Science China Technological Sciences

, Volume 61, Issue 10, pp 1431–1445 | Cite as

Progress review of US-China joint research on advanced technologies for plug-in electric vehicles

  • MingGao Ouyang
  • JiuYu DuEmail author
  • Huei Peng
  • HeWu Wang
  • XuNing Feng
  • ZiYou Song


The United States and China are the world’s largest automobile markets and oil consumers, and both face a severe challenge to conserve energy and reduce tailpipe emissions. Thus, both countries urgently need to transform conventional internal combustion engines to electrified powertrains. Targeting the advanced core technologies of plug-in electric vehicles (PEVs), a joint research collaboration between China and the US, called the “Clean Vehicle Consortium” (CVC), was set up in 2010. Six years of collaboration on PEV technologies has resulted in significant progress in three technical areas. Based on CVC publications, we review herein the progress made by the CVC research efforts on three key advanced PEV technologies. This includes the development of a safe battery with an energy density of 260 W h kg−1 and a systematic method for designing safe traction battery systems. Thus, a breakthrough in high power density and efficient traction motor systems has occurred. In addition to discussing advanced electric-drive powertrains, we also discuss global energy management strategies that aim to improve PEV energy efficiency. This discussion covers scientific and comprehensive analysis methods to analyze energy systems, which include cost-benefit analyses of plug-in hybrid electric vehicles, life-cycle assessments for evaluating vehicle emissions, and PEV-ownership projections.


US-China joint research plug-in electric vehicle safety of traction battery electric driving powertrain energy system analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gallant B M, Kwabi D G, Mitchell R R, et al. Influence of Li2O2 morphology on oxygen reduction and evolution kinetics in Li-O2 batteries. Energ Environ Sci, 2013, 6: 2518CrossRefGoogle Scholar
  2. 2.
    Kang S Y, Mo Y, Ong S P, et al. A facile mechanism for recharging Li2O2 in Li-O2 batteries. Chem Mater, 2013, 25: 3328–3336CrossRefGoogle Scholar
  3. 3.
    Kang S Y, Mo Y, Ong S P, et al. Nanoscale stabilization of sodium oxides: Implications for Na-O2 batteries. Nano Lett, 2014, 14: 1016–1020CrossRefGoogle Scholar
  4. 4.
    Akbar Ali M, Violi A. Reaction pathways for the thermal decomposition of methyl butanoate. J Org Chem, 2013, 78: 5898–5908CrossRefGoogle Scholar
  5. 5.
    Akbar Ali M, Dillstrom V T, Lai J Y W, et al. Ab initio investigation of the thermal decomposition of n-butylcyclohexane. J Phys Chem A, 2014, 118: 1067–1076CrossRefGoogle Scholar
  6. 6.
    Erickson E M, Schipper F, Penki T R, et al. Review—Recent advances and remaining challenges for lithium ion battery cathodes: II. lithiumrich, xLi2MnO3·(1−x)LiNiaCobMncO2. J Electrochem Soc, 2017, 164: A6341–A6348CrossRefGoogle Scholar
  7. 7.
    Lin J, Mu D, Jin Y, et al. Li-rich layered composite Li[Li0.2Ni0.2Mn0.6] O2 synthesized by a novel approach as cathode material for lithium ion battery. J Power Sources, 2013, 230: 76–80CrossRefGoogle Scholar
  8. 8.
    Zhang L, Wu B, Li N, et al. Rod-like hierarchical nano/micro Li1.2 Ni0.2Mn0.6O2 as high performance cathode materials for lithium-ion batteries. J Power Sources, 2013, 240: 644–652CrossRefGoogle Scholar
  9. 9.
    Zhang L, Borong W, Ning L, et al. Hierarchically porous micro-rod lithium-rich cathode material Li1.2Ni0.13Mn0.54Co0.13O2 for high performance lithium-ion batteries. Electrochim Acta, 2014, 118: 67–74CrossRefGoogle Scholar
  10. 10.
    Feng X, Ouyang M, Liu X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energ Storage Mater, 2018, 10: 246–267CrossRefGoogle Scholar
  11. 11.
