In actual operation of the vehicle, the output torque is an important parameter of the electronic control and condition monitoring of the diesel engine. Non-contact dynamic torque sensors are expensive and large-scale application of armored vehicle diesel engine real vehicle torque measurement is difficult, at the same time, practice has proved that the engine unstable operating conditions time accounted for 60% to 80% of the total working time; therefore, research diesel engine The dynamic characteristics under unstable conditions and the establishment of an accurate dynamic output torque model for diesel engines is of great significance for electronic control and condition monitoring of diesel engines.
Usually diesel engines are in non-failure conditions, there are three main causes of instability: the same throttle opening, the external load changes; the external load is unchanged, the throttle opening changes; throttle opening and external load changes. The literature system summarizes the methods for studying the dynamic characteristics of diesel engines. There are mainly two kinds of experimental studies and simulation calculations. According to the different research purposes, the advantages, disadvantages and application scope of various dynamic characteristics models are summarized and compared. The purpose and form of the research are as shown.
Diesel engine as a complex nonlinear, time-varying and time-delay system, about 30 state variables can accurately reflect the model of common operation characteristics. Using such a model not only has a complex calculation and a large workload, but also has a certain difficulty in solving the model. . Artificial neural network (ANN) has a strong ability of self-adaption and self-learning, and has the property of approximating arbitrary nonlinear functions with arbitrary precision. Many researchers at home and abroad have discussed in detail the application of ANN in internal combustion engine engineering. The authors of the literature developed a real-time prediction system for engine performance and emissions based on ANN. The authors used ANN to establish a model of a spark ignition engine and carried out bench test verification. The literature discussed the dynamic recurrent neural network for dynamic model identification of diesel engines. And diesel engine output torque prediction control model application. It can be seen that ANN can approximate the arbitrarily complex nonlinear relationships and the dynamic characteristics of learning and adapting uncertain systems; therefore, based on steady-state experimental data, artificial neural network is used to establish dynamic output torque prediction that can be used for electronic control and condition monitoring of diesel engines. The model has the advantages of small modeling workload, simple model, and good generality.
1 Diesel Engine Dynamic Characteristics Study Method Classification Research Method Research Purpose Expression Form Test Study Dynamic Characteristics Test (Based on Dynamic Test Bench) Test Data Used for Vehicle Performance Simulation (Based on Steady State Test Data) Dynamic Model Dynamic Correction Model Exponential Curve Approximation Model First-order inertial link model Neural network model Fuel economy model Neural network model Simulation calculation Control analysis Linear model Quasi-linear model Nonlinear model Used for parameter optimization and matching Nonlinear model
1The establishment of a diesel engine output torque neural network model
1. 1 neural network topology
BP neural network is a neural network learning algorithm with three or more layers of neurons, including input layer, middle layer (hidden layer) and output layer. It is called artificial neural network based on error back propagation algorithm. It is characterized by: each layer of neurons is only fully connected with adjacent layers of neurons, there is no connection between the neurons in the same layer, there is no feedback connection between the layers of neurons, constitute a hierarchical feed-forward neural network system. The single computational layer feedforward neural network can only solve the linear separable problem. The network that can solve nonlinear problems must be a multilayer neural network with hidden layers. Theoretically, it has been proved that the BP neural network with double hidden layers has very Good generalization ability. Therefore, a dynamic output torque prediction model for a diesel engine can be established using a BP neural network with two hidden layers.
To study the dynamic characteristics of a diesel engine, not only the relationship between the throttle control characteristics of the driver and the overall performance parameters of the diesel engine, but also the relationship between the accelerator opening or the displacement of the fuel rod and the speed and torque of the diesel engine, should also be introduced into the internal thermal process of the diesel engine. Effects such as intake air temperature, intake pressure, and exhaust temperature. Therefore, the dynamic output torque of a diesel engine is simply described as
M = fn, d, Tp, (1)
Where: M is the output torque of the diesel engine, the unit is Nm; n is the speed of the diesel engine, the unit is r/min; d is the displacement of the refueling rod; the unit is mm; Tp is the exhaust gas for the test turbo turbocharger after the turbine exhaust temperature The unit is °C; f is the nonlinear mapping between neural network input and output. Therefore, the input layer of the BP neural network is 3 neurons, which are diesel engine speed, refueling rod displacement and exhaust temperature after the turbine. The output layer has 1 neuron, which is the output torque of the diesel engine.
The number of neurons in the hidden layer is directly related to the network's simulation accuracy, training time, and generalization ability, and can only be determined after repeated experiments.
1. 2 Transfer Function Determination
The transfer function, also called the activation function, is an important part of the BP neural network and must be continuously differentiable. BP neural networks often use S-type logsig or tansig functions and pureline functions, as shown in 1.
