Sample construction method and control method for control model of hybrid vehicle

Abstract

The present invention provides a method for constructing a sample of a control model for a hybrid vehicle, which is applied to the technical field of data processing. The method includes: determining a target decision sequence corresponding to the minimum weighted value of a plurality of driving cost indicators while keeping the driving speed sequence of the hybrid vehicle in a historical driving cycle unchanged, wherein the target decision sequence includes a target engine power corresponding to each driving speed; determining a set of vehicle driving parameters corresponding to each driving speed according to the driving speed sequence and a plurality of target engine powers; constructing a training sample for training a control model of the hybrid vehicle according to the driving speed, the target engine power corresponding to the driving speed, and the set of vehicle driving parameters corresponding to the driving speed. The present invention also provides a control method for a hybrid vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more particularly to a method for constructing a sample of a control model of a hybrid vehicle and a control method therefor. Background Art

[0002] In the assessment of greenhouse gas emissions and energy consumption in various fields, the proportion of the transportation field is gradually increasing. Greenhouse gases generated by vehicle fuel consumption in the transportation field have become one of the key factors affecting the environment. To address greenhouse gas emissions and energy consumption on the road, the use of hybrid vehicles has become an important solution.

[0003] In the process of implementing the inventive concept, at least the following problems exist in the related art: Based on the characteristics of multi-energy coupling of hybrid vehicles, how to control the power ratio between multiple power devices during the driving process of hybrid vehicles to improve the hybrid performance of hybrid vehicles has become an urgent problem to be solved. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method for constructing a sample of a control model of a hybrid vehicle and a control method therefor.

[0005] According to a first aspect of the present invention, there is provided a method for constructing a sample of a control model of a hybrid vehicle, including: while keeping the driving speed sequence of the hybrid vehicle in a historical driving cycle unchanged, determining a target decision sequence corresponding to the minimum weighted value of multiple driving cost indicators, wherein the target decision sequence includes target engine powers corresponding to respective driving speeds; determining a set of vehicle driving parameters corresponding to each driving speed according to the driving speed sequence and multiple target engine powers; and constructing a training sample for training a control model of the hybrid vehicle according to the driving speed, the target engine power corresponding to the driving speed, and the set of vehicle driving parameters corresponding to the driving speed.

[0006] According to an embodiment of the present invention, while keeping the driving speed sequence of the hybrid vehicle in a historical driving cycle unchanged, determining a target decision sequence corresponding to the minimum weighted value of multiple driving cost indicators includes: while keeping the driving speed sequence of the hybrid vehicle in a historical driving cycle unchanged, using multiple weight coefficients as decision variables of an elitist non-dominated genetic algorithm, minimizing the weighted sum of multiple driving cost indicators as an optimization objective, and determining a set of driving cost indicator values and a preferred decision sequence corresponding to multiple sets of target weight coefficients respectively; determining a target set of driving cost indicator values from multiple sets of driving cost indicator values; and determining the preferred decision sequence corresponding to the target set of driving cost indicator values as the target decision sequence.

[0007] According to an embodiment of the present invention, while keeping the driving speed sequence of a hybrid vehicle in a historical driving cycle unchanged, using a plurality of weight coefficients as decision variables of an elitist non-dominated genetic algorithm, minimizing the weighted sum of a plurality of driving cost metrics as an optimization objective, and determining a set of driving cost metric values and a set of optimal decision sequences respectively corresponding to a plurality of sets of objective weight coefficients, including: under the constraint conditions of the plurality of weight coefficients, for each individual composed of a plurality of weight coefficients in the p-th generation population, while keeping the driving speed sequence unchanged, using the respective value ranges of a plurality of vehicle operating parameters as constraints, and using minimizing the weighted sum of a plurality of driving cost metrics in the historical driving cycle as an optimization objective, optimizing the engine power of the hybrid vehicle to obtain a decision sequence corresponding to the driving speed sequence and the p-th driving cost metric value set in the historical driving cycle, where p > 0 and p is an integer, and the p-th driving cost metric value set includes a plurality of p-th driving cost metric values; determining the p-th fitness of each individual according to the p-th driving cost metric value set of each individual; selecting each individual based on the p-th fitness of each individual to obtain the p-th sub-population; determining the (p + 1)-th generation population according to the p-th sub-population and repeating the above operations until a preset number of iteration rounds P is reached; determining the set of driving cost metric values corresponding to each individual in the P-th sub-population as a plurality of sets of driving cost metric values; and determining the P-th decision sequences corresponding to each individual in the P-th sub-population as a plurality of sets of optimal decision sequences.

