Overview of the Application of Big Data Analysis Technology in New Energy Vehicle Industry: Based on Operating Big Data of New Energy Vehicle

doi:10.3901/JME.2019.20.003

Abstract

Abstract: New energy vehicle(NEV) has been widely used around the world in response to the fossil energy crisis and environmental pollution problems. NEV will generate massive real-world data during its daily operating which is contributed by high electrification and intelligent networking. Applying these multi-source heterogeneous data for a security warning and technical analysis will play a key role in promoting the development of NEV industry in China. The current situation of data-driven analysis technology in the NEV field is reviewed. Firstly, the basic theory of big data analysis techniques are introduced and the development of big data technology is depicted. The structure and function of the National Monitoring and Management Platform for New Energy Vehicles are introduced, and the particular process of big data analysis on NEV is emphasized. The previous data-driven research and methods in power battery, NEV daily operation and charging behavior are proposed for discussion respectively. Some representative research results and applications are displayed at the same time. Finally, the issues and prospects of the data-driven method on NEV application field are summarized and forecasted.

Key words: new energy vehicle, big data platform, data-driven methods, power battery

CLC Number:

U469

SHE Chengqi, ZHANG Zhaosheng, LIU Peng, SUN Fengchun. Overview of the Application of Big Data Analysis Technology in New Energy Vehicle Industry: Based on Operating Big Data of New Energy Vehicle[J]. Journal of Mechanical Engineering, 2019, 55(20): 3-16.

