CN108791270B

CN108791270B - Power component working point regulation and control method of hybrid power vehicle based on working condition prediction

Info

Publication number: CN108791270B
Application number: CN201810634352.0A
Authority: CN
Inventors: 王燕勤; 王伟达; 徐丽丽; 韩立金; 刘亮
Original assignee: Beijing Institute of Technology BIT
Current assignee: Beijing Institute of Technology BIT
Priority date: 2018-06-20
Filing date: 2018-06-20
Publication date: 2021-10-29
Anticipated expiration: 2038-06-20
Also published as: CN108791270A

Abstract

The invention discloses a power component working point regulation and control method of a hybrid power vehicle based on working condition prediction, which comprises the following steps: the method comprises the steps of adopting an SVR model to predict the working condition according to the speed information of a vehicle under the historical working condition, solving the torque or rotating speed information required by each part in the prediction time domain according to the working condition prediction result, and determining the optimal working point required to be reached by an engine and a motor in the future time domain according to the required torque or rotating speed information, so that the working points of the engine and the motor are distributed near the high-efficiency region of the prediction time domain in advance, and each control variable is easy to respond to a target working point between the actual sampling time and the next time.

Description

Power component working point regulation and control method of hybrid power vehicle based on working condition prediction

Technical Field

The invention relates to a method for regulating and controlling a working point of a power part of a hybrid vehicle, in particular to a method for regulating and controlling a working point of a power part of a hybrid vehicle based on working condition prediction in a limited time domain.

Background

With the attention of cities on environmental protection, new energy automobiles are gradually moving to the market. New energy vehicles on the market are mainly divided into hybrid vehicles and pure electric vehicles. Because of the battery life and endurance problems of pure electric vehicles, the hybrid electric vehicle has not been widely popularized, and is accepted by consumers as a comprehensive product of a traditional vehicle and a pure electric vehicle. The engine is used as a main energy source, the storage battery is used as another energy source and is linked with the motor to adjust the working point of the engine, so that the engine works in the optimal fuel consumption area, and meanwhile, the battery can recover a part of braking energy to achieve the purposes of saving energy and reducing emission of the whole vehicle. In order to achieve the optimal energy-saving state of the whole vehicle and reduce fuel consumption, the whole vehicle control strategy plays a very important role.

In the aspect of reducing the energy consumption of the whole vehicle, Chen et al issue a hybrid vehicle based on equivalent fuel consumption, add the battery energy consumption into the conversion proportion, and provide a whole vehicle fuel consumption strategy on the basis of off-line simulation. In the aspect of working condition prediction and identification, Payr et al designs a whole vehicle control strategy by predicting driver behavior through a Markov chain. Sun et al obtains a vehicle predictive control sequence by predicting the vehicle speed off-line based on a neural network model. And the Z Chen utilizes a DPSO method to predict the global speed off line and utilizes the DPSO to search the global minimum fuel consumption at each moment. The control strategy based on the working condition prediction is to perform off-line prediction and calculation on the basis of the known global speed, predict the speed of the automobile off-line, design the automobile control strategy according to the off-line prediction, is suitable for the urban patrol service cars with stable speed and non-congestion road states, and can design an energy-saving and fuel-saving control strategy according to the routes and the driving start-stop frequency of the urban patrol service cars. However, the actual situation is much more complicated, and as the holding amount of the automobiles is gradually increased, the road congestion condition is gradually increased. Even for city patrol cars with dedicated lanes, it is difficult to avoid congested traffic. For the patrol car, as the patrol car has a fixed patrol track, the energy management can be extracted from historical information and then globally planned. However, if congestion or other temporary lane change situations of vehicles are met, the energy management strategy based on the global planning completely cannot meet the requirements of energy conservation and fuel saving. Therefore, the control strategy based on off-line prediction cannot achieve the best effect. The present invention is directed to solving the above problems.

Disclosure of Invention

The invention mainly aims to provide a hybrid electric vehicle power part working point regulation and control method based on working condition prediction in a limited time domain, so that the limitation of an offline fuel consumption optimization control strategy is solved.

The invention adopts a novel machine learning method to achieve the prediction purpose, namely Support Vector Regression (SVR).

And extracting the speeds of different preset driving cycles of the automobile, and repeatedly training the SVR model by a Particle Swarm Optimization (PSO) algorithm by using the speed characteristics of each driving cycle as training data. And training two SVR models corresponding to a speed change violent mode and a driving stable mode in the driving process. And (3) using a clustering (KNN) algorithm for a specific unknown working condition to complete speed pattern recognition of an actual driving process. And combining the speed pattern recognition result with the SVR model established by the corresponding pattern to obtain the speed information of each moment of the prediction time domain.

And evaluating the accuracy of the SVR prediction method according to the average root mean square error and the correlation degree of the prediction result and the actual result. The prediction result shows that the SVR method can accurately and efficiently predict the speed in the limited time domain. Obtaining predicted values such as throttle opening prediction, driving mode prediction, engine power and the like in a limited time domain according to the speed prediction information; and inputting the predicted values into the vehicle controller, and obtaining the distribution values of the control variables at the sampling moment by utilizing dynamic programming in a limited time domain. The results show that: the control strategy based on the prediction information has significant effects in optimizing fuel consumption of the whole vehicle and optimizing battery consumption.

The object of the present invention can be achieved by:

a method for regulating and controlling the working point of a power component of a hybrid vehicle based on working condition prediction comprises the following steps:

adopting an SVR model to predict the working condition according to the speed information of the vehicle under the historical working condition,

the torque or rotating speed information required by each component in the prediction time domain is solved according to the working condition prediction result,

the optimal working point which needs to be reached by the engine and the motor in a future time domain is determined according to the required torque or rotating speed information, so that the working points of the engine and the motor are distributed near a high-efficiency region of a prediction time domain in advance, and each control variable is easy to respond to a target working point between an actual sampling moment and the next moment.

The invention relates to a power component working point regulation and control method based on working condition prediction, wherein the working condition prediction comprises the following steps: extracting the speeds of different preset driving cycles of the automobile, using the speed characteristics of each driving cycle as training data, and repeatedly training an SVR model through a PSO algorithm; training two SVR models to respectively correspond to a mode with violent speed change and a driving stability mode in the driving process; a KNN algorithm is used for a specific unknown working condition to complete speed mode identification in the actual driving process; and combining the speed pattern recognition result with the SVR model established by the corresponding pattern to obtain the speed information of each moment of the prediction time domain.

The invention relates to a power component working point regulation and control method based on working condition prediction, wherein the accuracy of SVR model prediction is evaluated according to the average root mean square error and the correlation degree of a prediction result and an actual result.

The invention relates to a power component working point regulation and control method based on working condition prediction, wherein a radial basis kernel function is selected by an SVR model to carry out speed fitting, key parameter values of the corresponding SVR model are adjusted, and the combination of different model parameters and kernel functions is selected to evaluate the prediction accuracy through the correlation and the average mean square error of speed;

determining a key parameter gamma of the SVR kernel function and a penalty factor C of the model by adopting a Particle Swarm Optimization (PSO) iterative optimization;

and performing secondary optimization near the PSO optimal solution by using a grid type search method so as to determine SVR model parameters.

