Lithium-ion batteries are the most widely used and reliable power source for electric vehicles. With the development of electric vehicles, the safety performance, energy density, life and reliability of lithium-ion batteries have been continuously improved. However, in the field of automotive power battery technology, battery cells are grouped in series and parallel to provide sufficient energy, but a major problem faced by grouped battery is the problem of consistency between battery cells. In this paper, the lithium iron phosphate battery capacity increase curve (IC curve) was used as an analysis tool. It is found that the IC curve characteristic peaks of different monomers in the battery pack can reflect the consistency between the monomers. On this basis, a mathematical model was established, which used the IC curve II peak feature point of a single cell as a reference to characterize the consistency of other monomers one by one, so as to evaluate the battery pack consistency problem and calculate it with the SOC-OCV curve. The actual capacity was compared and found to be consistent with the battery consistency trend of capacity characterization. This method can quickly describe the battery pack consistency problem, and can be applied during the normal charging process of the battery pack. During the whole life of the battery pack, the battery consistency can be determined in real time, which has certain practical value for the utilization and accurate management of the battery pack.
{"title":"Research on consistency of Grouped lithium batteries Based on Capacity Increment Curve","authors":"Dongpei Qian, Jie Hong, Jionggeng Wang, Dong Dong, Yufeng Zhou, Yu Tian, Weixiong Sheng, Junjie Xu, Xiao Yan, Zhongcai Liu, Dong Liu","doi":"10.1145/3366194.3366332","DOIUrl":"https://doi.org/10.1145/3366194.3366332","url":null,"abstract":"Lithium-ion batteries are the most widely used and reliable power source for electric vehicles. With the development of electric vehicles, the safety performance, energy density, life and reliability of lithium-ion batteries have been continuously improved. However, in the field of automotive power battery technology, battery cells are grouped in series and parallel to provide sufficient energy, but a major problem faced by grouped battery is the problem of consistency between battery cells. In this paper, the lithium iron phosphate battery capacity increase curve (IC curve) was used as an analysis tool. It is found that the IC curve characteristic peaks of different monomers in the battery pack can reflect the consistency between the monomers. On this basis, a mathematical model was established, which used the IC curve II peak feature point of a single cell as a reference to characterize the consistency of other monomers one by one, so as to evaluate the battery pack consistency problem and calculate it with the SOC-OCV curve. The actual capacity was compared and found to be consistent with the battery consistency trend of capacity characterization. This method can quickly describe the battery pack consistency problem, and can be applied during the normal charging process of the battery pack. During the whole life of the battery pack, the battery consistency can be determined in real time, which has certain practical value for the utilization and accurate management of the battery pack.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124521349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on auto valve seat ring and the size of the spur gear slotting detection as an example, the visual inspection method is used to measure the key dimensions. Image feature recognition method based on Hough transform is put forward, and the size detecton algorithm is designed on this basis. this paper expounds the problems existing in the process of feature recognition, and adopt different methods to solve. Finally, the detection accuracy is verified. Inside of the valve seat diameter detection process, test results is not accurate because of the uncertainty in parameters, and multiple characteristics identification results. In order to solve this problem, in the algorithm design limiting detection range of valve seat ring diameter, the detection accuracy is higher, faster. Delimiting the scope of testing method has been used in solid round edge detection, this paper uses the method to detect hollow circular edge. In the feature recognition and dimension detection of spur gear kyway keyway, due to the similarity of some features of the keyway and spur gear teeth, which are straight lines, features of spur gear teeth appear in the identification results. The data that interfered with the size measurement of keyway appeared in the size measurement results. Because the size of the line segment on the tooth is much smaller than the size of the keyway, the detection result of the keyway size is obtained by using the data statistics method to exclude the data with too large difference from the actual value.
