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Here we introduce a metal‐free, catalytic and enantioselective strategy from α,β‐unsaturated 2‐acyl imidazoles to the chiral phosphorous 2‐acyl imidazoles. Interestingly, this methodology was catalyzed by the classical and commercial oxazaborolidine under mild conditions. This strategy features a wide range of substrates scope with good yields and excellent enantioselectivities. The possible mechanism further suggests the key of this reaction through the cleavage of diarylphosphine oxides using Frustrated Lewis Pairs theory. [ABSTRACT FROM AUTHOR]

In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The objective is to enhance calibration efficiency and reduce diesel engine emissions. The proposed model utilizes data collected under the thermal cycle according to the world harmonized transient cycle (WHTC) emission test standard for training and verifying the prediction model. The CNN is employed to extract features from the training data, while LSTM networks are used to fit the data, resulting in the precise prediction of training NOx emissions from diesel engines. Experimental verification was conducted and the results demonstrate that the fitting coefficient (R2) of the CNN-LSTM network model in predicting transient NOx emissions from diesel engines is 0.977 with a root mean square error of 33.495. Compared to predictions made by a single LSTM neural network, CNN neural network predictions, and back-propagation (BP) neural network predictions, the root mean square error (RMSE) decreases by 35.6%, 50.8%, and 62.9%, respectively, while the fitting degree R2 increases by 2.5%, 4.4%, and 6.6%. These results demonstrate that the CNN-LSTM network prediction model has higher accuracy, good convergence, and robustness. [ABSTRACT FROM AUTHOR]

The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively. [ABSTRACT FROM AUTHOR]

Due to the increasing sales of extended-range hybrid vehicles and the increasingly stringent emission regulations for light vehicles in China, the performance and emission of diesel engines for range extenders in the plateau region have attracted increasing attention. In order to obtain the superior performance of diesel engines for range extenders operating at high altitudes, a multi-objective optimization of the optimal economic operating point of the diesel engine was performed at an altitude of 1960 m. A diesel engine system model with MC-EGR-VNT (MEV) technology was developed using GT-Power based on the data of the engine bench to analyze the effects of the Miller cycle (MC), exhaust gas recirculation (EGR), and variable nozzle turbine (VNT) technologies on the power, economy, and emission performance of high-speed diesel engines. The response surface method (RSM) design was carried out with the Miller cycle rate (MCR), EGR value opening, VNT nozzle opening as variable factors and torque, brake-specific fuel consumption (BFSC), nitrogen oxide (NOx) emission as optimization objectives based on Box Behnken Design (BBD). The optimization results showed that the torque and BFSC remained almost constant, and NOx emission decreased by 59.5% compared with the original machine. The proposed multi-objective optimization method could make the diesel engine with a MEV system achieve a good comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper studies the fuel supply offset of diesel engines based on the crankshaft segment signal. Engine nonuniformity refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences caused by misfiring or differences in the fuel supply or air supply. Fuel injection offset can reduce the nonuniformity between cylinders to realize high-efficiency and low pollution combustion. Based on crankshaft segment signal characteristics, an individual cylinder fuel offset control (FOC) strategy was built. The high-pressure common-rail diesel engine operating nonuniform control strategy was put forward. Based on crankshaft segment signal characteristics at different operating conditions, the reasonable operating condition of detecting the engine individual cylinder nonuniform degree was put forward. The open-loop and closed-loop control mode based on the condition was set up. The proportional-integral (PI) control algorithm is proposed to quantify engine individual cylinder nonuniform degree, and the fuel amount offset value was obtained. According to the principle of FOC, based on the automotive electronics development ASCET software platform, the FOC strategy module of the electronic control unit (ECU) was designed, and the simulation experiment was carried out. Research shows that for Z cylinder engine, just the first Z/2 harmonic components below fire frequency can fully reflect the state of the engine's nonuniform operation. The control target to individual cylinder FOC is zero for the synthetic waveform amplitude of the first Z/2 harmonic components. Compared with the traditional quantization method, the fuel offset information extracted from the crankshaft segment signal has stronger anti-interference and more accurate parameters. FOC algorithm can accurately reflect the engine's operating nonuniformity. The control of the nonuniformity is reasonable. The offset fuel amount calculated by FOC is very consistent with the fuel supply state of each cylinder set by the experiment, which meets the requirement of accurate fuel injection control of the diesel engine. [ABSTRACT FROM AUTHOR]

