Fleet managers are constantly looking for ways to make their charging operations more efficient, cost-effective, eco-friendly, and secure.
Traditional fueling was simple. Drivers pull up to a pump, fill their tank, and they’re on their way.
With EVs, charging needs to be more coordinated and controlled. If an EV stays on charge for longer than needed, it may delay the charging of another vehicle leading to schedule disruptions.
Impact of Uncontrolled Charging
For example, imagine two logistics EVs that are scheduled for back-to-back deliveries.
One takes 30 minutes longer to charge than expected. The first driver doesn’t report it, thinking it was just a minor delay. This creates a domino effect, delaying not only the second vehicle, but several other deliveries on that day.
And that’s not to mention the cost and eco implications of the extra power usage.
Coordinating complex charging schedules is impossible without careful oversight.
There are additional security challenges too. Fleets often charge EVs at various locations across a large depot. Even if the site is private, it’s important to securely authorize the driver or vehicle and reconcile EV charging bills for internal audits.
To achieve this, you need to know which EV is at which charger and for how long. That’s where smart charging technology and AI can help.
But before we look at AI-powered EV identification, let’s take a deeper look at some of the challenges that arise when using traditional methods.
Traditional EV Identification Challenges
There are three main types of traditional EV identification. Let’s take a look at how each one works and what the limitations are.
1. No Authentication In Place
Many fleets have no authentication or EV identification measures in place at all. Under a setup like this, any driver can simply park up, plug-in, and the vehicle starts charging immediately.
This works okay for small, isolated, single-tenant sites where EV monitoring isn’t really relevant.
But for more complex settings, the no-authentication method falls down for the following reasons:
- Lack of Accountability – At shared sites, used by multiple firms, accountability is an issue.
- Public Access Issues – Places with public or semi-public access can cause debates over charger usage and cost allocation.
- Uptime Monitoring – If you need to monitor uptime efficiently, keeping track of vehicles is crucial.
- Oversight – On large, sprawling sites, it's impractical and time-consuming to manually oversee charging.
- Complex Scheduling – Specific EV tracking may be needed to ensure that complex running schedules are met.
2. RFID
The beauty of RFID is its simplicity and cost-effectiveness.
A card or tag is used to authenticate drivers and their vehicles at charging stations. Each RFID card is assigned to an individual EV and an RFID chip may be attached to the vehicle key.
With broad compatibility across both AC and DC chargers, it’s a solid choice for fleet managers that tightens up security. There’s no doubt that RFID is a big step up from the previous no-authentication method.
However, there are a few potential downsides to using RFID technology for EV identification:
- Extra Hardware – You may need additional hardware, such as card readers and scanners.
- Card Losses – When drivers lose or damage cards, an admin layer is needed to manage replacements.
- Inconvenience – If drivers use multiple vehicles they may need a variety of different cards or chips.
3. Plug-Based Identification
Plug-based or physical connector identification uses simple logic – match the plug to the vehicle type.
However, this simple system tends to only yield simplistic results, leading to a number of drawbacks:
- Basic ID – You only get a basic ID using this method, with very little in the way of specific vehicle and charging information
- Misleading – Can lead to misrepresentation of an EV’s battery size or state of charge, leading to inaccurate charging data.
- Lack of Scalability – Difficult to scale up, especially as the variety of EVs within a fleet grows or a mixed fleet is used.
- Not Future-Proof – New plugs and charging standards are likely to emerge over time.
- Security Issues – Any EV with a compatible plug can charge, potentially leading to unauthorized usage.
Common EV Identification Challenges
Lack of Real-time Data
One thing that all three traditional methods of EV identification have in common is a failure to provide real-time information about the vehicle or state of charge (SoC). Most commonly:
- AC Chargers don't supply SoC information.
- DC Chargers tend to fare better, often providing data on SoC and other pertinent details. But overall, the information available directly from the DC charger is limited.
Without access to real-time data, it’s difficult to optimize your charging strategies, as you won’t have a clear picture of what is going on.
Scalability Challenges
The rapid increase in EV adoption means that traditional identification methods are no longer good enough, for the following reasons:
- Infrastructure Management – Traditional systems don’t cope well with growing charging infrastructures as they are inherently inflexible.
- Congestion Issues – With more vehicles using slow, traditional identification processes, charger stations may get congested quickly.
- Wasted Resources – As EV fleets scale up, resources such as manpower and energy may be wasted.
AI-Powered EV Identification – A New Era for EV Fleets
Automatically identifying precise EV models and spec with AI engines is groundbreaking. It provides fleet managers with an adaptable, reliable, and scalable way to get reliable data and ensure accurate charger authorization.
How Do AI Algorithms Recognize EVs?
AI algorithms use machine vision and pattern recognition to identify EVs through a network of cameras and sensors.
Deep learning models have been trained to analyze vehicle characteristics, such as shape, logo, and license plate. The AI engine cross references captured images with data in the model to identify the EV, using a variety of reference points to ensure accuracy.
Benefits of AI Identification
- Streamlined Charging Operations – Reduces the time spent on verifications and eliminates human error.
- Cohesive Ecosystem – Gather and analyze data more efficiently with smooth integration between AI-powered smart charging systems (like Ampcontrol) and fleet management software.
- Highly Scalable – As you expand your fleet, the AI-driven solution evolves with you as it learns and adapts to new EV models.
- Boost Efficiency – Gather real-time data, allowing you to spot charging inefficiencies and deal with them instantly.
- Cost Saving – Efficiency gains translate to real cost savings, such as reduced energy consumption, minimized downtime, decreased charger wear and tear, and lower admin costs.
- More Sustainable – Achieve your environmental goals by optimizing charging to reduce energy waste.
- Predictive Analysis – Gain deep insights into the charging needs of different fleet vehicles. This allows you to plan the best charging methods and times in advance.
Conclusion
You need to move with the times if you want to stay ahead in the rapidly evolving world of EV fleet management.
AI and machine learning technology is revolutionizing all aspects of life at the moment – and EV charging is no exception.
Outdated EV identification methods hold back efficiency gains and cost saving opportunities, so don’t fall into that trap.
Investing in AI-powered EV identification and smart charging software like Ampcontrol for EV fleets will future-proof your operations, enable rapid scaling, and raise your eco-credentials.
To find out more about how Ampcontrol can help you reach new levels of charging efficiency, get in touch and book a demo today.