References

[1] LI L,YOU S,YANG C,et al. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy,2016,162(1):868-879.
[2] 张雷,胡晓松,王震坡. 超级电容管理技术及在电动汽车中的应用综述[J]. 机械工程学报,2017,53(16):32-43. ZHANG Lei,HU Xiaosong,WANG Zhenpo. Overview of supercapacitor management techniques in electrified vehicle applications[J]. Journal of Mechanical Engineering,2017,53(16):32-43.
[3] CHEN Z,XIONG R,TIAN J,et al. Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles[J]. Applied Energy,2016,184:365-374.
[4] KIM G H,SMITH K,IRELAND J,et al. Fail-safe design for large capacity lithium-ion battery systems[J]. Journal of Power Sources,2012,210(4):243-253.
[5] WANG Z,HONG J,LIU P,et al. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z -score for electric vehicles[J]. Applied Energy,2017,196:289-302.
[6] 王华伟. 铁路运输设备技术状态大数据平台研究[D]. 北京:中国铁道科学研究院,2017. WANG Huawei. Research on big data platform for railway transportation equipment technical condition[D]. Beijing:China Academy of Railway Sciences,2017.
[7] MANYIKA J,CHUI M,BROWN B,et al. Big data:The next frontier for innovation,competition and productivity[EB/OL].[2012-10-02]. http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation.
[8] DREMEL C,WULF J,HERTERICH M M,et al. How AUDI AG established big data analytics in its digital transformation[J]. MIS Quarterly Executive,2017,16(2):81-100.
[9] KIM G H,TRIMI S,CHUNG J H. Big-data applications in the government sector[J]. Communications of the ACM,2014,57(3):78-85.
[10] FILIP F G,HERRERA-VIEDMA E. Big data in the European Union[J]. The Bridge,2014,44(4):33-37.
[11] 张月茹. 我国政府数据开放与安全政策协同研究[D]. 哈尔滨:黑龙江大学,2018. ZHANG Yueru. Research on the data opening and security policy synergy of government data in China[D]. Harbin:Heilongjiang University,2018.
[12] 杨小娟,阳冬波,贾红,等. 新能源汽车可靠性大数据分析技术研究[J]. 装备维修技术,2018,165(1):25-31. YANG Xiaojuan,YANG Dongbo,JIA Hong,et al. Research on reliability of new energy vehicle based on Big data analysis technology[J]. Equipment Technology,2018,165(1):25-31.
[13] TU W,LI Q,FANG Z,et al. Optimizing the locations of electric taxi charging stations:A spatial-temporal demand coverage approach[J]. Transportation Research Part C:Emerging Technologies,2016,65:172-189.
[14] ARIAS M B,BAE S. Electric vehicle charging demand forecasting model based on big data technologies[J]. Applied Energy,2016,183:327-339.
[15] 张文,王东,郑静楠,等. 电动汽车领域的大数据研究与应用[J]. 大众用电,2016(S2):64-68. ZHANG Wen,WANG Dong,ZHENG Jingnan,et al. Research and application of big data in electric vehicle field[J]. Popular Utilization of Electricity,2016(S2):64-68.
[16] 张兰廷. 大数据的社会价值与战略选择[D]. 北京:中共中央党校,2014. ZHANG Lanting. Research on social value and strategic choice of China based on big data[D]. Beijing:Central Party School of the Communist Party of China,2014.
[17] LEETARU K,WANG S,CAO G,et al. Mapping the global Twitter heartbeat:The geography of Twitter[J]. First Monday,2013,18(5):18-20.
[18] ZHANG Q,YANG L T,CHEN Z,et al. A survey on deep learning for big data[J]. Information Fusion,2018,42:146-157.
[19] MANOGARAN G,THOTA C,LOPEZ D. Human-computer interaction with big data analytics[M]//HCI challenges and privacy preservation in big data security. Hershey:IGI Global,2018:1-22.
[20] LANEY D. 3D data management:Controlling data volume,velocity and variety[J]. META Group Research Note,2001,6(70):1.
[21] 顾荣. 大数据处理技术与系统研究[D]. 南京:南京大学,2016. GU Rong. Research on techniques and systems for big data processing[D]. Nanjing:Nanjing University,2016.
[22] GROPP W D,GROPP W,LUSK E,et al. Using MPI:portable parallel programming with the message-passing interface[M]. Boston:MIT Press,1999.
[23] DEAN J,GHEMAWAT S. MapReduce:Simplified data processing on large clusters[J]. Communications of the ACM,2008,51(1):107-113.
[24] BHANDARKAR M. MapReduce programming with apache Hadoop[C]//2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS),April 19-23,2010,the Downtown Sheraton Atlanta,Atlanta,Georgia. New York:IEEE,2010:1-1.
[25] ZAHARIA M,XIN R S,WENDELL P,et al. Apache spark:A unified engine for big data processing[J]. Communications of the ACM,2016,59(11):56-65.
[26] MENG X,BRADLEY J,YAVUZ B,et al. Mllib:Machine learning in apache spark[J]. The Journal of Machine Learning Research,2016,17(1):1235-1241.
[27] CARBONE P,KATSIFODIMOS A,EWEN S,et al. Apache flink:Stream and batch processing in a single engine[J]. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering,2015,36(4):28-38.
