1. Birth with zero-prototyping

Traditionally, simulation has been used to develop engineering solutions that help reduce CO2 emissions and improve air quality: lightweighting whilst maintaining performance and safety goals, aerodynamics for lower penetration coefficient in order to increase mileage, more effective exhaust systems, sturdier tyres for better adherence and reduced wear and tear and paint robot optimization for less waste.

With the massive transformation triggered by electrification and autonomy, modeling and simulation play an ever-increasing role in moving towards sustainability.

First and foremost, the trend toward zero-prototyping brings down the environmental costs of developing self-driving cars.

A recent paper by researchers at the University of Massachusetts, Amherst, found the process of training several common large AI models can emit the equivalent of more than 283000 kilograms of carbon dioxide. That’s approximately five times the lifetime emissions of the average American internal combustion engine car - including the manufacture of the vehicle itself. While the increase of Green AI will mitigate much of this issue, the use of modeling and simulation for the fast-paced development of autonomous vehicles is essential for the technology to become truly ubiquitous.

An effective alternative to physical testing is coupling computational fluid dynamics (CFD) and optical simulation solutions. These solutions can help self-driving car manufacturers rapidly develop weather-aware autonomous systems by providing a physics-based approach for different weather scenarios and performance testing for sensors and autonomous systems within a virtual environment.

2. Life cycle simulation

Life cycle simulation (LCS) (Umeda et al. 2000) is a technique to evaluate the performances of life cycles (e.g., life cycle costs and environmental impacts) using discrete event simulation. LCS simulates physical and functional deterioration of individual products in a market.

Extending the operation and the life cycle of EV batteries is a perfect use case for modeling and simulation. The deployment of « true to physics » digital twins, on the edge or in the cloud, helps monitor and operate complex battery packs through their operation until senescence, as well as facilitating their preventive maintenance for an extended lifespan.

Similarly, simulation-driven additive manufacturing becomes a credible alternative to large, multi-reference inventories of spare parts for the maintenance and repair of the vehicles in operation. This itself is expected to be one of the biggest contributors to the net-zeroification of the entire automotive industry.

3. Death is not the end

Finally, modeling and simulation play a critical role in the fast-growing recycling and upcycling sector of the mobility industry.

As re-, down- and up-cycling never performs better than when it is planned from the initial product inception, modeling and simulating the entire process, accounting for each use of a specific material and its alternative, is the most effective and sustainable way to manage the automotive product lifecycle, from cradle to grave to rebirth.

Ansys: Christophe Bianchi