Home Green Energy and Intelligent Transportation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
HIGHLIGHTS
Graphical Abstract
Abstract
Keywords
References
Show full outline
Hide outline
Review Article | Open Access

China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs

Hongwen He, Fengchun Sun(), Zhenpo Wang(), Cheng Lin, Chengning Zhang, Rui Xiong, Junjun Deng, Xiaoqing Zhu, Peng Xie, Shuo Zhang, Zhongbao Wei, Wanke Cao, Li Zhai
National Engineering Research Centre of Electric Vehicles, Beijing Institute of Technology, Beijing, 100081, PR China

Handling editor: Hailong Li

Show Author Information

HIGHLIGHTS

· China puts forward a system engineering-based technology system architecture consisting of three key components for BEVs.

· “BEV platform” improves the vehiclelevel all-climate environmental adaptability and all-day safety of BEVs.

· “Charging/swapping stations” reduce the charging time and improves BEVs' application convenience.

· “Real-time operation monitoring platform” guarantees BEVs' safety through active/passive safety protection of batteries.

Graphical Abstract

View original image Download original image

Abstract

Developing new energy vehicles has been a worldwide consensus, and developing new energy vehicles characterized by pure electric drive has been China's national strategy. After more than 20 years of high-quality development of China's electric vehicles (EVs), a technological R & D layout of “Three Verticals and Three Horizontals” has been created, and technological advantages have been accumulated. As a result, China's new energy vehicle market has ranked first in the world since 2015. To systematically solve the key problems of battery electric vehicles (BEVs) such as “driving range anxiety, long battery charging time, and driving safety hazards”, China took the lead in putting forward a “system engineering-based technology system architecture for BEVs” and clarifying its connotation. This paper analyzes the research status and progress of the three core components of this architecture, namely, “BEV platform, charging/swapping station, and real-time operation monitoring platform”, and their key technological points. The three major demonstration projects of the 2008 Beijing Olympic Games, the 2022 Beijing Winter Olympics, and the intelligent and connected autonomous battery electric bus project are discussed to specify the applications of BEVs in China. The key research directions for upgrading BEV technologies remain to be further improving the vehicle-level all-climate environmental adaptability and all-day safety of BEVs, systematically solving the charging problem of BEVs and improving their application convenience, and safeguarding safety with early warning and implementing active/passive safety protection for the whole life cycle of power batteries on the basis of BEVs' operation big data. BEVs have acquired new technological features such as intelligent and networked technology empowerment, extensive integration of control-by-wire systems, a platform of chassis hardware, and modularization of functional software.

Keywords

Battery electric vehicle (BEV)Charging/swapping stationOperation monitoring platformTechnology systemMotor drive systemAll-climateBatteryBattery safetyChina

References

[1]

Ehsani M, Singh KV, Bansal HO, Mehrjardi RT. State of the art and trends in electric and hybrid electric vehicles. Proc IEEE 2021;109:967–984.

Crossref Google Scholar
[2]

Feng S, Magee CL. Technological development of key domains in electric vehicles: improvement rates, technology trajectories and key assignees. Appl Energy 2020;260:114264.

Crossref Google Scholar
[3]
Pontes José. Top 20 electric cars in the world. 2022; Available from: https://cleantechnica.com/2022/04/01/the-top-20-electric-cars-in-the-world-february-2022/; February 2022.
[4]

Ouyang M. New energy vehicle research and development in China (In Chinese). Sci Technol Rev 2016;34(6):13–20.