    Zhang S, Zhou Q, Xia Y. Influence of mass distribution of battery and occupant on crash response of small lightweight electric vehicle. SAE Technical Paper, 2015Google Scholar
  12. 12.
    Jiang X, Luo H, Xia Y, et al. Mechanical behavior of lithium-ion battery component materials and error sources analysis for test results. SAE Int J Mater Manf, 2016, 9: 2016-01-0400CrossRefGoogle Scholar
  13. 13.
    Ouyang M, Ren D, Lu L, et al. Overcharge-induced capacity fading analysis for large format lithium-ion batteries with LiyNi1/3Co1/3Mn1/3O2+LiyMn2O4 composite cathode. J Power Sources, 2015, 279: 626–635CrossRefGoogle Scholar
  14. 14.
    Ren D, Feng X, Lu L, et al. An electrochemical-thermal coupled overcharge-to-thermal-runaway model for lithium ion battery. J Power Sources, 2017, 364: 328–340CrossRefGoogle Scholar
  15. 15.
    Guo R, Lu L, Ouyang M, et al. Mechanism of the entire overdischarge process and overdischarge-induced internal short circuit in lithium-ion batteries. Sci Rep, 2016, 6: 30248CrossRefGoogle Scholar
  16. 16.
    Liu L, He X, Lu L, et al. Evaluation of triggering approaches of internal short circuit in lithium ion batteries. In: The 231st ECS Meeting. New Orleans, 2017Google Scholar
  17. 17.
    Feng X, Weng C, Ouyang M, et al. Online internal short circuit detection for a large format lithium ion battery. Appl Energ, 2016, 161: 168–180CrossRefGoogle Scholar
  18. 18.
    Feng X, Fang M, He X, et al. Thermal runaway features of large format prismatic lithium ion battery using extended volume accelerating rate calorimetry. J Power Sources, 2014, 255: 294–301CrossRefGoogle Scholar
  19. 19.
    Feng X, Sun J, Ouyang M, et al. Characterization of large format lithium ion battery exposed to extremely high temperature. J Power Sources, 2014, 272: 457–467CrossRefGoogle Scholar
  20. 20.
    Feng X, He X, Ouyang M, et al. Thermal runaway propagation model for designing a safer battery pack with 25 Ah LiNixCoyMnzO2 large format lithium ion battery. Appl Energ, 2015, 154: 74–91CrossRefGoogle Scholar
  21. 21.
    Feng X, Sun J, Ouyang M, et al. Characterization of penetration induced thermal runaway propagation process within a large format lithium ion battery module. J Power Sources, 2015, 275: 261–273CrossRefGoogle Scholar
  22. 22.
    Feng X, Lu L, Ouyang M, et al. A 3D thermal runaway propagation model for a large format lithium ion battery module. Energy, 2016, 115: 194–208CrossRefGoogle Scholar
  23. 23.
    Lu L, Han X, Li J, et al. A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources, 2013, 226: 272–288CrossRefGoogle Scholar
  24. 24.
    Han X, Ouyang M, Lu L, et al. Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model. J Power Sources, 2015, 278: 802–813Google Scholar
  25. 25.
    Han X, Ouyang M, Lu L, et al. Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation. J Power Sources, 2015, 278: 814–825Google Scholar
  26. 26.
    Ouyang M, Chu Z, Lu L, et al. Low temperature aging mechanism identification and lithium deposition in a large format lithium iron phosphate battery for different charge profiles. J Power Sources, 2015, 286: 309–320CrossRefGoogle Scholar
  27. 27.
    Chu Z, Feng X, Lu L, et al. Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model. Appl Energ, 2017, 204: 1240–1250CrossRefGoogle Scholar
  28. 28.
    Han X, Ouyang M, Lu L, et al. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification. J Power Sources, 2014, 251: 38–54CrossRefGoogle Scholar
  29. 29.
    Han X, Ouyang M, Lu L, et al. A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation. J Power Sources, 2014, 268: 658–669CrossRefGoogle Scholar
  30. 30.
