Lifelong battery management via adaptive modelling and predictive control
Research Project , 2020 – 2023

There will be a tremendous shift in road transport from fossil to electric, in which the availability of capable batteries is a major issue. Without accurate knowledge of physical limits, existing battery systems are inappropriately designed and prematurely aged. This project aims to conduct fundamental and multidisciplinary research and significantly advance battery management technology for safe and optimal use. To enable online manipulation of internal battery electrochemical states, this project will first develop a novel modelling framework that incorporates physically meaningful circuit models for a range of battery applications. Theoretical and practical contributions will be made in model simplification, identification and adaptivity. Then, a robust multi-timescale observer will be designed that synthesizes model-based and machine learning algorithms, and to estimate unmeasurable yet crucial battery states in different timescales. Finally, based on the battery models and estimator, a hierarchical optimal control methodology will be proposed that optimally trades off different objectives, systematically handles constraints on input and states and ensures real-time implementability.


Changfu Zou (contact)

Forskarassistent vid Chalmers, Electrical Engineering, Systems and control, Automatic Control


Swedish Research Council (VR)

Funding Chalmers participation during 2020–2023

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