Crossref Google Scholar
[5]
The prospect of industrialized operation of my country's own brand pure electric buses is becoming clearer. Available from: https://www.chinabuses.com/2008/06/13003.htm; 2008.
[6]
Electric vehicles, China leads the way. Available from: https://news.sciencenet.cn/sbhtmlnews/2017/10/328152.shtm?from=singlemessage&isappinstalled=0; 2017.
[7]
The National Electric Vehicle Test Demonstration Zone is expected to become a national testing and testing center. 2010. Available from: http://www.chinadaily.com.cn/dfpd/2010-07/02/content_10053040.htm.
[8]
Beijing Olympics strives to achieve seven goals including "zero emission" of transportation in the Olympic venues. 2007. Available from: http://www.gov.cn/govweb/ztzl/beijing2008/content_709001.htm.
[9]
Xinhua News Agency. The sales of new energy vehicles in China accounts for about half of the global total. 2021. 2021-06-19]; Available from: http://www.gov.cn/xinwen/2021-06/19/content_5619459.htm.
[10]
China automotive industry development annual report (2021) (In Chinese); Available from: http://www.miit-eidc.org.cn/module/download/downfile.jsp?classid=0&filename=ab5936358eb244bf96b63fe818f1680e.pdf; 2021..
[11]
China Association of Automobile Manufacturers. CAAM: new energy vehicle market penetration rate reaches 19.1% in December 2021. 2021. Available from: https://www.chinanews.com.cn/auto/2022/01-19/9655947.html.
[12]
China Association of Automobile Manufacturers. The national ownership of new energy vehicles reached 7.84 million, including 6.4 million pure electric vehicles (In Chinese). 2022. 2022-01-13]; Available from: http://www.caam.org.cn/chn/11/cate_120/con_5235354.html.
[13]
Mengyuan Ge China's new energy vehicles exports tripled in 2021, with half made by Tesla. 2022 [cited 2022 13 January ]; Available from: https://kr-asia.com/chinas-new-energy-vehicles-exports-tripled-in-2021-with-half-made-by-tesla.
[14]
Global electric vehicle sales up 109% in 2021, with half in Mainland China. 2022. Available from: https://www.canalys.com/newsroom/global-electric-vehicle-market-2021.
[15]

Husain I, Ozpineci B, Islam MS, Gurpinar E, Su G, Yu W, et al. Electric drive technology trends, challenges, and opportunities for future electric vehicles. Proc IEEE 2021;109(6):1039–59.

Crossref Google Scholar
[16]
Ministry of Industry and Information Technology of the People's Republic of China. Recommended models for the promotion and application of new energy vehicles catalog (In Chinese). 2022 [cited 2020 August 21]; Available from: https://www.miit.gov.cn/jgsj/zbys/wjfb/art/2020/art_c0045d5656564c3492ca46111ec4d256.html.
[17]
Ministry of Industry and Information Technology of the People's Republic of China. Recommended models for the promotion and application of new energy vehicles catalog (In Chinese). 2022 [cited 2022 April 7]; Available from: https://www.miit.gov.cn/jgsj/zbys/wjfb/art/2022/art_96ae9db9f80e4260bf602a78b3d16cba.html.
[18]

Hossain LM, Hannan MA, Karim TF, Hussain A, Saad MH, Ayob A, et al. Intelligent algorithms and control strategies for battery management system in electric vehicles: progress, challenges and future outlook. J Clean Prod 2021;292:126044.

Crossref Google Scholar
[19]
Afshar S, MacEdo P, Mohamed F, Disfani V. A literature review on mobile charging station technology for electric vehicles. In: 2020 IEEE transportation electrification conference & expo (ITEC). IEEE; 2020.
Crossref
[20]

Wu W, Lin B. Benefits of electric vehicles integrating into power grid. Energy 2021;224:120108.

Crossref Google Scholar
[21]
Pontes ByJosé. Inventory of domestic electric vehicle fire accidents in 2020. 2020 [cited 2022 April 1]; Available from: https://www.china5e.com/news/news-1093720-1.html.
[22]
2021 electric vehicle safety Annual Report. 2021. Available from: https://baijiahao.baidu.com/s?id=1721164341983283279&wfr=spider&for=pc.
[23]

Gandoman FH, Jaguemont J, Goutam S, Gopalakrishnan R, Firouz Y, Kalogiannis T, et al. Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: basics, progress, and challenges. Appl Energy 2019;251:113343.

Crossref Google Scholar
[24]

Li W, Yang M, Sandu S. Electric vehicles in China: a review of current policies. Energy Environ 2018;29(8):1512–24.

Crossref Google Scholar
[25]

Zhao M, Shi J, Lin C. Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus. Appl Energy 2019;243:21–34.

Crossref Google Scholar
[26]

Lin C, Zhao M, Pan H, Yi J. Blending gear shift strategy design and comparison study for a battery electric city bus with AMT. Energy 2019;185:1–14.

