scholarly journals Optimization of fuel cell thermal management system based on back propagation neural network

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2975-2982
Author(s):  
Qianqian Ge ◽  
Cuncun Wei
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Thermal Management ◽  
Control Strategy ◽  
Management System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Current Control ◽  
Control Effect ◽  
Thermal Management System

Two thermal management control strategies, namely flow following current and power mode and back propagation neural network auto-disturbance rejection method, were proposed to solve significant temperature fluctuation problems, long regulation time, and slow response speed in fuel cell thermal management system variable load. The results show that the flow following current and power control strategy can effectively weaken the coupling effect between pump and radiator fan and significantly reduce the overshoot and adjustment time of inlet and outlet cooling water temperature and temperature difference reactor. Although the control effect of the neural network and strategy is insufficient under maximum power, the overall control effect is better than that of the flow following the current control strategy.

scholarly journals Optimization and control of fuel cell thermal management system based on neural network

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2933-2939
Author(s):  
Kunhao Tang ◽  
Sanhua Zhang ◽  
Youlong Wu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuel Cell ◽  
Thermal Management ◽  
Management System ◽  
Direct Methanol Fuel Cell ◽  
Battery Voltage ◽  
Thermal Management System ◽  
Direct Methanol ◽  
Methanol Fuel Cell

Aiming at the direct methanol fuel cell system is too complicated, difficult to model, and the thermal management system needs to be optimized. The article attempts to bypass the internal complexity of direct methanol fuel cell, based on experimental data, use neural networks to approximate arbitrarily complex non-linear functions ability to apply neural network identification methods to direct methanol fuel cell, a highly non-linear thermal management system optimization modelling. The paper uses 1000 sets of battery voltage and current density experimental data as training samples and uses an improved back propagation neural network to establish a battery voltage-current density dynamic response model at different temperatures. The simulation results show that this method is feasible, and the established model has high accuracy. It makes it possible to design the real-time controller of the direct methanol fuel cell and optimize the thermal energy manage?ment system?s efficiency.

scholarly journals System optimization of thermal management performance of fuel cell system for automobile

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2923-2931
Author(s):  
Wenfeng Bai ◽  
Caofeng He
Keyword(s):  
Fuel Cell ◽  
Thermal Management ◽  
Management System ◽  
Surface Wind ◽  
Cooling Water ◽  
System Optimization ◽  
Cell System ◽  
Management Control ◽  
Thermal Management System ◽  
Bypass Valve

Vehicle fuel cell systems release a large amount of heat while generating electricity. The suitable thermal management system must be built to ensure system performance and reliability. Based on the analysis of the working principle of the vehicle fuel cell thermal management system, the paper establishes a control-oriented fuel cell thermal management. The stack, air cooler, hydrogen heat exchanger, bypass valve, heat sink, and cooling water circulating pump model are taking into account. System model, and the relationship between stack current, coolant flow rate, fin surface wind speed, bypass valve opening, and fuel cell temperature are in established in simulation experiments. The paper discusses their effects on system as a whole, air coolers, hydrogen heat exchangers, and the influence of the temperature difference between the inlet and outlet of the radiator. The simulation results can provide guidance and help to design the fuel cell thermal management control system.

Modelling and control of vehicle integrated thermal management system of PEM fuel cell vehicle

Energy ◽  
2020 ◽  
Vol 199 ◽  
pp. 117495
Author(s):  
Jiamin Xu ◽  
Caizhi Zhang ◽  
Ruijia Fan ◽  
Huanhuan Bao ◽  
Yi Wang ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Thermal Management ◽  
Management System ◽  
Pem Fuel Cell ◽  
Fuel Cell Vehicle ◽  
Thermal Management System ◽  
And Control
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