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Optimizing Performance in Formula E Racing with System-Level Modeling
Technology Category
- Other - Battery
- Sensors - Autonomous Driving Sensors
Applicable Industries
- Automotive
- Electrical Grids
Applicable Functions
- Procurement
- Product Research & Development
Use Cases
- Smart Parking
- Vehicle-to-Infrastructure
Services
- System Integration
- Testing & Certification
The Challenge
Competitive racing, particularly in the all-electric class of Formula E, demands the utmost from both driver skill and engineering innovation. However, the regulations in this field are stringent, with the battery being standardized across all vehicles. This leaves teams with limited areas for customization and performance enhancement. A leading Formula E team, recognizing these constraints, sought to develop dynamic models of their car to find new ways to optimize their systems for peak performance. The team aimed to develop customized racing strategies for different tracks, weather conditions, and pit stops, ensuring optimal use of their battery power. They also wanted to incorporate real-time simulations to update the team with information as variables change during the race, a feature not commonly available without a system-level modeling tool.
About The Customer
The customer in this case is a leading Formula E team. Formula E is a class of auto racing that uses only electric-powered cars. The series was conceived in 2012, and the inaugural championship started in Beijing in September 2014. The series is sanctioned by the FIA. The customer team, recognizing the limitations imposed by the standardized battery, sought to optimize their car's performance by developing dynamic models of their car. They aimed to use these models to develop customized racing strategies for different tracks, weather conditions, and pit stops, ensuring optimal use of their battery power.
The Solution
Maplesoft was approached to develop a dynamic car model that would be validated against real-world performance data. The model needed to incorporate several important systems, including the chassis, steering, tires, battery, inverter, motor, and gearbox. Using the system-level modeling tool MapleSim, engineers were able to capture the dynamics of each system in one multidomain environment. A crucial step in the process was correlating the model’s parameters with test data provided by the Formula E team to ensure accurate data across the range of operating conditions. With all these components together in one system, the Formula E team could better understand trade-offs when trying to design a better car and a faster racing strategy. The model could simulate ideal performance in various scenarios and provide new insights for the team, such as modifying how the driver might accelerate on certain turns or the ideal timing for using their FanBoost accelerations in various kinds of weather.
Operational Impact
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