The invention relates to a power component working point regulation and control method based on working condition prediction, which establishes a verification set, and then uses a cross-validation method to cross-validate repeated correction parameters C and gamma until a prediction error is controlled within a certain range according to the optimal regression prediction accuracy and the fitted mean square error.

The invention relates to a power component working point regulation and control method based on working condition prediction, wherein a driving stability mode is used for predicting a more stable and uniform road section or a road section with the speed of 0 when an automobile is driven, and 5 models required by the prediction speed at different moments in a prediction time domain are obtained by training by utilizing the suburb expressway working condition and the Japanese light automobile fuel consumption economy test working condition (NEDC, 10-15 mode); the mode with violent speed change is used for predicting middle and high speed sections with violent speed change when the automobile runs, and 5 prediction models in different domains are obtained by training by utilizing three working conditions of a world light vehicle fuel consumption test cycle (WLTP class 1/2/3).

The invention also provides a vehicle speed control device of the hybrid vehicle, which is characterized in that the device adopts the working condition prediction-based power component working point regulation and control method to regulate and control the working points of the power components.

Drawings

Fig. 1 is a vehicle speed control flowchart of the present invention.

FIG. 2 is a diagram of the predicted time domain timing logic of the present invention.

FIG. 3 is a schematic diagram of the support vector regression machine fitting a strip region covering a large number of sample points.

FIG. 4 is a schematic diagram of the parameter correction flow of the PSO method of the invention in combination with cross validation.

Fig. 5 is a PSO algorithm fitness curve.

FIG. 6 is a grid search result.

FIG. 7 is the result of a search on another model mesh.

FIG. 8 is an intelligent segmented schematic diagram of acceleration conditions.

Fig. 9 is a prediction result based on the operating condition prediction.

FIG. 10 shows the predicted results and errors for the 1 st second future operating conditions

FIG. 11 shows the predicted operating conditions and errors for the 2 nd second future.

FIG. 12 shows the predicted operating conditions and errors for the 3 rd second in the future.

FIG. 13 shows the predicted operating conditions and errors for the 4 th future second.

Fig. 14 is a structure diagram of a series-parallel hybrid vehicle.

FIG. 15 is a flowchart of a programmed implementation of scroll prediction.

FIG. 16 is a comparison of the predicted results of the method with other methods.

Fig. 17 is a comparative analysis of battery SOC for FTTDP method versus ECMS method.

FIG. 18 is a hybrid vehicle engine operating point distribution using the FTTDP method.

FIG. 19 is a hybrid vehicle engine operating point distribution using the ECMS method.

Fig. 20 shows changes in control variable values of the hybrid vehicle using the FTTDP method.

Wherein, 1 represents a power battery pack, 2 represents a power distribution unit, 3 represents an electric machine B, 4 represents an electric machine A, 5 represents electric equipment, 6 represents a power coupling mechanism, 7 represents a front transmission, 8 represents a speed change mechanism, and 9 represents an engine.

Detailed Description

Considering the engine and the motor as a complex highly nonlinear system, the next-time operating point distribution cannot be determined from the last-time operating point. In order to enable the whole vehicle to meet the power performance and optimize the fuel economy, the dynamic planning method is combined with the prediction information to carry out dynamic planning.

The prediction information is mainly prediction of the working condition, and the prediction information such as torque or rotating speed required by each component in the prediction time domain is solved according to the working condition prediction result. Therefore, the optimal working points of the engine and the motor, which need to be reached in the future time domain, can be determined, so that the working points of the engine and the motor are distributed near a high-efficiency region of a prediction time domain in advance, and each control variable is easy to respond to a target working point between the actual sampling time and the next time. And the basis of the prediction information is the speed information of the whole vehicle under the historical working condition.

The unique data fitting method of SVR makes it possible to fit a bar region (bar region represents the best linear regression function) according to the historical speed, so that as many feature values as possible covered by the historical information fall in the region, and the predicted speed value is obtained according to the fitted curve value. Meanwhile, a penalty factor is searched to punish outliers, and an optimal hyperplane is searched to enable the fitted bar-shaped area to cover as many points as possible. The difference between the support vector regression and artificial intelligence learning methods such as the neural network is that the artificial intelligence learning method has the characteristic of multi-input and single-output, namely the speed at the next moment can be predicted by inputting a speed section.

The SVR model requires selection of appropriate kernel functions for spatial high-dimensional fitting. The method selects a radial basis kernel function to carry out speed fitting, adjusts key parameter values of a corresponding SVR model, selects a combination of different model parameters and kernel functions, and evaluates the prediction accuracy through the correlation and the average mean square error of the speed.

After the kernel function is determined, the key parameters of the kernel function and parameters such as penalty factors of the model need to be matched. The model parameters are estimated here using PSO in conjunction with SVR. The iterative optimization algorithm of the SVR parameters adopts a Particle Swarm Optimization (PSO), and the iterative optimization algorithm can determine kernel function parameters of the SVR, optimize an SVR model and narrow parameter optimization range.

Aiming at the problem of local optimal solution generated by overlarge resolving step length of a PSO algorithm, the method uses a grid search method to perform secondary optimization near the PSO optimal solution, so as to determine SVR model parameters.

Aiming at the problem of improving the prediction accuracy of the model, firstly, a verification set needs to be established, and then, a cross-validation method is used for repeatedly correcting prediction parameters according to a prediction result until the prediction error is controlled within a certain range. The step is carried out simultaneously with the three steps, and each weighted value of the cost function is corrected according to the prediction accuracy.

And aiming at different speed change conditions, carrying out KNN nearest clustering on any input working condition, and selecting a model corresponding to a speed mode for prediction according to a clustering result. After the prediction speed is obtained, the control boundary and the key prediction parameters of the control variable and the state variable of the prediction time domain can be determined.

And the MCU determines the control torque and the control rotating speed of the motor engine by utilizing dynamic programming in a limited time domain according to the predictive control variable and the predictive control boundary.

A flow chart of an embodiment based on operating condition prediction may be seen in FIG. 1.

SVR data annotation and preprocessing

The method can be determined to support input of multidimensional features according to the principle of SVR. And the multidimensional features can be projected to a high-dimensional space for learning and fitting. The predictive model may be trained with a large amount of sample data to generate a reliable predictive model. The invention selects various circulation working conditions as a sample training set: training by using two types of working conditions with obvious characteristics, and taking three working condition equal cycles of the world light vehicle oil consumption test cycle as a prediction model representative of severe acceleration and deceleration; and adopting cycles such as suburban highway working condition, Japanese light automobile fuel consumption economy test working condition and the like as a prediction model representative of stable speed. The distinct working conditions are selected mainly to cover most of automobile driving behaviors, so that the application coverage of the prediction model is wider. And also to be used as a cushion for the later speed pattern recognition.

