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• W2990240092 abstract "Abstract This paper develops a self-adaptive control strategy for a newly-proposed J-type air-based battery thermal management system (BTMS) for electric vehicles (EVs). The structure of the J-type BTMS is first optimized through surrogate-based optimization in conjunction with computational fluid dynamics (CFD) simulations, with the aim of minimizing temperature rise and maximizing temperature uniformity. Based on the optimized J-type BTMS, an artificial neural network (ANN)-based model predictive control (MPC) strategy is set up to perform real-time control of mass flow rate and BTMS mode switch among J-, Z-, and U-mode. The ANN-based MCP strategy is tested with the Urban Dynamometer Driving Schedule (UDDS) driving cycle. With a genetic algorithm optimizer, the control system is able to optimize the mass flow rate by considering several steps ahead. The results show that the ANN-based MPC strategy is able to constrain the battery temperature difference within a narrow range, and to satisfy light-duty daily operations like the UDDS driving cycle for EVs." @default.
• W2990240092 created "2019-12-05" @default.
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• W2990240092 date "2019-08-18" @default.
• W2990240092 modified "2023-10-16" @default.
• W2990240092 title "Self-Adapting Intelligent Battery Thermal Management System via Artificial Neural Network Based Model Predictive Control" @default.
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• W2990240092 doi "https://doi.org/10.1115/detc2019-98205" @default.
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