Batteries are increasingly used in many applications, such as: transportation (electric vehicles and e-bikes), drones, cordless (power) tools, energy buffering in smart power grids, and portable equipment. A state-of-the art Smart Battery Pack (SBP) is a high-tech system in itself, containing a Battery Management System (BMS), thermal management system, efficient charging and balancing algorithms, accurate prediction algorithms.
What does the project involve?
This research project focusses on Battery Packs for Smart Mobility Applications where the “range prediction algorithm” is a crucial aspect of the application. For example, travellers must feel confident that the destination can be reached under all circumstances possible.
Technical goals: develop a demonstrator application (the e-bike) with an improved smart battery pack (SBP) characterized by:
- A novel range prediction algorithm that takes user-behaviour into account via self-learning algorithms (Artificial intelligence, Machine Learning, Deep Learning).
- Giving intelligent feedback to the user, so that both the user and the system learn to adapt their behaviour for maximum performance and reliability, while safety is guaranteed.
- Enabling Energy Buffering and Energy Regeneration for Smart Battery Packs.
- A new or revised SBP control algorithm to maximize the benefit of the Energy Buffering in terms of Performance and Life-time, while safety is ensured.
Long-term and society related goals:
- Extend our knowledge about “fast charging of Li-ion battery packs” and its impact on society àPublish a white paper on this topic.
- Position ourselves to become a recognized centre of expertise on Energy Storage and Battery Technology, with focus on smart mobility applications. Integrate this knowledge into the curriculum.
September 2020 through July 2021
Fontys: Harold Benten, Chris Lee and Ralph Goes
ID-bike: Bas d'Herripon
Battery Point Eindhoven: Karel Smits