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State Estimation and Coordinated Control for Distributed Electric Vehicles pp 1–36Cite as

Introduction

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Abstract

In a broad sense, electric automobiles refer to the motor vehicles based on electric drive, which can be divided into electric vehicles, hybrid electric vehicles, fuel cell vehicles and other electric-driven vehicles in terms of energy consumed, and into centralized drive vehicles and distributed drive vehicles in terms of power system layout. As the most common mode for internal combustion engine automobiles, centralized drive was designed based on traditional vehicles; its power acts on the wheels through clutch, transmission, drive shaft, differential mechanism, half axle, etc. This sort of design maintains the compatibility between electric automobiles and traditional internal combustion engine automobiles to the maximal extent, which is mainly adopted by hybrid electric automobiles. However, its disadvantages, including large quantity of drive parts, low drive efficiency and complex control, are showing up themselves due to the restriction of traditional automobile design concept; on the contrary, the advantages of electric vehicles, including less mechanical drive links, short drive chain and flexible layout, are gradually discovered, with the constant deepening of design concept for electric automobiles and progress in developing electric drive system. With distributed drive, such drive parts as clutch, transmission, drive shaft, differential mechanism and half axle are spared and the drive motor can directly be installed in or near the driving wheel. As this brand-new chassis form designed for electric automobiles according to the motor characteristics greatly promotes the transformation of automobile structure, it has become a hot spot in research and design.

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References

  1. Yu Z, Gao X (2009) Review of vehicle state estimation problem under driving situation. J Mech Eng 45(5):20–33

    Article  MathSciNet  Google Scholar 

  2. Carlson C R (2004) Estimation with application for automobile dead reckoning and control. Stanford University, USA

    Google Scholar 

  3. Bevly DM, Gerdes JC, Wilson C (2002) Use of GPS based velocity measurements for measurement of sideslip and wheel slip. Veh Syst Dyn 38(2):127–147

    Google Scholar 

  4. Ryu J (2004) State and parameter estimation for vehicle dynamics control using GPS. Stanford University, USA

    Google Scholar 

  5. Schoepflin TN, Dailey DJ (2003) Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation. IEEE Trans Intell Transp Syst 4(2):90–98

    Article  Google Scholar 

  6. Liu L, Luo Y, Li K (2009) Observation of road surface adhesion coefficient based on normalized tire model. J Tsinghua Univ (Nat Sci) 49(5):116–120

    Article  Google Scholar 

  7. Kiencke U, Nielsen L (2010) Automotive control systems: for engine, driveline, and vehicle, 2nd edn. Springer, New York

    Google Scholar 

  8. Qi Z, Zhang J (2003) Study on reference vehicle velocity determination for ABS based on vehicle ABS/ASR/ACC integrated system. J Automot Eng 25(6):617–620

    Google Scholar 

  9. Jiang F, Gao Z (2000) An adaptive nonlinear filter approach to the vehicle velocity estimation for ABS. In: Proceedings of the international conference on control applications, Anchorage, Alaska, USA, Sept 2000, pp 490–495

    Google Scholar 

  10. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82(Series D):35–45

    Google Scholar 

  11. Welch G, Bishop G (2006) An introduction to the Kalman filter. Technical Report, Department of Computer Science, University of North Carolina at Chapel Hill. http://www.cs.unc.edu/~welch/kalman/

  12. Lee H (2006) Reliability indexed sensor fusion and its application to vehicle velocity estimation. J Dyn Syst Meas Contr 128(2):236–243

    Article  Google Scholar 

  13. Kobayashi K, Cheok KC, Watanabe K (1995) Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter. In: Proceedings of the American control conference, Seattle, WA, USA, June 1995, pp 3086–3090

    Google Scholar 

  14. Watanabe K, Kinase K, Kobayashi K (1993) Absolute speed measurement of vehicles from noisy acceleration and erroneous wheel speed. In: Proceedings of the intelligent vehicles symposium, Tokyo, Japan, pp 271–276