The output layer neurons use a linear transfer function, and the output of the entire network can take any value. Under the same conditions (the number of neurons in the first hidden layer is 12, the number of neurons in the second hidden layer is 9, the training function is the Levenberg2MarquardtBP training function, the weights and thresholds are initialized the same for each training), and the first implied The transfer functions of the neurons in the layer and the second hidden layer adopt four combinations of logsig and logsig functions, logsig and tansig functions, tansig and logsig functions, tansig and tansig functions, respectively. The training and test samples used are the test data of torque, rotation speed, exhaust temperature and refueling rod displacement of a turbocharged diesel engine under steady state conditions. The torque variation range is 223 to 1 854 Nm. For 800 to 2 200 r/min, the displacement range of the filler rod was changed from 1.3 to 9.2 mm, and the exhaust gas temperature varied from 238. 2 to 613.1 °C. After many trainings, it was found that when the transfer function of neurons in the first hidden layer and the second hidden layer adopts tansig and tansig functions respectively, the output is relatively smooth, the convergence characteristics are good, and the relative error between the target value and the predicted value of the test sample. The smallest. Therefore, the two hidden layers of the BP neural network adopt the S-type tangent function.
1.3 Determination of the number of neurons in the hidden layer
The number of hidden layer neurons is directly related to the network's simulation accuracy, training time, and generalization ability. If the number of neurons is too small, the information available to the network to solve the problem is too little; if the number of neurons is too much, not only the training time is increased, but also the so-called "overfitting" problem, that is, the test The increase in error leads to a decrease in the generalization ability; therefore, it is very important to choose the number of neurons in the hidden layer.
The neural network formed by the combination of different numbers of neurons is used to conduct trial calculations on the training samples. It is assumed that the number of neurons in the first hidden layer is s1 and the number of neurons in the second hidden layer is s2. Consider s1 and s2 respectively. 12 changes. After many trainings, it was found that the neural networks formed by the combination of various neuron numbers can all obtain convergence results. However, by comparing the test error, the ideal combination of numbers is,,,,, and. Taking into account the training time, training accuracy, and fault-tolerance factors of the network, the number of neurons in the two hidden layers of the BP neural network is taken as the final value. The network structure is as shown, in which the second hidden layer has the same structure as the first hidden layer but the number of neurons is not equal.
2 simulation results analysis
The simulation program is based on the neural network toolbox provided by MATLAB 6. 5. The training and test samples are bench test data for steady-state operating conditions of an exhaust turbo-supercharged diesel engine. Among them, the test samples are load characteristic test data for diesel engines at 800 r/min and 1 500 r/min to verify the generalization of the network. Ability; The training sample is the load characteristic test data of the diesel engine at other speeds. Set the expected mean square error to be 10 - 4, and the maximum number of trainings is 1 000.
2. 1 training sample simulation results
In the sample training process, the approximation of the sample point target value is used as the convergence condition of the program. Therefore, for successful training, there should be a minimum error between the predicted value of the training sample point and the target value (trial value). Due to limited space, only the target values ​​and predicted values ​​of the training samples for diesel engine at 1 400 r/min and 2 200 r/min were given. It can be seen that the trained network has high simulation accuracy, and the maximum relative error between the training sample target value and the predicted value is 3.0%.
Comparison of target and predicted values ​​of 2 training samples
2. 2 test sample simulation results
The load test data for the diesel engine at 800 r/min and 1 500 r/min were used as test samples. Because the diesel engine speed is 800 r/min outside the training sample speed range, it can better test the generalization ability of the network. The comparison of the target and predicted values ​​of the test sample is given. It can be seen that the maximum relative error between the target value and the predicted value of the test sample is 4.32%, and the trained network has strong generalization ability.
3 Comparison of test sample target value and predicted value
3 conclusions
1) Using the multi-layer feed-forward BP network in the MATLAB neural network toolbox to achieve non-linear prediction of dynamic output torque of the diesel engine, compared with the dynamic correction model, the exponential curve approximation model and the first-order inertial link model, to study the modeling work Small amount, simple model and good versatility.
2) Using the speed of the diesel engine's steady-state bench test, refueling rod displacement, exhaust temperature and corresponding output torque as training and test samples, using normalized training samples and test samples for BP with double hidden layers The network was trained and the training error obtained was 9.71×10-5, which reached the target.
3) The simulation results of the training sample and the test sample show that the trained BP network has a high prediction accuracy for the dynamic output torque of the diesel engine, the network generalization ability is strong, and the calculation time is short, and can be used for on-line real-time diesel engine control.
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