Electric Vehicle Design with Simscape

This repository show you how to model an automotive battery electric vehicle (BEV) for range estimation and battery sizing.

Electric Vehicle Design with Simscape

To reduce greenhouse gas emissions, meet climate goals, and arrest global warming, the automotive sector is rapidly developing and proposing innovative low-carbon solutions. Among these solutions, electric vehicles (EVs) have gained traction thanks to their reduced carbon footprint and overall efficiency. The mass adoption of EVs depends on factors including the cost of ownership, safety, and range anxiety. Typically, these vehicles employ large battery packs, which are often the most expensive component of the vehicle. Modeling and simulation play then an important role in reducing the development cost and enabling greater adoption of these vehicles.

The examples in this repository show you how to model an automotive battery electric vehicle (BEV) for range estimation and battery sizing. The battery pack comprises several battery modules as combinations of cells in series and parallel. The vehicle model is a coupled electrical, mechanical, and thermal model built using Simscape™ Battery™, Simscape Driveline™, Simscape Electrical™, and Simscape Fluids™ Libraries.

There are workflows in this project where you learn how to:

1. Simulate an all wheel drive (AWD) and a front wheel drive (FWD) vehicle.

2. Estimate the on-road range of the vehicle. Run drive cycles with different ambient conditions to determine the range of the vehicle with a given capacity.

3. Size your high-voltage (HV) battery pack to achieve your desired range. You learn how to simulate battery packs with different capacities and weights, and compare them based on how these factors affect the range of the vehicle.

4. Setup your electric motor test bench for system integration.

5. Find the fixed gear ratio suitable for BEV application.

6. Generate a loss map for the motor and inverter.

7. Estimate the inverter power module semiconductor device junction temperature variation due to switching and predict the lifetime of the inverter.

8. Build a neural network model to predict battery temperature.

Setup

• Clone the project repository.
• Open ElectricVehicleSimscape.prj to get started with the project.
• Requires MATLAB® release R2023b or newer.

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