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Cell behaviour-aware power capability prediction and operation optimization of large-scale battery systems

Project description

Advanced control algorithms for optimised battery power and safety

Large-scale battery systems are vital for the EU’s goal of net-zero emissions by 2050, as individual cell behaviours impact overall performance and safety. Thus, control algorithms that enhance battery management are crucial. With the support of the Marie Skłodowska-Curie Actions programme, the ProBat project will enhance the maximum allowable charge and discharge power of battery systems by accurately predicting the state of power (SoP) based on the operating limits of internal battery cells. The project will focus on battery system modelling and control, integrating circuit analysis, automatic control, and optimisation. Expected outcomes include an accurate long-term SoP prediction algorithm, a dynamic reconfiguration algorithm to boost efficiency, and an optimal control framework utilising deep reinforcement learning (DRL).

Objective

Large-scale battery system applications, e.g. transportation electrification and grid-level renewable energy integration, are vital for the EU to achieve net-zero emissions by 2050. In such battery systems, real-time operating behaviours of individual battery cells determine the system’s overall performance and safety, highlighting the need to develop cell behaviour-aware control algorithms for advanced battery system management. Thus, in this proposal, we will take into account the specific operating limits of all internal battery cells for accurately predicting and significantly boosting battery system's maximum allowable charge and discharge power, i.e. state of power (SoP). Then, the obtained cell behaviour-aware battery system SoPs will act as the operating power limits for further optimising the system's operation performance. As an MSCA-PF fellow, Dr. Weiji Han will receive crucial training at the Chalmers University of Technology and engage in detailed battery system modelling and control works which span the areas of circuit analysis, automatic control, and optimisation. The interplay between these areas will spark an innovative and productive throughput, including 1) an accurate prediction algorithm for the long-horizon SoPs of battery systems, 2) a high-efficiency dynamic reconfiguration algorithm for boosting battery system SoPs, and 3) an optimal control framework of dynamic reconfiguration for optimising battery system performance based on adapted deep reinforcement learning (DRL). The major novelties or contributions will include: considering both battery parameter update and operating constraints during the prediction horizon to enhance the prediction accuracy of long-horizon SoPs; unifying the modelling of reconfigurable battery systems to significantly reduce the modelling efforts and facilitate the formulation of control or optimisation problems; narrowing the search space and adapting the DRL method to achieve higher computational efficiency.

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Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

CHALMERS TEKNISKA HOGSKOLA AKTIEBOLAG
Net EU contribution
€ 252 180,00
Address
-
412 96 Goteborg
Sweden
Region
Södra Sverige Västsverige Västra Götalands län
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data