An Efficient Power Control Scheme for Heavy-Duty Hybrid Electric Vehicle With Online Optimized Variable Universe Fuzzy System - Wang, Muyao; Yang, Chao; Wang, Weida; Lu, Zhexi; Yang, Liuquan; Chen, Ruihu (IEEE transactions on fuzzy systems, 2024)
In heavy-duty series hybrid electric vehicles (SHEVs), the engine-generator set (EGS) functions as the main power source for propulsion. However, the limitation of engine power per liter and delayed computation of control algorithm result in the hysteretic response of EGS to high demand power. It leads to deteriorating operation of powertrain. Thus, the challenging technical issue lies in achieving stable powertrain operation, which is difficult to describe precisely by real-time control. In this work, an efficient power control scheme for heavy-duty HEV with online optimized variable universe fuzzy system is proposed. First, a splitting sequential clustering quadratic programming algorithm is designed to solve power distribution and achieve real-time control. The original subproblem is split into two subproblems with smaller scale to obtain iterative points. And clustering algorithm is introduced to gather up the points to improve the termination criterion. It turns to skip unnecessary short step in the iteration which fails to obtain sufficient descent. Then, the online optimized variable universe fuzzy system is established to achieve rapid response of EGS by adjusting power distribution. In this system, online optimization of membership function distribution parameters is considered. The optimization is constructed on real-time membership overlap degree and central value of fuzzy system rather than the traditional offline optimization using posterior information of vehicle. Finally, the effectiveness of the proposed scheme is validated both in simulation test and hardware-in-loop test. The results reveal that stable power output is maintained and calculation time is decreased by 40.9%, 46.0% under two driving cycles.