[0008] According to an embodiment of the present invention, determining a set of target driving cost metric values from a plurality of sets of driving cost metric values includes: constructing a decision matrix using the plurality of sets of driving cost metric values; normalizing the decision matrix to obtain a normalized matrix; determining an ideal optimal solution set and an ideal worst solution set based on the normalized matrix; calculating the relative closeness of each of the plurality of sets of driving cost metric values according to the ideal optimal solution and the ideal worst solution; and determining the set of target driving cost metric values from the plurality of sets of driving cost metric values according to the plurality of relative closeness values.

[0009] According to an embodiment of the present invention, the method further includes: weighting each driving cost metric value in the normalized matrix according to the metric type of each driving cost metric value to obtain a weighted normalized matrix, so as to determine the ideal optimal solution set and the ideal worst solution set based on the weighted normalized matrix.

[0010] According to an embodiment of the present invention, weighting each driving cost metric value in the normalized matrix according to the metric type of each driving cost metric value to obtain a weighted normalized matrix includes: constructing a judgment matrix according to the relative importance degrees of a plurality of metric types; calculating the arithmetic mean value corresponding to each metric type using the judgment matrix; and weighting each driving cost metric value using each arithmetic mean value respectively to obtain a weighted normalized matrix.

[0011] According to an embodiment of the present invention, the training samples include first training samples and second training samples; the vehicle driving parameter set includes motor power, vehicle power, and state of charge; based on the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed, training samples for training a control model of a hybrid vehicle are constructed, including: determining the driving mode of the hybrid vehicle based on the target engine power and the motor power; constructing first training samples based on the driving mode, the vehicle power, the state of charge, and the driving speed corresponding to the vehicle driving parameter set, where the driving mode is the label of the first training samples; constructing second training samples corresponding to the first training samples based on the driving mode, the vehicle power, the state of charge, the driving speed corresponding to the vehicle driving parameter set, and the target engine power corresponding to the driving speed, where the target engine power is the label of the second training samples.

[0012] According to an embodiment of the present invention, the value ranges of multiple vehicle operating parameters respectively include an engine power range, a state of charge range, and a motor power range, and the engine power range and the motor power range are preset; the state of charge range is determined by the following method: according to the (k + 1)th state of charge and the engine power range, determining the kth state of charge range corresponding to the kth target engine power, where K≥2 and K is an integer, k belongs to [1, K - 1], and k is an integer; taking the smaller maximum value among the first maximum value in the preset state of charge range and the second maximum value in the kth state of charge range as the target maximum value; taking the larger minimum value among the first minimum value in the preset state of charge range and the second minimum value in the kth state of charge range as the target minimum value; repeating the above steps until k = 1; determining the state of charge range based on the target minimum value and the target maximum value.

[0013] A second aspect of the present invention provides a control method for a hybrid vehicle, including: determining vehicle power based on the collected driving speed and the power transmission model of the hybrid vehicle; inputting the driving speed, the vehicle power, and the collected state of charge into the control model to output the target engine power of the hybrid vehicle, where the control model is trained according to training samples corresponding to multiple driving speeds, and the training samples are constructed according to the sample construction method of the control model of the hybrid vehicle as described above; determining the target motor power of the hybrid vehicle according to the target engine power, the vehicle power, and the power transmission model; controlling the operation of the hybrid vehicle based on the target motor power and the target motor power.

