A stochastic power management strategy with skid avoidance for improving energy efficiency of in-wheel motor electric vehicles

In this study, a stochastic power management strategy for in-wheel motor electric vehicles is proposed to reduce the energy consumption and increase the driving range, considering the unpredictable nature of the driving power demand. A stochastic dynamic programming approach, policy iteration algorithm, is used to create an infinite horizon problem formulation to calculate optimal power distribution policies for the vehicle. The developed stochastic dynamic programming strategy distributes the demanded power, Pdem between the front and rear in-wheel motors by considering states of the vehicle, including the vehicle speed and the front and the rear wheels’ slip ratios. In addition, a skid avoidance rule is added to the power management strategy to maintain the wheels’ slip ratios within the desired values. Undesirable slip ratios cause poor brake and traction control performances and therefore should be avoided. The resulting strategy consists of a time-invariant, rule-based controller which is fast enough for real time implementations, and additionally, it is not expensive to be launched since the future power demand is approximated without a need to vehicle communication systems or telemetric capability. A high-fidelity model of an in-wheel motor electric vehicle is developed in the Autonomie/Simulink environment for evaluating the proposed strategy. The simulation results show that the proposed stochastic dynamic programming strategy is more efficient in comparison to some benchmark strategies, such as an equal power distribution and generalized rule-based dynamic programming. The simulation results of different driving scenarios for the considered in-wheel motor electric vehicle show the proposed power management strategy leads to 3% energy consumption reduction in average, at no additional cost. If the resulting energy savings is considered for the total annual trips for the vehicle and also the total number of electric vehicles in the country, the proposed power management strategy has a significant impact.

[1]  Huei Peng,et al.  A stochastic control strategy for hybrid electric vehicles , 2004, Proceedings of the 2004 American Control Conference.

[2]  J. Fredriksson,et al.  Coordination of Vehicle Motion and Energy Management Control Systems for Wheel Motor Driven Vehicles , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[3]  Nasser L. Azad,et al.  Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions , 2015 .

[4]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[5]  Hiroshi Fujimoto.  Regenerative Brake and Slip Angle Control of Electric Vehicle with In-wheel Motor and Active Front Steering , 2011 .

[6]  Yan Chen,et al.  Adaptive Energy-Efficient Control Allocation for Planar Motion Control of Over-Actuated Electric Ground Vehicles , 2014, IEEE Transactions on Control Systems Technology.

[7]  Yan Chen,et al.  Energy-efficient control allocation with applications on planar motion control of electric ground vehicles , 2011, Proceedings of the 2011 American Control Conference.

[8]  Yan Chen,et al.  Fast and Global Optimal Energy-Efficient Control Allocation With Applications to Over-Actuated Electric Ground Vehicles , 2012, IEEE Transactions on Control Systems Technology.

[9]  Nasser L. Azad,et al.  Online optimization of automotive engine coldstart hydrocarbon emissions control at idle conditions , 2015, J. Syst. Control. Eng..

[10]  Michael Ellims,et al.  ISO 26262: Experience applying part 3 to an in-wheel electric motor , 2011 .

[11]  Kyongsu Yi,et al.  Coordinated Control of Tractive and Braking Forces Using High Slip for Improved Turning Performance of an Electric Vehicle Equipped with In-Wheel Motors , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[12]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[13]  John McPhee,et al.  Development of an Advanced Torque Vectoring Control System for an Electric Vehicle with In-Wheel Motors using Soft Computing Techniques , 2013 .

[14]  Xiaoyong Wang,et al.  Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data , 2009, 2009 American Control Conference.

[15]  Junmin Wang,et al.  Operational Energy Optimization for Pure Electric Ground Vehicles Based on Terrain Profile Preview , 2011 .

[16]  Mahyar Vajedi,et al.  Intelligent power management of plug-in hybrid electric vehicles, part II: real-time route based power management , 2014 .

[17]  Y. Hori,et al.  Body Slip Angle Estimation and Control for Electric Vehicle with In-Wheel Motors , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[18]  Yangsheng Xu,et al.  Energy management for four-wheel independent driving vehicle , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  John McPhee,et al.  Design and Evaluation of a Real-time Fuel-optimal Control System for Series Hybrid Electric Vehicles , 2012 .

[20]  Xiaoyu Huang,et al.  Model predictive regenerative braking control for lightweight electric vehicles with in-wheel motors , 2012 .

[21]  Namwook Kim,et al.  Comparison between Rule-Based and Instantaneous Optimization for a Single-Mode, Power-Split HEV , 2011 .

[22]  Huei Peng,et al.  Power management strategy for a parallel hybrid electric truck , 2003, IEEE Trans. Control. Syst. Technol..

[23]  Y. Hori,et al.  Future motion control to be realized by in-wheel motored electric vehicle , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[24]  Sue Hopkins,et al.  Magic formula? , 1999, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[25]  Yan Chen,et al.  Energy Management and Driving Strategy for In-Wheel Motor Electric Ground Vehicles With Terrain Profile Preview , 2014, IEEE Transactions on Industrial Informatics.

[26]  Aymeric Rousseau,et al.  Automated Model Based Design Process to Evaluate Advanced Component Technologies , 2010 .

[27]  Reza Sharif Razavian,et al.  Design and Evaluation of a Real-Time Optimal Control System for Series Hybrid Electric Vehicles , 2012 .

[28]  Shin-ichiro Sakai,et al.  Motion control in an electric vehicle with four independently driven in-wheel motors , 1999 .

[29]  Hosam K. Fathy,et al.  A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles , 2011, IEEE Transactions on Control Systems Technology.

[30]  Mahyar Vajedi,et al.  Intelligent power management of plug–in hybrid electric vehicles, part I: real–time optimum SOC trajectory builder , 2014 .

[31]  Xinbo Chen,et al.  Overview and Prospects on Distributed Drive Electric Vehicles and Its Energy Saving Strategy , 2012 .

[32]  Nasser L. Azad,et al.  An optimal power management strategy for power split plug-in hybrid electric vehicles , 2012 .

[33]  Junmin Wang,et al.  A global optimization algorithm for energy-efficient control allocation of over-actuated systems , 2011, Proceedings of the 2011 American Control Conference.

[34]  Yinghui Ge,et al.  Torque distribution control for electric vehicle based on traction force observer , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[35]  B Jacobsen.  Potential of electric wheel motors as new chassis actuators for vehicle manoeuvring , 2002 .

[36]  Hongyu Zheng,et al.  The Regenerative Braking Control Strategy of Four-Wheel-Drive Electric Vehicle Based on Power Generation Efficiency of Motors , 2013 .

[37]  Lam Van Dongen,et al.  ELECTRIC VEHICLES IN THE UNITED STATES , 1982 .

[38]  Hans B. Pacejka,et al.  THE MAGIC FORMULA TYRE MODEL , 1991 .

[39]  Languang Lu,et al.  Energy efficiency optimization of electric vehicle driven by in-wheel motors , 2013 .