Abstract
The lateral stability is critically important especially when the vehicle is steered at a high longitudinal speed. Loss of lateral stability would lead to severe accidents. Therefore, the study of lateral stability has been a hot topic for decades. In order to monitor the lateral stability or improve the stability by using the feedback control, the sideslip angle is an important index. However, the sideslip angle is not measurable by using an affordable physical sensor. An alternative approach is to estimate the sideslip angle with the measurements of relatively cheap sensors. In this chapter, we investigate the problem of sideslip angle observer design for an electric ground vehicle (EGV). The EGV is equipped with an advanced navigation system. The lateral velocity, the longitudinal velocity, and the yaw rate are available. Thus, the sideslip angle which is defined as the ratio of lateral velocity and longitudinal velocity is also available. Meanwhile, the hand-wheel steering angle is also measurable in this application. The main work is to estimate the sideslip angle with the measurements of yaw rate based on the vehicle lateral dynamics. The dynamic model is first established, and the parameters are identified with experimental data. Since the dynamic model is nonlinear, in order to facilitate the system analysis and observer design, the nonlinear model is transformed to a linear-parameter-varying (LPV) system. An observer is proposed based on the LPV form. By defining the estimation error, a compact system which contains the estimation error and the original dynamics is obtained. Considering the frequency of the front-wheel steering angle, the finite-frequency \(\mathcal {H}_{\infty }\) performance of the compact system is exploited. An optimal observer design method is then developed. For the EGV, the observer is designed according to the developed method. The performance of the designed observer is illustrated with experimental test data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
X. Hu, R. **ong, B. Egardt, Model-based dynamic power assessment of Lithium-Ion batteries considering different operating conditions. IEEE Trans. Ind. Inform. 10(3), 1948–1959 (2014)
A. Greco, D. Cao, X. Jiang, H. Yang, A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes. J. Power Sources 257(7), 344–355 (2014)
X. Hu, N. Murgovski, L.J. Mardh, B. Egardt, Comparison of three electrochemical energy buffers applied to a hybrid bus powertrain with simultaneous optimal sizing and energy management. IEEE Trans. Intell. Transp. 15(3), 1193–1205 (2014)
R. Wang, C. Hu, Z. Wang, F. Yan, N. Chen, Integrated optimal dynamics control of 4WD4WS electric ground vehicle with tire-road frictional coefficient estimation. Mech. Syst. Signal Process. 60–61, 727–741 (2015)
F. Meng, H. Chen, T. Zhang, X. Zhu, Clutch fill control of an automatic transmission for heavy-duty vehicle applications. Mech. Syst. Signal Process. 64–65, 16–28 (2015)
M. Montazeri-Gh, M. Soleymani, S. Hashemi, Impact of traffic conditions on the active suspension energy regeneration in hybrid electric vehicles. IEEE Trans. Ind. Electron. 60(10), 4546–4553 (2013)
M. Zhang, Y. Yang, C. Mi, Analytical approach for the power management of blended-mode plug-in hybrid electric vehicles. IEEE Trans. Veh. Technol. 61(4), 1554–1566 (2012)
S.M.M. Sangdehi, S. Hamidifar, N. Kar, A novel bidirectional DC/AC stacked matrix converter design for electrified vehicle applications. IEEE Trans. Veh. Technol. 63(7), 3038–3050 (2014)
F. Zhu, L. Chen, C. Yin, Design and analysis of a novel multimode transmission for a HEV using a single electric machine. IEEE Trans. Veh. Technol. 62(3), 1097–1110 (2013)
L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao, Z. Shao, Regulated charging of plug-in hybrid electric vehicles for minimizing load variance in household smart microgrid. IEEE Trans. Ind. Electron. 60(8), 3218–3226 (2013)
A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, A. Kapoor, Intelligent sensorless ABS for in-wheel electric vehicles. IEEE Trans. Ind. Electron. 61(4), 1957–1969 (2014)
Y. Wang, B.M. Nguyen, H. Fujimoto, Y. Hori, Multirate estimation and control of body slip angle for electric vehicles based on onboard vision system. IEEE Trans. Ind. Electron. 61(2), 1133–1143 (2014)
B. Li, H. Du, W. Li, Y. Zhang, Side-slip angle estimation based lateral dynamics control for omni-directional vehicles with optimal steering angle and traction/brake torque distribution. Mechatronics 30, 348–362 (2015)
X. **, G. Yin, N. Chen, Gain-scheduled robust control for lateral stability of four-wheel-independent-drive electric vehicles via linear parameter-varying technique. Mechatronics 30, 286–296 (2015)
Y. Sun, L. Li, B. Yan, C. Yang, G. Tang, A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle mass for a hybrid electric bus. Mech. Syst. Signal Process. 68–69, 416–430 (2016)