    Feng X, Li J, Ouyang M, et al. Using probability density function to evaluate the state of health of lithium-ion batteries. J Power Sources, 2013, 232: 209–218CrossRefGoogle Scholar
  31. 31.
    Weng C, Cui Y, Sun J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. J Power Sources, 2013, 235: 36–44CrossRefGoogle Scholar
  32. 32.
    Weng C, Sun J, Peng H. Model parametrization and adaptation based on the invariance of support vectors with applications to battery stateof-health monitoring. IEEE Trans Veh Technol, 2015, 64: 3908–3917CrossRefGoogle Scholar
  33. 33.
    Weng C, Feng X, Sun J, et al. State-of-health monitoring of lithiumion battery modules and packs via incremental capacity peak tracking. Appl Energ, 2016, 180: 360–368CrossRefGoogle Scholar
  34. 34.
    Cordoba-Arenas A, Onori S, Rizzoni G, et al. Aging propagation in interconnected systems with an application to advanced automotive battery packs. In: 7th IFAC Symposium on Advances in Automotive Control. Tokyo, 2013Google Scholar
  35. 35.
    Cordoba-Arenas A, Onori S, Rizzoni G. A control-oriented lithiumion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management. J Power Sources, 2015, 279: 791–808CrossRefGoogle Scholar
  36. 36.
    Ouyang M, Feng X, Han X, et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery. Appl Energ, 2016, 165: 48–59CrossRefGoogle Scholar
  37. 37.
    Zheng Y, Ouyang M, Lu L, et al. Understanding aging mechanisms in lithium-ion battery packs: From cell capacity loss to pack capacity evolution. J Power Sources, 2015, 278: 287–295CrossRefGoogle Scholar
  38. 38.
    Ouyang M, Gao S, Lu L, et al. Determination of the battery pack capacity considering the estimation error using a Capacity-Quantity diagram. Appl Energ, 2016, 177: 384–392CrossRefGoogle Scholar
  39. 39.
    Zheng Y, Lu L, Han X, et al. LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation. J Power Sources, 2013, 226: 33–41CrossRefGoogle Scholar
  40. 40.
    Zheng Y, Ouyang M, Lu L, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 1. Equalization based on remaining charging capacity estimation. J Power Sources, 2014, 247: 676–686Google Scholar
  41. 41.
    Zheng Y, Ouyang M, Lu L, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 2. Fuzzy logic equalization. J Power Sources, 2014, 247: 460–466CrossRefGoogle Scholar
  42. 42.
    Hua Y, Cordoba-Arenas A, Warner N, et al. A multi time-scale stateof-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control. J Power Sources, 2015, 280: 293–312CrossRefGoogle Scholar
  43. 43.
    Cordoba-Arenas A, Zhang J, Rizzoni G. Diagnostics and prognostics needs and requirements for electrified vehicles powertrains. IFAC Proc Volumes, 2013, 46: 524–529CrossRefGoogle Scholar
  44. 44.
    Zheng Y, Han X, Lu L, et al. Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles. J Power Sources, 2013, 223: 136–146CrossRefGoogle Scholar
  45. 45.
    Ouyang M, Zhang M, Feng X, et al. Internal short circuit detection for battery pack using equivalent parameter and consistency method. J Power Sources, 2015, 294: 272–283CrossRefGoogle Scholar
  46. 46.
    Cai Y, Ouyang M G, Yang F. Impact of power split configurations on fuel consumption and battery degradation in plug-in hybrid electric city buses. Appl Energ, 2017, 188: 257–269CrossRefGoogle Scholar
  47. 47.
    Ouyang M, Zhang W, Wang E, et al. Performance analysis of a novel coaxial power-split hybrid powertrain using a CNG engine and supercapacitors. Appl Energ, 2015, 157: 595–606CrossRefGoogle Scholar
  48. 48.
    Wang E, Guo D, Yang F. System design and energetic characterization of a four-wheel-driven series-parallel hybrid electric powertrain for heavy-duty applications. Energ Conv Manage, 2015, 106: 1264–1275CrossRefGoogle Scholar
  49. 49.
