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
An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles,as it provides the main power source for the transmission system.However,manufacturing defe...An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles,as it provides the main power source for the transmission system.However,manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration.This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution,which further lead to reduction of cycle life and battery capacity over time.To solve this problem,this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance.Experiments were first performed by dismantling battery modules for the measurement of performance parameters.The kmeans clustering and support vector clustering(SVC)algorithms were then employed to produce battery modules composed of 12 cells each.Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period,while air cooling was provided.It was found that the SVC-clustered battery module in Category 3 exhibited the best performance,with a maximum observed temperature of 32℃.By contrast,the maximum observed temperatures of the other battery modules were higher,at 40℃for Category 1(manufacturer),36℃for Category 2(manufacturer),and 35℃for Category 4(k-means-clustered battery module).展开更多
Rapid prototyping (RP) or layered manufacturing (LM) technologies have been extensively used to manufacture prototypes composed mainly of plastics, polymers, paper, and wax due to the short product development time an...Rapid prototyping (RP) or layered manufacturing (LM) technologies have been extensively used to manufacture prototypes composed mainly of plastics, polymers, paper, and wax due to the short product development time and low costs of these technologies. However, such technologies, with the exception of selective laser melting and sintering, are not used to fabricate metallic products because of the resulting poor life, short cycle, poor surface finish, and low structural integrity of the fabricated parts. The properties endowed by these parts do not match those of functional parts. Therefore, extensive research has been conducted to develop new additive manufacturing (AM) technologies by extending existing RP technologies. Several AM technologies have been developed for the fabrication of metallic objects. These technologies utilize materials, such as Ni-, A1-, and Ti-based alloys and stainless steel powders, to fabricate high-quality functional components. The present work reviews the type of materials used in laserbased AM processes for the manufacture of metallic products. The advantages and disadvantages of processes and different materials are summarized, and future research directions are discussed in the final section. This review can help experts select the ideal type of process or technology for the manufacturing of elements composed of a given alloy or material (Ni, Ti, Al, Pb, and stainless steel).展开更多
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.
基金This work was supported by the National Natural Science Foundation of China(51675196 and 51721092)the program for HUST Academic Frontier Youth Team(2017QYTD04)+2 种基金The authors acknowledge the grant(DMETKF2018019)from the State Key Lab of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technologythe Sailing Talent Program and the Guangdong University Youth Innovation Talent Project(2016KQNCX053)supported by the Department of Education of Guangdong Provincethe Shantou University Scientific Research Funded Project(NTF16002).
文摘An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles,as it provides the main power source for the transmission system.However,manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration.This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution,which further lead to reduction of cycle life and battery capacity over time.To solve this problem,this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance.Experiments were first performed by dismantling battery modules for the measurement of performance parameters.The kmeans clustering and support vector clustering(SVC)algorithms were then employed to produce battery modules composed of 12 cells each.Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period,while air cooling was provided.It was found that the SVC-clustered battery module in Category 3 exhibited the best performance,with a maximum observed temperature of 32℃.By contrast,the maximum observed temperatures of the other battery modules were higher,at 40℃for Category 1(manufacturer),36℃for Category 2(manufacturer),and 35℃for Category 4(k-means-clustered battery module).
文摘Rapid prototyping (RP) or layered manufacturing (LM) technologies have been extensively used to manufacture prototypes composed mainly of plastics, polymers, paper, and wax due to the short product development time and low costs of these technologies. However, such technologies, with the exception of selective laser melting and sintering, are not used to fabricate metallic products because of the resulting poor life, short cycle, poor surface finish, and low structural integrity of the fabricated parts. The properties endowed by these parts do not match those of functional parts. Therefore, extensive research has been conducted to develop new additive manufacturing (AM) technologies by extending existing RP technologies. Several AM technologies have been developed for the fabrication of metallic objects. These technologies utilize materials, such as Ni-, A1-, and Ti-based alloys and stainless steel powders, to fabricate high-quality functional components. The present work reviews the type of materials used in laserbased AM processes for the manufacture of metallic products. The advantages and disadvantages of processes and different materials are summarized, and future research directions are discussed in the final section. This review can help experts select the ideal type of process or technology for the manufacturing of elements composed of a given alloy or material (Ni, Ti, Al, Pb, and stainless steel).