The specific implementation steps are as follows:

(1) and performing data preprocessing on the trained speed sample set. Extracting main prediction information characteristics: short-time acceleration, maximum deceleration, maximum acceleration, percentage of each section of speed value in the speed section and other characteristic information. And combining the characteristic information with each step of model training, screening redundant characteristics, and extracting a representative characteristic combination.

(2) The speed features extracted by the invention are all speed relative values rather than absolute values, which ensures that speed changes starting from any speed value can be included. And the absolute value of the acceleration of the automobile in a short time generally does not exceed 1m/s2, so that the sample extracted by the characteristics under the limited working condition can basically cover most of the working condition change conditions. Besides the acceleration feature extraction, the overall velocity distribution feature is extracted. Therefore, the characteristic extraction mode of the invention has universal adaptability to the acceleration and deceleration working conditions of any speed starting point. Because the longer the interval between the prediction time and the current time in the prediction time domain is, the greater the prediction difficulty is, and the more the prediction information is, different speed prediction models need to be established for the prediction information at different times in the same time domain.

(3) And (4) speed prediction in a limited time domain. By adopting a method of feature mining and rolling prediction in a short time domain, the speed of the next moment in the future needs to be at least 5 moments before the history, and at least 9 features are extracted from the 5 speeds, so that the prediction model can reach certain precision.

(4) In the simulation process, the characteristic of clustering the speed working condition by the KNN nearest neighbor algorithm is different from the target characteristic of the SVR. The two extraction features are different in that: the characteristics of the clusters emphasize the effect of changes and fluctuations in speed on the classification results, while the characteristics of the SVR emphasize the characteristics of the operating conditions themselves. Taking any speed condition as an example, describing the speed variation of the vehicle can be:

at a certain initial speed, the automobile runs at a certain acceleration and deceleration according to the requirements of the driver. Then a corresponding SVR model needs to be established in order to predict the velocity at a certain time in the time domain. 5 kinds of finite time domain speed prediction models can be established according to the speed prediction requirement in the prediction time domain: the predicted temporal domain 1 st velocity prediction, the predicted temporal domain 2s velocity prediction … … predicts temporal domain 5s velocity prediction, respectively.

The invention sequentially explains the feature extraction method of each speed prediction model according to the time sequence of the prediction time domain.

TABLE 1 feature extraction

The features extracted by the present invention from historical speed information can be mainly explained by using the time sequence logic diagram shown in fig. 2. Assuming that a control decision at the time t0 needs to be made at the time t0-1, and the speed before the time t0-1 is taken as historical information, a plurality of prediction models can be obtained by predicting each predicted speed after the time t0 according to the historical speed information before the time t 0-1.

(1) In the first case: v10-v14 can be predicted in the prediction time domain from the first 9 velocities in the normal time domain, so that the control variables of the prediction time domain can be determined according to the predicted velocities of the prediction time domain. Thus determining at t₀To t₀A control strategy at time +1, by which t is reached₀Speed requirement at time + 1.

(2) In the second case: the speed v6 at the 6 th time can be predicted to ensure prediction accuracy by providing the speed of the first 5s to the original speed, that is, the speed at the 1 st time in the future time domain can be predicted from the speeds at the time points in the historical time domain as shown in table 1.

Characteristic 1 is an acceleration a₀I.e. the difference in velocity between adjacent time intervals. The characteristic 2 is the average value of the speed difference, the characteristic 3 is the mean square error of the adjacent acceleration of the time domain historical speed, the characteristic 4 is the sum of the historical speeds in the time domain, the characteristic 5 is the average value of the historical speeds in the time domain, the characteristic 6 is the sum of the acceleration values in the historical speed, the characteristic 7 is the sum of the acceleration values in the historical speed, the characteristic 8 is the acceleration percentage of the adjacent speed acceleration of less than 1m/s2 in the historical speed. Feature 9 is the acceleration of adjacent speed in the historical speed of the segmentThe number of less than 1m/s2 is proportional to all accelerations. As shown in the last two rows of equations in the third column of table 3, where N represents the total number of accelerations, where the historical speed number is 5, the acceleration (speed interval) number is 4.

And finally, normalizing each feature, thereby eliminating the phenomenon of local over-learning caused by different dimensions of each feature. As shown in formula 1, x is the characteristic shown in table 1, and y is the characteristic after regression.

Wherein MinValue is the minimum value of the single sample characteristics, MaxValue is the maximum value of the single sample characteristics, and the extracted speed characterizations can be classified into the same measurement range through the regression mode of the formula 1.

(3) In the third case: only the first 6s velocities are provided for the raw data, and the 7s and 8s velocities v7, v8 can be predicted to ensure prediction accuracy. The feature extraction can also be performed in the manner of equation 1.

(4) In a fourth case: only the first 7s velocities are provided for the raw data, and the velocities v8, v9, v10 at 8s, 9s, and 10s can be predicted to ensure prediction accuracy. Predicting v8 on the basis of the last 5 speeds; predicting v9 on the basis of the last 6 speeds; the method is characterized by predicting v8 on the basis of the last 6 speeds.

(5) In the fifth case: only the first 8s velocities are provided for the raw data, and the velocities v9, v10, v11 and v12 at the 9s, 10s,11s and 12s can be predicted in order to ensure prediction accuracy. Predicting v9 on the basis of the last 5 speeds; predicting v10 on the basis of the last 6 speeds; predicting v11 on the basis of the last 7 speeds; v12 is predicted on the basis of the first 8 velocities.

Combining the above 5 cases, the predictive model is abstracted into 5 as shown in table 2. From the different speed sequences, the speeds at different times in the future time domain can be predicted

Table 2 the first column is a known velocity sequence and the table can generate a velocity prediction model for the third column by extracting features of the known velocity sequence such as the second column of table 2. Because the historical reference speed has increased, the third column of Table 2 may generate a future time domain speed prediction model at different times based on the newly extracted model features. The newly generated prediction model is identified by underlining, and the underlined prediction model is generated through feature extraction in the second column of the table. The underline-free identified model may be generated by a feature extraction method on a line of the table.

TABLE 2 feature extraction and velocity prediction

SVR model construction

The support vector regression machine is developed on the basis of the support vector machine, and the idea of the support vector machine is basically similar to the classification principle of the support vector machine. Assume a sample is { (x)_i，y_i)，…，(x_l，y_l)}，x∈Rⁿ. Mapping with a non-linearity: x → phi (x), the input space is mapped to a high-dimensional feature space.

The data sample set is fitted with a function f (x) w · Φ (x) + b, provided that the fitting error precision of the training data is oa as shown in fig. 3, the curved surface to be fitted should be as flat as possible to prevent overfitting, and w should be as small as possible, according to the construction risk minimization criterion. If the outlier is considered as shown by "x" in the outlier sample point of fig. 3, the over-learning situation will occur if the fitting straight line region is forced to pass through the bar region where the point is located, so that a large fitting error is generated, and therefore, a relaxation factor xi is introduced_iThen the support vector regression can be expressed as:

introducing lagrange multiplier alpha_i,α_i ^*The dual problem can be obtained and Lagrange's equation as formula 3 can be established.

