EV Battery Failure Prediction Agent
Product: AI analytical agent for EV battery fleets
Duration: 2 Weeks
Role: Designer & Developer
Background
Fleet operators manage thousands of electric vehicles, and the battery is the most expensive part of each one. A battery that fails without warning means a vehicle off the road, an unhappy driver and a costly emergency repair. Most fleets already collect daily telemetry, but that data sits in spreadsheets that nobody has time to read.
Problem
Fleet managers need to know which batteries are likely to fail, how much life each one has left, and why. A risk score on its own is not enough: if people cannot see the reason behind a prediction, they do not trust it and they do not act on it.
Approach
I applied the same rule I use for UX work: start with the user's questions, not the technology. A fleet manager asks three things every morning: "What changed?", "Who do I need to look at today?" and "Why?" Every part of the agent answers one of those questions.
What I built
Prediction: failure probability with a risk band, and remaining life in cycles, kilometres or days, with a calibrated 90% range.
Explanation: for every vehicle, the factors that drive its risk, plus charts, outliers and the reason one model was chosen over the others.
Daily intake: takes in the fleet's daily export (CSV, Excel, JSON or Parquet), maps the columns, and checks the data for gaps, bad values and drift.
Reports and alerts: daily and weekly reports with an action list, alerts by email, Slack or Teams, and a dashboard that refreshes on every intake.
Learning loop: recorded outcomes ("failed", "false alarm") become training labels. A new model goes live only if it beats the current one.
Agent mode: Claude plans the analysis with 20 tools, reads its own charts and explains the results in plain language.
Results
Trained on 20,000 vehicles and tested on 4,000 it had never seen:
0.985 ROC-AUC for failure prediction (84% of failures caught at 75% precision)
R² 0.906 for remaining-life prediction, with a 90% range that holds 90.0% of the time
Runs fully offline with no API key; agent mode is optional
Takeaways
Trust is a design problem. The most useful feature was not the model's accuracy but the explanation next to each number. A decision log and a "Needs review" section make every run auditable, so a manager can check the agent's work instead of taking it on faith.