Battery charging is a complex task, which needs to be addressed by a proper control methodology to find the highest charging current while guaranteeing safety. Among the different approaches, model predictive control appears particularly suitable due to its ability in dealing with nonlinear systems and constraints. However, its use in a realistic scenario is limited due to the high computational burden required by the online solution of an optimal control problem. A neural network-based algorithm is here proposed to significantly reduce the real-time computational effort by approximating the predictive control law. In addition, for the first time to the authors’ knowledge, an adaptation of the proposed deep learning-based algorithm is presented for the case in which the battery's internal states are not measurable. The superiority of proposed methodology is highlighted in simulation by comparing it with a predictive controller coupled with a properly designed state observer.

Pozzi, A., Moura, S., Toti, D., A deep learning-based predictive controller for the optimal charging of a lithium-ion cell with non-measurable states, <<COMPUTERS & CHEMICAL ENGINEERING>>, 2023; 173 (N/A): N/A-N/A. [doi:10.1016/j.compchemeng.2023.108222] [https://hdl.handle.net/10807/233228]

A deep learning-based predictive controller for the optimal charging of a lithium-ion cell with non-measurable states

Pozzi, Andrea;Toti, Daniele
2023

Abstract

Battery charging is a complex task, which needs to be addressed by a proper control methodology to find the highest charging current while guaranteeing safety. Among the different approaches, model predictive control appears particularly suitable due to its ability in dealing with nonlinear systems and constraints. However, its use in a realistic scenario is limited due to the high computational burden required by the online solution of an optimal control problem. A neural network-based algorithm is here proposed to significantly reduce the real-time computational effort by approximating the predictive control law. In addition, for the first time to the authors’ knowledge, an adaptation of the proposed deep learning-based algorithm is presented for the case in which the battery's internal states are not measurable. The superiority of proposed methodology is highlighted in simulation by comparing it with a predictive controller coupled with a properly designed state observer.
2023
Inglese
Pozzi, A., Moura, S., Toti, D., A deep learning-based predictive controller for the optimal charging of a lithium-ion cell with non-measurable states, <<COMPUTERS & CHEMICAL ENGINEERING>>, 2023; 173 (N/A): N/A-N/A. [doi:10.1016/j.compchemeng.2023.108222] [https://hdl.handle.net/10807/233228]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/233228
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