The above formula should satisfy the KKT condition, i.e. the above formula is to parameter w, b, xi_iAll partial derivatives of (a) are equal to 0. I.e. w, b, ξ_iThe equation of equation 4 needs to be satisfied.

Obtaining a solution:

this equation gives the value of w at the redirection amount.

At the optimal solution according to the KKT condition, the equation shown in equation (5) can be obtained.

The following unification is made.

Based on this, alpha of the sample point correspondence in the fitting region can be derived_i，α_i ^*Are all 0, the outer points correspond to alpha_iEither C or alpha_i ^*C, on the fitting boundary, xi_iAnd xi_i ^*Are all 0, thus alpha_i,α_i ^*E (0, C) so that the value of b can be calculated according to equation 6:

and alpha_i,α_i ^*Samples x corresponding to values other than 0_iI.e. samples not within the fitting strip boundary are support vectors. Therefore, the function of the fit is:

where SVs represents a set of support vectors. The kernel function here is chosen to be a radial basis kernel function such as 8.

φ(x)＝exp(-||x-x_i||²/γ²) Formula 8

Therefore, the method needs to determine the values of the kernel function parameter gamma and the penalty parameter C, because the former influences the distribution form of the sample points in the space, and the latter influences whether the machine can generate over-learning in the space or can effectively fit in the high-dimensional space. When alpha is_i ^*≠α_iThen the data point is the eligible support vector. From equation 8, equation 7 can yield a regression plane that meets the conditions.

Then determining the kernel function parameter gamma, and punishing the parameter C to obtain the optimal plane supporting the regression vector. The two parameters are independent from each other and simultaneously influence the regression result, so that parameter optimization is an important part of SVR generalization learning.

In order to make the trained model achieve accurate prediction, firstly, a standard for prediction accuracy evaluation needs to be established, and the standard is used for repeatedly optimizing and determining the optimal parameters so as to minimize the cost function.

Prediction accuracy evaluation of SVR model

The predictive effect of the predictive model is first related to the fitting effect of the model. The quality of the model fitting effect can be obtained by comparing the fitting trend of the SVR model fitting original speed curve with the speed trend of the original speed curve. And (4) substituting the fitting speed curve into a test set support vector according to the existing sample characteristic points, and calculating the error between the fitting value and the target value of the test set so as to evaluate the quality of the prediction effect.

(1) R-squre is the main evaluation criterion for evaluating regression quality. The degree of deviation of the prediction of the speed can be analyzed here by the R-squre method. And setting the original cycle working condition speed as x, and taking y as the predicted speed of x. Var represents variance, see equation 9.

(2) The root Mean Square Error (MSE) is the primary evaluation criterion for evaluating regression errors. He solves the error between the predicted value and the true value of the velocity to obtain the average variance of the error.

SVR parameter matching based on PSO method

The PSO method is also called particle swarm optimization, and a method for simulating bird migration to search for habitat is used for obtaining a global optimal habitat. The optimal perching point of the invention is the point with the highest accuracy of the prediction cross validation.

The PSO algorithm has the advantages that the main kernel function parameter gamma (formula 8) and the penalty parameter C (formula 2) enable the cross validation prediction accuracy to be highest, and the predicted value mean square error to be minimum. It also relates to cross-validation solutions that support vector regression machines. Cross-validation is a corrective mechanism for adaptive optimization of prediction parameters. In order to fit the prediction model to as many feature set variations as possible for a limited sample.

(1) Cross validation flow

Firstly, dividing a sample data set into k parts, wherein each test set only comprises 1 part of data, the rest k-1 parts are used as training set data, and the steps of cross validation can be obtained by analogy:

a. one of the samples is taken as a test set repeatedly, the remaining k-1 samples are used as a training set to train the model, and then the MSEi of the model in the test set is calculated.

b. Averaging the k-folded MSEi to obtain the final MSE

This special cross validation method is called k-fold cross validation.

The SVR parameter optimization based on the minimum mean square error of the cross validation comprises the following steps:

and (4) a discrete kernel function gamma and a penalty parameter C are in a determined discrete range, and the boundary of the particle migration range is determined so as to determine the particle search area. The particles are searched in this region at a certain speed, and the particles of the N generation are initialized first. For each particle, its fitness is compared to its best position (with the least prediction error) and if better, the best position for the current particle. For this generation of microparticles, their fitness is compared with the global best fitness (prediction error minimum), and if better than the global best fitness, the microparticle position is reset to the best position.

And updating the searching speed and the population in the region, and performing the operation again, wherein the iterative optimization knows that the best fitness of the particles of the current generation is found. And repeating the operations on the next generation of particles, comparing the operations with the global optimal position of the previous generation of particles, adjusting the global optimal fitness of each generation, and performing iterative optimization until the minimum fitness in all generations is found.

The method mainly comprises the following steps:

the first step is as follows: for the initialized 1 st generation of 1 particle, each particle is defined by [ C_i,γ_j]Combined formation of x_kThe subscript k represents the fitness of the particle at position k, i.e.

The superscript p represents the number of individuals in each population.

Initializing the velocity of the 1 st generation individual particles

The second step is that: initializing individuals of generation 1 particles at a certain migration velocity in domain U_c,U_γSearching within the range to find the position of the best fitness (minimum MSE)

Wherein

Is the best fitness position of the single particle within the search domain at the initialized migration velocity. Comparing the individual best fitness positions found by all particles in the population:

and (3) carrying out speed updating on the 1 st generation particles, and searching the optimal fitness in the search domain according to the updating speed by each particle according to the current position of each individual, the best fitness of the population and the best fitness of each individual.

The third step: iterative optimization, if

Updating the position of the individual optimal fitness for each particle in each population, so that the individual tends to the position of the individual optimal fitness; comparing the individual best fitness in the particle swarm, and comparing the group best fitness to make the individual tend to the group best fitness position if

And repeatedly updating the particle velocity according to the position of the optimal fitness

The fourth step: updating to the final generation, comparing the best fitness in all generations, and selecting [ C ] with the minimum mean square error_i,γ_j]And (4) taking values.

Taking the first prediction model as an example, as shown in table 2, for example, feature extraction and speed prediction, the sixth speed is predicted through the first five speed values, then a penalty parameter C discrete range 0:1:1000 is set, a kernel function parameter γ discrete range 0:1:1000 is set, first, the values of C and γ are randomly initialized in the search range, and the speed weight of the particles in the search range is set to be 1.5 and 1.7.

Initializing the population quantity 20, randomly initializing different values of particles C and particles gamma for each population, searching C and gamma in a discrete range at a certain speed by using a set speed weight, and selecting the optimal fitness in each population. The population optimal fitness is the mean square error of SVR cross validation, and the whole process carries out model training and parameter matching according to the process of figure 1.

Cross validation of adjustment parameters to accommodate various operating conditions

By adopting the cross validation method, the over-learning and under-learning phenomena of the model caused by too much or too little sample set can be avoided.

