Battery: Gigafactory process optimization

Process optimization with artificial intelligence, rapid error analysis, and customized voice output, using the example of battery system production

Initial situation

Electric vehicles require powerful and cost-effective batteries. Currently, battery production accounts for approximately 30% of the manufacturing costs of an electric car.

Depending on the vehicle model, various new battery systems will have to be brought into series production in the coming years, with a series ramp-up to process stability being a challenge.

Battery systems are composed of many individual components (including battery cells, cooling system, charge controller, contacts, frame, etc.) and are assembled over many production steps.

Even small errors lead to rework on half-finished battery systems and also to unplanned scrap, which has a significant impact on the overall costs of battery production.

Reducing rework and rejects through rapid optimization of the entire manufacturing process is key to achieving cost-effective electric cars.

Solution approach

Modern processes and data analysis make it possible to keep an eye on the entire value stream for manufacturing a battery system. Process engineers and other user groups receive recommendations for potential error causes and tips for rapid process optimization. 

Questions from various users are answered in seconds by AI agents (chatbots) using user-oriented data, text, and graphics. This can improve manufacturing processes and ultimately reduce the manufacturing costs of battery systems.

Artificial intelligence and machine learning, from sensor monitoring to human-machine communication, enable the time-efficient continuous improvement of business processes. 

Cloud analysis services monitor trends in metric and image data, communicate with other IT systems, and respond to deviations. 

In addition, the processes can also identify and report unexpected types of deviations in both metric and image data in a timely manner. 

To reduce operational costs in IT/data science, maintenance cycles for AI models can be automated, with our design guidelines supporting a high level of conformity and acceptance.

Key technical points

Capgemini develops these error detection systems using AWS cloud data services such as Sagemaker (AI) and Bedrock (genAI), focusing on analytics without changing a company’s existing organizational structures. Data from various IT systems (ERP, MES, machine PLC) is monitored in real time in various data formats across the entire production process to quickly identify weak points and explain contexts company-wide and appropriately (role, language, area of ​​responsibility). 

In collaboration with AWS, Capgemini places particular emphasis on the clear, linguistic explanation of analytical results using the company’s technical terminology in any local language. 

The causes of failures can thus be eliminated in an interdisciplinary manner, and new disruptions can be avoided. Modern analysis methods from the fields of machine learning, computer vision, and artificial intelligence are used.

Key elements and benefits of holistic process analysis

1. Economically competitive production of battery systems
   Production costs are significantly reduced through data integration, anomaly detection, and AI-driven process optimization. 
2. Root cause analysis across the entire process with an end-to-end approach
   Automated root cause determination, leveraging all relevant information and formats, to quickly identify deviations within the production steps
3. Reduction of rework and scrap
   By optimizing the end-to-end production process using data analytics and AI, sources of errors are identified and prevented early on.
4. Real-time recommendations for process engineers
   Advanced machine learning algorithms provide actionable insights into potential causes of errors and opportunities for process improvement.
5. Automated, user-friendly communication via AI agents
   AI systems translate technical reports into understandable messages tailored to different user groups – “around the clock.”
6. Integration of diverse data sources
   The solution processes data from various IT systems (ERP, MES), machines/PLCs, and formats to create a comprehensive view of changes in the manufacturing process.

Our capabilities

Capgemini is a global business and technology transformation partner for organizations. The company supports them in their dual transformation towards a more digital and sustainable world – always focused on making tangible progress for society. Capgemini is a responsible, diverse group of companies with over 55 years of history and 340,000 employees in more than 50 countries. Clients trust Capgemini to unlock the potential of technology for the full range of their business needs. With its strong strategy, design, and engineering expertise, Capgemini develops comprehensive services and end-to-end solutions, leveraging its leading capabilities in AI, generative AI, cloud, and data, as well as its deep industry expertise and partner ecosystem. The group generated revenues of €22.1 billion in 2024.

Developed in collaboration with AWS

Capgemini’s E2E process optimization is built on the AWS cloud and leverages a scalable and modular architecture with proven capabilities to continuously analyze manufacturing processes such as battery system production using laser welding, screwing, bonding, and riveting, up to gigafactories, support data management processes, and enable time-efficient expansion to other advanced analytics use cases.

Our goal is to help utility companies across multiple manufacturers with similar use cases understand the otherwise overwhelming influx of complex data sets from manufacturing processes, including the associated network and customer systems, using architecture, cloud support, platform-based business processes, data models, and AI-based insights