{"title":"Research On Key Dimension Detection Algorithm Of Auto Parts Based On Hough Transformation","authors":"Lijuan Jia, GuoQiang","doi":"10.1145/3366194.3366226","DOIUrl":"https://doi.org/10.1145/3366194.3366226","url":null,"abstract":"Based on auto valve seat ring and the size of the spur gear slotting detection as an example, the visual inspection method is used to measure the key dimensions. Image feature recognition method based on Hough transform is put forward, and the size detecton algorithm is designed on this basis. this paper expounds the problems existing in the process of feature recognition, and adopt different methods to solve. Finally, the detection accuracy is verified. Inside of the valve seat diameter detection process, test results is not accurate because of the uncertainty in parameters, and multiple characteristics identification results. In order to solve this problem, in the algorithm design limiting detection range of valve seat ring diameter, the detection accuracy is higher, faster. Delimiting the scope of testing method has been used in solid round edge detection, this paper uses the method to detect hollow circular edge. In the feature recognition and dimension detection of spur gear kyway keyway, due to the similarity of some features of the keyway and spur gear teeth, which are straight lines, features of spur gear teeth appear in the identification results. The data that interfered with the size measurement of keyway appeared in the size measurement results. Because the size of the line segment on the tooth is much smaller than the size of the keyway, the detection result of the keyway size is obtained by using the data statistics method to exclude the data with too large difference from the actual value.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article analyzes and designs the motion structure, platform design and transmission mode of the food 3D printer, determines the structure of the nozzle to make the three-dimensional motion as the motion structure, the Z axis adopts the ball screw drive, and the XY axis adopts the transmission mode of the synchronous belt drive;The automatic tool change system of the machine tool has designed a new type of nozzle fixture, adopting array type nozzle arrangement structure, which can realize the automatic conversion function of the nozzle and the mixed printing of various food materials; the heating device and the refrigeration system in the design body are designed to adopt the secondary semiconductor refrigeration method;For the improvement of traditional PID temperature control, the fuzzy adaptive PID control system is adopted, and the Matlab simulation is used for comparative analysis. The improved system improves the accuracy of temperature control and reduces the overshoot of traditional PID control. Protect the nutritional properties of the ingredients.
{"title":"Optimization of Mechanical Structure and Temperature Control Design of FDM Smart Food 3D Printer","authors":"Peng He, Yun Guo","doi":"10.1145/3366194.3366257","DOIUrl":"https://doi.org/10.1145/3366194.3366257","url":null,"abstract":"The article analyzes and designs the motion structure, platform design and transmission mode of the food 3D printer, determines the structure of the nozzle to make the three-dimensional motion as the motion structure, the Z axis adopts the ball screw drive, and the XY axis adopts the transmission mode of the synchronous belt drive;The automatic tool change system of the machine tool has designed a new type of nozzle fixture, adopting array type nozzle arrangement structure, which can realize the automatic conversion function of the nozzle and the mixed printing of various food materials; the heating device and the refrigeration system in the design body are designed to adopt the secondary semiconductor refrigeration method;For the improvement of traditional PID temperature control, the fuzzy adaptive PID control system is adopted, and the Matlab simulation is used for comparative analysis. The improved system improves the accuracy of temperature control and reduces the overshoot of traditional PID control. Protect the nutritional properties of the ingredients.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129904016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mechanical equipment is becoming much larger, more precise and more autonomous in current industrial society. The mechanical equipment fault detection is entering the age of 'big data' for much more monitoring points and sampling rate. Traditional diagnosis methods based on "signal processing feature extraction + machine learning classification" require a large amount of signal processing technology and diagnostic experience and can no longer meet the requirements of mechanical 'big data'. To solve this problem, an important part bearing in mechanical equipment is taken as the research object, and a diagnosis method based on convolutional neural network is proposed. This method uses the vibration signal as the monitoring signal and uses the Fourier transform to generate the vibration signal spectrum picture as the input of the whole system. Using the powerful feature extraction capability of convolutional neural network can automatically complete fault feature extraction and fault identification. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.
{"title":"Fault Diagnosis Method of Mechanical Equipment Based on Convolutional Neural Network","authors":"Jun Zhou, Wenfeng Zhang, Weizhao Sun","doi":"10.1145/3366194.3366276","DOIUrl":"https://doi.org/10.1145/3366194.3366276","url":null,"abstract":"Mechanical equipment is becoming much larger, more precise and more autonomous in current industrial society. The mechanical equipment fault detection is entering the age of 'big data' for much more monitoring points and sampling rate. Traditional diagnosis methods based on \"signal processing feature extraction + machine learning classification\" require a large amount of signal processing technology and diagnostic experience and can no longer meet the requirements of mechanical 'big data'. To solve this problem, an important part bearing in mechanical equipment is taken as the research object, and a diagnosis method based on convolutional neural network is proposed. This method uses the vibration signal as the monitoring signal and uses the Fourier transform to generate the vibration signal spectrum picture as the input of the whole system. Using the powerful feature extraction capability of convolutional neural network can automatically complete fault feature extraction and fault identification. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123619815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a kind of intelligent installation robot for rail fasteners. Through visual technology, the type and position coordinates of the fasteners are identified, the position of the rail bolts is obtained by sensor technology. The manipulator and the electric wrench are controlled by the PLC to complete the automatic identification of the rail fastener, automatic feeding and automatic fastening, etc. The robot is tested and can realize the installation automation of the rail fasteners.