And (5) repeatedly training, and cross-verifying the values of the correction parameters C and gamma according to the optimal regression prediction accuracy and the fitted mean square error. By repeatedly searching and comparing all the generations of all the populations, the result can be converged to a global optimal fitness point.

Taking Model1 as an example, the Model predicts the first speed in the future time domain according to the previous 5 speeds, at this time, the optimal value of C particle is 340.1207, the optimal value of γ particle is 871.054, and the fitness (mean square error of cross validation) is 0.251.

Because the particle swarm optimization method is limited by the assignment of the speed weight, the particles can fly over the optimal point when the velocity weight assignment is too large, and the search time can be increased when the velocity weight assignment is too small, so that the global optimal position can be searched as soon as possible, and the computer load is reduced. A relatively large velocity weight is selected to enable the population of particles to find a globally optimal position.

And after the global optimal fitness is found, carrying out grid search on points near the fitness corresponding to the values C and gamma. The grid search determines the parameter values to make the model prediction result more accurate and reliable.

Using a Model₁For example, at C at 2⁸～2⁹Within a range of dispersion, gamma is 2⁹～2^9.8The range is discrete, so according to the result of grid search, the optimal cross validation mean square deviation value is determined, as shown in fig. 6 and fig. 7, the grid search result graph, and the optimal mean square deviation value is 0.207.

Substituting the C value and the gamma value of the training optimal result parameters into an SVR Model to obtain the SVR Model (Model) for predicting the first speed of the future time domain₁). On the basis of the characteristic training model, the invention trains two different models to correspond to the speed prediction in each time domain. The first type mainly predicts a more stable and uniform road section or a road section with the speed of 0 when the automobile runs, extracts the characteristics of the table by using the NEDC and the 10-15mode, and trains 5 models required for predicting the speed at different moments in a prediction time domain. The second type mainly predicts the medium and high speed sections of the automobile when the automobile is in violent speed change during running. The characteristics of the table above are extracted mainly by using WLTP class 1, WLTP class2 and

WLTP class

3, and 5 prediction models in different time domains are trained to make the prediction.

Through the method, 10 SVR models are trained in total, and 5 SVR models are used for the first type of prediction with a relatively flat speed. Mainly corresponding to the low-speed slow-moving road section in the urban area. The 5 models are used for predicting the second type of severe speed change and mainly correspond to the road type frequent acceleration and deceleration road sections. In order to select a proper type of model for prediction in the real-time driving process of the automobile, any test cycle condition needs to be clustered, and the optimal model is selected to be matched with the optimal model according to the classification corresponding condition.

And (3) performing clustering processing on the test speed cycle, as shown by a blue line in fig. 8, calibrating the speed types by 10,20 and 30, wherein 20 represents a medium-high speed rapid acceleration and deceleration section, and predicting by using a second type model, and 10 represents a low-speed relatively stable section, and predicting by using a first type model. 30, the model is in the middle speed section, the first type model and the second type model are compared to predict the speed of the section, and the model with high prediction accuracy is selected for prediction.

Therefore, the model corresponding to the speed sequence selection number in the limited time domain is predicted, and the predicted speed is obtained as shown in figure 9.

And intercepting four

speed prediction graphs

10, 11, 12 and 13 at different moments to obtain comparison graphs of the predicted speed and the original speed at different moments in the predicted time domain, thereby evaluating the prediction effect of the speed.

The model was trained to apply to more conditions, resulting in the results of Table 3. Comparing the rows in the table 3, wherein the R-square of the predicted speed and the actual speed at the fifth moment of the predicted time domain gradually decreases, and the MSE gradually increases, which means that the prediction precision is affected along with the prediction time domain. In order to make the results of FTTDP more accurate and reliable, the DP-based energy management strategy assigns more weight to the results of the future time domain first few seconds of speed calculation.

And meanwhile, the prediction effect of the SVR model under different road conditions is analyzed by adopting an R-square method and MSE.

Table 3 describes the variation of R-square under different road conditions and table 4 describes the variation of correlation coefficient under different road conditions. When R-square is larger and MSE is smaller, the speed prediction speed error is smaller, the correlation degree of the trend of the predicted speed and the actual speed is higher, namely the SVR prediction effect under the road condition is better.

And (3) combining the MSE and the R-square of the table 4, and comparing prediction results of the prediction models at the same time of the prediction time domain. The LA92 working condition is compared with a plurality of working conditions, the predicted result of the same line is compared, the R-square is high, the MSE error is large, and the fact that the speed change trend of the predicted speed and the speed change trend of the actual speed are similar but the accuracy of the predicted speed is not high under the stable working condition is proved.

Comparing the same line of the UNIF01 shows that R-square is smaller, MSE of the UNIF01 is larger than LA92 and WVUCITY, so that the speed prediction accuracy of the UNIF01 working condition is lower, the speed change of the working condition is more severe, and the speed prediction effect is poor.

TABLE 3 correlation coefficient of speed prediction for each condition

From the results in tables 3 and 4, it can be known that the speed prediction of the model has a better prediction effect on the conditions with less drastic acceleration and deceleration changes, for example, the UDDS speed acquisition condition is shown in fig. 9, and the prediction effects on the conditions with drastic acceleration and deceleration, LA92 and Unif, are not as good as that of WVUCity, so that the prediction effect of the SVR model is good for the medium-speed or high-speed road sections with little vehicle speed fluctuation during continuous driving.

TABLE 4 mean square error of predicted speed value and actual speed value

In order to achieve the purpose of predicting fuel saving based on working conditions, the control strategy of the invention adopts a method of dynamic programming in a limited time domain to enable an engine and a motor to respond to the vicinity of a high-efficiency working area in advance under the condition of known prediction speed so as to achieve the purposes of fuel saving and energy saving.

Whole vehicle structure

The invention relates to a series-parallel hybrid electric vehicle, which comprises an engine, two driving motors, a power battery, a power coupling device, a speed change mechanism and the like. The basic transmission route of the hybrid electric vehicle takes an engine as a power source, one part of energy drives a motor to generate electricity through a front transmission and power coupling mechanism, electric energy generated by the motor is used by electric equipment, and the other part of the electric energy is converted into mechanical energy.

When the generator is higher than the total power of the motor and the electric equipment, the power battery absorbs the residual power for storage. When the generator is insufficient to meet the external equipment and motor drive demands, the power cell discharges electrical power.

And finally, coupling the mechanical power of the engine and the mechanical power of the motor through a power coupling mechanism, transmitting the coupled mechanical power of the engine and the mechanical power of the motor to wheels through a speed change mechanism, overcoming external resistance and driving the automobile to run, wherein the main structure of the series-parallel hybrid electric automobile is shown in figure 14.

1. The power battery pack: the function of the whole vehicle power supply is achieved 2. the power distribution unit: the control core unit VCU 3 of the whole vehicle, the motor A can generate power to charge a battery on the one hand, and can drive the whole vehicle to move forward by using electric energy on the other hand, the motor B: complementary to the operating mode of motor a 5. consumer: generally, the power consumption instruments and air conditioners in the vehicle are 6. power coupling mechanism: it is composed of planetary gears 7. front transmission: drive gear 8, speed change mechanism: multiple gear box 9. engine: the main power source of the whole vehicle drives the whole vehicle by the chemical energy of fuel combustion.