Crossref Google Scholar
[27]

Lin C, Yu X, Zhao M, Yi J, Zhang R. Collaborative control of novel uninterrupted propulsion system for all-climate electric vehicles. Automot Innovat 2022:1–11.

Crossref Google Scholar
[28]

Zhang G, Ge S, Xu T, Yang X, Tian H, Wang C. Rapid self-heating and internal temperature sensing of lithium-ion batteries at low temperatures. Electrochim Acta 2016;218:149–55.

Crossref Google Scholar
[29]

Wang Z, Sun F. Study of the EV battery pack attended mode. J Asian Electr Veh 2004;2(1):517–20.

Crossref Google Scholar
[30]
Li J, Tian H, Wu P. Analysis of random vibration of power battery box in electric vehicles. In: 2014 IEEE conference and expo transportation electrification AsiaPacific (ITEC Asia-Pacific). IEEE; 2014.
[31]

Zhu X, Wang Z, Wang H, Wang C. Review of thermal runaway and safety management for lithium-ion traction batteries in electric vehicles [J] J Mech Eng 2020;56(14):91–118.

Crossref Google Scholar
[32]

Khateeb SA, Amiruddin S, Farid M, Selman JR, Al-Hallaj S. Thermal management of Li-ion battery with phase change material for electric scooters: experimental validation. J Power Sources 2005;142(1–2):345–53.

Crossref Google Scholar
[33]

Zhao R, Zhang S, Gu J, Liu J, Carkner S, Lanoue E. An experimental study of lithium ion battery thermal management using flexible hydrogel films. J Power Sources 2014;255:29–36.

Crossref Google Scholar
[34]
Aoki Kenji. The Great Wall Motor of China announced success of development of highly safety “Dayu Battery”. 2021 [cited 2021 August 12]; Available from: https://enviliance.com/regions/east-asia/cn/report_3793.
[35]

Xue Q, Li G, Zhang Y, Shen S, Chen Z, Liu Y. Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution. J Power Sources 2021;482:228964.

Crossref Google Scholar
[36]

Li D, Zhang Z, Liu P, Wang Z, Zhang L. Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model. IEEE Trans Power Electron 2020;36(2):1303–15.

Crossref Google Scholar
[37]

Wang Z, Hong J, Liu P, Zhang L. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles. Appl Energy 2017;196:289–302.

Crossref Google Scholar
[38]

Li X, Dai K, Wang Z, Han W. Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method. J Energy Storage 2020;27:101121.

Crossref Google Scholar
[39]

Hong J, Wang Z, Chen W, Wang L. Multi-fault synergistic diagnosis of battery systems based on the modified multi-scale entropy. Int J Energy Res 2019;43(14):8350–69.

Crossref Google Scholar
[40]

He H, Xiong R, Guo H, Li S. Comparison study on the battery models used for the energy management of batteries in electric vehicles. Energy Convers Manag 2012;64:113–21.

Crossref Google Scholar
[41]

Xiong R, Li L, Li Z, Yu Q, Mu H. An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application. Appl Energy 2018;219:264–75.

Crossref Google Scholar
[42]

Xiong R, Huang J, Duan Y, Shen W. Enhanced Lithium-ion battery model considering critical surface charge behavior. Appl Energy 2022;314:118915.

Crossref Google Scholar
[43]

Tian J, Xiong R, Shen W, Lu J, Yang X. Deep neural network battery charging curve prediction using 30 points collected in 10 min. Joule 2021;5(6):1521–34.

Crossref Google Scholar
[44]

Chen C, Xiong R, Shen W. A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation. IEEE Trans Power Electron 2017;33(1):332–42.

Crossref Google Scholar
[45]

Xiong R, He H, Sun F, Zhao K. Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach. IEEE Trans Veh Technol 2012;62(1):108–17.

Crossref Google Scholar
[46]

Sun F, Xiong R, He H. A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 2016;162:1399–409.

Crossref Google Scholar
[47]

Xiong R, Li L, Tian J. Towards a smarter battery management system: a critical review on battery state of health monitoring methods. J Power Sources 2018;405:18–29.