[0014] According to an embodiment of the present invention, the control model includes a first control module and a second control module. Among them, the first control module is used to output a driving mode of the hybrid vehicle according to the driving speed, state of charge, and vehicle power; the second control module is used to output a target engine power according to the driving mode, driving speed, state of charge, and vehicle power.

[0015] According to an embodiment of the present invention, taking the driving speed of the hybrid vehicle in the historical driving cycle as a fixed value, while keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, optimizing the power ratio between the engine and the motor in the historical driving cycle by using the minimum weighted value of multiple driving cost indicators to determine the target decision sequence, and using the driving speed of the hybrid vehicle and the target decision sequence to determine the vehicle driving parameter set corresponding to the driving speed. Furthermore, using the corresponding relationship among the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed, constructing the corresponding relationship between the driving state and the power ratio of the hybrid vehicle, and constructing training samples for training the control model based on this corresponding relationship, so that the control model can make decisions that are beneficial to optimizing energy management and improving overall performance during the driving process of the hybrid vehicle. Description of the Drawings

[0016] Through the following description of the embodiments of the present invention with reference to the drawings, the above content and other objects, features, and advantages of the present invention will become clearer. In the drawings:

[0017] Figure 1 The application scenario diagram of the sample construction method of the control model of the hybrid vehicle according to the embodiment of the present invention is shown.

[0018] Figure 2 The flowchart of the sample construction method of the control model of the hybrid vehicle according to the embodiment of the present invention is shown.

[0019] Figure 3 The schematic diagram of the principle for determining the decision sequence according to the embodiment of the present invention is shown.

[0020] Figure 4 The schematic diagram of determining the target driving cost indicator set according to the embodiment of the present invention is shown.

[0021] Figure 5 The flowchart of the control method of the hybrid vehicle according to the embodiment of the present invention is shown.

[0022] Figure 6 The block diagram of the electronic device suitable for implementing the sample construction method of the control model of the hybrid vehicle according to the embodiment of the present invention is shown. Detailed Embodiments

[0023] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, numerous specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0024] The terms used herein are merely for describing specific embodiments and are not intended to limit the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] In the case of using expressions such as "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0027] It should be noted that in the embodiments of the present application, some existing industry solutions such as certain software, components, models, etc. may be mentioned. They should be considered exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0028] In the scenario of making automated decisions using personal information, the methods, devices, and systems provided by the embodiments of the present invention all provide corresponding operation entrances for users to choose to agree or reject the automated decision results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision" here refers to the activity of automatically analyzing and evaluating an individual's behavior habits, hobbies, or economic, health, credit status, etc. through a computer program and making a decision. The expression "expert decision" here refers to the activity of making a decision by a person who specializes in a certain field, has specialized experience, knowledge, and skills, and has reached a certain professional level.

[0029] With the progress of society and the growth of energy demand, the issue of global warming has prompted countries to strengthen the control of greenhouse gas emissions. Against this backdrop, to reduce greenhouse gas emissions and energy consumption in the transportation sector, the use of hybrid vehicles has become a key solution to reducing carbon emissions in road transportation. Hybrid vehicles have improved from relying solely on engine fuel to provide driving force for the vehicle to being able to partially or fully replace the engine with an electric motor to provide driving force for the vehicle.

[0030] Due to the characteristics of multi - energy coupling in hybrid vehicles, compared with traditional single - energy systems, they exhibit significant advantages in energy conservation and emission reduction. This system intelligently controls each power device, distributes power according to the changes in working conditions, and ensures that each power device operates in the optimal working range, thereby improving the overall performance of hybrid vehicles.

[0031] Since hybrid vehicles introduce new components such as batteries and electric motors, and there are multiple power ratios between the electric motor and the engine, the performance evaluation of hybrid vehicles not only needs to focus on fuel consumption but also consider multiple objectives such as battery life, pollutant emissions, and engine working conditions. Therefore, an intelligent and efficient multi - objective energy management strategy has become an important research direction for hybrid vehicles.