    Google Scholar 

  15. Chu W, Li S, Jiang Q et al (2011) Estimation of full-wheel independently-driven electric vehicle based on multi-information integration. J Autom Eng 33(11):962–966

    Google Scholar 

  16. Imsland L, Johansen TA, Fossen TI et al (2006) Vehicle velocity estimation using nonlinear observers. Automatica 42(12):2091–2103

    Article  MATH  MathSciNet  Google Scholar 

  17. Shraim H, Ananou B, Fridman L, et al (2006) Sliding mode observers for the estimation of vehicle parameters, forces and states of the center of gravity. In: Proceedings of the IEEE conference on decision & control, San Diego, California, USA, Dec 2006, pp 1635–1640

    Google Scholar 

  18. Cherouat H, Braci M, Diop S (2005) Vehicle velocity, side slip angles and yaw rate estimation. In: Proceedings of the IEEE international symposium on industrial electronics, Dubrovnik, Croatia, June 2005, pp 349–354

    Google Scholar 

  19. Xu T, Gao X (2007) Overview of Development Trend of Vehicle Longitudinal Velocity Estimation Algorithm. Shanghai Auto 6:39–42

    Google Scholar 

  20. Piyabongkarn D, Rajamani R, Grogg JA et al (2009) Development and experimental evaluation of a slip angle estimator for vehicle stability control. IEEE Trans Control Syst Technol 17(1):78–88

    Article  Google Scholar 

  21. van Zanten AT (2000) Bosch ESP systems: 5 Years of experience. SAE technical paper: 2000–01–1633

    Google Scholar 

  22. Aoki Y, Uchida T, Hori Y (2005) Experimental demonstration of body slip angle control based on a novel linear observer for electric vehicle. In: Proceedings of the IEEE conference on industrial electronics society, Raleigh, North Carolina, USA, Nov 2005, pp 2620–2625

    Google Scholar 

  23. Venhovens PJT, Naab K (1999) Vehicle dynamics estimation using Kalman filters. Veh Syst Dyn 32(2):171–184

    Article  Google Scholar 

  24. Farrelly J, Wellstead P (1996) Estimation of vehicle lateral velocity. In: Proceedings of the IEEE conference on control applications, Dearborn, Michigan, USA, Sept 1996, pp 552–557

    Google Scholar 

  25. Geng C, Mostefai L, Denaï M et al (2009) Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer. IEEE Trans Ind Electron 56(5):1411–1419

    Article  Google Scholar 

  26. Unger I, Isermann R (2006) Fault tolerant sensors for vehicle dynamics control. In: Proceedings of the American control conference, Minneapolis, Minnesota, USA, June 2006, pp 3948–3953

    Google Scholar 

  27. Chumsamutr R, Fujioka T, Abe M (2006) Sensitivity analysis of side-slip angle observer based on a tire model. Veh Syst Dyn 44(7):513–527

    Article  Google Scholar 

  28. Zou G (2008) Integral dynamical control of four-wheel independently-driven electric vehicle system. Tsinghua University, Beijing

    Google Scholar 

  29. Yu Z (2009) Automobile theory, No.5 version. Tsinghua University Press, Beijing

    Google Scholar 

  30. Nagai M (2007) The perspectives of research for enhancing active safety based on advanced control technology. Veh Syst Dyn 45(5):413–431

    Article  Google Scholar 

  31. Stphant J, Charara A, Meiz D (2004) Virtual sensor: application to vehicle sideslip angle and transversal forces. IEEE Trans Ind Electron 51(2):278–289

    Article  Google Scholar 

  32. Stphant J, Charara A (2005) Observability matrix and parameter identification application to vehicle tire cornering stiffness. In: Proceedings of the European control conference, Sevile, Spain, Dec 2005, pp 6734–6739

