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Nonlinear model predictive control for integrated thermal management of electric vehicle battery and cabin environment.

Number: 2484

Author(s) : PAN C., LI Y.

Summary

Energy efficiency and operating range of an electric vehicle (EV) are strongly affected by the performance of thermal management for its battery pack and cabin environment. By use of air-source heat pump (ASHP) with multiple evaporators/condensers, integral thermal management system (ITMS) can synchronize the cabin and battery thermal management efficiently. In this paper, we propose a nonlinear model predictive control (NMPC) strategy for ASHP-ITMS for EV with all-cooling operation. The NMPC is designed to optimize the overall efficiency while regulating thermal requirements based on driving cycle information. For NMPC design, a nonlinear autoregressive model with exogenous inputs using recurrent neural network (NARX-RNN) is adopted, in which the states include cabin supply air temperature, and battery return liquid temperature, and the outputs include COP and superheats of the battery and cabin evaporators. A model selection procedure that fits the RNN structure is applied to obtain structurally parsimonious model to reduce computational load of NMPC design. The proposed control strategy is evaluated with a Modelica model of EV ASHP-ITMS. With the NMPC is designed and implemented in Python, a Modelica-Python co-simulation platform is developed using the Functional Mockup Interface (FMI). Simulation study is performed to validate the effectiveness of the proposed strategy.

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Pages: 13 p.

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  • Original title: Nonlinear model predictive control for integrated thermal management of electric vehicle battery and cabin environment.
  • Record ID : 30030747
  • Languages: English
  • Subject: Technology
  • Source: 2022 Purdue Conferences. 19th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2022

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