Mode-switching-based active control of a powertrain system with non-linear backlash and flexibility for an electric vehicle during regenerative deceleration
Restricted accessResearch articleFirst published online September, 2015
Mode-switching-based active control of a powertrain system with non-linear backlash and flexibility for an electric vehicle during regenerative deceleration
Regenerative braking provided by an electric powertrain is very different from conventional friction braking with respect to the system dynamics. During regenerative decelerations, the powertrain backlash and flexibility excite driveline oscillations, causing the vehicle driveability and the blended brake performance to deteriorate. In this article, system models, including a powertrain model with non-linear backlash and flexibility, and a hydraulic brake system model, are developed. The effects of the powertrain backlash and the flexibility on the vehicle driveability during regenerative deceleration are analysed. To improve the driveability and the blended braking performance of an electric vehicle further, a mode-switching-based active control algorithm with a hierarchical architecture is developed providing compensation for the backlash and the flexibility. The proposed control algorithms are simulated and compared with the baseline strategy under the regeneration braking process. The simulation results show that the vehicle driveability and the blended braking performance can be significantly enhanced by the developed active control algorithm.
GaoYEhsaniM. Electronic braking system of EV and HEV – integration of regenerative braking, automatic braking force control and ABS. SAE paper 2001-01-2478, 2001.
2.
ZhangJLvCGouJKongD. Cooperative control of regenerative braking and hydraulic braking of an electrified passenger car. Proc IMechE Part D: J Automobile Engineering2012; 226(10): 1289–1302.
3.
SovranGBlaserD. Quantifying the potential impacts of regenerative braking on a vehicle’s tractivefuel consumption for the US, European, and Japanese driving schedules. SAE paper 2006-04-03, 2006.
4.
ChiaraFCanovaM. A review of energy consumption, management, and recovery in automotive systems, with considerations of future trends. Proc IMechE Part D: J Automobile Engineering2013; 227(6): 914–936.
5.
KanarachosSAlirezaeiMJansenSMauriceJP. Control allocation for regenerative braking of electric vehicles with an electric motor at the front axle using the state-dependent Riccati equation control technique. Proc IMechE Part D: J Automobile Engineering2014; 228(2): 129–143.
6.
ZhangJLvCQiuM. Braking energy regeneration control of a fuel cell hybrid electric bus. Energy Conversion Managmt2013; 76: 1117–1124.
7.
ZhangJKongDChenLChenX. Optimization of control strategy for regenerative braking of an electrified bus equipped with an anti-lock braking system. Proc IMechE Part D: J Automobile Engineering2012; 226(4): 494–506.
8.
LvCZhangJLiY. Hardware-in-the-loop simulation of pressure-difference-limiting modulation of the hydraulic brake for regenerative braking control of electric vehicles. Proc IMechE Part D: J Automobile Engineering2014; 228(6): 649–662.
9.
NakamuraESogaMSakaiA. Development of electronically controlled brake system for hybrid vehicle. SAE paper 2002-01-0300, 2002.
10.
OhtaniYInnamiTObataT. Development of an electrically-driven intelligent brake unit. SAE paper 2011-01-0572, 2011.
11.
FlemingB. Electric vehicle collaboration – Toyota Motor Corporation and Tesla Motors. IEEE Veh Technol Mag2013; 8: 4–9.
12.
ZhangJLiYLvCYuanY. New regenerative braking control strategy for rear-driven electrified minivans. Energy Conversion Managmt2014; 82: 135–145.
13.
HuangXWangJ. Model predictive regenerative braking control for lightweight electric vehicles with in-wheel motors. Proc IMechE Part D: J Automobile Engineering2012; 226(9): 1220–1232.
14.
LvCZhangJLiYYuanY. Regenerative braking control algorithm for an electrified vehicle equipped with a by-wire brake system. SAE paper 2014-01-1791, 2014.
15.
KawamuraHItoKKarikomiTKumeT. Highly-responsive acceleration control for the Nissan LEAF electric vehicle. SAE paper 2011-01-0397, 2011.
16.
LagerbergAEgardtB. Backlash estimation with application to automotive powertrains. IEEE Trans Control Systems Technol2007; 15: 483–493.
17.
TawareATaoGTeolisC. Design and analysis of a hybrid control scheme for sandwich nonsmooth nonlinear systems. IEEE Trans Autom Control2002; 47: 145–150.
18.
TaoGBurkholderJOGuoJ. Adaptive state feedback actuator nonlinearity compensation for multivariable systems. Int J Adaptive Control Signal Processing2013; 27:. 82–107.
19.
BottiglioneFSorniottiASheadL. The effect of half-shaft torsion dynamics on the performance of a traction control system for electric vehicles. Proc IMechE Part D: J Automobile Engineering2012; 226(9): 1145–1159.
20.
AmannNBockerJPrennerFActive damping of drive train oscillations for an electrically driven vehicle. IEEE/ASME Trans Mechatronics2004; 9: 697–700.
21.
TemplinP. Simultaneous estimation of driveline dynamics and backlash size for control design. In: IEEE international conference on control applications, San Antonio, Texas, USA, 3–5 September 2008, pp. 13–18. New York: IEEE.
22.
TemplinPEgardtBS. Experimental results for a powertrain LQR-torque compensator with backlash handling. In: 2009 IFAC workshop on engine and powertrain control, simulation and modeling, Rueil-Malmaison, France, 30 November–2 December 2009, pp. 148–153. Laxenburg: IFAC.
23.
FarshidianfarAEbrahimiMBartlettH. Hybrid modelling and simulation of the torsional vibration of vehicle driveline systems. Proc IMechE Part D: J Automobile Engineering2001; 215(2): 217–229.
24.
BerririMChevrelPLefebvreD. Active damping of automotive powertrain oscillations by a partial torque compensator. Control Engng Practice2008; 16: 874–883.
25.
BayarKWangJRizzoniG. Development of a vehicle stability control strategy for a hybrid electric vehicle equipped with axle motors. Proc IMechE Part D: J Automobile Engineering2012; 226(6): 795–814.
26.
LagerbergAEgardtB. Backlash gap position estimation in automotive powertrains. In: 7th European control conference, Cambridge, UK, 1–4September2003, pp. 1–6. Cambridge, UK: University of Cambridge.
27.
ZhangJLvCYueX. Study on a linear relationship between limited pressure difference and coil current of on/off valve and its influential factors. ISA Trans2014; 53: 150–161.
28.
ZhangJLvCYueXQiuM. Development of the electrically-controlled regenerative braking system for electrified passenger vehicle. SAE paper 2013-01-1463, 2013.
29.
YeoHHwangSKimH. Regenerative braking algorithm for a hybrid electric vehicle with CVT ratio control. Proc IMechE Part D: J Automobile Engineering2006; 220(11): 1589–1600.
30.
FazeliAZeinaliMKhajepourA. Application of adaptive sliding mode control for regenerative braking torque control. IEEE/ASME Trans Mechatronics2012;17: 745–755.
31.
WangBHuangXWangJ. A robust wheel slip ratio control design combining hydraulic and regenerative braking systems for in-wheel-motors-driven electric vehicles. J Franklin Inst2014; in press. doi:10.1016/j.jfranklin.2014.06.004
32.
EzalKKokotovicPVTaoG. Optimal control of tracking systems with backlash and flexibility. In: 36th IEEE conference on decision and control, San Diego, California, USA, 10–12 December 1997, Vol 2, pp. 1749–1754. New York: IEEE.
