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Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

European Union
Geo-spatial type
Infrastructure Node
Total project cost
EU Contribution
€230 774
Project Acronym
STRIA Roadmaps
Transport electrification (ELT)
Transport mode
Road icon
Transport policies
Environmental/Emissions aspects
Transport sectors
Passenger transport,
Freight transport


Call for proposal
Link to CORDIS
Background & Policy context

Electric vehicles (EV) are the future. So what does the future hold for EV batteries? To make these batteries less complicated and easy to charge, it’s important to improve the battery management system (BMS) that monitors, controls and protects the EV lithium-ion battery packs. Considering that EV batteries are subjected to harsh operating cycles and varying environmental conditions leading to very complicated interactions, the EU-funded DeepBMS project will develop a new model to improve the BMS functionality in EVs. Efficient deep reinforcement learning-based algorithms will capture the convoluted time-varying behaviour of battery. DeepBMS will also boost reliability and extend battery lifetime by improving the estimation accuracy in a wide temperature range and over the full life span of the batteries.


Battery Management System (BMS) plays a pivotal role in monitoring, control, and protecting the Electric Vehicle (EV) Lithium-ion battery packs. In vehicular applications, batteries are usually subjected to harsh operating cycles and varying environmental conditions leading to very complicated interactions of different aging factors and unforeseeable modeling uncertainties. Therefore, the classical model-based techniques cannot completely handle the foregoing factors, which always leave an unwanted state estimation error in the BMS. This project intends to apply a multidisciplinary approach by combining the advantages of deep reinforcement learning and classical model-based techniques to improve the BMS functionality in EVs. Specifically, DeepBMS aims to: 1-Develop efficient deep reinforcement learning-based algorithms which are able to capture the convoluted time-varying behavior of battery and can gradually improve themselves by learning in real-time 2- Combine the beneficial features of model-based and data-driven techniques to improve the state estimation accuracy in a wide temperature range and over the full life span of the batteries, thereby increasing the reliability and extending the battery lifetime. The interdisciplinary nature of DeepBMS is very strong, involving a combination of control and state estimation theory, power electronics, battery storage systems, and machine learning. The supervisor and candidate have excellent complemental research experiences in these fields providing the necessary competencies to bring the project to successful completion. The project ensures two-way transfer of knowledge including training of the candidate in cutting-edge advanced techniques in a state-of-the-art laboratory, which improves his future career prospects. Likewise, DeepBMS is in line with the EU strategic action plan on batteries and its results have a great potential to be further developed at the fundamental and applied levels through follow-up research.


Specific funding programme
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
Other Programme
HORIZON-MSCA-2021-PF-01-01 MSCA Postdoctoral Fellowships 2021


Lead Organisation
Aalborg Universitet
Organisation website
EU Contribution
€230 774


Technology Theme
Electric vehicle batteries (and energy management)
Battery energy management and sensors
Development phase
Demonstration/prototyping/Pilot Production

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