Battery pack thermal prediction and fault detection using machine learning

This project aims to use machine learning to determine the thermal state of a battery pack used in electrical vehicles. In fact, battery pack are made of numerous individual cells, and it is essential to monitor their respective state to ensure the battery pack operates in ideal conditions. By doing so, the autonomy of the electrical vehicle can be increased, and the duration of the battery pack useful life can be extended. With this project, we would like to infer the thermal distribution in the whole battery pack by monitoring as few cells as possible to reduce hardware cost.

Faculty Supervisor:

François Grondin

Student:

Partner:

Calogy Solutions

Discipline:

Engineering

Sector:

Manufacturing

University:

Université de Sherbrooke

Program:

Accelerate

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