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Balancing catalytic activity and durability remains a challenge in the development of high performance, inexpensive and durable electrocatalysts. In this study, fully hydrolyzed composite catalyst V–Fe 2 P@Co 3 (PO 4) 2 /NF with ping-pong daisy-like morphology was synthesized using conventional hydrothermal and chemical vapor deposition (CVD) processes. In alkaline electrolyte, V–Fe 2 P@Co 3 (PO 4) 2 /NF showed excellent electrochemical performance, achieving a current density of 10 mA cm−2 when the driving overpotentials for the hydrogen production reaction (HER) and oxidation reaction (OER) were 56 mV and 178 mV, respectively. Meanwhile, V–Fe 2 P@Co 3 (PO 4) 2 /NF demonstrated good durability in terms of both HER and OER stability. Furthermore, the driving voltage required for the electrolyser assembled with V–Fe 2 P@Co 3 (PO 4) 2 /NF as the electrode is lower than that of the RuO 2 /NF||Pt/C/NF electrode, both of which have driving voltages of 1.503 and 1.562 V (10 mA cm−2), respectively. Meanwhile, it achieves a Faraday efficiency of 95%. This study offers a feasible solution towards synthesizing and applying bifunctional water decomposition electrocatalysts. The synthesized V–Fe 2 P@Co 3 (PO 4) 2 /NF catalyst exhibits excellent catalytic properties for bifunctional electrolysis of water in 1 M KOH solution. [Display omitted] •V doping alters Co3(PO4)2 and Fe2P nanosheets morphology and catalytic properties. • Synthesized samples show superior catalytic activity for total hydrolysis in 1 M KOH. • Ping Pong daisy's shape leads to more pores, larger active area and better stability. [ABSTRACT FROM AUTHOR]

Abnormal combustion is an important factor in the development process of hydrogen engine and it mainly includes pre-ignition, backfire and knocking, among which pre-ignition has the most serious impact on hydrogen engine. In this paper, it is divided into four types: normal combustion, slight pre-ignition, moderate pre-ignition and severe pre-ignition according to different crankshaft rotation angles. In order to identify different combustion types, this paper proposes a fault diagnosis model based on the fusion of SOM neural network and Multi-Agent System (SOM-MAS). Firstly, different combustion types are identified by SOM. Secondly, the abnormal combustion is tracked and located mainly through the Multi-Agent System, and the location of the abnormality is identified. Finally, based on 44 sets of pressure data samples collected from the in-cylinder combustion of a hydrogen engine on the experimental bench, different combustion types were diagnosed and identified, and the location of abnormal combustion faults was tracked, which verifies the effectiveness of the proposed method shows that the method has certain feasibility and superiority for the diagnosis of hydrogen engine pre-ignition. • Research carried out by the 492Q hydrogen engine, close to actual conditions. • The dataset is original and contains data for normal and abnormal combustion. • SOM-MAS can be applied in abnormal combustion of hydrogen fuel engines. • Less relevant dataset, we will publish the dataset for future research. [ABSTRACT FROM AUTHOR]

With the development of new energy, hydrogen fuel engines have become a research boom in the automotive field. But there are abnormal problems such as backfire and pre-ignition during the combustion of hydrogen engines. This paper is based on the Ant Colony Optimization-Back Propagation (ACO-BP) algorithm to study the influence of different speed and load conditions on the ignition advance angle, so as to optimize the control of the hydrogen engine. The experimental system is established on a hydrogen engine converted from a 492Q gasoline engine. The prediction of the optimal ignition advance angle was obtained through experiments, and the optimal ignition MAP diagram of the hydrogen engine is constructed. The optimal ignition advance angle under different working conditions can effectively avoid the occurrence of hydrogen engine pre-ignition. The accuracy reaches 0.0018209 when the training reaches 14 times, the fitness between the actual value and the predicted value of ACO-BP training is 0.99921, the verification accuracy reaches 0.99913, and the test accuracy reaches 0.99932. Compared with the three optimization methods, the convergence speed and error accuracy of ACO-BP are significantly better than the BP neural networks and Genetic Algorithm-Back Propagation (GA-BP). This method realized the model of the nonlinear mapping model from hydrogen engine speed and load to optimal ignition advance angle, which is of great significance for solving the problem of abnormal combustion in hydrogen engine. • Research carried out by the 492Q hydrogen engine, close to actual conditions. • The dataset is original, collected under different speed and load conditions. • ACO-BP can be applied in abnormal combustion of hydrogen fuel engines. • Less relevant dataset, we will publish the dataset for future research. [ABSTRACT FROM AUTHOR]

Abstract: An optimization control model which is about the relationship between power, economy, emission performances and operating parameters of hydrogen-fueled engines is established in the text, and optimality of the solution''s Pareto is realized through multi-objective optimization algorithm which is combined with Pareto rank and partial decision-making, and the optimization process of multi-objective is carried out parallelly in the searching process, the solution converges to the direction of decision partiality, and the convergence speed is improved; at the same time, dynamic adjustment of partial decision-making changes as the operating condition of hydrogen-fueled engines can be realized through weighting function, thus the control of multi-variable, multi-objective, multi-working status is unified to single-objective integrated optimal control technique. This develops a new avenue to solve the controlling problem of optimization objective''s diversification and complication in hydrogen-fueled engines and improve the controlling precise further. [Copyright &y& Elsevier]

When hydrogen internal combustion engine works using the intake manifold injection mode to provide the hydrogen, it has to face a contradiction between the abnormal combustion (preignition, backfire, etc.). Currently, most researches on abnormal combustion in hydrogen internal combustion engines have focused primarily on backfire and detonation, with limited investigation of pre-ignition, particularly when initiated by in-cylinder hot spots. In order to explore the relationship between the hot spot and the pre-ignition of hydrogen internal combustion engine, the influence of characteristic parameters of hot spot, such as hot spot area, initial temperature, duration of hot spot and temperature change rate, on pre-ignition of hydrogen internal combustion engines were analyzed. On the basis of using the isolated factor method to research the influence of each characteristic parameter on the occurrence time of pre-ignition of hydrogen internal combustion engines, the comprehensive effect of these characteristic parameters on the occurrence moment of pre-ignition in hydrogen internal combustion engines was also investigated with the orthogonal test method. It is found that the occurrence of pre-ignition of hydrogen internal combustion engines is the result of multi-factor interaction. Pre-ignition is most affected by hot spot area, and when the hot spot area is the same, the order of influence of pre-ignition timing is initial temperature > temperature change rate > duration of hot spot. The research results can promote the practical application of hydrogen fuel in internal combustion engines and provide guidance for the structural design of hydrogen internal combustion engines. • Hydrogen engine pre-ignition results from combined effects of several parameters such as the area, initial temp. • Strategies to control temp change rates could mitigate pre-ignition risks and optimize combustion performance. • Compared to initial temp, the moment of pre-ignition is more sensitive to changes in the area of the hot spot. • With equal hot spot area: initial temp > temp change rate > duration influence hydrogen engine pre-ignition. [ABSTRACT FROM AUTHOR]