[0032] The control strategies for electric motors and engines in hybrid vehicles need to be balanced among multiple objectives such as power performance, cost, emissions, and energy efficiency. For example, to improve energy efficiency, battery life may be sacrificed or emissions may increase. The complexity of these problems makes traditional single - objective optimization methods difficult to apply. Therefore, an optimization method that can objectively and reasonably balance various objectives is needed to optimize the hybrid vehicle strategy.

[0033] An embodiment of the present invention provides a method for constructing a sample of a control model for a hybrid vehicle, including: while keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, determining a target decision sequence corresponding to the minimum weighted value of multiple driving cost indicators, where the target decision sequence includes the target engine power corresponding to each driving speed; determining a set of vehicle driving parameters corresponding to each driving speed according to the driving speed sequence and multiple target engine powers; constructing a training sample for training the control model of the hybrid vehicle according to the driving speed, the target engine power corresponding to the driving speed, and the set of vehicle driving parameters corresponding to the driving speed.

[0034] Figure 1 The application scenario diagram of the method for constructing a sample of the control model of a hybrid vehicle according to an embodiment of the present invention is shown.

[0035] As Figure 1As shown, the application scenarios according to this embodiment may include a first hybrid vehicle 101, a second hybrid vehicle 102, a third hybrid vehicle 103, and a server 104.

[0036] The first hybrid vehicle 101, the second hybrid vehicle 102, and the third hybrid vehicle 103 support a combination of a traditional fuel engine and one or more electric motors to drive the vehicle forward. The first hybrid vehicle 101, the second hybrid vehicle 102, and the third hybrid vehicle 103 are equipped with a system for controlling vehicle driving, a control model, and a device for detecting the vehicle driving speed and the remaining battery power.

[0037] The server 104 may be a server that provides various services for the first hybrid vehicle 101, the second hybrid vehicle 102, and the third hybrid vehicle 103. Each model parameter in the control models of the first hybrid vehicle 101, the second hybrid vehicle 102, and the third hybrid vehicle 103 may be trained on the server 104, and the method for constructing samples of the control model of the hybrid vehicle may be executed in the server 104.

[0038] It should be understood that Figure 1 the number and types of the hybrid vehicles and the server in

[0039] are merely illustrative. Figure 1 Based on the scenario described below Figures 2 to 4 a method for constructing samples of the control model of the hybrid vehicle according to the embodiments of the invention will be described in detail through

[0040] Figure 2 FIG. shows a flowchart of a method for constructing samples of the control model of a hybrid vehicle according to an embodiment of the present invention.

[0041] As Figure 2 shown, the method for constructing samples of the control model of the hybrid vehicle according to this embodiment includes: operation S210 to operation S230.

[0042] In operation S210, while keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, a target decision sequence corresponding to the minimum weighted value of multiple driving cost metrics is determined, where the target decision sequence includes the target engine power corresponding to each driving speed.

[0043] In operation S220, according to the driving speed sequence and multiple target engine powers, a set of vehicle driving parameters corresponding to each driving speed is determined.

[0044] In operation S230, a training sample for training a control model of a hybrid vehicle is constructed based on the driving speed, the target engine power corresponding to the driving speed, and the set of vehicle driving parameters corresponding to the driving speed.

[0045] According to an embodiment of the present invention, the historical driving cycle is the continuous driving process of the hybrid vehicle within a historical time period. The driving speed sequence can be obtained by sampling the driving speed of the hybrid vehicle at multiple time points in the historical driving cycle.

[0046] For example, during the continuous 60 - minute driving process of the hybrid vehicle, the data of the change in the driving speed of the hybrid vehicle over time is recorded. The driving speed at 60 time points from 1 to 60 minutes can be collected at a 1 - minute interval as the driving speed sequence of the hybrid vehicle.