    Google Scholar 

  33. Hiemer M, von Vietinghoff A, Kiencke U (2005) Determination of the vehicle body side slip angle with non-linear observer strategies. SAE technical paper: 2005–01–0400

    Google Scholar 

  34. Doumiati M, Victorino A, Charara A, et al (2009) Estimation of vehicle lateral tire-road forces: a comparison between extended and unscented filtering. In: Proceedings of the European control conference, Budapest, Hungary, Aug 2009, pp 4804–4809

    Google Scholar 

  35. Gao X, Yu Z, Chen X (2009) Model based yaw rate estimation of electric vehicle with 4 in-wheel motors. SAE technical paper: 2009–01–0463

    Google Scholar 

  36. Hac A, Simpson M (2000) Estimation of vehicle side slip angle and yaw rate. SAE technical paper: 2000–01–0696

    Google Scholar 

  37. Gao Y, Gao Z, Li X (2005) Vehicle yaw velocity soft measurement algorithm based on adaptive Kalman filter method. J Jiangsu Univ (Nat Sci) 26(1):24–27

    Google Scholar 

  38. Dugoff H, Fancher PS, Segel L (1970) The Influence of lateral load transfer on directional response. SAE technical paper 700377

    Google Scholar 

  39. Liu Q, Guo K, Chen B (2000) Analysis on tire brush model. J Agric Mach 31(1):19–22

    Google Scholar 

  40. Pacejka HB (2012) Tire Veh Dyn, 3rd edn. Elsevier, Oxford

    Google Scholar 

  41. Dugoff H, Fancher PS, Segel L (1969) Tire performance characteristics affecting vehicle response to steering and braking control inputs. Final Report. Technical Report, Office of Vehicle Systems Research, US National Bureau of Standards. http://hdl.handle.net/2027.42/1387

  42. Nagai M, Yamanaka S, Hirano Y (1996) Integrated control law of active rear steering control. In: Proceedings of the international symposium on advanced vehicle control. Aachen, Germany, July 1996, pp 451–469

    Google Scholar 

  43. Nagai M, Shino M, Gao F (2002) Study on integrated control of active front steer angle and direct yaw moment. JSAE Rev 23(3):309C315

    Google Scholar 

  44. Antonov S, Fehn A, Kugi A (2011) Unscented Kalman filter for vehicle state estimation. Veh Syst Dyn 49(9):1497C1520

    Google Scholar 

  45. Reif K, Renner K, Saeger M (2008) Using the unscented Kalman filter and a non-linear two-track model for vehicle state estimation. In: Proceedings of the international federation of automatic control. Seoul, Korea, July 2008, pp 8570–8575

    Google Scholar 

  46. Yu X (2010) Study on integral control strategy of vehicle active suspension. Jilin University, Changchun

    Google Scholar 

  47. Schofield B, Hägglund T (2008) Optimal control allocation in vehicle dynamics control for rollover mitigation. In: Proceedings of the American control conference, Seattle, Washington, USA, June 2008, pp 3231–3236

    Google Scholar 

  48. Hyun D, Langari R (2003) Modeling to predict rollover threat of tractor-semitrailers. Veh Syst Dyn 39(6):401–414

    Article  Google Scholar 

  49. Schofield B (2006) Vehicle dynamics control for rollover prevention. Lund University, Sweden

    Google Scholar 

  50. Nam K, Oh S, Fujimoto H, et al (2011) Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors. In: Proceedings of the American control conference, San Francisco, California, USA, July 2011, pp 5372–5377

    Google Scholar 

  51. Cho W, Yoon J, Yim S et al (2010) Estimation of tire forces for application to vehicle stability control. IEEE Trans Veh Technol 59(2):638–649

    Article  Google Scholar 

  52. Yoon J, Yi K (2006) A rollover mitigation control scheme based on rollover index. In: Proceedings of the American control conference, Minneapolis, Minnesota, USA, June 2006, pp 4853–4858

