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Elemental composition design is a common strategy for tuning the crystallographic structure of films, thereby optimizing their mechanical properties. The evolution of film structure is highly dependent on both selection and concentration of cation and anion elements. In this work, we utilized strong nitride-forming elements (Hf, Nb, Zr, and Ta) and the weak nitride-forming element Mo as the metallic cations in a multi-principal element nitride system. The nitrogen content in the (HfMoNbZrTa)1-xNxfilm was manipulated by varying the N2/Ar flow ratio (RN) during reactive magnetron sputtering. The results showed that the HfMoNbZrTa metallic film crystallized weakly into a body-centered cubic (BCC) structure, yielding a hardness (H) of approximately 10.7 ± 0.2 GPa. The incorporation of nitrogen immediately induced a transformation from BCC to a face-centered cubic (FCC) predominated multi-phase structure at substoichiometric regimes (10% ≤ RN ≤ 20%). A further increase of RNto ≥25%, i.e., at near-stoichiometric regimes, resulted in the formation of single-phase FCC structure. Hardness and wear rates both reached their optimal values at RN = 20–25%. The strengthening mechanism was elucidated through density functional theory (DFT) calculations, which suggest that higher Hof the substoichiometric film is mainly associated with the increased local lattice distortion and the formation of a multi-phase structure.

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.

Energy management strategy (EMS) is regarded as a crucial measure to further leverage the benefits of plug-in hybrid electric vehicles (PHEVs). However, existing EMSs often fail to balance applicability and optimality. As a widely recognized instantaneous optimization strategy, equivalent consumption minimization strategy (ECMS) is considered to be competitive to achieve the desired performance, provided that equivalent factor (EF) is properly tuned. To achieve this goal, a rolling convergent ECMS for energy management of PHEV is proposed in this article. Instead of seeking the instantaneous solution solely from two-dimensional scale like traditional ECMS, this article extends the analysis to three-dimensional global scale. And the analytic expression of the optimal EF initial interval is derived, which can be quantitatively described by the component specifications of PHEV and driving condition information. Subsequently, inheriting the obtained optimal EF initial interval, a rolling convergent method using tabu search is developed for more efficient optimization. Finally, the effectiveness of the proposed strategy is verified by simulation and experiment. The results show that the proposed strategy improves the fuel economy of PHEV by 3.7% and 5.2% over that using the traditional adaptive ECMS under two real-world driving cycles, respectively. And the validity of the optimal EF initial interval is corroborated under several typical driving cycles. Furthermore, the proposed strategy is performed on the test bench, demonstrating its superiority and practicality.