    Cai Y, Ouyang M, Yang F. Energy management and design optimization for a series-parallel PHEV city bus. Int J Automot Technol, 2017, 18: 473–487CrossRefGoogle Scholar
  50. 50.
    Hu Y, Yang F, Ouyang M. Fuel consumption analysis and optimizing of a heavy duty dual motor coaxial series-parallel hybrid lorry under C-WTVC. SAE Technical Paper, 2017Google Scholar
  51. 51.
    Gao G J, Yang F Y, Chen L et al. Transient control of low-temperature premixed combustion using ISG motor dynamic torque compensation. In: IEEE Vehicle Power and Propulsion Conference VPPC. New York, 2012. 846–850Google Scholar
  52. 52.
    Yang F, Gao G, Ouyang M, et al. Research on a diesel HCCI engine assisted by an ISG motor. Appl Energ, 2013, 101: 718–729CrossRefGoogle Scholar
  53. 53.
    Zhang Y, Hou Z, Yang F, et al. Failure mechanism of the transmission shaft of a new power split hybrid vehicle. In: Vehicle Symposium and Exhibition (EVS27). Barcelona, 2013CrossRefGoogle Scholar
  54. 54.
    Zhang J, Hu Y, Yang F, et al. Simulations on special structure ISG motor used for hybrid electrical vehicles aimed at active damping. SAE Technical Paper, 2017Google Scholar
  55. 55.
    Wang L, Zhang Y, Yin C, et al. Hardware-in-the-loop simulation for the design and verification of the control system of a series-parallel hybrid electric city-bus. Simul Model Practice Theor, 2012, 25: 148–162CrossRefGoogle Scholar
  56. 56.
    Du L, Yang F, Xu L, et al. Research on TITO coupling control strategy for parallel hybrid excavator. In: ASME Internal Combustion Engine Fall Technical Conference. Dearborn, 2013CrossRefGoogle Scholar
  57. 57.
    Jie S, Yong Z, Chengliang Y. Longitudinal brake control of hybrid electric bus using adaptive fuzzy sliding mode control. Int J Model Indet Control, 2012, 15: 147–155CrossRefGoogle Scholar
  58. 58.
    Zhu F, Chen L, Yin C, et al. Dynamic modelling and systematic control during the mode transition for a multi-mode hybrid electric vehicle. P I Mech Eng D-J Aut, 2013, 227: 1007–1023CrossRefGoogle Scholar
  59. 59.
    Zhu F, Chen L, Yin C. Design and analysis of a novel multimode transmission for a HEV using a single electric machine. IEEE Trans Veh Technol, 2013, 62: 1097–1110CrossRefGoogle Scholar
  60. 60.
    Chen L, Zhu F, Zhang M, et al. Design and analysis of an electrical variable transmission for a series-parallel hybrid electric vehicle. IEEE Trans Veh Technol, 2011, 60: 2354–2363CrossRefGoogle Scholar
  61. 61.
    Zhang H, Zhang Y, Yin C. Hardware-in-the-loop simulation of robust mode transition control for a series-parallel hybrid electric vehicle. IEEE Trans Veh Technol, 2016, 65: 1059–1069CrossRefGoogle Scholar
  62. 62.
    Cai Y, Yang F, Ouyang M. Impact of control strategy on battery degradation for a plug-in hybrid electric city bus in China. Energy, 2016, 116: 1020–1030CrossRefGoogle Scholar
  63. 63.
    Zhang X, Mi C C, Yin C. Active-charging based powertrain control in series hybrid electric vehicles for efficiency improvement and battery lifetime extension. J Power Sources, 2014, 245: 292–300CrossRefGoogle Scholar
  64. 64.
    Du J, Yang F, Cai Y, et al. Testing and analysis of the control strategy of Honda Accord plug-in HEV. IFAC-Papersonline, 2016, 49: 153–159CrossRefGoogle Scholar
  65. 65.
