Challenge:
Stellantis faced several key challenges in its traditional product development process, particularly in the area of e-mobility services:
- Long Design Cycles: Traditional vehicle design and validation processes were time-consuming, relying heavily on physical prototypes and manual testing, which slowed the overall development timeline.
- Complexity of EV Design: Developing electric vehicles and e-mobility services involved complex systems, including battery management, energy efficiency, vehicle dynamics, and integration with renewable energy sources. These factors made it difficult to optimize designs quickly and iteratively.
- Sustainability Goals: Stellantis needed to meet ambitious sustainability targets, which required accelerated innovation in the design of electric vehicles (EVs) and related infrastructure. This meant reducing development time without compromising quality or performance.
- Inadequate Performance Testing: Testing new vehicle designs under different operating conditions, such as temperature variations, road types, and energy consumption scenarios, was costly and inefficient without the use of advanced simulation tools.
To address these challenges, Stellantis required a solution that would enable faster iterations, improve design optimization, and facilitate the rapid development of e-mobility solutions.
Solution:
UpGradelle CS brought its expertise in Digital Twin technologies and CAD modeling to Stellantis, transforming the company’s approach to vehicle design and e-mobility services development. By implementing a digital twin approach and integrating real-time simulations, Stellantis could create virtual prototypes, test them under a variety of scenarios, and optimize designs more efficiently.
1. Digital Twin Implementation for Real-Time Simulations:
The core of the solution was the implementation of Digital Twin technologies to create virtual replicas of Stellantis’s electric vehicles and key components. These digital models mirrored real-world vehicle behavior, allowing engineers to simulate real-time performance under different conditions and iterate faster.
- Vehicle Simulation: Digital twins enabled Stellantis engineers to simulate and test the behavior of electric vehicles in virtual environments. They could assess vehicle performance in various real-world scenarios, such as changes in terrain, driving conditions, and battery performance.
- Battery Management System (BMS) Optimization: With the digital twin, Stellantis could simulate the performance of its EV battery systems under different charging, discharging, and temperature conditions. This facilitated faster optimization of battery lifespan, charging efficiency, and energy consumption.
- Energy Efficiency Testing: Digital simulations allowed engineers to test energy consumption in real-time, optimizing vehicle designs for maximum efficiency while ensuring compliance with environmental standards.
2. Advanced CAD Modeling for Design Iteration:
Stellantis integrated CAD modeling to create highly detailed, data-rich digital models of their electric vehicles. This allowed engineers to optimize the structural design, aerodynamic efficiency, and overall performance of the vehicles, and rapidly iterate based on performance feedback from digital simulations.
- Design Optimization: Engineers could make changes to the vehicle’s design in real-time, testing these changes in virtual environments before moving to physical prototypes. This reduced the number of design iterations and the need for costly physical testing.
- Vehicle Dynamics and Aerodynamics: Using CAD models, Stellantis optimized aerodynamics and vehicle dynamics, crucial factors for improving energy efficiency and performance in EVs. Detailed CAD simulations allowed for precise adjustments to the design, minimizing drag, enhancing handling, and improving overall efficiency.
3. Accelerated Product Development Cycle:
By combining digital twin simulations with CAD modeling, Stellantis could shorten the development timeline for its electric vehicle designs. This allowed for more rapid prototyping, fewer design cycles, and faster validation of new features.
- Virtual Prototyping: Engineers were able to test and validate vehicle designs virtually, without the need for physical prototypes. This reduced the time required for product validation, cutting design time by up to 25%.
- Faster Iteration: Engineers could run multiple simulations in parallel, testing different configurations and materials in virtual environments, which led to faster refinement of vehicle designs and improved product quality.
- Reduced Costs: The use of digital twins and CAD modeling minimized the need for costly physical testing, reduced the amount of prototype iterations, and streamlined the manufacturing process, resulting in significant cost savings.
4. Performance and Sustainability Optimization:
In line with Stellantis’s sustainability goals, the digital twin and CAD modeling solutions allowed the company to optimize its electric vehicles for sustainability and performance, aligning with both regulatory standards and customer expectations.
- Battery Performance: Through detailed simulations, Stellantis optimized battery charging and discharging cycles to improve the overall lifespan and energy efficiency of its electric vehicles, reducing the environmental impact of battery disposal and increasing the sustainability of the fleet.
- Sustainable Materials Testing: CAD modeling enabled the testing of lightweight, sustainable materials in the design of vehicle components, helping Stellantis reduce the carbon footprint of its vehicles without compromising safety or performance.
Key Achievements:
The collaboration between Stellantis and UpGradelle CS led to several key achievements that significantly impacted the company’s ability to accelerate its e-mobility solutions and improve design processes:
- Accelerated Design Validation: By leveraging digital twins and CAD modeling, Stellantis reduced the time spent on design validation by 25%, allowing engineers to test and iterate designs much faster.
- Improved Product Efficiency: The use of real-time simulations allowed for the optimization of battery systems, energy consumption, and vehicle dynamics, resulting in electric vehicles that were up to 15% more energy-efficient.
- Faster Time-to-Market: With virtual prototyping and faster iterations, Stellantis was able to reduce the overall product development cycle by 20%, speeding up the time-to-market for new e-mobility solutions.
- Cost Savings: The integration of digital twins and CAD simulations led to significant cost savings by reducing the need for physical prototypes, cutting prototype development costs by up to 30%.
- Enhanced Innovation: The digital twin technology and CAD modeling approach allowed Stellantis to experiment with new designs and innovations without the constraints of traditional development processes, leading to the introduction of more sustainable and innovative electric vehicles.
Quantitative KPIs and Performance Metrics:
The impact of Digital Twin technologies and CAD modeling on Stellantis’s product development lifecycle can be measured through the following KPIs:
- Design Iteration Time:
- KPI: Time required for each design iteration.
- Impact: The time required for design iterations was reduced by 25%, enabling faster refinement of vehicle prototypes.
- Energy Efficiency:
- KPI: Energy consumption per kilometer.
- Impact: Optimizations in vehicle dynamics and battery systems improved energy efficiency by 15%, contributing to the development of more sustainable EVs.
- Prototype Development Costs:
- KPI: Cost of producing physical prototypes.
- Impact: The use of virtual prototyping reduced physical prototype development costs by 30%.
- Time-to-Market:
- KPI: Time from concept to market-ready product.
- Impact: The product development cycle was reduced by 20%, allowing for faster delivery of new e-mobility solutions.
- Battery Lifespan and Efficiency:
- KPI: Battery charging cycles and efficiency.
- Impact: Digital twin simulations optimized battery lifespan and charging cycles, contributing to improved vehicle performance and sustainability.
- Sustainability Impact:
- KPI: Reduction in CO2 emissions from manufacturing and vehicle operation.
- Impact: The optimized design and use of sustainable materials contributed to a 10% reduction in the overall environmental footprint of Stellantis’s EV models.
Conclusion:
The partnership between Stellantis and UpGradelle CS has successfully revolutionized the company’s approach to e-mobility services development. By implementing Digital Twin technologies and CAD modeling simulations, Stellantis has significantly accelerated its product development lifecycle, optimized vehicle designs for energy efficiency, and reduced costs associated with physical prototyping.
This innovative approach has not only allowed Stellantis to meet its sustainability goals but also enabled the company to remain competitive in the rapidly evolving e-mobility market. The collaboration demonstrates how digital twins and advanced CAD modeling can be used to drive innovation in automotive design, improve product performance, and bring sustainable solutions to market faster and more efficiently.