The hybrid electric vehicle has two working modes, which respectively correspond to a low-speed mode EVT1 and a high-speed mode EVT2, and the two modes control the oil charging and discharging of the clutch according to the speed of a switching point so as to achieve the effect of mode connection and mode disconnection.

The main judgment basis for switching the vehicle speeds in the mode 1 and the mode 2 is the vehicle speed:

in equation 16, α is the accelerator opening degree. If the vehicle is in mode 1 and the speed satisfies the first-row condition of equation 16, the vehicle switches to mode 2, and if the vehicle is in mode 2 and the second-row condition of equation 16 is satisfied, the vehicle switches from mode 2 to mode 1.

The principle of the complex coupling mechanism is simplified, under the state of EVT1, mechanical power output by an engine is transmitted to a motor A through a power coupling mechanism, the motor A generates electricity, the motor B is in an electric state at the moment, part of the generated power of the motor A is supplied to an external power supply, part of the generated power of the motor A meets the driving requirement of the motor B, and finally the power of the engine and the power of the motor B are coupled through a speed change mechanism to drive wheels to move.

In the EVT2 state, the engine outputs mechanical power to motor B through the power coupling mechanism, motor B is in a power generation state, and motor B outputs electric power for external power utilization and motor A driving.

The EVT1 and the EVT2 power coupling mechanisms incorporate different clutches, so the gear ratios are different in different modes. Since the transmission principle of the power coupling mechanism is not the key point of the invention, the details are not repeated and only the formula is quoted. In EVT1 mode, speeds n of Motor A and Motor B_A、n_BInput and output rotating speed n of power coupling mechanism_i、n_oThe relationship is shown in equation 17.

In EVT2 mode, Motor AAnd the rotational speed n of the motor B_A、n_BInput and output rotating speed n of power coupling mechanism_i、n_oThe relationship is shown in equation 18.

The relationship between the input rotating speed of the power coupling mechanism and the rotating speed of the engine and the relationship between the output rotating speed of the coupling mechanism and the wheel speed are as shown in formula 19.

Therefore, the output rotating speed of the coupling mechanism of the automobile can be obtained by predicting the change of the automobile speed, and the rotating speed of the engine and the required power P of the engine_eIn this regard, the engine required vehicle speed is related to the accelerator opening degree. The required power of the engine can be obtained by predicting the vehicle speed, so that the predicted required rotating speed of the engine can be obtained by interpolation according to the optimal working curve of the engine.

The predicted speed combined control strategy mainly comprises the following steps: simulating real-time running of the automobile, and reading the speed of the automobile 9s before the historical time domain of the current moment from the automobile data recorder, wherein the speed is v (t) at the moment. Where t represents the sampling instant and i represents the predicted temporal ith instant from the sampling instant.

At this sampling time t, prediction is started, and the prediction speed in the prediction time domain is denoted as v (i | t). The real-time rolling prediction of 5 speeds in the future time domain, i ═ t + P-1(P ≦ 5), represents the 10s,11s,12s,13s and 14s speeds predicted under the condition of the known historical speed of the first 9 s.

And by analogy, the sampling time is advanced for 1s every time, and the speed of 5s later at the time is predicted until the automobile completes the driving cycle.

The predicted accelerator opening α (i | t) of the vehicle can be determined from the predicted speed v (i | t), and the predicted required engine power Pe (i | t) can be determined from the accelerator opening of the vehicle in equation 20.

The mode to which the hybrid vehicle is switched in the prediction time domain can be determined based on the current speed v (t), and the predicted speed v (i | t). If the automobile is switched from EVT1 to EVT2, the optimal torque T of the engine of the automobile at the current moment_eg(i | t) the power demand of the engine at that moment is divided by the actual engine speed n at that moment_e(i-1| t) is as in formula 21. The actual engine speed value is the engine response speed at the time i-1 in the prediction time domain.

And interpolating according to the working curve with the lowest optimized oil consumption of the automobile engine. Equation 22 is where the engine optimum speed is a function of engine power at the current engine power, where p₀、p₁、p₂、p₃For the fitting coefficients, 2698, -4158, 2877, and 701, respectively. The control speed value of the engine at the current power at the i s th predicted time domain is

Through the same thinking, the time domain ith second is predicted, and the engine reaches the optimal rotating speed value at the moment

The engine is predicted to have the optimum torque value of

And so on, the optimal working speed control sequence of the engine in the prediction time domain can be obtained:

engine optimum control torque sequence T_eg(i | t). Suppose that the engine reaches the optimal rotating speed at i s th in the prediction time domain

And the predicted speed v (i | t) of the whole vehicle is reached, n can be calculated by the formula 19_eAnd n_o. Through n_e,n_oAnd equation 17, equation 18 determines the rotational speed ω of the motor A and the motor B in different speed modes_AAnd ω_B。

Determining omega_AAnd ω_BThe maximum torque limit of the motor corresponding to the rotating speed in the time domain can be determined according to the motor torque external characteristic curve as shown in the formula 23, wherein phi is the external characteristic curve of the motor.

For the torque distribution of the electric machines at the present time, the electric machines a and B are determined by the engine output shaft torque Te and the transmission output shaft torque To at the previous time. Assuming that the engine is responsive to the control torque T_egIn the low-speed mode state, the corresponding torque of the motor and the torque of the engine are calculated according to the following relation:

from equation 24, equation 25 yields the relationship between the output torque and the motor torque in the low speed mode and the high speed mode. T is_oL,T_oHThe relationship between the output torque of the planetary gear set in the low-speed mode and the output torque of the planetary gear set in the high-speed mode and the torques of the motor A and the motor B is shown in the formula 26.

To find the appropriate motor control torque to match the engine speed and torque requirements, the torques for discrete motor a and motor B are within the torque region determined by equation 23. The superscripts m, n are control sequence numbers within the control range of motor a and motor B.

In order to achieve the purpose that the speed of the automobile is required and the speed and the torque of the engine are adjusted at the same time, the motor is used as a load mechanism at the input end of the engine, and for different combinations of control variables

It is necessary to convert the motor torque to the load torque on the engine side. The torque conversion of the coupled type 24, 25 motor a and motor B to the engine side is determined by equation 28. T is_LLThe relationship between the load torque and the motor torque in the low-speed mode, T_LHIs the load torque in the high speed mode. k is the number of the different control combination sequences.

According to the speed regulation requirement of the automobile, if the driver needs to accelerate at the predicted time moment i, the engine torque and the load torque need to satisfy the torque speed regulation relation of the formula 29.

When the engine needs to accelerate to meet the acceleration requirement of the automobile, the load torque of the motor is smaller than the engine torque, and the load torque is smaller when the acceleration of the engine is larger. Meanwhile, the engine control torque needs to be controlled within the maximum engine torque at the current time t. Formula 29 medium psi_eIs an engine external characteristic curve.

