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Artificial intelligence (AI) is evolving rapidly and is making its way into almost every industry. And why not? After all, when used thoughtfully, AI solutions have the potential to enable organizations and businesses to achieve better operational efficiencies, measure key performance indicators (KPIs), gain insights into customer beliefs and behaviors, and leverage the power of big data.
When we look at where AI is making its mark, the electric vehicle (EV) market is no exception. Improved performance, better safety features, and increased efficiency are all benefits that AI can bring to EVs. Before we dive more into that, let’s take a look at where the EV market is headed: Experts believe that EVs will make up a third – or even half – of all light vehicles sold yearly in the United States by 2030, up from about 7% in 2022.
Multiple factors are driving the adoption of EVs, and sustained policy support is a main pillar. Additionally, public spending on subsidies and incentives for EVs has increased dramatically: In 2021, it almost doubled to nearly $30 billion USD. A growing number of countries have pledged to phase out internal combustion engines or have ambitious vehicle electrification targets for the coming decades. With five times more new EV models available in 2021 than in 2015, the attraction factor for consumers has increased, and on top of that, several carmakers have plans to electrify their fleets.
Of course, as the EV market and adoption continue to expand, the demand for electricity and supporting infrastructure will grow. And it will grow tremendously: 60% from 2019 to 2050. This will put pressure on power generation, transmission, trade, and distribution. All those things are subject to optimization through digital data management. As such, there is an opportunity for AI to play a significant role in converting to EV. More specifically, AI can play a major role in the rapid growth, efficiency, safety, and reliability of EV charging networks. This can assist your fleet in its energy demand management – including using high renewable generation sources such as solar – to optimize supply and match demand whether for charging at home or at a private depot.
In fact, according to the World Economic Forum’s white paper titled “Harnessing Artificial Intelligence to Accelerate the Energy Transition:” ‘AI has tremendous potential to support and accelerate a reliable and lowest-cost energy transition, with potential applications ranging from optimizing and efficiently integrating variable renewable energy resources into the power grid, to supporting a proactive and autonomous electricity distribution system, to opening up new revenue streams for demand-side flexibility. However, despite its promise, AI’s use in the energy sector is limited, with it primarily deployed in pilot projects for predictive asset maintenance. While it is useful there, a much greater opportunity exists for AI to help accelerate the global energy transition than is currently realized.’
Let’s take a deeper look at how.
Vehicle drivers have unique characteristics, patterns, and preferences in how and when they fuel their vehicles, and EV drivers are no exception. With AI, such charging patterns and preferences can be predicted, and personalized charging recommendations and incentives can be made. From there, AI can make accurate predictions about charging behavior and requirements for future users. In turn, that helps charging station operators make the most of their business strategies and resources. Profits can be maximized while managing energy better and providing an optimal user experience. Most importantly, by anticipating drivers’ charging needs, charging station operators can ensure that their charging stations are available when and where they are most needed.
Charging behavior goes hand in hand with charging schedules. To ensure a reliable and steady supply of electricity, it’s critical to maximize the efficiency of charging schedules. AI can play a powerful role by analyzing real-time power system conditions. Advanced algorithms can then identify the most efficient schedules. Plus, AI can optimize charging schedules based on various factors, including time-of-use tariffs, which use different prices to encourage consumers to use electricity at times when more electricity is available at a lesser cost.
By predicting behavior and optimizing charging schedules, AI can help charge station operators provide dynamic pricing for charging services, which can be affected by the energy required, the time of day, and the location. AI can determine the most cost-effective times to charge and adjust the pricing accordingly by analyzing real-time power system conditions.
AI algorithms can also optimize the power consumption of the vehicle by adjusting the speed, acceleration, and deceleration to maximize range for most efficient power usage. Additionally, AI can assist in better energy management by identifying the smartest route to a destination based on traffic, weather, and other factors.
AI is also transforming fleet management for EVs. Fleet managers can use AI to monitor and optimize the performance of their EV fleets, including tracking fuel efficiency, predicting maintenance needs, tracking traffic and vehicle movements through geospatial tagging, and analyzing data to optimize routes and reduce downtime. This can result in significant cost savings and improved operational efficiency.
Electric mobility as a service (eMaaS) offerings are changing regimes from preventive maintenance to predictive maintenance with the help of AI. Using AI to create a new industry benchmark in EV lifecycle management can bring significant cost savings and help make the adoption of EVs easier and more affordable.
AI is also transforming EV safety features. Advanced driver assistance systems (ADAS) are using AI to detect and avoid potential road obstructions and hazards such as pedestrians, cyclists, and other vehicles. By analyzing data from sensors, cameras, and radar systems, AI can detect potential collisions and alert the rider. Advanced systems go even further and take control of the vehicle in order to avoid accidents.
With the advancement of dash cameras, fleet managers are able to make data-informed decisions that will protect and enhance their business. Adding dash cameras to a fleet can save money on incident-related costs and reduce overall operating expenses. Driving behavior is improved, and increased safety and fuel efficiency are achieved. This AI-influenced technology provides a significant return on investment and can be seamlessly integrated into existing fleet card solutions. WEX prioritizes its customers’ needs to find top-tier dash cameras with the best features at competitive prices.
AI will play a significant role in the future of EVs. AI is transforming and helping improve performance, creating better safety features, and increasing fleet efficiency. And it’s only just begun. Measuring the possible impact of AI is complicated, but a 1% improvement on demand side efficiency could generate significant funds (trillions of dollars) to reinvest back into accelerating the energy transition globally.
“Companies and policy-makers must play an active role in governing and shaping the use of AI in the energy sector in a responsible way, and creating an enabling environment to unlock AI’s full potential.” – World Economic Forum
At WEX we see the power of EV, and we are well positioned to help drive the mixed fleet revolution and power the electric fleet future. AI will help accelerate energy transition with line of sight applications on energy demand management, renewable power generation and demand forecasting, grid optimization, and materials discovery. As an industry leader, WEX is working to help shape data standards and data sharing mechanisms through WEX-managed vehicles and the real-time data that they can provide.
WEX is powering the electric fleet transition with:
Learn more and get your fleet operations ready for a mixed fleet that makes the most sense for your business.
Editorial note: This article was originally published on July 26, 2023, and has been updated for this publication.
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