Crossref Google Scholar
[48]

Xiong R, Sun F, Chen Z, He H. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles. Appl Energy 2014;113:463–76.

Crossref Google Scholar
[49]

Xiong R, Wang J, Shen W, Tian J, Mu H. Co-estimation of State of charge and capacity for Lithium-ion batteries with multi-stage model fusion method. Engineering 2021;7(10):1469–82.

Crossref Google Scholar
[50]

Tian J, Xiong R, Shen W, Sun F. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries. Energy Storage Mater 2021;37:283–95.

Crossref Google Scholar
[51]

Xiong R, Zhang Y, Wang J, He H, Peng S, Pecht M. Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles. IEEE Trans Veh Technol 2018;68(5):4110–21.

Crossref Google Scholar
[52]

Zhang Y, Xiong R, He H, Pecht M. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans Veh Technol 2018;67(7):5695–705.

Crossref Google Scholar
[53]

Zhang Y, Xiong R, He H, Pecht M. Lithium-ion battery remaining useful life prediction with Box–Cox transformation and Monte Carlo simulation. IEEE Trans Ind Electron 2018;66(2):1585–97.

Crossref Google Scholar
[54]

Yang R, Xiong R, He H, Chen Z. A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. J Clean Prod 2018;187:950–9.

Crossref Google Scholar
[55]

Xiong R, Yang R, Chen Z, Shen W, Sun F. Online fault diagnosis of external short circuit for lithium-ion battery pack. IEEE Trans Ind Electron 2019;67(2):1081–91.

Crossref Google Scholar
[56]

Yang R, Xiong R, Shen W. On-board soft short circuit fault diagnosis of lithium-ion battery packs for electric vehicles using extended Kalman filter. CSEE J Power Energy Syst 2022;8(1):258–70.

Google Scholar
[57]

Yang R, Xiong R, Shen W, Lin X. Extreme learning machine-based thermal model for lithium-ion batteries of electric vehicles under external short circuit. Engineering 2021;7(3):395–405.

Crossref Google Scholar
[58]
Zhu Z, Chan C. Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles. In: 2008 IEEE Vehicle Power and Propulsion Conference. IEEE; 2008.
Crossref
[59]

Alberti L, Bianchi N. A coupled thermal–electromagnetic analysis for a rapid and accurate prediction of IM performance. IEEE Trans Ind Electron 2008;55(10):3575–82.

Crossref Google Scholar
[60]

Bramerdorfer G, Tapia JA, Pyrhönen JJ, Cavagnino A. Modern electrical machine design optimization: techniques, trends, and best practices. IEEE Trans Ind Electron 2018;65(10):7672–84.

Crossref Google Scholar
[61]

Li J, Zhang C, Shao G. New enhanced magnetism motor drive control system. Chin J Mech Eng 2008;44(11):197–202+208.

Crossref Google Scholar
[62]

Feng Y, Zhang C. Core loss analysis of interior permanent magnet synchronous machines under SVPWM excitation with considering saturation. Energies 2017;10(11):1716.

Crossref Google Scholar
[63]

Wu X, Wrobel R, Mellor PH, Zhang C. A computationally efficient PM power loss mapping for brushless AC PM machines with surface-mounted PM rotor construction. IEEE Trans Ind Electron 2015;62(12):7391–401.

Crossref Google Scholar
[64]

Ding S, Guo B, Feng H. Temperature field investigation of permanent magnet synchronous motors controlled by the frequency conversion control system. Proc CSEE 2014;34(9):1368–75.

Google Scholar
[65]

Wang X, Gao P. Analysis of 3-D temperature field of in-wheel motor with inner-oil cooling for electric vehicle. Electr Mach Control 2016;20(3):37–42.

Google Scholar
[66]

Guo F, Zhang C. Oil-cooling method of the permanent magnet synchronous motor for electric vehicle. Energies 2019;12(15):2984.

Crossref Google Scholar
[67]

Yang Y, Bianchi N, Zhang C, Zhu X, Liu H, Zhang S. A method for evaluating the worst-case cogging torque under manufacturing uncertainties. IEEE Trans Energy Convers 2020;35(4):1837–48.