    Google Scholar 

  53. Doumiati M, Victorino A, Charara A et al (2008) An estimation process for vehicle wheel-ground contact normal forces. In: Proceedings of the world congress of the international federation of automatic control, Seoul, Korea, July 2008, pp 7110–7115

    Google Scholar 

  54. Bae HS, Gerdes JC (2000) Parameter estimation and command modification for longitudinal control of heavy vehicles. In: Proceedings of the international symposium on advanced vehicle control, Ann Arbor, Michigan, USA

    Google Scholar 

  55. Bae HS, Ryu J, Gerdes JC (2001) Road grade and vehicle parameter estimation for longitudinal control using GPS. In: Proceedings of the IEEE conference on intelligent transportation systems. Oakland, California, USA, Aug 2001

    Google Scholar 

  56. Johansson K (2005) Road slope estimation with standard truck sensors. KTH Royal Institute of Technology, Sweden

    Google Scholar 

  57. Jansson H, Kozica E, Sahlholm P et al (2006) Improved road grade estimation using sensor fusion. In: Proceedings of the 12th Reglermöte, Stockholm, Sweden, May 2006

    Google Scholar 

  58. Sahlholm P, Johansson KH (2010) Road grade estimation for look-ahead vehicle control using multiple measurement runs. Control Eng Pract 18(11):1328–1341

    Article  Google Scholar 

  59. Parviainen J, Hautamäki J, Collin J et al (2009) Barometer-aided road grade estimation. Proceedings of the world congress of the international association of institutes of navigation. Stockholm, Sweden, Oct 2009

    Google Scholar 

  60. Zhang T (2010) Behavior matching of vehicle driving roads. Tsinghua University, Beijing

    Google Scholar 

  61. Zhang T, Yang D, Li T et al (2010) Vehicle state estimation system aided by inertial sensors in GPS navigation. In: Proceedings of the international conference on electrical and control engineering, Wuhan, China, June 2010

    Google Scholar 

  62. Grewal M, Weill L, Andrewsa A (2007) Global positioning systems, inertial navigation, and integration. Wiley, Hoboken

    Book  Google Scholar 

  63. Parkum JE, Poulsen NK, Holst J (1992) Recursive forgetting algorithms. Int J Control 55(1):109–128

    Article  MATH  MathSciNet  Google Scholar 

  64. Saelid S, Foss B (1983) Adaptive controllers with a vector variable forgetting factor. In: Proceedings of the IEEE conference on decision and control, San Antonio, TX, USA, Dec 1983, pp 1488–1494

    Google Scholar 

  65. Vahidi A, Druzhinina A, Stefanopoulou A et al (2003) Simultaneous mass and time-varying grade estimation for heavy-duty vehicles. In: Proceedings of the American control conference, Denver, Colorado, USA, June 2003, pp 4951–4956

    Google Scholar 

  66. Vahidi A, Stefanopoulou A, Peng H (2003) Experiments for online estimation of heavy vehicles mass and time-varying road grade. In: Proceedings of the ASME international mechanical engineering congress and exposition, Washington, DC, USA

    Google Scholar 

  67. Vahidi A, Stefanopoulou A, Peng H (2005) Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Veh Syst Dyn 43(1):57–75

    Article  Google Scholar 

  68. McIntyre ML, Ghotikar TJ, Vahidi A et al (2009) A two-stage lyapunov-based estimator for estimation of vehicle mass and road grade. IEEE Trans Veh Technol 58(7):3177–3185

    Article  Google Scholar 

  69. Sastry S, Bodson M (2011) Adaptive control: stability, convergence and robustness. Dover Publications, Mineola

    Google Scholar 

  70. Holm EJ (2011) Vehicle mass and road grade estimation using Kalman filter. Linköping University, Sweden

    Google Scholar 

  71. Lingman P, Schmidtbauer B (2002) Road slope and vehicle mass estimation using Kalman filtering. Veh Syst Dyn 37(Supplement):12C–23