[28] 徐华. 基于云的大数据处理系统性能优化问题研究[D].合肥:中国科学技术大学,2018. XU Hua. Performance optimization for big data progressing systems in the cloud[D]. Hefei:University of Science and Technology of China,2018.
[29] XU Y,SHAW S L,ZHAO Z,et al. Understanding aggregate human mobility patterns using passive mobile phone location data:A home-based approach[J]. Transportation,2015,42(4):625-646.
[30] CHU X,ILYAS I F,KRISHNAN S,et al. Data cleaning:Overview and emerging challenges[C]//Proceedings of the 2016 International Conference on Management of Data,June 26-July 01,2016,San Francisco,California. New York:ACM,2016:2201-2206.
[31] RONG H,ZHANG H,XIAO S,et al. Optimizing energy consumption for data centers[J]. Renewable and Sustainable Energy Reviews,2016,58:674-691.
[32] FAN Y,GUTHRIE A,LEVINSON D. Waiting time perceptions at transit stops and stations:Effects of basic amenities,gender,and security[J]. Transportation Research Part A:Policy and Practice,2016,88:251-264.
[33] AGRAWAL R,SRIKANT R. Privacy-preserving data mining[C]//ACM Sigmod Record,May 16-18,2000,Dallas,Texas. New York:ACM,2000,29(2):439-450.
[34] KHANDPUR R P,JI T,JAN S,et al. Crowdsourcing cybersecurity:Cyber attack detection using social media[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management,November 06-10,2017,Singapore. New York:ACM,2017:1049-1057.
[35] SMITH M,SZONGOTT C,HENNE B,et al. Big data privacy issues in public social media[C]//20126th IEEE International Conference on Digital Ecosystems and Technologies (DEST),June 18-20,2012,Campione d'Italia,Italy. Piscataway:IEEE,2012:1-6.
[36] XU L,JIANG C,WANG J,et al. Information security in big data:privacy and data mining[J]. Ieee Access,2014,2:1149-1176.
[37] LAM C. Hadoop in action[M]. Greenwich:Manning Publications,2011.
[38] MEHRAGHDAM S,KELLER M,KARL H. Specifying and placing chains of virtual network functions[C]//2014 IEEE 3rd International Conference on Cloud Networking (CloudNet),October 8-10,2014,Luxembourg. Piscataway:IEEE,2014:7-13.
[39] MURDOCH T B,DETSKY A S. The inevitable application of big data to health care[J]. JAMA,2013,309(13):1351.
[40] 王丽,王苹,沈俊辉. 基于Hadoop的中医药大数据平台基础架构的设计与研究[J]. 中国医药导报,2018,15(6):158-162. WANG Li,WANG Ping,SHEN Junhui. Design and research on the infrastructure of Chinese medicine big data platform based on Hadoop[J]. China Medical Herald,2018,15(6):158-162.
[41] 王磊,陈青,高洪雨,等. 基于大数据挖掘技术的智能变电站故障追踪架构[J]. 电力系统自动化,2018,42(3):84-91. WANG Lei,CHEN Qing,GAO Hongyu,et al. Intelligent substation fault tracking architecture based on big data mining technology[J]. Automation of Electric Power Systems,2018,42(3):84-91.
[42] ZHAO Y,LIU P,WANG Z,et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods[J]. Applied Energy,2017,207:354-362.
[43] 曹军威,袁仲达,明阳阳,等. 能源互联网大数据分析技术综述[J]. 南方电网技术,2015,9(11):1-12. CAO Junwei,YUAN Zhongda,MING Yangyang,et al. Review of energy net based on big data analysis[J]. Southern Power System Technology,2015,9(11):1-12.
[44] 李学龙,龚海刚. 大数据系统综述[J]. 中国科学:信息科学,2015,45(1):1-44. LI Xuelong,GONG Haigang. Review of big data systems[J]. Scientia Sinica Informationis,2015,45(1):1-44.
[45] 雷亚国,贾峰,周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报,2015,51(21):49-56. LEI Yaguo,JIA Feng,ZHOU Xin,et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015,51(21):49-56.
[46] GARCÍA S,LUENGO J,HERRERA F. Data preprocessing in data mining[M]. New York:Springer,2015.
[47] 张引,陈敏,廖小飞. 大数据应用的现状与展望[J]. 计算机研究与发展,2013,50(S2):216-233. ZHANG Yin,CHEN Min,LIAO Xiaofei. The status and prospects of big data application[J]. Journal of Computer Research and Development,2013,50(S2):216-233.
[48] AHLQVIST E,STORM P,KÄRÄJÄMÄKI A,et al. Novel subgroups of adult-onset diabetes and their association with outcomes:a data-driven cluster analysis of six variables[J]. The Lancet Diabetes & Endocrinology,2018,6(5):361-369.
[49] LEE D,QUADRIFOGLIO L,TEULADA E S D,et al. Discovering relationships between factors of round-trip car sharing by using association rules approach[J]. Procedia Engineering,2016,161:1282-1288.
[50] 朱雪初,乔非. 基于工业大数据的晶圆制造系统加工周期预测方法[J]. 计算机集成制造系统,2017(10):95-102. ZHU Xuechu,QIAO Fei. Machining cycle prediction method of wafer manufacturing system based on industrial big data[J]. Computer Integrated Manufacturing Systems,2017(10):95-102.