Crossref Google Scholar
[68]

Yang Y, Zhang C, Bramerdorfer G, Bianchi N, Qu J, Zhao J, et al. A computationally efficient surrogate model based robust optimization for permanent magnet synchronous machines. IEEE Trans Energy Convers 2022;37(3):1520–32.

Crossref Google Scholar
[69]

Yang J, Chen WH, Li S, Guo L, Yan Y. Disturbance/uncertainty estimation and attenuation techniques in PMSM drives—a survey. IEEE Trans Ind Electron 2016;64(4):3273–85.

Crossref Google Scholar
[70]

Zheng Z, Sun D. Model predictive flux control with cost function-based field weakening strategy for permanent magnet synchronous motor. IEEE Trans Power Electron 2019;35(2):2151–9.

Crossref Google Scholar
[71]

Wu L, Guo Y, Huang X, Fang Y, Liu J. Harmonic torque suppression methods for single-phase open-circuit fault-tolerant operation of PMSM considering third harmonic BEMF. IEEE Trans Power Electron 2020;36(1):1116–29.

Crossref Google Scholar
[72]

Huang Q, Huang Y, Zhang F, Gu Z, Zhang C. Engine-generator set and its control strategy research based on tracked vehicle electric drive system [J]. Veh Power Technol 2006;2:29–33.

Google Scholar
[73]

Li X, Tian W, Gao X, Yang Q, Kennel R. A generalized observer-based robust predictive current control strategy for PMSM drive system. IEEE Trans Ind Electron 2021;69(2):1322–32.

Crossref Google Scholar
[74]

Yuan X, Zhang S, Zhang C. Improved model predictive current control for SPMSM drives with parameter mismatch. IEEE Trans Ind Electron 2019;67(2):852–62.

Crossref Google Scholar
[75]

Qu J, Zhang C, Jatskevich J, Zhang S. Deadbeat harmonic current control of permanent magnet synchronous machine drives for torque ripple reduction. IEEE J Emerg Sel Top Power Electron 2021;10:3357–70.

Crossref Google Scholar
[76]

Qu J, Jatskevich J, Zhang C, Zhang S. Torque ripple reduction method for permanent magnet synchronous machine drives with novel harmonic current control. IEEE Trans Energy Convers 2021;36(3):2502–13.

Crossref Google Scholar
[77]

Shen ZJ, Feng B, Mao C, Ran L. Optimization models for electric vehicle service operations: a literature review. Transp Res Part B Methodol 2019;128:462–77.

Crossref Google Scholar
[78]

Xie R, Wei W, Khodayar ME, Wang J, Mei S. Planning fully renewable powered charging stations on highways: a data-driven robust optimization approach. IEEE Transp Electrification 2018;4(3):817–30.

Crossref Google Scholar
[79]

Lin Y, Zhang K, Shen ZJ, Ye B, Miao L. Multistage large-scale charging station planning for electric buses considering transportation network and power grid. Transport Res C Emerg Technol 2019;107:423–43.

Crossref Google Scholar
[80]

Hu D, Zhang J, Liu Z-W. Charging stations expansion planning under government policy driven based on Bayesian regularization backpropagation learning. Neurocomputing 2020;416:47–58.

Crossref Google Scholar
[81]

Shin J, Shin S, Kim Y, Ahn S, Lee S, Jung G, et al. Design and implementation of shaped magnetic-resonance-based wireless power transfer system for roadway-powered moving electric vehicles. IEEE Trans Ind Electron 2013;61(3):1179–92.

Crossref Google Scholar
[82]

Wang W, Deng J, Chen D, Wang Z, Wang S. A novel design method of LCC-S compensated inductive power transfer system combining constant current and constant voltage mode via frequency switching. IEEE Access 2021;9:117244–56.

Crossref Google Scholar
[83]

Wu HH, Gilchrist A, Sealy KD, Bronson D. A high efficiency 5 kW inductive charger for EVs using dual side control. IEEE Trans Ind Inf 2012;8(3):585–95.

