Vehicle Powertrain Thermal Management System Using Model Predictive Control

2016 ◽  
Vol 9 (3) ◽  
pp. 525-533 ◽  
Author(s):  
Amey Y. Karnik ◽  
Adrian Fuxman ◽  
Phillip Bonkoski ◽  
Mrdjan Jankovic ◽  
Jaroslav Pekar
Keyword(s):  
Model Predictive Control ◽  
Thermal Management ◽  
Predictive Control ◽  
Management System ◽  
Thermal Management System ◽  
Vehicle Powertrain

Self-Adapting Intelligent Battery Thermal Management System via Artificial Neural Network Based Model Predictive Control

2019 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Predictive Control ◽  
Mass Flow Rate ◽  
Thermal Management ◽  
Predictive Control ◽  
Flow Rate ◽  
Battery Thermal Management ◽  
Thermal Management System ◽  
Battery Thermal Management System

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.

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