    Google Scholar 

  72. Winstead V, Kolmanovsky IV (2005) Estimation of road grade and vehicle mass via model predictive control. In: Proceedings of the IEEE conference on control applications, Toronto, Canada, Aug 2005

    Google Scholar 

  73. Eriksson A (2009) Implementation and evaluation of a mass estimation algorithm. KTH Royal Institute of Technology, Sweden

    Google Scholar 

  74. Kamachi M, Walters K (2006) A research of direct yaw-moment control on slippery road for in-wheel motor vehicle. In: Proceedings of the international battery, hybrid and fuel cell electric vehicle symposium & exposition, Yokohama, Japan, Oct 2006, pp 2122–2133

    Google Scholar 

  75. Raksincharoensak P, Nagai M (2006) Adaptive direct yaw moment control based on driver intention. Proceedings of the international symposium on advanced vehicle control, Taipei, China, Aug 2006, pp 753–758

    Google Scholar 

  76. Tahami F, Farhangi S, Kazemi R (2004) A fuzzy logic direct yaw-moment control system for all-wheel-drive electric vehicles. Veh Syst Dyn 41(3):203–221

    Article  Google Scholar 

  77. Kim J, Kim H (2007) Electric vehicle yaw rate control using independent in-wheel motor. In: Proceedings of the power conversion conference, Nagoya, Japan, April 2007, pp 705–710

    Google Scholar 

  78. Li D (2008) Study on vehicle dynamical integrated control based on optimal allocation. Shanghai Jiaotong University, Shanghai

    Google Scholar 

  79. Li L, Song J, Wang H et al (2006) Linear subsystem model for real-time control of vehicle stability control system. In: Proceedings of the IEEE conference on robotics, automation and mechatronics, Bangkok, Thailand, Dec 2006

    Google Scholar 

  80. Abe M, Kano Y, Suzuki K et al (2001) Side-slip control to stabilize vehicle lateral motion by direct yaw moment. JSAE Rev 22(4):413–419

    Article  Google Scholar 

  81. Boada BL, Boada MJL, Dłaz V (2005) Fuzzy-logic applied to yaw moment control for vehicle stability. Veh Syst Dyn 43(10):753–770

    Google Scholar 

  82. Wang B (2009) Study on experiment platform and driving force control system for four-wheel independently-driven electric vehicle. Tsinghua University, Beijing

    Google Scholar 

  83. Pi D, Chen N, Wang J (2008) Application of fuzzy logics in vehicle stability control system. J Southeast Univ (Nat Sci) 38(1):43–48

    MATH  Google Scholar 

  84. Kim D, Hwang S, Kim H (2010) Vehicle stability enhancement of four-wheel-drive hybrid electric vehicle using rear motor control. IEEE Trans Veh Technol 57(2):727–735

    Google Scholar 

  85. Park JH, Ahn WS (1999) \(H_{\infty }\) yaw-moment control with brakes for improving driving performance and stability. In: Proceedings of the international conference on advanced intelligent mechatronics, Atlanta, Georgia, USA, Sept 1999, pp 747–752

    Google Scholar 

  86. Mokhiamar O, Abe M (2006) How the four wheels should share forces in an optimum cooperative chassis control. Control Eng Pract 14(3):295–304

    Article  Google Scholar 

  87. Yih P, Gerdes JC (2005) Modification of vehicle handling characteristics via steer-by-wire. IEEE Trans Control Syst Technol 13(6):965–976

    Article  Google Scholar 

  88. Nagai M, Hirano Y, Yamanaka S (1997) Integrated control of active rear wheel steering and direct yaw moment control. Veh Syst Dyn 27(5):357–370