Crossref Google Scholar
[84]
Onar OC, Miller JM, Campbell SL, Coomer C, White CP, Seiber LE. Oak ridge national laboratory wireless power transfer development for sustainable campus initiative. In: 2013 IEEE Transportation Electrification Conference and Expo (ITEC). IEEE; 2013.
Crossref
[85]
Budhia M, Covic GA, Boys JT, Huang CY. Development and evaluation of single sided flux couplers for contactless electric vehicle charging. In: 2011 IEEE Energy Conversion Congress and Exposition. IEEE; 2011.
Crossref
[86]

Budhia M, Boys JT, Covic GA, Huang CY. Development of a single-sided flux magnetic coupler for electric vehicle IPT charging systems. IEEE Trans Ind Electron 2011;60(1):318–28.

Crossref Google Scholar
[87]

Ahmad A, Alam MS, Mohamed AA. Design and interoperability analysis of quadruple pad structure for electric vehicle wireless charging application. IEEE Transp Electrification 2019;5(4):934–45.

Crossref Google Scholar
[88]

Li S, Lu S, Mi CC. Revolution of electric vehicle charging technologies accelerated by wide bandgap devices. Proc IEEE 2021;109(6):985–1003.

Crossref Google Scholar
[89]

Bosshard R, Iruretagoyena U, Kolar JW. Comprehensive evaluation of rectangular and double-D coil geometry for 50 kW/85 kHz IPT system. IEEE J Emerg Sel Top Power Electron 2016;4(4):1406–15.

Crossref Google Scholar
[90]

Wang Z, Li L, Deng J, Zhang B, Wang S. Magnetic coupler robust optimization design for electric vehicle wireless charger based on improved simulated Annealing algorithm. Automot Innovat 2022:1–14.

Crossref Google Scholar
[91]

Feng H, Tavakoli R, Onar OC, Pantic Z. Advances in high-power wireless charging systems: overview and design considerations. IEEE Transp Electrification 2020;6(3):886–919.

Crossref Google Scholar
[92]

Deng J, Pang B, Shi W, Wang Z. A new integration method with minimized extra coupling effects using inductor and capacitor series-parallel compensation for wireless EV charger. Appl Energy 2017;207:405–16.

Crossref Google Scholar
[93]

Deng J, Mao Q, Wang W, Li L, Wang Z, Wang S, et al. Frequency and parameter combined tuning method of LCC-LCC compensated resonant converter with wide coupling variation for EV wireless charger. IEEE J Emerg Sel Top Power Electron 2021;10:956–68.

Crossref Google Scholar
[94]

Sohn YH, Choi BH, Lee ES, Lim GC, Cho GH, Rim CT. General unified analyses of two-capacitor inductive power transfer systems: equivalence of current-source SS and SP compensations. IEEE Trans Power Electron 2015;30(11):6030–45.

Crossref Google Scholar
[95]

Zhang X, Cai T, Duan S, Feng H, Hu H, Niu J, et al. A control strategy for efficiency optimization and wide ZVS operation range in bidirectional inductive power transfer system. IEEE Trans Ind Electron 2018;66(8):5958–69.

Crossref Google Scholar
[96]

Wu M, Yang X, Chen W, Wang L, Jiang Y, Zhao C, et al. A dual-sided control strategy based on mode switching for efficiency optimization in wireless power transfer system. IEEE Trans Power Electron 2021;36(8):8835–48.

Crossref Google Scholar
[97]

Fu N, Deng J, Wang Z, Wang W, Wang S. A hybrid mode control strategy for LCC–LCC-compensated WPT system with wide ZVS operation. IEEE Trans Power Electron 2021;37(2):2449–60.

Crossref Google Scholar
[98]
Li M, Deng J, Chen D, Wang W, Wang Z, Li Y. A control strategy for ZVS realization in LCC-S compensated WPT system with semi bridgeless active rectifier for wireless EV charging. In: 2021 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE; 2021.
Crossref
[99]

Zhao D, Lam H, Peng H, Bao S, LeBlanc DJ, Nobukawa K, et al. Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques. IEEE Trans Intell Transport Syst 2016;18(3):595–607.

Crossref Google Scholar
[100]

Kim T, Makwana D, Adhikaree A, Vagdoda JS, Lee Y. Cloud-based battery condition monitoring and fault diagnosis platform for large-scale lithium-ion battery energy storage systems. Energies 2018;11(1):125.

