Collaborative Ecosystem Resilient Product & Production System Engineering for Electric Battery
General Information
Challenge, Value & Description
Performance, Access & Contact
1 – General Information
Partners
Sectors addressed
Application categories covered
Lifecycle level covered
Digital Engineering
Planning & Commissioning
Smart Production & Operations
Smart Logistics
Smart Maintenance
Customer Service
Circularity
Peter-Sander-Straße 32, 55252 Wiesbaden, Alemania
Geographical Scope
- Europe
- DACH (Germany, Austria, Switzerland)
2 – Challenge, Value & Description
Challenge
In the RE4DY Project, the AVL pilot addresses the complexity of battery module and pack manufacturing, focusing on process optimization, flexibility, and sustainability.
Traditional battery production is rigid, making it difficult to adapt to new designs, scale production, and ensure quality. High energy consumption, waste generation, and inefficient resource use further challenge sustainability. The lack of integrated digital tools limits real-time decision-making and predictive maintenance.
This pilot aims to enhance production agility, leveraging AI, digital twins, and simulation to reduce waste, optimize energy use, and improve production efficiency, ensuring a resilient and scalable battery manufacturing process.
Value
The AVL pilot enhances battery manufacturing efficiency, flexibility, and sustainability through AI, digital twins, and process simulation.
By enabling real-time optimization, it reduces production waste, energy consumption, and defects, ensuring higher quality and lower costs. The digital twin approach allows manufacturers to test and refine processes before implementation, improving scalability and adaptability to new battery designs. Automated data-driven decision-making minimizes downtime and enhances predictive maintenance.
This pilot supports a resilient, future-proof battery production ecosystem, accelerating innovation and competitiveness in the growing e-mobility sector while contributing to sustainable manufacturing practices.
Description
The AVL pilot’s data value chain begins with data acquisition from battery module production systems, sensors, and digital twin simulations.
This data is then processed and analyzed using AI-driven models to optimize manufacturing efficiency, energy usage, and defect detection. The insights generated are used for real-time decision-making, predictive maintenance, and process adjustments.
Data is shared across stakeholders (AVL, Fill, Visual Components) to improve production scalability and flexibility. Finally, the optimized data supports continuous improvement, enabling resilient, high-quality, and sustainable battery manufacturing while ensuring adaptability to new designs and production requirements.
Infraestructure Elements
- HPC
- PLC / Industrial PC
- Data Centre
- Field Devices
Platforms & Tools used
Acquisition: Sensors & Industrial Monitoring Systems to collect real-time data from battery production lines. | PLC / Industrial PC – to control and monitor manufacturing processes.
Analysis: AI & Machine Learning Models for process optimization, defect detection, and predictive analytics. | Simulation Tools (Visual Components, Fill) to provide process modeling, 3D visualization, and digital twin simulations.
Preparation: Big Data ETL (Extract, Transform, Load) Systems – Clean, harmonize, and integrate production data before analysis.
Storage: Data Centers & HPC Infrastructure – Store large volumes of battery production and simulation data.
Usage: Digital Twin Platforms for real-time optimization of battery production | Manufacturing Execution Systems (MES) to ensure efficient execution of production plans.
Sharing: Collaboration Platforms (AVL, Fill, Visual Components) to facilitate data exchange between partners.
Other platforms & tools: Energy Load Management Systems to optimize energy efficiency in production. | Industrial Robotics & Automation Software to support battery module assembly and customization.
3 – Performance, Access & Contact Info
Performance
The AVL pilot has transformed battery production by integrating AI-driven digital twins, process simulations, and automation.
Previously, production relied on manual optimization, static layouts, and trial-and-error adjustments, leading to inefficiencies, waste, and long ramp-up times. Now, real-time monitoring, predictive analytics, and process automation enable faster decision-making, reduced defects, and optimized energy use.
The ramp-up phase has been shortened by 20%, customization has increased, and operational flexibility has improved. This transition ensures higher efficiency, cost savings, and sustainability, making the manufacturing process more adaptive and scalable for future battery production.
Lessons Learned & Observations
The AVL pilot highlighted the importance of digital continuity in battery manufacturing. Integrating AI, digital twins, and simulation tools significantly improved process efficiency, quality control, and adaptability. However, data standardization and interoperability remain key challenges for seamless integration. The ramp-up phase was reduced by 20%, proving the value of predictive analytics in minimizing waste.
Automated decision-making and real-time monitoring enhanced energy efficiency and production scalability. A major takeaway is that flexible, AI-driven manufacturing systems are crucial for sustainable, high-quality battery production, ensuring resilience and competitiveness in the evolving e-mobility market.
Replication Potential & Feasibility Assessment
The AVL pilot has high replication potential across battery manufacturing, e-mobility, and advanced production industries. Technically, the integration of AI, digital twins, and process simulation is feasible but requires standardized data models and IT infrastructure. Economically, initial implementation costs are significant, but efficiency gains, reduced waste, and energy savings ensure long-term ROI.
Replication feasibility depends on data availability, automation capabilities, and workforce adaptation. Industries adopting flexible, AI-driven production systems can benefit from enhanced scalability, predictive maintenance, and sustainable manufacturing, making the approach viable for future battery and smart factory developments.