When the engine is decelerating, the greater the motor load torque needs to be greater than the engine torque and meet the engine deceleration demand, the greater the load torque. On the other hand, in order to maintain the power exchange balance between the automobile battery and the motor, the charge and discharge of the battery needs to be balanced. The electric energy generated or consumed by the motor and the battery are charged and discharged as shown in formula 30. Where ψ is the motor power outer characteristic curve. P_sThe electric power produced or consumed by the two motors in the prediction time domain i. For a battery, charging is performed when the SOC is low, and discharging is performed when the SOC is high, which is advantageous for maintaining the life and good charge-discharge characteristics of the battery.

One part of the battery power is coordinated with the motor power, and the other part of the battery power supplies electric energy for external electrical appliances. Therefore P is_s>0, the motor is in an electric state, the motor and the external electric accessory are mainly used as battery loads to consume electric energy, and when P is reached_s<And 0, the motor is in a power generation state, and the power generated by the battery and the motor is supplied to an electric accessory. Meanwhile, if the battery needs to maintain the SOC of the battery to be stable, the battery needs power P_nIs a function of the SOC of the battery, and when the SOC is smaller, the battery needs power P_nNegative values represent the required charging power of the battery. When the SOC is higher, the battery can consume the self power to maintain the external power and the SOC balance of the battery, and the power P required by the battery_nPositive, discharge. P_battAnd P_nKeeping the same number can maintain the requirement of battery balance SOC, and meanwhile, in order to avoid the phenomenon of overcharge or overdischarge, P_battAnd P_nThe closer the value, the better.

For the instantaneous charge and discharge process of the battery, the SOC of the battery movesState change process and battery power P_battThe variation of (c) is shown in formula 32.

Equation 32 represents a state space equation of battery charge and discharge, with the SOC value of this equation as the state quantity.

According to this concept, there is also a state space equation 33 for the acceleration for the vehicle speed.

At the same time, to overcome the ground resistance, the output power of the power coupling should be as large as possible, see formula 34. Formula 34 output power T_oControlling torque T for an electric machine_A,T_BIs calculated (equation 26). To solve the optimal output shaft speed, a cost function needs to be determined: output power P of coupling mechanism_outFinding the optimal motor control variable combination with the difference between the power of the automobile and the power of the resistance to be overcome

The cost function is minimized.

The optimization problem is changed into the search of appropriate control variable combinations TA and TB, so that the motor control torque combination meets the minimum cost optimization function as much as possible, and simultaneously, the working point of the engine is met to work in a fuel economy area and the cooperative control of all parts is met.

The limited time domain dynamic programming can determine the optimized energy management strategy from the t th to the (t +1) th s of the predicted time. The method adopts the steps of forward recursion to predict the cost optimization function of each time of the time domain to the last step of the time domain prediction, searches the optimal control variable of the power system of each step to optimize the cost function, and then carries out reverse recursion optimization. Hair-like deviceMotive torque in the neighborhood T of the engine's optimized torque_e＝{T_e∈U|T_emin≤T_eg+δT_e≤T_emax). The control strategy cost function of the invention mainly optimizes the fuel economy of the engine, and simultaneously, in order to meet the characteristic of the dynamic response of the whole vehicle, the control variable u-T of the invention_A(t)T_B(t)T_e(t)w_e(t)]^TConstrained by control boundary conditions, the state variable of the present invention is x ═ SOC (t) v (t)]^T。

And adding a limited time domain dynamic programming to determine a control strategy from the sampling time t to (t +1) s, predicting the fuel consumption of each time of the time domain to the No. P step by adopting forward prediction and recursion, searching a cost function which minimizes the fuel consumption and the battery storage electric energy, and then carrying out reverse recursion optimization. In the recursive optimization process, the control variables and state variables for each step are subject to the conditional constraints and state constraints, see equation 35.

The cost function for each step mainly optimizes the minimum fuel consumption of the engine under the required power and the balance of the charge and discharge power of the battery, so the optimized cost function for the first step is shown as formula 36.

Step 1:

in the ith step, a weight ω is added for limiting the fluctuation of the battery SOC and the fluctuation of the engine torque_s,ω_eLet the migration cost function L_i-1The minimum is as in equation 37.

Step i

In the last step P of the prediction time domain, in order to limit the batteryAdding weight omega into the fluctuation range of SOC and the initial SOC of the battery_fThe limit battery final step SOC is as in equation 38.

Step P:

J_P ^*(x, u) is the cumulative cost function of the prediction time domain, the cost function of the step P is optimized to be minimum, the optimal control variable of the prediction time domain at each moment is reversely recurrently calculated,

and obtaining a control decision u of the prediction time domain for the t s th sampling time to (t +1) s according to the dynamic programming forward recursion and the backward solution of the P step^*(t)＝[T_A(t)T_B(t)T_e(t)w_e(t)]^T。

The sampling time is advanced, the rotating speed and the torque of the engine are predicted and distributed in a rolling mode, and the rotating speed and the torque of the motor can achieve the control targets of fuel consumption economy and battery charge and discharge balance.

The engine is supposed to work on the optimal torque curve, and the speed regulation characteristic of the engine and the output torque of the engine and the motor coupling meet the requirement of overcoming the road resistance when the automobile normally runs. The engine does not need to be dispersed in the neighborhood near the optimal working torque, and the minimum fuel consumption and the optimal dynamic performance can be optimized only by adjusting the control torque of the motor. The main logic flow diagram of the speed prediction based power allocation scheme for dynamic programming in the limited time domain is shown in fig. 15.

Control effect verification

The invention takes UDDS working condition as an example, and analyzes compared with an automobile equivalent fuel consumption control strategy ECMS. The actual overall parameters of the hybrid electric vehicle adopted by the invention are shown in the table 5.

As can be seen in FIG. 16, the FTTDP and ECMS control strategies respond in time with the speed of the input UDDS operating conditions.

Therefore, the overall vehicle dynamics performance based on the FTTDP control strategy is good. The invention adopts the circulation generated by the collection of the actual road condition to simulate, inputs the historical time domain speed of the automobile into the SVR model, and can carry out real-time on-line prediction on the future speed of the automobile. According to the online predicted speed value, the basis and the control range of main control parameters can be determined, so that the comprehensive control strategy of the automobile is determined.

TABLE 5 Whole vehicle parameters

For the UDDS regime, the speed following of the vehicle during actual driving is good see fig. 17. Aiming at the SOC fluctuation of the automobile under the UDDS working condition, the SOC fluctuation value of the FTTDP method is smaller than that of the ECMS, and the service life of the battery is prolonged.

In the aspect of power cooperation of all parts, the rotating speed and the torque of the engine and the motor are within the range of a control interval, the rotating speed and the torque of the engine are small in fluctuation, the dynamic characteristic of the motor is good, the dynamic performance of the automobile in short-time acceleration and high-speed acceleration is made up, and the automobile is favorable for maintaining good acceleration performance and the characteristic of minimum fuel consumption.

