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

Jalalmaab, Mehdi; Azad, Nasser L · 2018 · Crossref

DOI: 10.1177/0954407018772377

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Summary

This paper addresses the challenge of optimizing energy efficiency in in-wheel motor electric vehicles (IWM-EVs) by developing a stochastic power management strategy. While IWM-EVs offer improved controllability and efficiency compared to conventional battery electric vehicles, their limited driving range remains a significant barrier to adoption. The authors propose a strategy that distributes demanded power between front and rear in-wheel motors to minimize energy consumption while accounting for the unpredictable nature of real-world driving conditions. Crucially, the strategy incorporates a skid avoidance rule to maintain wheel slip ratios within safe limits, ensuring stability and traction performance. The methodology employs Stochastic Dynamic Programming (SDP) using a policy iteration algorithm to formulate an infinite horizon optimization problem. This approach determines optimal power distribution policies based on vehicle states, including speed and front/rear wheel slip ratios, without requiring future power demand predictions or telemetric data. The study utilizes two modeling approaches: a high-fidelity model developed in the Autonomie/Simulink environment for evaluation, and a simplified control-oriented model for parameter optimization. The vehicle specifications include an 800 kg mass, a 200 Ah battery, and 7.5 kW in-wheel motors. The SDP algorithm minimizes a cost function comprising battery state-of-charge changes and penalties for power demand deviation, resulting in a time-invariant, rule-based controller suitable for real-time implementation. Simulation results demonstrate that the proposed SDP strategy outperforms benchmark methods, specifically equal power distribution and generalized rule-based dynamic programming. The strategy achieves an average energy consumption reduction of 3% across various driving scenarios. Sensitivity analysis reveals that optimal power distribution is dependent on vehicle speed, demanded power, and tire slip ratios, with the SDP algorithm effectively handling these nonlinear relationships. The convergence of the policy iteration algorithm was verified, confirming the stability of the derived control policies. The significance of this work lies in providing a cost-effective, computationally efficient solution for IWM-EV power management that does not rely on expensive sensors or communication systems. By integrating safety constraints like skid avoidance directly into the energy optimization framework, the strategy enhances both efficiency and vehicle stability. The authors conclude that while the 3% energy saving per trip may appear modest, the cumulative impact across the total annual trips and the broader population of electric vehicles is substantial, contributing meaningfully to extending driving range and reducing overall energy demand.

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