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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.

Inspired by the imbalance between extrinsic and intrinsic tendon healing, this study fabricated a new biofilter scaffold with a hierarchical structure based on a melt electrowriting technique. The outer multilayered fibrous structure with connected porous characteristics provides a novel passageway for vascularization and isolates the penetration of scar fibers, which can be referred to as a biofilter process. In vitro experiments found that the porous architecture in the outer layer can effectively prevent cell infiltration, whereas the aligned fibers in the inner layer can promote cell recruitment and growth, as well as the expression of tendon-associated proteins in a simulated friction condition. It was shown in vivo that the biofilter process could promote tendon healing and reduce scar invasion. Herein, this novel strategy indicates great potential to design new biomaterials for balancing extrinsic and intrinsic healing and realizing scarless tendon healing.

Braking energy recovery (BER) aims to recover the vehicle’s kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle (EV). To achieve this goal, the motor/generator mode requires frequent switching and prolonged operation during driving. In this case, the motor temperature will unavoidably rise, potentially triggering motor thermal protection (MTP). Activating MTP increases the risk of motor component failure, and the EV typically disables the BER function. Thus, maximizing BER while reducing the risk of motor overheating is a challenging problem. To address this issue, this article proposes a predictive BER strategy with MTP using the non-smooth Pontryagin Minimum Principle (NSPMP) for EVs. Firstly, a Markov long short-term memory (MLSTM) model is designed to obtain future velocity information. Secondly, the BER problem with MTP in the studied EV is embedded in a model predictive control (MPC) framework. Then, under the MPC framework, the NSPMP strategy is proposed to resolve the problem of MTP. Finally, the performance of the proposed strategy is verified through simulation and a hardware-in-loop test. The results show that in two real-world driving cycles, compared to the rule-based strategy, the proposed strategy reduced power consumption by 1.24% and 0.96%, respectively, and effectively limited motor temperature. Additionally, under global cycle conditions, this strategy demonstrated better MTP control performance compared to other benchmark strategies.

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.

• Accurate coating wear quantification at nanoscale level is established by employing a Raman sensing layer (silicon) under target coating and successfully applied to quantify the wear of target coating in dry and oil-lubricated conditions. • This approach can effectively avoid the remarkable errors caused by tribo-induced effects in the interface regions, demonstrating its advantage in the capacity of error tolerance. For gaining fundamental insight into coating wear mechanisms and increasing operational efficiency and automation degree of equipment in important application fields of coating techniques, it is of great importance to developing novel wear measurement techniques enabling nanoscale studies of coating wear in the running process, but this remains a significant challenge. Here, a facile strategy is reported to achieve accurate coating thickness quantification at nanoscale level, which is based on a bilayer structure: a top target layer of a-C:H (hydrogenated amorphous carbon) film is considered as a light attenuating and anti-wear layer while underlayer of silicon serves as Raman-sensing layer. Through constructing the relationship between the thickness of a-C:H and Raman intensity of attenuated silicon signal, the coating thickness quantification method is established and successfully applied to quantify coating wear in the friction process. This approach can effectively avoid remarkable errors caused by tribo-induced effects in the interface regions, demonstrating its advantage in error tolerance. Details about these tribo-induced effects are also elucidated by a combination of Raman spectroscopy, optical profilometer, EELS, and TEM. In particular, the proposed approach enables the possibility of measuring coating wear with oil film on top, which breaks an important limitation of existed wear measurement methods, i.e., incapable of applying in oil-lubricated conditions. This approach can be used to quantify the wear condition of diverse target coatings and has the potential of online wear monitoring when combining a compact laser excitation and detection system. [Display omitted] [ABSTRACT FROM AUTHOR]

Power flow optimization control, which governs the energy flow among engine, battery, and motor, plays a very important role in plug-in hybrid electric vehicles (PHEVs). Its performance directly affects the fuel economy of PHEVs. For the purpose of improving fuel economy, the electric system including battery and motor will be frequently scheduled, which would affect battery life. Therefore, a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary, which is also a research focus in field of hybrid vehicles. Motivated by this issue, this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory. Firstly, since the demand power of driver which is necessary for the power flow optimization control, cannot be known in advance, the demand power of driver can be modelled using a Markov chain to obtain predicted demand power. Secondly, based on the predicted demand power, the multi-objective optimization control problem is transformed into a game problem. A novel non-cooperative game model between engine and battery is established, and the benefit function with fuel economy and battery life as the optimization objective is proposed. Thirdly, under the premise of satisfying various constraints, the participants of the above game maximize their own benefit function to obtain the Nash equilibrium, which comprises of optimal power split scheme. Finally, the proposed strategy is verified compared with two baseline strategies, and results show that the proposed strategy can reduce equivalent fuel consumption by about 15% compared with baseline strategy 1, and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.

Solid–liquid lubricating systems have received significant attention as a promising way for energy saving and emission control. For deeply understanding their tribological behaviors, it is necessary to study interaction mechanisms between solid and liquid lubricants from the tribochemical viewpoint, as tribofilms formed by tribochemical products on contact surfaces critically affect the whole tribological process. Continually or periodically monitoring tribofilm formation and evolution can contribute significantly to clarifying its dominating role in tribological behavior under boundary lubrication. However, detecting tribofilms in situ remains a big challenge for conventional surface analytical approaches, mainly due to their limitations in accessing tribofilms or low signal intensities of thin tribofilms. In this study, highly sensitive Raman-based profilometry with in situ potential has been developed for detecting molybdenum dialkyldithiocarbamate (MoDTC)-derived tribofilms and exploring their effect on a-C:H wear over time. The optical properties of tribochemical products formed on the coating surface in different wear stages could result in extra attenuation of Raman signal intensities in the form of measurement deviations in wear depth. By monitoring the deviations, key information of tribofilm compositions was obtained and a two-stage wear progression mechanism was proposed for the first time to clarify the detrimental effect of MoDTC-derived tribofilms on a-C:H wear by combining detailed structure and composition analyses.

Additive Layer Manufacturing (ALM) of metals is rapidly changing the landscape of industrial manufacturing. Its deployment is however still hindered by extremely rough native surfaces, and drastic difficulties in efficiently applying conventional finishing methods. This paper presents the PALMS process, derived from electrolytic plasma polishing, as a solution to this problem. The viability of the process on a scale compatible with commercial use is demonstrated with a prototype industrial implementation. PALMS was applied on AISI 316 stainless steel pieces produced either by ALM or by conventional machining (CM.) Surface states, microstructures and other properties were compared pre- and post-PALMS. Significant improvements in surface state were observed after a 10 min treatment, with a 5-fold reduction in roughness. ALM surfaces were not affected negatively by PALMS in any way measured, and showed slight improvements in hardness and pore density. Two PVD coatings (TiN and WCC) were finally applied Post-PALMS, to test the compatibility of the process with further industrially relevant surface treatments. PALMS enables good coating adhesion on ALM pieces, with improved friction and wear properties compared to their CM counterparts.