To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling...To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling.The primary innovation of the new hybrid BTMS lies in the use of an OHP with vertically arranged evaporator and condenser,enabling dual heat transfer pathways through liquid cooling plate and OHP.This study experimentally investigates the performance characteristics of the⊥-shaped OHP and hybrid BTMS.Results show that lower filling ratios significantly enhance the OHP’s startup performance but reduce operational stability,with optimal performance achieved at a 26.1%filling ratio.Acetone,as a single working fluid,exhibited superior heat transfer performance under low-load conditions compared to mixed fluids,while the acetone/ethanol mixture,forming a non-azeotropic solution,minimized temperature fluctuations.At 100 W,the⊥-shaped OHP with a horizontally arranged evaporator demonstrated better heat transfer performance than 2D-OHP designs.Compared to a liquid BTMS using water coolant at 280 W,the hybrid BTMS reduced the equivalent thermal resistance(RBTMS)and maximum temperature difference(ΔTmax)by 8.06%and 19.1%,respectively.When graphene nanofluid was used as the coolant in hybrid BTMS,the battery pack’s average temperature(Tb)dropped from 52.2℃ to 47.9℃,with RBTMS andΔTmax decreasing by 20.1%and 32.7%,respectively.These findings underscore the hybrid BTMS’s suitability for high heat load applications,offering a promising solution for electric vehicle thermal management.展开更多
The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board...The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.展开更多
基金funded by the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ2404911)the Ministry of Higher Education,Malaysia through the Fundamental Research Grant Scheme:FRGS/1/2024/TK10/UMP/02/15 and Universiti Malaysia Pahang Al-Sultan Abdullah(RDU240117).
文摘To improve the thermal performance and temperature uniformity of battery pack,this paper presents a novel battery thermal management system(BTMS)that integrates oscillating heat pipe(OHP)technology with liquid cooling.The primary innovation of the new hybrid BTMS lies in the use of an OHP with vertically arranged evaporator and condenser,enabling dual heat transfer pathways through liquid cooling plate and OHP.This study experimentally investigates the performance characteristics of the⊥-shaped OHP and hybrid BTMS.Results show that lower filling ratios significantly enhance the OHP’s startup performance but reduce operational stability,with optimal performance achieved at a 26.1%filling ratio.Acetone,as a single working fluid,exhibited superior heat transfer performance under low-load conditions compared to mixed fluids,while the acetone/ethanol mixture,forming a non-azeotropic solution,minimized temperature fluctuations.At 100 W,the⊥-shaped OHP with a horizontally arranged evaporator demonstrated better heat transfer performance than 2D-OHP designs.Compared to a liquid BTMS using water coolant at 280 W,the hybrid BTMS reduced the equivalent thermal resistance(RBTMS)and maximum temperature difference(ΔTmax)by 8.06%and 19.1%,respectively.When graphene nanofluid was used as the coolant in hybrid BTMS,the battery pack’s average temperature(Tb)dropped from 52.2℃ to 47.9℃,with RBTMS andΔTmax decreasing by 20.1%and 32.7%,respectively.These findings underscore the hybrid BTMS’s suitability for high heat load applications,offering a promising solution for electric vehicle thermal management.
基金supported by the Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project(Grant No.2022YFB4300500).
文摘The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.
