Batteries are complex systems that need to be properly managed to guarantee safe and optimal operations. Model predictive control (MPC) is an advanced control strategy that, thanks to its characteristics, can be embedded into battery management systems (BMS) to derive optimal charging strategies. However, deterministic MPC, which relies on a nominal model only, is not adequate in a realistic scenario in which cells parameters are not known exactly. In this paper, stochastic MPC is proposed for the optimal charging of a Li-ion battery pack to account for the presence of parameter uncertainties. The adopted scheme relies on the polynomial chaos expansion paradigm for the propagation of uncertainties through the model equations and allows to satisfy safety constraints with a guaranteed probability. The results highlight the advantages of stochastic MPC over different scenarios when compared to a deterministic MPC approach.

Stochastic model predictive control for optimal charging of electric vehicles battery packs

Raimondo D. M.
2022-01-01

Abstract

Batteries are complex systems that need to be properly managed to guarantee safe and optimal operations. Model predictive control (MPC) is an advanced control strategy that, thanks to its characteristics, can be embedded into battery management systems (BMS) to derive optimal charging strategies. However, deterministic MPC, which relies on a nominal model only, is not adequate in a realistic scenario in which cells parameters are not known exactly. In this paper, stochastic MPC is proposed for the optimal charging of a Li-ion battery pack to account for the presence of parameter uncertainties. The adopted scheme relies on the polynomial chaos expansion paradigm for the propagation of uncertainties through the model equations and allows to satisfy safety constraints with a guaranteed probability. The results highlight the advantages of stochastic MPC over different scenarios when compared to a deterministic MPC approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3073204
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