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Parameters optimum matching of pure electric vehicle dual-mode coupling drive system

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Abstract

As an important development direction of pure electric vehicle drive system, the distributed drive system has the advantages of compact structure, high transmission efficiency, and flexible control, but there are some serious problems such as high performance requirements to the drive motors, complex control strategies, and poor reliability. To solve these problems, a two motors dual-mode coupling drive system has been developed at first, which not only has the capacity of two-speed gear shifting, but also can automatically switch between the distributed drive and the centralized drive by means of modes change control. So, the performance requirements to the drive motors can be reduced, the problem of abnormal running caused by the fault of unilateral distributed drive systems also can be resolved by replacing the drive mode with centralized drive. Then, the system parameters primary and the optimum matching under the principle of efficiency optimization have been carried out, which makes the drive system achieve predetermined functions and meet the actual demands of different operating statuses. At last, the economic comparison of a pure electric vehicle installation with a dual-mode coupling drive system, a single-motor centralized drive system or a dual-motor distributed drive system in the simulation conditions has been completed. Compared with other systems, the driving range of the electric vehicle driven by the designed system is significantly increased, which proves the better efficiency and application value of the system.

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References

  1. Zhu H, Li L, Jin M, et al. Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control. Proc I Mech E Part D: J Aut, 2013, 227: 1431–1445

    Article  Google Scholar 

  2. Murata S. Innovation by in-wheel-motor drive unit. Vehicle Syst Dyn, 2012, 50: 807–830

    Article  Google Scholar 

  3. Dadashnialehi A, Bab-Hadiashar A, Cao Z W, et al. Intelligent sensorless ABS for in-wheel electric vehicles. IEEE T Ind Electron, 2014, 61: 1957–1969

    Article  Google Scholar 

  4. Hori Y. Future vehicle driven by electricity and control-research on 4 wheels motored “UOT March II”. IEEE T Ind Electron, 2004, 51: 954–96

    Article  Google Scholar 

  5. Wang J N, Wang Q N, Jin L Q, et al. Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering. Mechatronics, 2011, 21: 63–76

    Article  MathSciNet  Google Scholar 

  6. Zhao W Z, Xu X H, Wang C Y. Multidiscipline collaborative optimization of differential steering system of electric vehicle with motorized wheels. Sci China Tech Sci, 2012, 55: 3462–3468

    Article  Google Scholar 

  7. Xiong L, Yu Z P, Wang Y, et al. Vehicle dynamics control of four in wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation. Vehicle Syst Dyn, 2012, 50: 831–846

    Article  Google Scholar 

  8. Wang R R, Wang J M. Fault-tolerant control with active fault diagnosis for four-wheel independently driven electric ground vehicles. IEEE T Veh Technol, 2011, 60: 4276–4287

    Article  Google Scholar 

  9. Chu W B, Luo Y G, Han Y W, et al. Rule-based traction system failure control of distributed electric drive vehicle. J Mech Eng, 2012, 48: 90–95, 102

    Article  Google Scholar 

  10. Mutoh N, Nakano Y. Dynamics of front-and-rear-wheel-independent-drive-type electric vehicles at the time of failure. IEEE T Ind Electron, 2012, 59: 1488–1499

    Article  Google Scholar 

  11. Zhang L P, Lin C, Qi B N. Mechanism and design of EV changing modes propulsion system. In: Bilof R, eds. International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, 2009. 91–94

    Google Scholar 

  12. Zhang L P, Qi B N, Lin C. Torque behavior of electric vehicle change mode drive system. In: Guerrero J E, ed. International Conference on Computing, Control and Industrial Engineering, Wuhan, China, 2010. 87–90

    Google Scholar 

  13. Nagaya G, Wakao Y, Abe A. Development of an in-wheel drive with advanced dynamic-damper mechanism. JSAE Review, 2003, 24: 477–481

    Article  Google Scholar 

  14. Yang C, Jiao X H, Li L, et al. Electromechanical coupling drive control for single-shaft parallel hybrid powertrain. Sci China Tech Sci, 2014, 57: 541–549

    Article  Google Scholar 

  15. Yu Z S. Theory of Automobile. Beijing: Machinery Industry Press, 2009. 75

    Google Scholar 

  16. Chiasson J, Vairamohan B. Estimating the state of charge of a battery. IEEE T Cont Syst T, 2005, 13: 465–470

    Article  Google Scholar 

  17. Goldberg D E, Holland J H. Genetic Algorithms and Machine Learning, Machine Learning 3. Dordrecht: Kluwer Academic Publishers, 1988. 95–99

    Google Scholar 

  18. Zhao W Z, Wang C Y. Mixed H2/H road feel control of EPS based on genetic algorithm. Sci China Tech Sci, 2012, 55: 72–80

    Article  Google Scholar 

  19. Leung Y W, Wang Y P. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE T Evol Comput, 2001, 5: 41–53

    Article  Google Scholar 

  20. Sadjadi F A. Comparison of fitness scaling functions in genetic algorithms with applications to optical processing. Proc SPIE, 2004, 5557: 356–364

    Article  Google Scholar 

  21. Montazeri-Gh M, Poursamad A, Ghalichi B. Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles. J Franklin Institute, 2006, 343: 420–435

    Article  MATH  Google Scholar 

  22. Nada M, Al-Salami A. Ant colony optimization algorithm. UbiCC J, 2009, 4: 823–826

    Google Scholar 

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Correspondence to Liang Li.

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Zhang, L., Li, L., Qi, B. et al. Parameters optimum matching of pure electric vehicle dual-mode coupling drive system. Sci. China Technol. Sci. 57, 2265–2277 (2014). https://doi.org/10.1007/s11431-014-5651-9

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  • DOI: https://doi.org/10.1007/s11431-014-5651-9

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