[0047] According to an embodiment of the present invention, when collecting the driving speed of the hybrid vehicle in the historical driving cycle, two time - interval methods can be adopted: fixed time interval and non - fixed time interval. The fixed time interval means maintaining the same time period between each time point. For example, the driving speed is recorded once per second; while the non - fixed time interval allows the interval between time points to vary flexibly according to the actual situation. For example, the interval is shortened when the driving speed changes greatly and extended when the driving speed is stable. These two methods can be selected according to actual needs.

[0048] According to an embodiment of the present invention, the driving cost index is the index that needs to be optimized during the driving process of the hybrid vehicle. Multiple driving cost indices can include fuel consumption, power consumption, battery degradation, engine start - stop times, etc. Different driving cost indices have different energy - saving and emission - reduction effects.

[0049] For example, fuel consumption refers to the amount of fuel consumed by the hybrid vehicle during driving. This index can be used to measure the fuel economy of the hybrid vehicle, that is, the amount of fuel consumed per unit distance (such as per 100 kilometers). The lower the fuel consumption, the higher the fuel efficiency of the hybrid vehicle, the lower the operating cost, and at the same time, it also means less carbon emissions.

[0050] For example, power consumption refers to the amount of electrical energy consumed by the hybrid vehicle during driving. This index reflects the energy utilization efficiency of the hybrid vehicle in generating power through the motor. The lower the power consumption, the higher the energy efficiency of the motor, which can extend the driving range, reduce the dependence on fuel, thereby reducing the operating cost and emissions.

[0051] For example, battery degradation refers to the phenomenon that the battery capacity gradually decreases during use, which can be expressed as the percentage reduction of battery life. Battery degradation is an important factor affecting the performance and cost of hybrid vehicles. The slower the degradation, the longer the battery life, the lower the maintenance and replacement costs, and at the same time, it also means that the hybrid vehicle can maintain good electric performance for a longer time and reduce the fuel consumption of the vehicle.

[0052] For example, the number of engine starts and stops refers to the number of times the engine starts and stops during vehicle driving. This indicator is closely related to the fuel economy and emissions of the vehicle. Frequent starts and stops may increase fuel consumption and emissions. Optimizing the engine start-stop strategy can reduce unnecessary start-stop times, thereby improving fuel economy and reducing emissions.

[0053] According to an embodiment of the present invention, the weights of multiple driving cost indicators can be determined based on expert experience. Based on the Bellman optimality principle, the optimization problem in the entire historical driving cycle is transformed into a backward optimization sequence. Each discrete stage is regarded as a sub-problem, and the optimal solution of each sub-problem is solved backward and recorded. Iterate according to the stage order until the initial state, and then determine an optimal decision path based on the initial state.

[0054] For example, when the historical driving cycle is 120 minutes and the driving speed of the hybrid vehicle in the historical driving cycle is collected every 2 minutes, the 120 minutes can be divided into 60 time periods, such as 118 - 120, 116 - 118, …… 0 - 2. Backward, while keeping the driving speed sequence of the hybrid vehicle unchanged, determine the target engine power corresponding to the minimum weighted value of multiple driving cost indicators, so as to determine the target decision sequence.

[0055] According to an embodiment of the present invention, the vehicle driving parameter set may include the state of charge, vehicle power, driving mode, etc. of the hybrid vehicle. The dynamic model of the hybrid vehicle can be used to determine the vehicle driving parameter set of the hybrid vehicle according to the driving speed and target engine power of the hybrid vehicle. The dynamic model of the hybrid vehicle is a complex system used to simulate and analyze the linkage relationship of components including internal combustion engines, electric motors, batteries, and others. For example, the vehicle power of the hybrid model can be determined in the dynamic model through the driving speed.

[0056] According to an embodiment of the present invention, the training sample includes the input data and labels input into the control model. The input data may include the driving speed and the vehicle driving parameter set corresponding to the driving speed, and the label may be the target engine power corresponding to the driving speed.