[51] WAMBA S F,GUNASEKARAN A,AKTER S,et al. Big data analytics and firm performance:Effects of dynamic capabilities[J]. Journal of Business Research,2017,70:356-365.
[52] HAFEZ M M,SHEHAB M E,EL FAKHARANY E,et al. Effective selection of machine learning algorithms for big data analytics using apache spark[C]//International Conference on Advanced Intelligent Systems and Informatics,October 24-26,2016,Cairo,Egypt. Cham,Switzerland:Springer,2016:692-704.
[53] 任磊,杜一,马帅,等. 大数据可视分析综述[J]. 软件学报,2014(9):1909-1936. REN Lei,DU Yi,MA Shuai,et al. Review of visual analysis of big data[J]. Journal of Software,2014(9):1909-1936.
[54] 邵景峰,贺兴时,王进富,等. 大数据环境下的纺织制造执行系统设计[J]. 机械工程学报,2015,51(5):160-170. SHAO Jingfeng,HE Xingshi,WANG Jinfu,et al. Design of textile manufacturing execution system based on big data[J]. Journal of Mechanical Engineering,2015,51(5):160-170.
[55] 王震坡,孙逢春,张承宁. 电动汽车动力蓄电池组不一致性统计分析[J]. 电源技术,2003,27(5):438-441. WANG Zhenpo,SUN Fengchun,ZHANG Chengning. Study on inconsistency of electric vehicle battery pack[J]. Chinese Journal of Power Sources,2003,27(5):438-441.
[56] 王震坡,孙逢春. 电动汽车电池组连接可靠性及不一致性研究[J]. 车辆与动力技术,2002(4):11-15. WANG Zhenpo,SUN Fengchun. Research on connection reliability and inconsistency of battery pack on electric vehicle[J]. Vehicle & Power Technology,2002(4):11-15.
[57] 王震坡,孙逢春,林程. 不一致性对动力电池组使用寿命影响的分析[J]. 北京理工大学学报,2006,26(7):577-580. WANG Zhenpo,SUN Fengchun,LIN Cheng. An analysis on the influence of inconsistencies upon the service life of power battery packs[J]. Transactions of Beijing Institute of Technology,2006,26(7):577-580.
[58] LIU Z,HE H. Model-based sensor fault diagnosis of a lithium-ion battery in electric vehicles[J]. Energies,2015,8(7):6509-6527.
[59] SIDHU A,IZADIAN A,ANWAR S. Adaptive nonlinear model-based fault diagnosis of li-ion batteries[J]. Industrial Electronics IEEE Transactions on,2015,62(2):1002-1011.
[60] YAO L,WANG Z,MA J. Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles[J]. Journal of Power Sources,2015,293:548-561.
[61] YAN W,ZHANG B,WANG X,et al. Lebesgue sampling-based diagnosis and prognosis for lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics,2016,63(3):1804-1812.
[62] ZHU X,WANG Z,WANG C,et al. Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O2 cathode for electric vehicles:degradation and failure mechanisms[J]. Journal of The Electrochemical Society,2018,165(16):A3613-A3629.
[63] PANCHAL S,DINCER I,AGELIN-CHAAB M,et al. Experimental and theoretical investigations of heat generation rates for a water cooled LiFePO₄ battery[J]. International Journal of Heat and Mass Transfer,2016,101:1093-1102.
[64] LIU K,LI K,DENG J. A novel hybrid data-driven method for Li-ion battery internal temperature estimation[C]//2016 UKACC 11th International Conference on Control (CONTROL),August 31-September 2,Belfast,Northern Ireland,UK. Piscataway:IEEE,2016:1-6.
[65] HONG J,WANG Z,LIU P. Big-data-based thermal runaway prognosis of battery systems for electric vehicles[J]. Energies,2017,10(7):919.
[66] 袁博. 新能源汽车技术发展与趋势综述[J]. 现代商贸工业,2018,39(35):16-20. YUAN Bo. Review of development and trend of new energy vehicle technology[J]. Modern Business Trade Industry,2018,39(35):16-20.
[67] FRISK E,KRYSANDER M,LARSSON E. Data-driven lead-acid battery prognostics using random survival forests[C]//Proceedings of the Annual Conference of The Prognostics and Health Management Society,September 29-October 2,2014,Fort Worth,Texas,USA. Scottsdale,Arizona:PMH Society,2014:92-101.
[68] RICHARDSON R R,OSBORNE M A,HOWEY D A. Battery health prediction under generalized conditions using a Gaussian process transition model[J]. Journal of Energy Storage,2019,23:320-328.
[69] WENG C,FENG X,SUN J,et al. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking[J]. Applied Energy,2016,180:360-368.
[70] LI Y,ABDEL-MONEM M,GOPALAKRISHNAN R,et al. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter[J]. Journal of Power Sources,2018,373:40-53.
[71] WANG Z,MA J,ZHANG L. State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the gaussian process regression[J]. IEEE Access,2017,5:21286-21295.
[72] MCGORDON A,POXON J E W,CHENG C,et al. Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2011,225(11):1518-1530.
[73] PIAO C H,DUAN C X,LI Y S,et al. Research on the driver's following behavior based on hybrid electric vehicle model[J]. Applied Mechanics and Materials,2013,281:159-162.
