A stochastic power management strategy with skid avoidance for improving energy efficiency of in-wheel motor electric vehicles (1708.06859v1)

Abstract: In this study, a stochastic power management strategy for in-wheel motor electric vehicles (IWM-EV) is proposed to reduce the energy consumption and increase the driving range by considering the unpredictable nature of the driving power demand. A stochastic dynamic programming (SDP) approach, policy iteration algorithm, is used to create an infinite horizon problem formulation to calculate optimal power distribution policies for the vehicle. The developed SDP strategy distributes the demanded power, between the front and rear IWMs 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 IWM-EV is developed in the Autonomie/Simulink environment for evaluating the proposed strategy. The simulation results show that the proposed SDP strategy is more efficient in comparison to some benchmark strategies, such as an equal power distribution (ED) and generalized rule- based dynamic programming (GRDP). The simulation results of different driving scenarios for the considered IWM-EV shows the proposed power management strategy leads to considerable energy consumption reduction in average, at no additional cost.