R. Wang, H. Zhang, J. Wang, Linear parameter-varying based fault-tolerant controller design for a class of over-actuated nonlinear systems with applications to electric vehicles. IET Control Theory Appl. 8(9), 705–717 (2014)
R. Wang, H. Zhang, J. Wang, Linear parameter-varying controller design for four wheel independently-actuated electric ground vehicles with active steering systems. IEEE Trans. Control Syst. Technol. 22(4), 1281–1296 (2014)
D.M. Bevly, J.C. Gerdes, C. Wilson, The use of GPS based velocity measurements for measurement of sideslip and wheel slip. Veh. Syst. Dyn. 38(2), 127–147 (2002)
D. Piyabongkarn, R. Rajamani, J.A. Grogg, J.Y. Lew, Development and experimental evaluation of a slip angle estimator for vehicle stability control. IEEE Trans. Control Syst. Technol. 17(1), 78–88 (2009)
Y.H. Hsu, S.M. Laws, J.C. Gerdes, Estimation of tire slip angle and friction limits using steering torque. IEEE Trans. Control Syst. Technol. 18(4), 896–907 (2010)
K. Nam, S. Oh, H. Fujimoto, Y. Hori, Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and Kalman filter approaches. IEEE Trans. Ind. Electron. 60(3), 988–1000 (2013)
R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME, J Basic Eng. 82(Series D), 35–45 (1960)
H. Gao, T. Chen, \(\cal{H} _{\infty }\) estimation for uncertain systems with limited communication capacity. IEEE Trans. Autom. Contr. 52(11), 2070–2084 (2007)
H. Gao, X. Meng, T. Chen, A parameter-dependent approach to robust \(\cal{H} _{\infty }\) filtering for time-delay systems. IEEE Trans. Autom. Contr. 53(10), 2420–2425 (2008)
H. Zhang, A. Saadat Mehr, Y. Shi, Improved robust energy-to-peak filtering for uncertain linear systems. Signal Process. 90(9), 2667–2675 (2010)
D.-W. Ding, G. hong Yang, Fuzzy filter design for nonlinear systems in finite-frequency domain. IEEE Trans. Fuzzy Syst. 18(5), 935–945 (2010)
X. Li, H. Gao, Robust finite frequency filtering for uncertain 2-D roesser systems. Automatica 48(6), 1163–1170 (2012)
H. Wang, G. hong Yang, A finite frequency approach to filter design for uncertain discrete-time systems. Int. J. Adapt. Control Signal Process. 22(6), 533–550 (2008)
K. Nam, H. Fujimoto, Y. Hori, Lateral stability control of in-wheel-motor-driven electric vehicles based on sideslip angle estimation using lateral tire force sensors. IEEE Trans. Veh. Technol. 61(5), 1972–1985 (2012)
K. Nam, S. Oh, H. Fujimoto, Y. Hori, Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and Kalman filter approaches. IEEE Trans. Ind. Electron. 60(3), 988–1000 (2013)
J.-H. Yoon, H. Peng, Robust vehicle sideslip angle estimation through a disturbance rejection filter that integrates a magnetometer with GPS. IEEE Trans. Intell. Transp. 15(1), 191–204 (2014)
S. Melzi, E. Sabbioni, On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results. Mech. Syst. Signal Process. 25(6), 2005–2019 (2011)
L. Li, G. Jia, X. Ran, J. Song, K. Wu, A variable structure extended kalman filter for vehicle sideslip angle estimation on a low friction road. Veh. Syst. Dyn. 52(2), 280–308 (2014)
R. Wang, Y. Chen, D. Feng, X. Huang, J. Wang, Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors. J. Power Sources 196(8), 3962–3971 (2011)
H. Zhang, X. Huang, J. Wang, H.R. Karimi, Robust energy-to-peak sideslip angle estimation with applications to ground vehicles. Mechatronics 30, 338–347 (2015)
L. **e, Y.C. Soh, Robust control of linear systems with generalized positve real uncertainty. Automatica 33(5), 963–967 (1997)
W.M. Haddad, D.S. Bernstein, Controller design with regional pole constraints. IEEE Trans. Autom. Contr. 37(1), 54–69 (1992)
D.H. Lee, An improved finite frequency approach to robust \(\cal{H} _\infty \) filter design for LTI systems with polytopic uncertainties. Int. J. Adapt. Control Signal Process. 27(11), 944–956 (2013)
H. Zhang, R. Wang, J. Wang, Y. Shi, Robust finite frequency static-output-feedback control with application to vibration active control of structural systems. Mechatronics 24(4), 354–366 (2014)
H. Zhang, G. Zhang, J. Wang, Sideslip angle estimation of an electric ground vehicle via finite-frequency \(\cal{H} _{\infty }\) approach. IEEE Trans. Transp. Electrification 2(2), 200–209 (2016)
Acknowledgements
This chapter is from the previous work in [40], and some typos are corrected here.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2023 Huazhong University of Science and Technology Press
About this chapter
Cite this chapter
Zhang, H., Wang, R., Wang, J. (2023). Sideslip Angle Estimation of An Electric Ground Vehicle Via Finite-Frequency \(\mathcal {H}_{\infty }\) Approach. In: Robust Gain-Scheduled Estimation and Control of Electrified Vehicles via LPV Technique. Key Technologies on New Energy Vehicles. Springer, Singapore. https://doi.org/10.1007/978-981-19-8509-6_3
Download citation
DOI: https://doi.org/10.1007/978-981-19-8509-6_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8508-9
Online ISBN: 978-981-19-8509-6
eBook Packages: EngineeringEngineering (R0)