• An SCR state estimator based on multi-model fusion is proposed. • Intelligent optimization method of urea injection for different modes. • NSGA-II algorithm is introduced for optimal Target MAP. • Real-time intelligent optimization of urea injection under transient conditions. • Achieving 95 % NO x conversion, reducing NH 3 slip (<10 ppm). To address the challenge of enhancing NO x conversion efficiency and reducing NH 3 slip in diesel vehicle Selective Catalytic Reduction (SCR) system, a multi-model optimization-based intelligent optimization method for SCR urea injection was proposed. An SCR state estimator employing multi-model fusion accurately predicts NH 3 coverage, SCR downstream NH 3 concentration, and NO x concentration. The intelligent optimization strategy of urea injection used Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize MAP in intelligent calibration mode. In the real-time optimization mode, the optimal control parameters and singular perturbation adaptive correction method were used to optimize the urea injection amount in real-time. Validation through Worldwide Harmonized Transient Cycle (WHTC) testing demonstrates significant results. The results show that the state estimation closely aligns with SCR catalyst simulation, with an average error of less than 5 %. Urea injection strategy under calibration mode, particularly at lower exhaust gas temperature, NO x conversion rate increases from 45 % to 75 %·NH 3 slip decreases at higher exhaust temperature, especially with increased exhaust flow rates and SCR carrier temperature, consistently staying below 10 ppm. Under real-time optimization mode, a significant reduction in NO x emission from 8.9 g/kW·h to 0.46 g/kW·h, with NO x conversion efficiency exceeding 95 %, representing a 13 % improvement·NH 3 slip is optimized, decreasing from an average of 12.89 ppm to 7.99 ppm, with an average NH 3 slip consistently below 10 ppm. This method intelligently adjusts urea injection quantity under actual operating conditions, achieving maximum NO x conversion rates while meeting regulatory requirement. This research offers new theoretical and methodological support for optimizing SCR system performance in diesel vehicles. [ABSTRACT FROM AUTHOR]

This research proposed a multi-objective optimization approach that combines Non-dominated Sorting Genetic Algorithms (NSGA) Ⅲ and support vector machine (SVM) to reduce diesel engine emissions while enhancing economic performance and calibration efficiency. In order to obtain accurate experimental data on diesel engines, a space-filling design method was proposed based on the prediction modeling of diesel engine performance. The SVM prediction model for diesel engine performance was established. A genetic algorithm (GA) was introduced to optimize the SVM model's penalty factor and radial basis parameters, thereby improving its prediction accuracy. The multi-objective optimization approach optimized the braking specific fuel consumption (BSFC), NO x , and CO. The results show that: the GA-SVM diesel engine performance prediction model has excellent prediction performance and generalization ability for BSFC, NO x , and CO, with R2 values of 0.981, 0.979, and 0.968, respectively. GA-SVM was used to evaluate the fitness of the NSGA-III optimal set. This not only ensures optimization accuracy but also improves working efficiency. After optimization, the BSFC of the diesel engine was reduced by 1.67%, NO x emission was reduced by 27.01%, CO emission was reduced by 19.15%, and noticeable optimization results were obtained. This work has important reference value for the automatic calibration of diesel engine control parameters, improving the economy and emission of diesel engines. • A design method based on diesel engine performance is proposed. • Diesel engine performance is accurately predicted by GA-SVM. • The fuel MAPs of engine were optimized based on NSGA-Ⅲ and GA-SVM. • The reduced rates of BSFC, NO x , and CO reach 1.67%, 27.01%, 19.15%. [ABSTRACT FROM AUTHOR]

