Why Telematics Can't Tell You Why Your EV Fleet Fails to Charge

What Does Telematics Data Actually Show?

Telematics platforms collect machine data from the vehicle. This is valuable for route optimization, maintenance scheduling, and energy accounting. But it is a narrow slice of what actually happens during a charging session.

• → Location. Where the vehicle was plugged in, based on GPS coordinates.
• → Duration. How long the vehicle was connected to a charger.
• → Energy delivered. How many kWh were added to the battery during the session.
• → State of charge. Battery percentage at the start and end of the session.

This is machine data. It describes what happened to the vehicle. It says nothing about what happened to the person. A session that shows zero energy delivered could mean a broken charger, a payment failure, a wrong connector, or a confused first-time user who gave up after two minutes. Telematics treats all of these the same: a failed session. No cause, no context, no actionable detail.

What Does Telematics Miss?

The human layer is invisible to telematics. Every charging session involves a person making decisions, and those decisions determine whether the session succeeds or fails.

Did the person know which connector to use? Did they understand why charging slowed down after 80%? Did the charging app fail to authenticate? Was the payment process confusing? Did they drive away because the queue was too long, or because they couldn't figure out how to start the charger?

Telematics sees the outcome. It does not see the cause. You get a data point that says "the vehicle was at a charger for four minutes and received zero energy." What you don't get is the reason. And without the reason, you cannot fix the problem.

This matters more than most companies realize. When someone on your team has a bad charging experience and doesn't know why, they don't file a report. They just avoid that charger next time, or they charge less efficiently, or they spend 20 extra minutes at a different station. These inefficiencies compound across a fleet. They show up as higher energy costs, longer downtime, and frustrated people, but they never show up in telematics.

Why Are Public Chargers a Blind Spot?

At the depot, you see what happens. You control the hardware, you set the schedule, you can walk over and check if something goes wrong.

At public chargers, you're blind. Your team uses chargers you don't control, at locations you can't monitor, operated by networks you have no relationship with. A delivery person charging between stops at a public DC station is on their own. If the charger doesn't start, they troubleshoot alone. If the app crashes, they figure it out alone. If they don't understand why charging is slow, they wait longer than they need to.

For companies with depot charging, the public charger problem might seem minor. But most small and mid-size fleets don't have depots. Last mile delivery companies, home care providers, courier services, and companies with electric company cars all depend on public infrastructure. Their people charge at public stations every day.

Telematics can tell you that a vehicle sat at a public charger for 45 minutes and only received 12 kWh. It cannot tell you that the person didn't realize the station was power-sharing between four stalls, or that they arrived at 75% state of charge and spent most of the session in the slow part of the charging curve. The fix for each of those situations is different, but the telematics data looks identical.

What Does Behavioral Data Look Like?

Behavioral data fills the gap that telematics leaves open. It comes in two layers, and both are necessary.

Scenario engagement data shows which problems come up most. When someone on your team opens a step-by-step guide about "charger won't start" or "charging speed lower than expected," that's a signal. It means they encountered that problem and needed help. Aggregated across a team, this data reveals patterns. If eight people looked up connector types in the last month, that's a knowledge gap you can address.

Charging feedback data captures what your team reports from the charger. After each session, a quick log (good, okay, or bad, plus a reason if something went wrong) creates a continuous record of what's actually happening at the charger. This is active data, reported by the person who was there.

Together, these two layers give you something telematics never will: a view into the human side of charging. Not just where and how long, but why things went wrong and what people didn't understand. This is the difference between knowing that a session failed and knowing that it failed because the payment process was confusing, or because the person didn't know which plug to use.

Why Does This Matter Now?

The number of electric vehicles in commercial fleets is growing. The charging infrastructure they depend on is fragmented, inconsistent, and often confusing. As your fleet scales, the gap between what telematics shows and what actually happens at the charger gets wider.

Companies that rely only on telematics data for charging insights are making decisions with half the picture. They can optimize routes and schedule maintenance, but they cannot identify why charging sessions fail, which problems repeat, or where their team needs help.

Closing this gap doesn't require new hardware or a complex integration. It requires a layer of human data that sits alongside your telematics. Machine data tells you what the vehicle did. Behavioral data tells you what the person experienced. You need both.