Perspective: AI Will Drive the Future of Electric Vehicles
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For many fleet operators, day-to-day operational complexities are the key barrier to success with the adoption of electric vehicles. Ensuring vehicles are fully charged and ready to depart on schedule, managing energy costs, avoiding demand charges, achieving charger uptime goals, and maintaining healthy vehicle and infrastructure systems can overwhelm teams. But AI is beginning to emerge as the orchestrator that brings order to that post-deployment complexity.
AI Enhances EV Charging
One of the biggest operational challenges with electric vehicles for fleet managers is ensuring every vehicle reaches the right state of charge (SoC) based on its route, duty cycle and schedule. In traditional systems, this is a semi-automated process in which planners estimate energy needs and schedule charging with limited data. Looking ahead, AI could improve this equation by automating route-aligned charging.
Also, by pairing AI-supported charging capabilities with a human-managed network operations center (NOC), operational anomalies could be minimized thanks to the two working in tandem.
Consider a scenario in which vehicles are returning to a depot with a low SoC during cold weather; a fleet management platform supported by AI could trigger adjustments to charging patterns based on route planning and forecast energy demand. This enables the NOC to monitor performance and provide insights, allowing fleets to make timely adjustments to their operations during any given delivery period.
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While this level of automation is still evolving, it illustrates how combining load-shifting strategies with human oversight and forward-looking systems could help fleets become more resilient and cost-efficient. By analyzing factors like temperature, terrain, driver behavior and battery health, AI could help ensure every vehicle is charged adequately.
Though much of AI’s potential in fleet management lies ahead, its impact can already be seen in today’s EV operations. Take a real-world scenario at a food and beverage industry depot: Operators believed a vehicle had been properly plugged in at the end of a shift. In reality, the charger plug wasn’t properly inserted. Within minutes, the NOC — supported by AI — alerted the fleet to this issue, thus preventing what could have been a major disruption the next morning.
Looking ahead, AI platforms could take efficiencies even further by pulling data directly from hardware manuals and diagnostic logs to identify the causes of failures, recommend precise fixes, refine projections around spare parts inventory and coordinate service calls autonomously.
Cost Control
Energy costs are one of the most variable and misunderstood components of EV operations. While many fleets use charge management systems to help schedule charging and track utility rates, AI can add a layer of intelligence — in scenarios like route changes, weather shifts, vehicle maintenance schedules or unexpected delays — where traditional tools may fall short.
AI-enabled platforms don’t just automate charging, they also learn and adapt. The technology may analyze day-ahead price signals, detect demand charge windows and shift loads accordingly. The result? Charging schedules that align with the lowest-cost windows while still meeting operational needs.
Take, for instance, depots operating in extreme weather. As more data is collected — such as weather trends, driver habits, utility rate fluctuations and infrastructure lifespans — AI-enabled platforms can become smarter, revealing insights that operators may not have considered. The compounding effect of this learning is where AI’s true potential lies: Small optimizations set the stage for accelerating improvements that may feel exponential. By optimizing charging to avoid peak hours and adjusting to shifting variables, the platforms reduce energy costs while ensuring vehicles are ready to roll the next morning.
From Electric to Intelligent
To lead in this next era of fleet electrification, operators must move beyond the mindset of “infrastructure deployment” and embrace operational intelligence. The fleets that thrive won’t just be electric; they’ll be adaptable, data-driven and optimized. AI will play a central role in making that happen, especially when paired with human expertise and on-the-ground support.
Siemens’ Alan White spearheads initiatives in e-mobility, microgrids and sustainable energy for commercial fleets.
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