Crossref Google Scholar
[101]

She C, Li Y, Zou C, Wik T, Wang Z, Sun F. Offline and online blended machine learning for lithium-ion battery health state estimation. IEEE Trans Transp Electrification 2021;8(2):1604–18.

Crossref Google Scholar
[102]

She C, Wang Z, Sun F, Liu P, Zhang L. Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network. IEEE Trans Ind Inf 2019;16(5):3345–54.

Crossref Google Scholar
[103]

Li X, Wang Z, Yan J. Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression. J Power Sources 2019;421:56–67.

Crossref Google Scholar
[104]

Liang K, Zhang Z, Liu P, Wang Z, Jiang S. Data-driven ohmic resistance estimation of battery packs for electric vehicles. Energies 2019;12(24):4772.

Crossref Google Scholar
[105]

Xiong R, Sun W, Yu Q, Sun F. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles. Appl Energy 2020;279:115855.

Crossref Google Scholar
[106]

Yao L, Xiao Y, Gong X, Hou J, Chen X. A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network. J Power Sources 2020;453:227870.

Crossref Google Scholar
[107]

Sun Z, Han Y, Wang Z, Chen Y, Liu P, Qin Z, et al. Detection of voltage fault in the battery system of electric vehicles using statistical analysis. Appl Energy 2022;307:118172.

Crossref Google Scholar
[108]

Sun Z, Wang Z, Chen Y, Liu P, Wang S, Zhang Z, et al. Modified relative entropy based lithium-ion battery pack online short circuit detection for electric vehicle. IEEE Trans Transp Electrification 2021;8:1710–23.

Crossref Google Scholar
[109]

Zhao Y, Liu P, Wang Z, Zhang L, Hong J. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Appl Energy 2017;207:354–62.

Crossref Google Scholar
[110]

Hong J, Wang Z, Ma F, Yang J, Xu X, Qu C, et al. Thermal runaway prognosis of battery systems using the modified multiscale entropy in real-world electric vehicles. IEEE Transp Electrification 2021;7(4):2269–78.

Crossref Google Scholar
[111]

Cui D, Wang Z, Liu P, Wang S, Zhang Z, Dorrell DG, et al. Battery electric vehicle usage pattern analysis driven by massive real-world data. Energy 2022:123837.

Crossref Google Scholar
[112]

Ding Y, Ji Z, Liu P, Wu Z, Li G, Cui D, et al. Gas station recognition method based on monitoring data of heavy-duty vehicles. Energies 2021;14(23):8011.

Crossref Google Scholar
[113]

Cui D, Wang Z, Zhang Z, Liu P, Wang S, Dorrell DG. Driving event recognition of battery electric taxi based on big data analysis. IEEE Trans Intell Transport Syst 2021;23(7):9200–9.

Crossref Google Scholar
[114]

Zhang J, Wang Z, Liu P, Zhang Z, Li X, Qu C. Driving cycles construction for electric vehicles considering road environment: a case study in Beijing. Appl Energy 2019;253:113514.

Crossref Google Scholar
[115]

Zhao Y, Wang Z, Shen ZJ, Sun F. Assessment of battery utilization and energy consumption in the large-scale development of urban electric vehicles. Proc Natl Acad Sci USA 2021;118(17).

Crossref Google Scholar
[116]

Zhang J, Wang Z, Liu P, Zhang Z. Energy consumption analysis and prediction of electric vehicles based on real-world driving data. Appl Energy 2020;275:115408.

Crossref Google Scholar
[117]

Lǎzǎroiu G, Machová V, Kucera J. Connected and autonomous vehicle mobility: socially disruptive technologies, networked transport systems, and big data algorithmic analytics. Contemp Read Law Soc Justice 2020;12(2):61–9.

Crossref Google Scholar
Green Energy and Intelligent Transportation
Volume 1 Issue 1,
June 2022
DOI: 10.1016/j.geits.2022.100020
Cite this article:
He H, Sun F, Wang Z, et al. China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation, 2022, 1(1). https://doi.org/10.1016/j.geits.2022.100020
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return