In order to reduce the complexity of the brushless DC motor (BLDC)-control-system algorithm while improving the estimation performance of the rotor phase position and the speed of the sensorless motor, a neural network (ANN) control algorithm based on multi-layer perceptron (MLP) topology is proposed. The phase voltage of the motor is conditioned to obtain the phase-voltage signal with a high signal-to-noise ratio, which is used as the input eigenvalue of the multi-layer-perceptron-topology neural network algorithm. The encoder signal is used as the training test data of the MLP-ANN. The first layer of the perceptual neural network estimates the position according to the voltage characteristics with incremental time characteristics. The second layer of the perceptual neural network estimates the speed according to the collected time characteristics and the characteristics of rotor position error. The algorithm after learning and training is digitally discretized and integrated into the motor control system. Experimental tests were carried out under no-load, speed step and load mutation conditions. The experimental results show that the algorithm can accurately estimate the rotor position and speed. The absolute error of the rotor position is within 0.02 rad, and the absolute error of the rotor speed is within 4 rpm. The control system with strong robustness has good dynamic and static characteristics. [ABSTRACT FROM AUTHOR]

With YN30 electronically-controlled diesel engine as the object of study, a detection method for the unevenness between different cylinders is proposed based on band-pass filtering. Firstly the instantaneous rotation speed signal of engine is picked up by crankshaft signal sensors. It is then processed by low-pass filtering and re-sampling, and the curves of unevenness between cylinders are obtained in the form of band-pass filtering. Finally the unevenness is quantified by PI algorithm to get the specific fuel correction required for each cylinder. [ABSTRACT FROM AUTHOR]

This research proposed a dependable model for predicting Diesel Particulate Filter (DPF) regeneration conditions and emission performance in diesel engines. The proposed model aims to aid engineers in optimizing diesel engine DPF regeneration efficiency, reducing emissions, and minimizing fuel consumption, thereby ensuring a highly efficient and safe DPF regeneration process. In this paper, A novel multi-model fusion prediction model, combining Improved Seagull Optimization Algorithm (AMSO)-Backpropagation neural network (BPNN) with the extreme Gradient Boosting (XGBoost) model was proposed. A test design method for DPF regeneration conditions was proposed, and the test data were obtained from the engine bench. An AMSO was proposed to improve the prediction capability of the BPNN. To enhance O 2 and smoke prediction, the XGBoost model was introduced in conjunction with the AMSO-BP. Finally, the prediction model's response was experimentally validated. The results show that: After AMSO optimization, the BPNN model's predictions for T4, T5, NO x , brake specific fuel consumption (BSFC), and exhaust temperature was significantly improved, which were 0.97, 0.99, 0.99, and 0.98, respectively. The improved model Mean absolute percentage error (MAPE) is reduced by 0.01 %, 0.38 %, 0.08 %, 0.36 %, and 0.17 %. However, in the prediction of O 2 and smoke density, the model's fitting coefficient (R2) remains relatively low, at 0.95 and 0.96, respectively. After adding XGBoost, the prediction accuracy of O 2 and smoke is significantly improved, R2 is increased to 0.97 and 0.98, and MAPE is reduced by 1.76 % and 14.93 %, respectively. The results of the AMSO-BP-XGBoost model are consistent with the experimental, providing a foundational model for optimizing the performance of DPF. • Improved SOA optimization algorithm and proposed AMSO • A multi-model fusion prediction for DPF regeneration condition is proposed. • AMSO-BP-XGBoost can predict engine regeneration performance with high precision [ABSTRACT FROM AUTHOR]

Aiming at the problem that the control performance of permanent magnet synchronous motor is easily affected by load disturbance during operation, a fractional‐order sliding mode control method combining fractal‐order sliding mode controller (FOSMC) and fractional‐order sliding mode load torque observer (FOSMO) is proposed. The parameters of this method are tuned using the improved salp swarm algorithm (ISSA). Initially, the fractional‐order sliding mode controller is designed by applying fractional‐order calculus theory to enhance the system's response speed. Subsequently, a fractional‐order sliding mode load torque observer is developed to improve the system's anti‐interference capability and robustness due to the slow response and low identification accuracy of traditional load torque observers. Lastly, the control parameters are self‐tuned using the improved salp swarm algorithm due to the difficulty of adjusting them manually. Simulation and experimental results demonstrate that the proposed control method exhibits good convergence, no overshoot, minimal buffeting, and strong robustness against uncertain factors like load disturbances. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]