    Jungst R G, Nagasubramanian G, Case H L, et al. Accelerated calendar and pulse life analysis of lithium-ion cells. J Power Sources, 2003, 119–121: 870–873Google Scholar
  66. 66.
    Song Z, Hofmann H, Li J, et al. Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energ, 2015, 139: 151–162CrossRefGoogle Scholar
  67. 67.
    Song Z, Li J, Han X, et al. Multi-objective optimization of a semiactive battery/supercapacitor energy storage system for electric vehicles. Appl Energ, 2014, 135: 212–224CrossRefGoogle Scholar
  68. 68.
    Song Z, Hofmann H, Li J, et al. Energy management strategies comparison for electric vehicles with hybrid energy storage system. Appl Energ, 2014, 134: 321–331CrossRefGoogle Scholar
  69. 69.
    Song Z, Hofmann H, Li J, et al. A comparison study of different semiactive hybrid energy storage system topologies for electric vehicles. J Power Sources, 2015, 274: 400–411CrossRefGoogle Scholar
  70. 70.
    Song Z, Hou J, Xu S, et al. The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses. Energy, 2017, 135: 91–100CrossRefGoogle Scholar
  71. 71.
    Ji Y, Wang C Y. Heating strategies for Li-ion batteries operated from subzero temperatures. Electrochim Acta, 2013, 107: 664–674CrossRefGoogle Scholar
  72. 72.
    Song Z, Hofmann H, Li J, et al. The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis. Appl Energ, 2015, 159: 576–588CrossRefGoogle Scholar
  73. 73.
    Jung H, Wang H, Hu T. Control design for robust tracking and smooth transition in power systems with battery/supercapacitor hybrid energy storage devices. J Power Sources, 2014, 267: 566–575CrossRefGoogle Scholar
  74. 74.
    Song Z, Hou J, Hofmann H, et al. Sliding-mode and Lyapunov function-based control for battery/supercapacitor hybrid energy storage system used in electric vehicles. Energy, 2017, 122: 601–612CrossRefGoogle Scholar
  75. 75.
    Caruntu C F, Lazar M, Gielen R H, et al. Lyapunov based predictive control of vehicle drivetrains over CAN. Control Eng Practice, 2013, 21: 1884–1898CrossRefGoogle Scholar
  76. 76.
    Shuai Z, Zhang H, Wang J, et al. Combined AFS and DYC control of four-wheel-independent-drive electric vehicles over CAN network with time-varying delays. IEEE Trans Veh Technol, 2014, 63: 591–602CrossRefGoogle Scholar
  77. 77.
    Shuai Z, Zhang H, Wang J, et al. Lateral motion control for fourwheel-independent-drive electric vehicles using optimal torque allocation and dynamic message priority scheduling. Control Eng Pract, 2014, 24: 55–66CrossRefGoogle Scholar
  78. 78.
    Shuai Z, Zhang H, Wang J, et al. Network control of vehicle lateral dynamics with control allocation and dynamic message priority assignment. In: Asme 2013 Dynamic Systems And Control Conference, 2013CrossRefGoogle Scholar
  79. 79.
    Song Z, Li J, Wei Y, et al. Interaction of In-wheel permanent magnet synchronous motor with tire dynamics. Chin J Mech Eng, 2015, 28: 470–478CrossRefGoogle Scholar
  80. 80.
    Li J, Song Z, Wei Y, et al. Influence of tire dynamics on slip ratio estimation of independent driving wheel system. Chin J Mech Eng, 2014, 27: 1203–1210CrossRefGoogle Scholar
  81. 81.
    Song Z, Li J, Shuai Z, et al. Fuzzy logic torque control system in fourwheel-drive electric vehicles for active damping vibration control. Int J Vehicle Des, 2015, 68: 55–80CrossRefGoogle Scholar
  82. 82.
    Yin D Y, Oh S, Hori Y. A novel traction control for EV based on maximum transmissible torque estimation. IEEE Trans Ind Electron, 2009, 56: 2086–2094CrossRefGoogle Scholar
  83. 83.