    Article  Google Scholar 

  89. Nagai M, Hirano Y, Yamanaka S (1998) Integrated robust control of active rear wheel steering and direct yaw moment control. Veh Syst Dyn 28(Supplement):416–421

    Google Scholar 

  90. Shino M, Miyamoto N, Wang Y et al (2000) Traction control of electric vehicles considering vehicle stability. In: Proceedings of the international workshop on advanced motion control. Nagoya, Japan, April 2000, pp 311–316

    Google Scholar 

  91. Shino M, Nagai M (2001) Yaw-moment control of electric vehicle for improving handling and stability. JSAE Rev 22(4):473–480

    Article  Google Scholar 

  92. Shino M, Nagai M (2003) Independent wheel torque control of small-scale electric vehicle. JSAE Rev 24(4):449–456

    Article  Google Scholar 

  93. Shino M, Raksincharoensak P, Kamata M et al (2004) Slide slip control of small-scale electric vehicle by DYC. Veh Syst Dyn 41(Supplement):487–496

    Google Scholar 

  94. Xiong L, Yu Z, Wang Y et al (2012) Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tire cornering stiffness estimation. Veh Syst Dyn 50(6):831–846

    Article  Google Scholar 

  95. Yu Z, Jiang W, Zhang L (2008) Torque allocation control over four-wheel hub motor-driven electric vehicle. J Tongji Univ (Nat Sci) 36(8):1115–1119

    Google Scholar 

  96. Liu L, Luo Y, Jiang Q et al (2011) AFS/DYC chassis integrated control based on generalized prediction theory. J Autom Eng 33(1):52–56

    Google Scholar 

  97. Zou G, Luo Y, Li K et al (2008) Improvement maneuverability and stability of independent 4WD EV by DYC based on dynamic regulation of control target. J Mech Syst Transp Logistic 1(3):305–318

    Article  Google Scholar 

  98. Chu W, Luo Y, Zhao F et al (2012) Driving force coordinated control of distributed electric drive vehicle. J Autom Eng 34(3):185–189

    Google Scholar 

  99. Harkegard O (2003) Backstepping and control allocation with applications to flight control. LinkOping University, Sweden

    Google Scholar 

  100. Omerdic E, Roberts G (2004) Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Control Eng Prac 12(12):1575–1598

    Article  Google Scholar 

  101. Podder TK, Sarkar N (2001) Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy. Robot Auton Syst 34(1):39–52

    Article  Google Scholar 

  102. Bo W, Yugong L, Fan J et al (2010) Driving force allocation algorithm of four- wheel independently-driven electric vehicle based on control allocation. J Autom Eng 32(2):128–132

    Google Scholar 

  103. Chu W, Luo Y, Dai Y et al (2012) Traction fault accommodation system for four wheel independently driven electric vehicle. In: Proceedings of the international battery, hybrid and fuel cell electric vehicle symposium & exposition, Los Angeles, CA, USA, May 2012

    Google Scholar 

  104. Liu L, Luo Y, Li K (2010) Study on vehicle all-wheel longitudinal force allocation based on dynamic target control. J Autom Eng 32(1):60–64

    Google Scholar 

  105. Hu J, Yin D, Hori Y (2011) Fault-tolerant traction control of electric vehicles. Control Eng Pract 19(2):204–213

    Article  Google Scholar 

  106. Kondo K, Sekiguchi S, Tsuchida M (2002) Development of an electrical 4WD system for hybrid vehicles. SAE technical paper: 2002–01–1043

    Google Scholar 

  107. Mutoh N, Takahashi Y, Tomita Y (2008) Failsafe drive performance of FRID electric vehicles with the structure driven by the front and rear wheels independently. IEEE Trans Ind Electron 55(6):2306–2315

    Article  Google Scholar 

  108. Mutoh N (2009) Front-and-rear-wheel-independent-drive type electric vehicle (FRID EV) with the outstanding driving performance suitable for next-generation adavanced EVs. In: Proceedings of the vehicle power and propulsion conference, Dearborn, Michigan, USA, Sept 2009, pp 1064–1070

