A Predictive Model of SOFC Thermal Management Based on LS-SVM

2014 ◽  
Vol 538 ◽  
pp. 274-277
Author(s):  
Ying Ying Zhang ◽  
Jing Dong Huang ◽  
Ying Zhang

The thermal management is crucial to the safety and lifespan of Solid Oxide Fuel Cell (SOFC) generation system. For the model-predictive control design, a model of SOFC thermal management system is proposed on the least squares support vector machine (LS-SVM). The model is composed of some thermal modules including SOFC stack, combustor, heat-exchanger and thermal equilibrium apparatus. It predicts the temperature distribution in SOFC generation system by computing the electrochemical reaction in the stack, the gas flow and the heat exchange through the modules. Checked by the experimental data, the model can be used for system temperature fast prediction with high precision and strong generalization ability, which meets the requirement of the research on the online predictive control design of SOFC generation system.

Author(s):  
Yuanzhi Liu ◽  
Jie Zhang

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.


2016 ◽  
Vol 9 (3) ◽  
pp. 525-533 ◽  
Author(s):  
Amey Y. Karnik ◽  
Adrian Fuxman ◽  
Phillip Bonkoski ◽  
Mrdjan Jankovic ◽  
Jaroslav Pekar

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