{"title":"A Robot for Automatic Installation of Rail Fasteners","authors":"S. Guo, Guangyuan Zhang, C. Qi","doi":"10.1145/3366194.3366208","DOIUrl":"https://doi.org/10.1145/3366194.3366208","url":null,"abstract":"This paper proposes a kind of intelligent installation robot for rail fasteners. Through visual technology, the type and position coordinates of the fasteners are identified, the position of the rail bolts is obtained by sensor technology. The manipulator and the electric wrench are controlled by the PLC to complete the automatic identification of the rail fastener, automatic feeding and automatic fastening, etc. The robot is tested and can realize the installation automation of the rail fasteners.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130015833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marine filters are widely used in various marine auxiliary equipment and power module equipment. The quality of filter directly affects the performance of the ship's power system and decides whether the ship can operate normally or not. Therefore, the quality detection of the filters plays a key role in the whole system. However, the structure of marine filter is very complex, the input and output of the system are inconspicuous, so it is difficult to describe the filter effectively with an accurate model. But with the development of pattern recognition and neural network theory, the new methodologies provide a new way for fault diagnosis. In this paper, we use the non-linear mapping properties of SOM network, and improve the inadequacy of initialization of network weights, use "probability normal distribution" to distribute the initial weights reasonably, and by balancing the difference between weights and input vectors to determine the neighborhood range. The fault is effectively diagnosed and analyzed combined with the detection of flow and pressure signals filtered by filters, and the filters with different faults in internal structure can be distinguished, so as to achieve the purpose of analyzing the fault grade and category of filters.
{"title":"Fault Diagnosis and Analysis of Marine Filter Based on SOM Network","authors":"Kan Xu, Kun Zhang, Jiangguo Wu","doi":"10.1145/3366194.3366287","DOIUrl":"https://doi.org/10.1145/3366194.3366287","url":null,"abstract":"Marine filters are widely used in various marine auxiliary equipment and power module equipment. The quality of filter directly affects the performance of the ship's power system and decides whether the ship can operate normally or not. Therefore, the quality detection of the filters plays a key role in the whole system. However, the structure of marine filter is very complex, the input and output of the system are inconspicuous, so it is difficult to describe the filter effectively with an accurate model. But with the development of pattern recognition and neural network theory, the new methodologies provide a new way for fault diagnosis. In this paper, we use the non-linear mapping properties of SOM network, and improve the inadequacy of initialization of network weights, use \"probability normal distribution\" to distribute the initial weights reasonably, and by balancing the difference between weights and input vectors to determine the neighborhood range. The fault is effectively diagnosed and analyzed combined with the detection of flow and pressure signals filtered by filters, and the filters with different faults in internal structure can be distinguished, so as to achieve the purpose of analyzing the fault grade and category of filters.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131254471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For solving the compatibility problem of the control system to different track vehicle platforms, a track vehicle cross-platform control system is designed, which reduces the development difficulty of track vehicle control system based on different control platforms, avoids repeated development, and also provides an application interface for the subsequent development of other functions. In the design, the hardware and software parts of the control system are constructed with the general multi-processor hardware architecture and μC/OS operating system kernel. For adapting the multi-processor hardware architecture, a multi-processor collaboration management task is designed. At the same time, the hardware interface of the bottom hardware management layer and the module package of the top application layer are added, so that the hardware development and software development are isolated from each other. The control module developed by this method can be portable to the track vehicle of other platforms, which is convenient for the development and application of different control system.
{"title":"Design and Implementation of Cross-platform Control system for Track Vehicle","authors":"Chenyu Yang, Lixue Zhu, Weifeng Huang, Genping Fu, Tianci Chen, Chao Li","doi":"10.1145/3366194.3366310","DOIUrl":"https://doi.org/10.1145/3366194.3366310","url":null,"abstract":"For solving the compatibility problem of the control system to different track vehicle platforms, a track vehicle cross-platform control system is designed, which reduces the development difficulty of track vehicle control system based on different control platforms, avoids repeated development, and also provides an application interface for the subsequent development of other functions. In the design, the hardware and software parts of the control system are constructed with the general multi-processor hardware architecture and μC/OS operating system kernel. For adapting the multi-processor hardware architecture, a multi-processor collaboration management task is designed. At the same time, the hardware interface of the bottom hardware management layer and the module package of the top application layer are added, so that the hardware development and software development are isolated from each other. The control module developed by this method can be portable to the track vehicle of other platforms, which is convenient for the development and application of different control system.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124481713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem that the traditional formula method is very complicated to calculate the pipeline pressure loss in the design process of pneumatic conveying system, the paper proposes a prediction model of pipeline pressure loss based on deep neural network (DNN). By supervising and analyzing the signals of flow parameters in the process of conveying, it can effectively extract the characteristics of signal by self-adaptive learning. The advantage of this prediction model is that it does not need to extract the characteristics of flow parameters signal in advance, and directly realizes the prediction of pipeline pressure loss end-to-end. This model avoids the complexity and signal loss in the process of artificially extracting parameter features, has higher stability and better prediction effect.