    Google Scholar 

  109. Chu W, Luo Y, Han Y et al (2012) Failure control over distributed electric drive vehicle based on rules. J Mech Eng 48(10):90–95

    Article  Google Scholar 

  110. Fan J, Mao M (2007) Study on driving force allocation strategy of three-axle electric drive vehicle based on economy. Veh & Power Technol 1:49–51

    Google Scholar 

  111. Yu Z, Zhang L, Xiong L (2005) Optimal torque allocation control based on economy improvement for four-wheel electric drive vehicle. J Tongji Univ (Nat Sci) 33(10):1115–1119

    Google Scholar 

  112. Abe M, Mokhiamar O (2004) Effects of an optimum cooperative chassis control from the viewpoint of tire workload. Trans Soc Autom Eng Jpn 35(3):215–221

    Google Scholar 

  113. Mokhiamar O, Abe M (2004) Simultaneous optimal distribution of lateral and longitudinal tire forces for the model following control. J Dyn Syst Meas Control 126(4):753–763

    Article  Google Scholar 

  114. Nishihara O, Kumamoto H (2006) Minimax optimizations of tire workload exploiting complementarities between independent steering and traction/braking force distributions. In: Proceedings of the international symposium on advanced vehicle control. Taipei, China, Aug 2006, pp 713–718

    Google Scholar 

  115. Nishihara O, Hiraoka T, Kumamoto H (2006) Optimization of lateral and driving/braking force distribution of independent steering vehicle (minimax optimization of tire workload). Trans Soc Autom Eng Jpn 72(714):537–544

    Google Scholar 

  116. Ono E, Hattori Y, Muragishi Y et al (2006) Vehicle dynamics integrated control for four-wheel-distributed steering and four-wheel-distributed traction/braking systems. Veh Syst Dyn 44(2):139–151

    Article  Google Scholar 

  117. He P, Hori Y (2006) Improvement of EV maneuverability and safety by disturbance observer based dynamic force distribution. In: Proceedings of the international battery, hybrid and fuel cell electric vehicle symposium & exposition. Yokohama, Japan, Oct 2006, pp 1818–1827

    Google Scholar 

  118. Peng H, Sabahi R, Chen S et al (November 2011) Integrated vehicle control based on tire force reserve optimization concept. In: Proceedings of the ASME international mechanical engineering congress and exposition, Denver, Colorado, USA, Nov 2011

    Google Scholar 

  119. Dai Y, Luo Y, Chu W et al (2012) Optimum tire force distribution for four-wheel-independent drive electric vehicle with active front steering. In: Proceedings of the international symposium on advanced vehicle control, Seoul, Korea, Sept 2011

    Google Scholar 

  120. Zou G, Luo Y, Li K (2009) Optimal method for all-wheel longitudinal force allocation of four-wheel independently-driven electric vehicle. J Tsinghua Univ (Nat Sci) 49(5):719–722

    Google Scholar 

  121. Sun H, Zhang C, Cao L (2005) Study on straight driving stability control over double-motor independently-driven crawler vehicle. Veh Pow Technol 4:26–29

    Google Scholar 

  122. Wang X, Zhou X (2007) Study on straight driving correction method for full hydraulic bulldozer. J Mech Eng 38(2):17–21

    Google Scholar 

  123. Zhang H, Wang Q, Jin L (2007) Study on the straight-line running stability of the four-wheel independent driving electric vehicles. SAE technical paper: 2007–01–3488

    Google Scholar 

  124. Zhang H (2009) Study on torque coordinated control over electric vehicle driven by electric wheel. Jilin University, Changchun

    Google Scholar 

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    Wenbo Chu

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Chu, W. (2016). Introduction. In: State Estimation and Coordinated Control for Distributed Electric Vehicles. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48708-2_1

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