Ageing-Aware Charging of Lithium-ion Batteries Using a Surrogate Model

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In this paper, we present an optimal-control-based method for ageing-aware charging. A surrogate modeling approach is used to approximate ageing-related Doyle-Fuller-Newman (DFN) model states, where the surrogate model is a combination of a black-box finite-dimensional linear-time-invariant model and a static nonlinear model that is a function of state-of-charge. We formulate the optimal-control problem as minimizing the side reactions for a given charging time and subject to several ageing-related constraints that are commonly used in literature. We will show that the ageing-related DFN model states can be well approximated by the proposed surrogate model. Furthermore, we will show that with the surrogate modeling approach, even in an open-loop execution of the optimal-control-based method, the considered constraints are only marginally violated when applied to the DFN model. Finally, we will compare the Pareto front achieved with the proposed optimal-control-based method with the Pareto fronts achieved with various multi-stage charging protocols. Here, we will show that the proposed optimal-control-based method achieves a significantly improved Pareto front over the multi-stage charging protocols.
Original languageEnglish
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)978-1-6654-4197-1
Publication statusPublished - 28 Jul 2021
Event2021 American Control Conference, ACC 2021 - Virtual, Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021


Conference2021 American Control Conference, ACC 2021
Abbreviated titleACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Internet address


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