{"title":"Study on the Optimum Design of Pneumatic Conveying System Based on DNN","authors":"Xuexia Zhang, Juyang Lei","doi":"10.1145/3366194.3366305","DOIUrl":"https://doi.org/10.1145/3366194.3366305","url":null,"abstract":"Aiming at the problem that the traditional formula method is very complicated to calculate the pipeline pressure loss in the design process of pneumatic conveying system, the paper proposes a prediction model of pipeline pressure loss based on deep neural network (DNN). By supervising and analyzing the signals of flow parameters in the process of conveying, it can effectively extract the characteristics of signal by self-adaptive learning. The advantage of this prediction model is that it does not need to extract the characteristics of flow parameters signal in advance, and directly realizes the prediction of pipeline pressure loss end-to-end. This model avoids the complexity and signal loss in the process of artificially extracting parameter features, has higher stability and better prediction effect.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper expounds the natural gas heating furnace from two aspects of AI automatic control and maintenance robot.The automatic control system combines grey prediction and generalized regression neural network with big data analysis to improve the intelligent control of the system, analyze data ability, reduce manpower input, reduce cost budget and improve work efficiency.Intelligent in order to meet the demand, but also designed for natural gas heating furnace of intelligent robot, China in this area is still in the blank stage, according to the characteristics of the heating furnace, design has practical significance and value of robots, robot with ultrasonic testing system, scale observation system, such as image acquisition system, provide a powerful guarantee for gas furnace overhaul.
{"title":"Artificial Intelligent Control and Maintenance of Natural Gas Heating Furnace","authors":"Hao Chen, Yun Guo, Yeyu Ouyang","doi":"10.1145/3366194.3366278","DOIUrl":"https://doi.org/10.1145/3366194.3366278","url":null,"abstract":"This paper expounds the natural gas heating furnace from two aspects of AI automatic control and maintenance robot.The automatic control system combines grey prediction and generalized regression neural network with big data analysis to improve the intelligent control of the system, analyze data ability, reduce manpower input, reduce cost budget and improve work efficiency.Intelligent in order to meet the demand, but also designed for natural gas heating furnace of intelligent robot, China in this area is still in the blank stage, according to the characteristics of the heating furnace, design has practical significance and value of robots, robot with ultrasonic testing system, scale observation system, such as image acquisition system, provide a powerful guarantee for gas furnace overhaul.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, our study takes lithium iron phosphate battery as the research object. In order to solve the problem of deviation in HPPC test, we propose a double pulse test method which is suitable for the calculation of characteristic internal resistance(CIR). Secondly, three lithium iron phosphate 18650 batteries were selected for the whole life cycle accelerated aging experiment, and the CIR was calculated according to the above method. The change rule of CIR in the whole life cycle was obtained. The relationship between CIR and SOH was divided into three stages in the whole life cycle: SOH=1~0.5, SOH=0.5~0.35, SOH < 0.35. Among them, there is a linear relationship between the CIR and SOH in the first stage, according to this relationship, a 280s double pulse test can be added to the charging process to estimate the SOH in practical application. In the second stage CIR increases rapidly and reaches the peak in a short time, wobbles near the peak after SOH<0.35. This phenomenon provides a reference for the utilization of second-use lithium-ion battery. It is recommended that second-use batteries retired when the SOH=0.35.
{"title":"Study on Characteristic Internal Resistance of Lithium Batteries Based on Double Pulse Test","authors":"Yiquan Wang, Bixiong Huang, Xiao Yan, Wenheng Lin, Gengjiong Wang, Zhongcai Liu","doi":"10.1145/3366194.3366239","DOIUrl":"https://doi.org/10.1145/3366194.3366239","url":null,"abstract":"In this paper, our study takes lithium iron phosphate battery as the research object. In order to solve the problem of deviation in HPPC test, we propose a double pulse test method which is suitable for the calculation of characteristic internal resistance(CIR). Secondly, three lithium iron phosphate 18650 batteries were selected for the whole life cycle accelerated aging experiment, and the CIR was calculated according to the above method. The change rule of CIR in the whole life cycle was obtained. The relationship between CIR and SOH was divided into three stages in the whole life cycle: SOH=1~0.5, SOH=0.5~0.35, SOH < 0.35. Among them, there is a linear relationship between the CIR and SOH in the first stage, according to this relationship, a 280s double pulse test can be added to the charging process to estimate the SOH in practical application. In the second stage CIR increases rapidly and reaches the peak in a short time, wobbles near the peak after SOH<0.35. This phenomenon provides a reference for the utilization of second-use lithium-ion battery. It is recommended that second-use batteries retired when the SOH=0.35.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123800822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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