With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers...With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers＇ purchase decisions. In order to guarantee a precise range estimation over the usage life of battery electric vehicles, a method is presented that combines adaptive filter algorithms with statistical approaches. The statistical approach uses recurring driving cycles over the lifetime in order to derive the aging status of the traction battery. It is implied that the variance of the energy usage of these driving cycles is within certain bounds. This fact should be proven by an experimental case study. The dataset used in this paper is open to the public.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
A great amount of work addressed methods for predicting the battery lifetime in wireless sensor systems. In spite of these efforts, the reported experimental results demonstrate that the duty-cycle current average met...A great amount of work addressed methods for predicting the battery lifetime in wireless sensor systems. In spite of these efforts, the reported experimental results demonstrate that the duty-cycle current average method, which is widely used to this aim, fails in accurately estimating the battery life time of most of the presented wireless sensor system applications. The aim of this paper is to experimentally assess the duty-cycle current average method in order to give more effective insight on the effectiveness of the method. An electronic metering system, based on a dedicated PCB, has been designed and developed to experimentally measure node current consumption profiles and charge extracted from the battery in two selected case studies. A battery lifetime measurement (during 30 days) has been carried out. Experimental results have been assessed and compared with estimations given by using the duty-cycle current average method. Based on the measurement results, we show that the assumptions on which the method is based do not hold in real operating cases. The rationality of the duty-cycle current average method needs reconsidering.展开更多
文摘With a growing consumer market of battery electric vehicles, customers＇ demand for technology and features is on the rise. The range and, to a certain extent, the range estimation will play a key factor in customers＇ purchase decisions. In order to guarantee a precise range estimation over the usage life of battery electric vehicles, a method is presented that combines adaptive filter algorithms with statistical approaches. The statistical approach uses recurring driving cycles over the lifetime in order to derive the aging status of the traction battery. It is implied that the variance of the energy usage of these driving cycles is within certain bounds. This fact should be proven by an experimental case study. The dataset used in this paper is open to the public.
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.
文摘A great amount of work addressed methods for predicting the battery lifetime in wireless sensor systems. In spite of these efforts, the reported experimental results demonstrate that the duty-cycle current average method, which is widely used to this aim, fails in accurately estimating the battery life time of most of the presented wireless sensor system applications. The aim of this paper is to experimentally assess the duty-cycle current average method in order to give more effective insight on the effectiveness of the method. An electronic metering system, based on a dedicated PCB, has been designed and developed to experimentally measure node current consumption profiles and charge extracted from the battery in two selected case studies. A battery lifetime measurement (during 30 days) has been carried out. Experimental results have been assessed and compared with estimations given by using the duty-cycle current average method. Based on the measurement results, we show that the assumptions on which the method is based do not hold in real operating cases. The rationality of the duty-cycle current average method needs reconsidering.
