Tire-road-friction-estimation-based braking force distribution for AWD electrified vehicles with a single electric machine

Braking force distribution (BFD) for electrified vehicles for maximizing energy regeneration has been a challenging research topic, due to the complex operating conditions and tradeoff among different vehicle performance measures. It is known that the level of tire-road friction has a significant impact on the braking force boundaries that define the locking conditions of front and rear wheels. However, conventional BFD strategies for electrified vehicles have not taken full advantage of tire-road friction and generally preferred conservative algorithms. These suggest a potential for exploiting tire-road friction estimation for BFD so as to enhance energy regeneration of electrified vehicles while retaining vehicle stability. This study tackles this challenge by proposing a tire-road-friction-estimation-based BFD for all-wheel-drive (AWD) electrified vehicles with a single electric motor. The specific topology considered in this study is a plug-in hybrid electric vehicle that is powered by an internal combustion engine and a single electric machine. The AWD capacity is provided by a propeller differential shaft connecting the front and rear axles, which imposes a constraint on the ratio of front/rear regenerative brake forces, which is always equal on both axles for the vehicle topology considered here. For the proposed BFD strategy, a fuzzy-logic based tire-road friction estimation algorithm is developed, which uses the longitudinal wheel slip estimated from sensor measurements of vehicle acceleration and wheel speeds. The tire-road friction estimation algorithm is accordingly integrated within the braking controller for front and rear braking force generation and allocation. Simulation analyses are conducted, and the results and discussions demonstrate the effectiveness of the proposed tire-road friction estimation algorithm, and that the tire-road friction estimation-oriented BFD strategy can help to improve the braking energy recovery.

[1]  Yimin Gao,et al.  Design and Control Principles of Hybrid Braking System for EV, HEV and FCV , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[2]  Rui Esteves Araujo,et al.  Real-time estimation of tyre-road friction peak with optimal linear parameterisation , 2012 .

[3]  Binggang Cao,et al.  Application of Genetic Algorithm for Braking Force Distribution of Electric Vehicles , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[4]  Yan Chen,et al.  Adaptive Vehicle Speed Control With Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation , 2011, IEEE Transactions on Vehicular Technology.

[5]  Yantao Tian,et al.  A new braking force distribution strategy for electric vehicle based on regenerative braking strength continuity , 2013 .

[6]  Ali Emadi,et al.  Modern electric, hybrid electric, and fuel cell vehicles : fundamentals, theory, and design , 2009 .

[7]  Changsun Ahn,et al.  Robust estimation of road friction coefficient , 2011, Proceedings of the 2011 American Control Conference.

[8]  Binggang Cao,et al.  Regenerative braking strategy for electric vehicles , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[9]  F Wang,et al.  Regenerative braking strategy for hybrid electric vehicles based on regenerative torque optimization control , 2008 .

[10]  Kevin Knowles,et al.  Effect on regenerative braking efficiency with deceleration demand and terrain condition , 2010 .

[11]  Mara Tanelli,et al.  Active Braking Control Systems Design for Vehicles , 2010 .

[12]  Hans B. Pacejka,et al.  Tire and Vehicle Dynamics , 1982 .

[13]  Guoqing Xu,et al.  Regenerative braking for electric vehicle based on fuzzy logic control strategy , 2010, 2010 2nd International Conference on Mechanical and Electronics Engineering.

[14]  Emilio Frazzoli,et al.  On steady-state cornering equilibria for wheeled vehicles with drift , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[15]  Ali Charara,et al.  Onboard Real-Time Estimation of Vehicle Lateral Tire–Road Forces and Sideslip Angle , 2011, IEEE/ASME Transactions on Mechatronics.

[16]  Kyongsu Yi,et al.  Tire-Road Friction-Coefficient Estimation , 2010, IEEE Control Systems.