more than it should?"

As electric vehicles (EVs) become increasingly common, a network of charging stations is springing up to keep them powered. Like gas stations, these EV charging hubs compete to attract customers. But there's a twist: many of these hubs are using sophisticated artificial intelligence (AI) algorithms to set their prices, and new research suggests this could lead to a hidden form of collusion, potentially impacting how much you pay to charge your EV.

The concept of "tacit algorithmic collusion" is raising concerns among experts and antitrust agencies. It suggests that competing businesses, guided by AI, might inadvertently coordinate their pricing to maximize profits, without any explicit agreements or communication. This could result in higher prices than what would exist in a truly competitive market.

A recent study dives into this issue, focusing specifically on the EV charging market. Researchers examined how AI-powered pricing algorithms might lead to tacit collusion among EV charging hubs, and what the implications are for consumers. Their findings reveal a nuanced picture, suggesting the potential for collusion exists, but the extent varies based on several factors.

How Do EV Charging Hubs Use AI to Set Prices?

Illustration of algorithmic connections between EV charging stations.

EV charging hubs face a complex challenge in determining the right price for their services. They need to consider several factors, including:

AI algorithms help charging hubs navigate this intricate landscape, making real-time adjustments to pricing based on market conditions and competitor behavior. The goal is to maximize their profits while remaining competitive enough to attract EV owners.

Electricity Costs: Prices fluctuate based on the time of day and market demand.
Demand: The number of EVs needing a charge varies depending on location and time.
Competition: Prices at nearby charging stations influence customer choices.
Battery Storage: Some hubs use battery systems to store energy, adding another layer of complexity.

The study uses a "two-step data-driven methodology" to understand this pricing dynamic. First, the hubs determine their day-ahead commitment by solving a stochastic model, anticipating future energy needs. Second, they develop pricing strategies using a competitive Markov decision process model, guided by multi-agent deep reinforcement learning (MADRL).

What Does This Mean for EV Owners?

While the study suggests the potential for tacit collusion, it also indicates that the level of collusion can vary from low to moderate. However, the possibility of AI-driven price coordination raises important questions about fairness and transparency in the EV charging market. As EV adoption continues to grow, it will be crucial to monitor these pricing practices and ensure that consumers are getting a fair deal. Further research and potential regulatory oversight may be needed to prevent algorithms from inadvertently driving up the cost of electric vehicle ownership.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2401.15108,

Title: Tacit Algorithmic Collusion In Deep Reinforcement Learning Guided Price Competition: A Study Using Ev Charge Pricing Game

Subject: cs.lg cs.ai cs.sy econ.gn eess.sy q-fin.ec

Authors: Diwas Paudel, Tapas K. Das

Published: 25-01-2024

Everything You Need To Know

What is tacit algorithmic collusion, and how does it relate to EV charging stations?

Tacit algorithmic collusion is a form of hidden agreement where businesses, in this case, EV charging hubs, use AI to coordinate their pricing strategies without explicit communication or agreements. AI algorithms analyze factors like electricity costs, demand, competition, and battery storage to set prices. This can lead to higher prices for consumers than would exist in a truly competitive market, as the algorithms might inadvertently align pricing to maximize profits across multiple charging hubs. This is a concern in the EV charging market because the sophisticated AI algorithms used by charging hubs could be inadvertently leading to this type of collusion.

How do EV charging hubs use AI to set prices, and what factors influence these prices?

EV charging hubs employ AI algorithms to dynamically adjust prices, considering several key factors. These include electricity costs, which fluctuate based on time of day and market demand; demand for charging, which varies by location and time; the pricing strategies of competing charging stations; and the presence of battery storage systems, which adds complexity. The AI algorithms analyze these factors to determine the optimal price, aiming to maximize profits while remaining competitive. The study mentions a two-step methodology: First, they determine their day-ahead commitment, and second, they develop pricing strategies using a competitive Markov decision process model, guided by multi-agent deep reinforcement learning (MADRL).

What is the potential impact of AI-driven pricing on the cost of charging my EV?

The potential impact is that the cost of charging your EV could increase due to tacit algorithmic collusion. Although the study suggests that the extent of collusion can vary, the possibility exists that AI-driven price coordination among EV charging hubs could lead to higher prices than in a genuinely competitive market. This is because the AI algorithms, while not explicitly colluding, may learn to set prices that are mutually beneficial for the hubs, reducing price competition and increasing consumer costs. This could mean you pay more to charge your EV than you would in a market without this type of AI-driven coordination.

What methodologies are used by researchers to study AI-driven pricing in EV charging?

Researchers use a "two-step data-driven methodology" to understand the pricing dynamics in EV charging. The first step involves determining the day-ahead commitment by solving a stochastic model, which helps to anticipate future energy needs. The second step focuses on developing pricing strategies using a competitive Markov decision process model, guided by multi-agent deep reinforcement learning (MADRL). This approach enables the researchers to examine how AI algorithms influence pricing decisions and whether they lead to any form of tacit collusion.

What steps might be taken to ensure fair pricing in the EV charging market, given the potential for AI-driven collusion?

To ensure fair pricing, several steps might be considered. Monitoring these pricing practices is crucial to identify any instances of tacit collusion. Further research is needed to fully understand the extent and impact of AI-driven pricing strategies. Potential regulatory oversight could be implemented to prevent algorithms from inadvertently driving up the cost of electric vehicle ownership. The goal is to ensure that the EV charging market remains competitive and transparent, and that consumers receive a fair deal. This might involve establishing guidelines for AI-powered pricing or enforcing antitrust laws to prevent anti-competitive behavior.