    Song Z, Li J, Xu L, et al. Traction control system for EV based on modified maximum transmissible torque estimation. In: IEEE Vehicle Power and Propulsion Conference. 2013. 384–390Google Scholar
  84. 84.
    Li J, Song Z, Shuai Z, et al. Wheel slip control using sliding-mode technique and maximum transmissible torque estimation. J Dyn Sys Meas Control, 2015, 137: 111010CrossRefGoogle Scholar
  85. 85.
    Li P, Yang Y, Ge Z, et al. Analysis and calculation on energy consumption of 300 MW CHP heating systems. In: Proceedings of the Chinese Society for Electrical Engineering, 2012. 15–20Google Scholar
  86. 86.
    Yang C Y, Yang Z P, Yang Y P, et al. Unit consumption analysis in 600 MW coal fired power plant. J North China Electric Power University, 2010, 216: 845-850Google Scholar
  87. 87.
    Yang Z P Yang Y P. Energy consumption and distribution of 1000 MW coal-fired power generating unit. J North China Elect Power Univ, 2012, 39: 76–80Google Scholar
  88. 88.
    Yang Z P, Wang N L, Yang Y P. Energy consumption spatial temporal distribution characteristics of large thermal power generating units. J North China Electric Power Univ, 2014, 41: 84–89Google Scholar
  89. 89.
    Yang Y, Yang Z, Xu G, et al. Situation and prospect of energy consumption for China’s thermal power generation. In: Proceedings of the Chinese Society for Electrical Engineering, 2013. 1–11Google Scholar
  90. 90.
    Hu Y, Xu G, Duan W, et al. Current situation and performance comparison of carbon capture technologies. Thermal Power Generation, 2017, 46: 1–6, 14Google Scholar
  91. 91.
    China Electricity Council. China Clean Coal Power Development Report. 2017Google Scholar
  92. 92.
    Hou C, Wang H, Ouyang M. Survey of daily vehicle travel distance and impact factors in Beijing. IFAC Proc Volumes, 2013, 46: 35–40CrossRefGoogle Scholar
  93. 93.
    Wang H, Wu L, Hou C, et al. A GPS-based research on driving range and patterns of private passenger vehicle in Beijing. In: Electric Vehicle Symposium and Exhibition (EVS27). Barcelona, 2013CrossRefGoogle Scholar
  94. 94.
    Zhang X B, Wang H W. Utility factors derived from Beijing passenger car travel survey. In: The FISITA 2014 world automotive congress. Maastricht, 2014Google Scholar
  95. 95.
    Xu H. Electric distance ratio of PHEV in China mega city—Based on mass driving and charging data. In: FISITA. Busan. 2016Google Scholar
  96. 96.
    Hou C, Wang H, Ouyang M. Battery sizing for plug-in hybrid electric vehicles in Beijing: A TCO model based analysis. Energies, 2014, 7: 5374–5399CrossRefGoogle Scholar
  97. 97.
    Du J, Wang H, Ouyang M. Parameters optimization of PHEV based on cost-effectiveness from life cycle view in China. In: Proceedings of the FISITA 2012 World Automotive Congress. Heidelberg, 2013. 697–704CrossRefGoogle Scholar
  98. 98.
    Wang H, Zhang X, Wu L, et al. Beijing passenger car travel survey: Implications for alternative fuel vehicle deployment. Mitig Adapt Strateg Glob Change, 2015, 20: 817–835CrossRefGoogle Scholar
  99. 99.
    Wang H W, Zhang X B, Ouyang M G. Energy and environmental lifecycle assessment of passenger car electrification based on Beijing driving patterns. Sci China Tech Sci, 2015, 58: 659–668CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • MingGao Ouyang
    • 1
  • JiuYu Du
    • 1
    Email author
  • Huei Peng
    • 2
  • HeWu Wang
    • 1
  • XuNing Feng
    • 1
  • ZiYou Song
    • 1
  1. 1.State Key Laboratory of Automotive Safety and EnergyTsinghua UniversityBeijingChina
  2. 2.Mechanical EngineeringUniversity of MichiganAnn ArborUSA

Personalised recommendations