EXPERIMENT OVERVIEW
This experiment focuses on integrating OVOS (Open Voice OS) as a Digital Assistance solution into the quality control process of Wall-AI battery manufacturing. Wall-AI is an advanced energy management device designed to improve how solar energy is stored and used in self-consumption photovoltaic systems, helping users reduce electricity costs and increase energy efficiency.
At the core of Wall-AI is the CAVE battery, which is built using recycled electric vehicle battery modules. After being carefully inspected and tested, they are reconditioned and assembled into new battery packs. This approach supports circular economy principles by reducing electronic waste and lowering the environmental footprint of renewable energy systems.
The manufacturing process takes place in Tenerife and is organized into two main stages: battery disassembly and module reassembly. During disassembly, electric vehicle battery modules are extracted, tested, and classified according to their condition and remaining capacity. In the assembly stage, selected modules are interconnected, integrated with control electronics, and prepared for final validation.
The experiment will introduce OVOS to digitally support and enhance the quality control workflow. The system will guide technicians through inspection procedures, assist in data collection, and help detect performance deviations or potential safety issues. By digitalizing and structuring the validation process, the solution aims to improve consistency, traceability, and efficiency.
This experiment is relevant because ensuring the safety and reliability of second-life batteries is essential for large-scale deployment in renewable energy systems. By combining battery reuse with intelligent digital support, the project contributes to more sustainable, reliable, and cost-effective solar energy solutions.
The experiment addresses several operational and technical challenges related to the quality control of second-life batteries used in the Wall-AI. The manufacturing process involves the disassembly of electric vehicle batteries and the reassembly of selected modules into new CAVE battery packs. While this approach supports circular economy and sustainability goals, it introduces complexity in ensuring consistent quality, safety, and performance.
One of the main challenges is the lack of full digital traceability throughout the disassembly and assembly stages. Currently, information related to battery module testing, classification, and integration may be recorded manually. This can limit the ability to track each module’s history, monitor its condition over time, and ensure complete transparency across the production workflow.
Another important issue is the dependence on manual data entry and subjective evaluations during inspection and classification. Technicians must assess parameters such as voltage behavior, remaining capacity, and physical condition. While their expertise is critical, manual processes can introduce variability and inconsistencies between operators.
There is also limited real-time visibility of quality metrics and test results across the manufacturing line. Without centralized and structured digital monitoring, it becomes more difficult to detect anomalies quickly or compare performance data between batches. This reduces the ability to react proactively to emerging issues and may delay corrective actions.
Finally, identifying early trends or deviations that could affect long-term battery performance remains a challenge. Reused battery modules may show gradual performance changes due to aging or prior usage conditions. Without systematic data analysis and structured monitoring, subtle patterns may go unnoticed until they affect system reliability.
The experiment therefore focuses on improving digitalization, standardization, and data-driven quality control within the Wall-AI manufacturing process.
Objective 1:
Enhancement of product quality:
Integrating the Digital Assistant (DA) into the testing process enables precise, consistent, and automated quality control of each Wall-AI battery. This integration ensures standardized testing procedures, minimizes variability, and improves traceability. As a result, product reliability and long-term performance are strengthened, enhancing customer satisfaction and reinforcing EAVE’s reputation as a provider of high-quality, sustainable energy solutions.
Objective 2:
Competitiveness and cost reduction:
The incorporation of digital assistance into the testing workflow increases operational efficiency by providing operators with clear, standardized guidance and automated data management. This reduces process time, minimizes errors and rework, and optimizes resource utilization. Consequently, production costs are lowered, and EAVE’s competitive position in the energy storage market is reinforced.
Objective 3:
Labour security and process effectiveness:
Although operators remain responsible for all physical testing tasks, the Digital Assistant supports them by delivering step-by-step guidance, real-time instructions, and consistent enforcement of safety protocols. This support enhances workplace safety, reduces the likelihood of human error, and improves overall process reliability and effectiveness.
The experiment is positioned within the renewable energy sector, specifically in the field of solar energy integration and second-life battery reuse. It focuses on the application of OVOS to support the quality control of Wall-AI battery manufacturing. Wall-AI is an advanced energy management device designed to optimize energy storage and consumption in self-consumption photovoltaic systems, helping users reduce electricity costs and improve system efficiency.
The experiment will be carried out at the Institute of Technology and Renewable Energy (ITER) in Tenerife, Spain. It will take place in a real industrial production environment where Wall-AI batteries are disassembled, tested, reassembled, and validated. The Digital Assistance solution will therefore be tested under operational manufacturing conditions rather than in a laboratory-only setting.
The main users involved are battery manufacturing quality control operators. Key stakeholders include renewable energy system operators and future end users of Wall-AI systems.
The implementation of the Digital Assistant must comply with data protection regulations, as limited operational and user interaction data may be processed. The system is designed to comply with the General Data Protection Regulation (GDPR) by minimizing personal data collection and focusing primarily on process-related information.
EXPECTED IMPACT
EXPECTED IMPACT
The primary technological impact involves applying DA to optimise product testing and production processes, leading to reduced waste and testing times, lower energy consumption, and enhanced product quality. Additionally, WASABI aims to equip the workforce to efficiently manage Wall-AI production using AI technologies, fostering human-AI collaboration.
The experiment’s emphasis on reducing energy consumption and improving production efficiency can lead to substantial economic impact through cost savings in manufacturing. The Wall-AI device also provides potential savings for end-users by lowering electricity bills. By integrating advanced technologies and sustainability practices, the experiment can boost the competitive advantage of EAVE, enabling them to stand out in the market.
The commitment to sustainability and technological innovation can enhance the brand reputation of companies involved in the EAVE and WASABI projects, leading to increased commercial impact with customer loyalty. Furthermore, the success of the Wall-AI device and the associated production improvements can facilitate expansion into new geographic markets.