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- W4310974525 abstract "The exposure of electric vehicle batteries to rapid charging and discharging profiles, particularly under uncertainty in the drive schedules, requires effective strategies to forecast and avoid thermal excursions during operation. In this work, we present a light gradient boosting-based machine learning model to create probabilistic forecasts of the discharge current over a forecast horizon in real time. A surrogate of a physics-based computational model provides the functional relationship for the battery temperature, using which a stochastic model-predictive control strategy is developed to derive the optimal cooling schedule based on the probabilistic forecast. The effectiveness of the stochastic model-predictive control approach is assessed on the US06 driving cycle, in comparison to a constant coolant flow and persistence forecast-based control. It is shown that the total number of temperature excursion instances using the stochastic model-predictive control is reduced by about 69% compared to the constant coolant flow case and by over 51% compared to persistence-forecast-based control. Further, the total coolant usage is reduced by about 73% compared to persistence forecast-based control. The stochastic model-predictive control approach provides more flexibility by allowing a wider range of control and battery pack design parameters to obtain optimal performance with minimum coolant use." @default.
- W4310974525 created "2022-12-21" @default.
- W4310974525 creator A5045564294 @default.
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- W4310974525 date "2023-02-01" @default.
- W4310974525 modified "2023-09-27" @default.
- W4310974525 title "Robust model-predictive thermal control of lithium-ion batteries under drive cycle uncertainty" @default.
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- W4310974525 doi "https://doi.org/10.1016/j.jpowsour.2022.232496" @default.
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