Lithium heat pipes have broad applications in heat pipe cooling reactors and hypersonic vehicles owing to their ultra-high working temperature.In particular,when the length of the lithium heat pipe is ultra-long,the f...Lithium heat pipes have broad applications in heat pipe cooling reactors and hypersonic vehicles owing to their ultra-high working temperature.In particular,when the length of the lithium heat pipe is ultra-long,the flow and heat transfer characteristics are more complex.In this study,an improved lumped parameter model that considers the Marangoni effect,bending effect,and different vapor flow patterns and Mach numbers was developed.Thereafter,the proposed model was verified using the University of New Mexico’s Heat Pipe and HTPIPE models.Finally,the verified model was applied to simulate the steady-state operation of an ultra-long lithium heat pipe in a Heat PipeSegmented Thermoelectric Module Converters space reactor.Based on the results:(1)Vapor thermal resistance was dominant at low heating power and decreased with increasing heating power.The vapor flow inside the heat pipe developed from the laminar to the turbulent phase,whereas the liquid phase in the heat pipe was always laminar.(2)The vapor pressure drop caused by bending was approximately 22–23%of the total,and the bending effect on the liquid pressure drop could be ignored.(3)The Marangoni effect reduced the capillary limit by hindering the liquid reflux,especially at low vapor temperatures.Without considering the Marangoni effect,the capillary limit of the lithium heat pipe was overestimated by 9%when the vapor temperature was 1400 K.(4)The total thermal resistance of the heat pipe significantly increased with increasing adiabatic length when the vapor temperature was low.Further,the wick dryness increased with increasing adiabatic length at any vapor temperature.Such findings improve on current knowledge for the optimal design and safety analysis of a heat pipe reactor,which adopts ultra-long lithium heat pipes.展开更多
Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles.However,lithium-ion cells generate immense heat at high-current charging rates.In order to address this probl...Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles.However,lithium-ion cells generate immense heat at high-current charging rates.In order to address this problem,an efficient fast charging–cooling scheduling method is urgently needed.In this study,a liquid cooling-based thermal management system equipped with mini-channels was designed for the fastcharging process of a lithium-ion battery module.A neural network-based regression model was proposed based on 81 sets of experimental data,which consisted of three sub-models and considered three outputs:maximum temperature,temperature standard deviation,and energy consumption.Each sub-model had a desirable testing accuracy(99.353%,97.332%,and 98.381%)after training.The regression model was employed to predict all three outputs among a full dataset,which combined different charging current rates(0.5C,1C,1.5C,2C,and 2.5C(1C=5 A))at three different charging stages,and a range of coolant rates(0.0006,0.0012,and 0.0018 kg·s^(-1)).An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments.The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min,with an energy consumption lower than 0.02 J.The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8C,respectively.The approach described herein can be used by the electric vehicles industry in real fast-charging conditions.Moreover,optimal fast charging-cooling schedule can be predicted based on the experimental data obtained,that in turn,can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.展开更多
基金the CASHIPS Director’s Fund(No.YZJJ2021QN36)the Key Research Program of the Chinese Academy of Sciences(No.ZDRW-KT-2019-1-0202).
文摘Lithium heat pipes have broad applications in heat pipe cooling reactors and hypersonic vehicles owing to their ultra-high working temperature.In particular,when the length of the lithium heat pipe is ultra-long,the flow and heat transfer characteristics are more complex.In this study,an improved lumped parameter model that considers the Marangoni effect,bending effect,and different vapor flow patterns and Mach numbers was developed.Thereafter,the proposed model was verified using the University of New Mexico’s Heat Pipe and HTPIPE models.Finally,the verified model was applied to simulate the steady-state operation of an ultra-long lithium heat pipe in a Heat PipeSegmented Thermoelectric Module Converters space reactor.Based on the results:(1)Vapor thermal resistance was dominant at low heating power and decreased with increasing heating power.The vapor flow inside the heat pipe developed from the laminar to the turbulent phase,whereas the liquid phase in the heat pipe was always laminar.(2)The vapor pressure drop caused by bending was approximately 22–23%of the total,and the bending effect on the liquid pressure drop could be ignored.(3)The Marangoni effect reduced the capillary limit by hindering the liquid reflux,especially at low vapor temperatures.Without considering the Marangoni effect,the capillary limit of the lithium heat pipe was overestimated by 9%when the vapor temperature was 1400 K.(4)The total thermal resistance of the heat pipe significantly increased with increasing adiabatic length when the vapor temperature was low.Further,the wick dryness increased with increasing adiabatic length at any vapor temperature.Such findings improve on current knowledge for the optimal design and safety analysis of a heat pipe reactor,which adopts ultra-long lithium heat pipes.
基金This work was supported by the Program for Huazhong University of Science and Technology(HUST)Academic Frontier Youth Team(2017QYTD04)the Program for HUST Graduate Innovation and Entrepreneurship Fund(2019YGSCXCY037)+2 种基金Authors acknowledge Grant DMETKF2018019 by State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and TechnologyThis study was also financially supported by the Guangdong Science and Technology Project(2016B020240001)the Guangdong Natural Science Foundation(2018A030310150).
文摘Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles.However,lithium-ion cells generate immense heat at high-current charging rates.In order to address this problem,an efficient fast charging–cooling scheduling method is urgently needed.In this study,a liquid cooling-based thermal management system equipped with mini-channels was designed for the fastcharging process of a lithium-ion battery module.A neural network-based regression model was proposed based on 81 sets of experimental data,which consisted of three sub-models and considered three outputs:maximum temperature,temperature standard deviation,and energy consumption.Each sub-model had a desirable testing accuracy(99.353%,97.332%,and 98.381%)after training.The regression model was employed to predict all three outputs among a full dataset,which combined different charging current rates(0.5C,1C,1.5C,2C,and 2.5C(1C=5 A))at three different charging stages,and a range of coolant rates(0.0006,0.0012,and 0.0018 kg·s^(-1)).An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments.The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min,with an energy consumption lower than 0.02 J.The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8C,respectively.The approach described herein can be used by the electric vehicles industry in real fast-charging conditions.Moreover,optimal fast charging-cooling schedule can be predicted based on the experimental data obtained,that in turn,can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.