The operating point distribution of the FTTDP and ECMS is different at the engine operating point. The fuel consumption rates of different working points are different, and as can be seen from fig. 18 and fig. 19, the distribution of the FTTDP-based engine working points is more dense near the optimal fuel consumption distribution curve, so that the overall fuel consumption of the FTTDP control strategy is superior to that of ECMS. Because the FTTDP solves the problem of frequent acceleration and deceleration like UDDS, the overload working condition can predict the speed change in advance so as to predict the start-stop time of the automobile in advance, and then the rotating speed and the torque of the engine slowly transition to the control range with smaller idling oil consumption in advance.

The invention tests various cycle conditions to verify results. According to the prediction result of the predicted time domain speed, the fuel consumption testing working condition of the invention is the same as that of the fuel consumption testing working condition of the invention.

According to the invention, the SOC value under ECMS and FTTDP methods under each working condition is compared to judge whether the charging and discharging of the battery are balanced or not according to the Fuel oil consumption increased value (FIP) under each working condition, and the initial SOC value of the battery under the table is set to be 60 as shown in Table 6.

According to the table, the final SOC value of the battery is related to the total required power of the automobile under the working condition, when the power requirement is large, the battery consumption is large, and the final SOC of the battery is low. Under the condition, the final SOC value of the FTTDP is always higher than the SOC final value of the ECMS, which shows that the SOC fluctuation of the battery of the FTTDP is smaller than that of the ECMS, and the FTTDP algorithm is very effective for the charge-discharge energy balance of the battery.

Similarly, comparing the Fuel consumption improvement rate (FIR), the FTTDP control strategy has better oil saving effect on the working condition of repeated start and stop. For the congested deceleration section, the engine can enter an oil saving area in an idling state in advance, and the average fuel consumption improvement ratio reaches 6.13%. The method has good oil saving effect on the congested and unblocked routes in cities like WVUCITY, and the state of the automobile to be braked is predicted in advance according to the historical speed, so that the working point of the engine is changed to reduce the fuel consumption.

TABLE 6 test results of various cycle conditions

The invention takes the actual road (Typical cycle) acquisition working condition as an example, the engine torque, the engine rotating speed and the double-motor torque of the automobile under the working condition are acquired, the battery power is acquired, and the response values of all variables under the control torque are shown in a graph 20. Through the adjustment of the motors A and B, the torque of the engine can work in an optimized working range. As shown in fig. 20 (c), the dual motors work in a complementary mode state, one generator is used as a motor, so that the pressure of battery power supply is effectively relieved, meanwhile, the battery can be charged in a speed-reducing section of the automobile and other sections, the fluctuation of the charging and discharging power of the battery is small as shown in fig. 20 (d), and the service life of the battery is effectively prolonged.

The finite time domain optimization algorithm based on the working condition prediction is combined with AI algorithm Support Vector Regression (SVR), and the dynamic planning in the finite time domain obtains a better effect, so that the effects of reducing the SOC fluctuation of the battery and maximizing the fuel economy are achieved. The invention judges the fuel-saving area of the engine which should work in the future through the reliable prediction of the SVR to the future working condition, so that the working point of the engine is distributed near the fuel-saving area in advance, and the engine can quickly respond to the optimal working point at the later moment according to the prediction result to reduce the whole fuel consumption.

The prediction accuracy is the factor that has the greatest impact on the engine operating point assignment. In order to achieve higher prediction accuracy, the SVR model needs to be trained continuously, various working condition parameters and characteristics are collected, and model parameters are adjusted in time, so that the global optimal prediction efficiency is achieved.

Meanwhile, the research needs to judge the research accuracy so as to evaluate the weights of different moments in the dynamic planning time domain. The invention verifies whether the fuel economy of the whole vehicle is effective under the FTTDP algorithm by comparing with an equivalent fuel consumption method. According to the control result of the invention, the algorithm can improve the fuel economy of the automobile, and the engine and the motor are matched for linkage so as to reduce the large-amplitude charge and discharge condition of the battery and effectively prolong the service life of the battery.

It should be understood, however, that the above description is only one embodiment of the present invention, and it should be understood that a person skilled in the art may make several modifications and improvements without departing from the principle of the present invention, and the modifications and improvements are within the protection scope of the appended claims.

Claims

1. A method for regulating and controlling the working point of a power component of a hybrid vehicle based on working condition prediction is characterized by comprising the following steps: adopting a Support Vector Regression (SVR) model to predict the working condition according to the speed information of the vehicle under the historical working condition;

solving torque or rotating speed information required by each part in a prediction time domain according to a working condition prediction result;

determining the optimal working points of the engine and the motor, which need to be reached in a future time domain, according to the required torque or rotating speed information, so that the working points of the engine and the motor are distributed near a prediction time domain high-efficiency region in advance, and each control variable is easy to respond to a target working point between the actual sampling time and the next time;

the working condition prediction comprises the following steps: extracting the speeds of different preset driving cycles of the automobile, using the speed characteristics of each driving cycle as training data, and repeatedly training a Support Vector Regression (SVR) model through a particle swarm algorithm (PSO); training two Support Vector Regression (SVR) models to respectively correspond to a violent speed change mode and a stable driving mode in the driving process; a clustering algorithm is used for a certain specific unknown working condition to complete speed pattern recognition in the actual driving process; combining the speed pattern recognition result with a Support Vector Regression (SVR) model established by a corresponding pattern to obtain speed information of each moment of a prediction time domain;

the support vector regression SVR model selects a radial basis kernel function to perform speed fitting, adjusts key parameter values of the corresponding support vector regression SVR model, and selects the combination of different model parameters and kernel functions to evaluate the prediction accuracy through the correlation and the speed average mean square error;

determining a key parameter gamma of a kernel function of a Support Vector Regression (SVR) model and a penalty factor C of the model by adopting Particle Swarm Optimization (PSO) iterative optimization; establishing a verification set, and then repeatedly correcting the values of the parameters C and gamma by using a cross-validation method according to the optimal regression prediction accuracy and the fitted mean square error until the prediction error is controlled within a certain range;

and performing secondary optimization near the PSO optimal solution of the particle swarm optimization by using a grid search method, thereby determining Support Vector Regression (SVR) model parameters.

2. The power component operating point regulation and control method based on operating condition prediction as claimed in claim 1, wherein the accuracy of Support Vector Regression (SVR) model prediction is evaluated by the average root mean square error and the correlation of the predicted result and the actual result.

3. The power component working point regulation and control method based on working condition prediction as claimed in claim 1 or 2, wherein the driving stability mode is used for predicting a more stable and uniform speed section or a section with a speed of 0 when the automobile is driven, and 5 models required for predicting speeds at different moments in a prediction time domain are obtained by training by using suburb highway working conditions and japanese light automobile fuel consumption economy test working conditions; the mode with violent speed change is used for predicting a middle-high speed section with violent speed change when the automobile runs, and 5 prediction models in different time domains are obtained by training by utilizing the test working condition of the light automobile.

4. A vehicle speed control apparatus for a hybrid vehicle, characterized in that the apparatus performs power component operating point control using the power component operating point control method based on operating condition prediction according to any one of claims 1 to 3.