AI for Electric Vehicle Batteries: A Hands-On Python Guide to State of Charge, State of Health, Remaining Life and Smart Charging

Turn battery data into insight, with code that runs.

Every electric vehicle has to answer two questions that it cannot measure directly: how much energy is left, and how long will the battery last? This practical guide shows engineers, students and data scientists how to answer them with physics and machine learning, from the first line of Python to a working battery health monitor on a Raspberry Pi.

You need no laboratory and no downloads to start. The book includes evbatt, a compact battery simulator that generates realistic drive cycles, voltages, temperatures and ageing histories for whole fleets of cells, with the hidden ground truth included, so you can learn every method before you touch real data. It also points you to the major public battery datasets for when you are ready.

Inside you will learn to:

• Build coulomb counting, an equivalent-circuit model and an extended Kalman filter, and see exactly where they fail
• Estimate state of charge across -10 to 45 °C with gradient boosting and LSTM neural networks in PyTorch
• Estimate state of health from partial charging curves, with calibrated uncertainty from Gaussian process regression
• Predict remaining useful life from the first 100 cycles, with honest conformal prediction intervals
• Detect internal short circuits, loose connections and thermal-runaway precursors in battery modules
• Optimise health-aware EV charging around solar power and time-of-day tariffs
• Deploy models on the edge with NumPy, 8-bit quantisation and C code for the ESP32
• Build a self-calibrating Raspberry Pi battery health monitor as a capstone project

Every code listing in the book was run before publication, and every figure was produced by the code beside it. Written for engineering students, researchers, BMS and EV engineers, data scientists moving into energy and mobility, and educators looking for meaningful hands-on AI projects.