Estimation of the vehicle speed in the driving mode for a hybrid electric car based on an unscented Kalman filter

Abstract

Preconditions for the effective implementation of a control strategy in an automobile’s active safety system rely on timely and accurate information on the vehicle’s running status, especially the vehicle speed. However, the vehicle speed cannot be measured directly without using advanced onboard sensors or specialized test equipment owing to their high mass production cost. In this study, a model-based method for estimating the vehicle speed in different driving modes is conducted in real time, which makes full use of information on the driving wheel’s torque for a four-wheel-drive hybrid car. First, a simulation platform that integrates the models of the powertrain system, the non-linear seven-degree-of-freedom vehicle dynamics system and the dynamic UniTire model is established. Next, an unscented Kalman filter algorithm is adopted to estimate the vehicle speed, and the estimated results and the simulated results are compared under different driving modes. Finally, real-vehicle tests at medium and low speeds are performed using a prototype car. The simulations and the test results confirm that the proposed unscented Kalman filter estimation algorithm without a linearizing truncation process can estimate the vehicle speed with high precision and strong adaptability.

Keywords

Four-wheel-drive hybrid electric car estimation of the vehicle speed seven-degree-of-freedom non-linear vehicle dynamics model unscented Kalman filter

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References

Gao

. Review of vehicle state estimation problem under driving situation. Chin J Mech Engng2009; 45(5): 20–33.

Guo

Chen

Zhao

. State and parameter estimation for running vehicle: recent developments and perspective. Control Theory Applic2013; 30(6): 661–672.

Ding

. Estimation of vehicle speed based on Kalman filter and ABS control inputs. J Beijing Univ Aeronaut Astronaut2011; 37(1): 67–71.

Wang

Liu

. Vehicle reference speed determination using adaptive Kalman filter algorithm. Trans Chin Soc Agric Mach2006; 37(4): 9–11.

Kobayashi

Cheok

Watanabe

. Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter. In: 1995 American control conference, Seattle, Washington, USA, 21–23 June 1995, Vol 5, pp. 3086–3090. New York: IEEE.

Zong

Pan

. Information fusion algorithm for vehicle state estimation based on Extended Kalman Filtering. J Mech Engng2009; 45(10): 272–277.

Zong

Yang

. Vehicle driving state estimation based on extended Kalman filter. J Jilin Univ (Engng Technol Edn)2009; 39(1): 7–11.

Julier

Uhlmann

. A new approach for filtering nonlinear systems. In: 1995 American control conference, Seattle, Washington, USA, 21–23 June 1995, Vol 3, pp. 1628–1632. New York: IEEE.

Cheng

. Unscented transformation and unscented Kalman filtering. Comput Engng Applic2008; 44(24): 25–35.

10.

Pan

Yang

. Survey of a kind of nonlinear filters – UKF (in Chinese). Control Decision2005; 20(5): 481–489.

11.

Zhang

Luo

. The study on form and performance of unscented Kalman filtering algorithm. J Projectiles, Rockets, Missiles Guidance2012; 32(3): 189–196.

12.

Chu

Shi

Zhang

. Vehicle lateral and longitudinal velocity estimation based on adaptive Kalman filter. In: 2010 3rd international conference on advanced computer theory and engineering, Chengdu, Sichuan, People’s Republic of China, 20–22 August 2010, Vol 3, pp. 325–329. Piscataway, New Jersey: IEEE.

13.

Antonova

Fehnb

Kugic

. Unscented Kalman filter for vehicle state estimation. Veh System Dynamics2011; 49(9): 1496–1520.

14.

Zong

Deng

. Study on velocity estimation for four-wheel independent drive electric vehicle by UKK. In: 5th conference on measuring technology and mechatronics automation, Hong Kong, People’s Republic of China, 16–17 January 2013, pp. 1111–1114. Washington, DC: IEEE Computer Society, 2013.

15.

Zhao

Zhu

. Mode transition control for four wheel drive hybrid electric car. Chin J Mech Engng2011; 47(4): 100–109.

16.

Zhao

. The study of acceleration slips regulation strategy for four wheel drive hybrid electric car. Chin J Mech Engng2011; 47(14): 83–98.

17.

Guo

Yuan

. A Study on the prediction capability of UniTire model for combined slips mode. Automot Engng2006; 28(6): 565–568.

18.

Guo

Jin

. Application of UniTire in vehicle dynamics simulation. J Jilin Univ (Engng Technol Edn)2009; 39(2): 241–245.

19.

Cui

Chang

Cui

. Research on observability analysis-based autonomous navigation method for deep space. J Astronaut2011; 32(10): 2115–2124.

20.

ISO 3888-1:1999 Passenger cars – test track for a severe lane-change manoeuvre – Part 1: double lane-change. Geneva: International Organization for Standardization, 1999.