Imagine if your car knew exactly how you drive and how the weather affects your battery, then adjusted itself to save power. It's like having a smart coach that looks at the road and charging stations to make sure you don't run out of juice. This tech makes batteries last longer and charge faster by using real-world data instead of just guesses.
Electric vehicles suffer from limited autonomy and slow charging, which hinders mass adoption. Current battery systems often ignore individual driver behavior and external environmental factors.
A modular Battery Management System (BMS), AI-based battery state estimation algorithms, and a cloud-based data preprocessing infrastructure using Docker.
If you are an EV manufacturer dealing with short driving ranges — this project developed a modular Battery Management System (BMS) and AI algorithms that reduce energy consumption by more than 10%. This allows for smaller batteries or longer range for the same battery size.
If you are a charging station operator dealing with inefficient power delivery — this project developed smart data collection systems and charging interface optimizations that decrease charging time by at least 10%. This increases the number of vehicles your stations can serve daily.
If you are a logistics company dealing with unpredictable EV downtime — this project developed a performance and consumption model specific to individual vehicles and drivers. This enables more accurate route planning and reduces operational risks.
Based on available project data, the project focused on cost-effective analysis of charging interfaces and creating affordable light mobility solutions, though specific price reductions are not listed.
The project has deployed demonstrators showcasing innovative EV technologies and developed a modular BMS, indicating a transition from lab to real-world testing.
Based on available project data, licensing details are not provided, but the consortium includes 24 industry partners and 19 SMEs who likely share the developed IP.
It uses a modular Battery Management System (BMS) and aggregates data from internal sensors and external back-end systems via cloud infrastructure using Docker.
The project runs from 2023-01-01 to 2025-12-31, with second-year progress already showing deployed demonstrators.
The project is heavily industry-driven with a 65% industry ratio, comprising 24 companies, 19 of which are SMEs. This strong commercial presence, spanning 11 countries and 37 partners, suggests the results are designed for immediate market application rather than pure academic research.
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Contact us to connect with the OPEVA consortium for BMS licensing.
