EAVE - Company and Team

EAVE as an innovative SME operating in the Renewable Energy industry is completely aligned with WASABI, particularly in its focus on enhancing energy efficiency, sustainability, and technological innovation.

For our WALLABI project, here are the colleagues involved:

Antonio Sánchez Miranda, CRO
Rubén González Lorenzo, PM & Cloud Architect
Jorge Gutiérrez Cejudo, Electronic Engineer & Renewable Energy expert

Antonio Sánchez Miranda

CRO

Rubén González Lorenzo

PM & Cloud Architect

Jorge Gutiérrez Cejudo

Electronic Engineer & Renewable Energy expert

EXPERIMENT OVERVIEW

OVERVIEW

The experiment focuses on the application of COALA-OVOS during the quality control process of Wall-AI batteries manufacturing. These batteries are designed with a strong focus on circular economy principles, being constructed from recycled electric vehicle (EV) batteries. The Wall-AI is an advanced electronic device designed for better management of solar energy within photovoltaic systems. The OVOS implementation will be carried out:

As part of the QA manufacturing process, the OVOS solution will support technicians by automatically collecting and storing data, recording performance data under various operational and environmental conditions.

CHALLENGES

Several challenges for EAVE are presented during QA process:

- Lack of digital traceability throughout disassembly and assembly stages.
- Dependence on manual data entry and subjective evaluations, which can lead to inconsistencies.
- Limited real-time visibility of quality data and test results.
- Difficulty in identifying early trends or deviations that could affect product reliability.

OBJECTIVES

Integrate the COALA-OVOS digital assistant to enhance the existing QA process in Wall-AI battery manufacturing.
Improve efficiency by automating data management, reducing testing time and optimizing energy use.
Implement strategies for a greener approach in Wall-AI battery assembly, promoting more sustainable production.

EXPERIMENT IMPACT

EXPECTED RESULTS (KPIs)

Number of queries resolved by OVOS (per month)
Decrease in product testing time (measured in minutes)
Reduction in production costs
Growth in the number of successfully validated batteries (QA-approved battery units per day)
Decrease energy consumption per unit produced (measured in kWh/unit)
Increase in battery units sold in new markets, especially in Europe

AS-IS SITUATION

The QA testing is performed manually, requiring operators to record results and issued by hand.
Data collection and storage are time-consuming and prone to human error.
Performance monitoring lacks real-time visibility and traceability.
Test results and reports are shared manually, increasing the risk of information loss or delays.

TO-BE SITUATION

The system automatically collects and stores test data in real time.
When anomalies occur, the operator can analyse possible causes and corrective actions based on data.
New or replacement operators can seamlessly continue ongoing tests with full process traceability.
Test datasets are generated and updated according to defined fields.
QA testing time, energy consumption, and human error rates are significantly reduced.

EXPERIMENT WORK PLAN

WP1 (M1 - M12): Project Management

Managing daily project duties in the areas of technology, finance, administration and IPR strategy.
Liaising with the WASABI team during the monitoring meetings, as well as supervising financial controls and reporting protocols.

WP2 (M1 - M3): Adaptation of OVOS to the EAVE environment

Analyse the EAVE environment in detail, paying close attention to the demands and limitations unique to the Wall-AI battery testing procedures.
Adapt the OVOS system to the Wall-AI battery testing process.
Develop a testing protocol tailored to Wall-AI batteries requirements.

WP3 (M4 - M9): OVOS deployment in EAVE’s Wall-AI Production Line

Integrate OVOS into our facility’s production line.
Provide comprehensive training to EAVE employees on how to interact with OVOS, focusing on executing validation protocols, optimising WALL-AI batteries testing conditions, and ensuring safety and sustainability standards are met.
Conduct a series of tests to ensure OVOS operates correctly within the production environment. Address any integration issues, refine interaction protocols, and ensure that OVOS’s performance meets expectations.

WP4 (M9 - M12): Optimisation of OVOS in Wall-AI Production

Perform comprehensive testing to evaluate OVOS’s effectiveness in reducing QA time, reducing energy consumption, and enhancing product quality during batteries manufacturing.
Collect and analyse data from the testing phase, focusing on OVOS’s impact on time reduction, energy efficiency, and product quality. Use this data to identify areas for further optimization.

WP5 (M6 - M12): Project dissemination and exploitation

Planning and execution of communication and dissemination activities to raise awareness, generate interest, and drive action among target audiences and stakeholders.

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