[74] VATANPARVAR K,FAEZI S,BURAGO I,et al. Extended range electric vehicle with driving behavior estimation in energy management[J]. IEEE Transactions on Smart Grid,2018,10(3):2959-2968.
[75] 毛正涛,张金喜. 基于新能源汽车远程监控数据的驾驶行为识别建模与应用[J]. 汽车与配件,2017(17):80-83. MAO Zhengtao,ZHANG Jinxi. Driving behavior recognition modeling and application based on new energy vehicle remote monitoring data[J]. Automobile & Parts,2017(17):80-83.
[76] LEE C H,WU C H. A novel big data modeling method for improving driving range estimation of EVs[J]. IEEE Access,2015,3:1980-1993.
[77] FIORI C,AHN K,RAKHA H A. Power-based electric vehicle energy consumption model:Model development and validation[J]. Applied Energy,2016,168:257-268.
[78] 陈燎,杨帆,盘朝奉. 基于电池能量状态和车辆能耗的电动汽车续驶里程估算[J]. 汽车工程学报,2017(2):113-122. CHEN Liao,YANG Fan,PAN Chaofeng. A driving range estimation model for electric vehicles based on battery energy state and vehicle energy consumption[J]. Chinese Journal of Automotive Engineering,2017(2):113-122.
[79] ELKAHKY A M,SONG Y,HE X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web,May 18-22,2015,Florence,Italy. New York:ACM,2015:278-288.
[80] MAPLE C. Security and privacy in the internet of things[J]. Journal of Cyber Policy,2017,2(2):155-184.
[81] HIGGINS A,PAEVERE P,GARDNER J,et al. Combining choice modelling and multi-criteria analysis for technology diffusion:An application to the uptake of electric vehicles[J]. Technological Forecasting and Social Change,2012,79(8):1399-1412.
[82] KIECKHÄFER K,VOLLING T,SPENGLER T S. A hybrid simulation approach for estimating the market share evolution of electric vehicles[J]. Transportation Science,2014,48(4):651-670.
[83] CUI J,LIU F,HU J,et al. Identifying mismatch between urban travel demand and transport network services using GPS data:A case study in the fast growing Chinese city of Harbin[J]. Neurocomputing,2016,181:4-18.
[84] LI W,LONG R,CHEN H,et al. Household factors and adopting intention of battery electric vehicles:a multi-group structural equation model analysis among consumers in Jiangsu Province,China[J]. Natural Hazards,2017,87(2):945-960.
[85] LI W,LONG R,CHEN H,et al. A review of factors influencing consumer intentions to adopt battery electric vehicles[J]. Renewable and Sustainable Energy Reviews,2017,78:318-328.
[86] DARABI Z,FERDOWSI M. Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile[J]. IEEE Transactions on Sustainable Energy,2011,2(4):501-508.
[87] SOARES J,BORGES N,GHAZVINI M A F,et al. Scenario generation for electric vehicles' uncertain behavior in a smart city environment[J]. Energy,2016,111:664-675.
[88] CAI H,JIA X,CHIU A S F,et al. Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet[J]. Transportation Research Part D:Transport and Environment,2014,33:39-46.
[89] GUO S,ZHAO H. Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective[J]. Applied Energy,2015,158:390-402.
[90] ZHANG A,KANG J E,KWON C. Incorporating demand dynamics in multi-period capacitated fast-charging location planning for electric vehicles[J]. Transportation Research Part B:Methodological,2017,103:5-29.
[91] CLEMENT-NYNS K,HAESEN E,DRIESEN J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems,2010,25(1):371-380.
[92] HAIDAR A M A,MUTTAQI K M. Behavioral characterization of electric vehicle charging loads in a distribution power grid through modeling of battery chargers[J]. IEEE Transactions on Industry Applications,2016,52(1):483-492.
[93] ALHAZMI Y A,SALAMA M M A. Economical staging plan for implementing electric vehicle charging stations[J]. Sustainable Energy,Grids and Networks,2017,10:12-25.
[94] PLÖTZ P,GNANN T,WIETSCHEL M. Modelling market diffusion of electric vehicles with real world driving data-Part I:Model structure and validation[J]. Ecological Economics,2014,107:411-421.
[95] GANDOMI A,HAIDER M. Beyond the hype:Big data concepts,methods,and analytics[J]. International Journal of Information Management,2015,35(2):137-144.
[96] WANG W C,CHAU K W,CHENG C T,et al. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series[J]. Journal of Hydrology,2009,374(3-4):294-306.
[97] DAWKINS S,TIAN A W,NEWMAN A,et al. Psychological ownership:A review and research agenda[J]. Journal of Organizational Behavior,2017,38(2):163-183.