[0057] According to an embodiment of the present invention, the vehicle driving parameter set may include the motor power of a hybrid vehicle. The data input into the control model may include the driving speed, the state of charge corresponding to the driving speed, the vehicle power, the driving mode, etc. The label may be the motor power corresponding to the driving speed, and the motor power is the battery power in the hybrid vehicle.

[0058] According to an embodiment of the present invention, taking the driving speed of the hybrid vehicle in the historical driving cycle as a fixed value, while keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, optimizing the power ratio between the engine and the motor in the historical driving cycle by using the minimum weighted value of multiple driving cost indicators to determine the target decision sequence, and using the driving speed of the hybrid vehicle and the target decision sequence to determine the vehicle driving parameter set corresponding to the driving speed. Furthermore, using the correspondence relationship between the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed, constructing the correspondence relationship between the driving state and the power ratio of the hybrid vehicle, and constructing training samples for training the control model based on this correspondence relationship, so that the control model can make decisions beneficial to optimizing energy management and improving overall performance during the driving process of the hybrid vehicle.

[0059] According to an embodiment of the present invention, when keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, determining the target decision sequence corresponding to the minimum weighted value of multiple driving cost indicators includes: when keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, using multiple weight coefficients as the decision variables of the elitist non-dominated genetic algorithm, minimizing the weighted sum of multiple driving cost indicators as the optimization goal, and determining the driving cost indicator value sets and the preferred decision sequences corresponding to multiple target weight coefficient sets respectively; determining the target driving cost indicator value set from multiple driving cost indicator value sets; and determining the preferred decision sequence corresponding to the target driving cost indicator value set as the target decision sequence.

[0060] According to an embodiment of the present invention, the weight coefficients can be set as unknowns. By utilizing the search ability of the elitist non-dominated genetic algorithm in the optimization objective composed of multiple driving cost indicators, through iterative calculations and genetic operations such as selection, crossover, and mutation, the optimal solution is gradually approximated, and finally the weight coefficients are determined. These weight coefficients reflect the importance of different driving cost indicators in the optimization process. In the "selection" process, multiple weight coefficients in the set of weight coefficients are used as known quantities. By minimizing the weighted sum of multiple driving cost indicators, the set of driving cost indicator values and the optimal decision sequence corresponding to the set of weight coefficients are determined, and selection is made based on each driving cost indicator in multiple sets of weight coefficients. Through multiple iterations, the Pareto optimal solutions composed of multiple sets of objective weight coefficients, as well as the sets of driving cost indicator values and the optimal decision sequences respectively corresponding to multiple sets of objective weight coefficients, are determined.

[0061] According to an embodiment of the present invention, the set of driving cost indicator values is a set of multiple driving indicator values generated by a hybrid vehicle in a historical driving cycle. The optimal decision sequence is the target engine power corresponding to each driving speed determined based on the corresponding set of weight coefficients in the historical driving cycle.

[0062] According to an embodiment of the present invention, the set of target driving cost indicator values is the set of driving cost indicator values in multiple sets of driving cost indicator values, and the comprehensive performance of which most conforms to the driving cost indicator values set with the minimum consumption cost of the hybrid vehicle. The optimal decision sequence corresponding to the set of target driving cost indicator values is determined as the target decision sequence.

[0063] According to an embodiment of the present invention, by utilizing the powerful global search ability of the elitist non-dominated genetic algorithm, multiple sets of Pareto optimal solution sets, that is, multiple sets of driving cost indicator values, are found among multiple driving cost indicators. The set of target driving cost indicator values is determined from multiple sets of driving cost indicator values, and the optimal decision sequence corresponding to the set of target driving cost indicator values is determined as the target decision sequence, rather than directly using the weighted driving cost indicator value containing the weight coefficient as the selection direction. Thus, the balanced optimization of multiple driving cost indicators can be fully considered, the limitations brought by single-index optimization are reduced, and the comprehensiveness and robustness of the target decision sequence are improved, so as to train a control model with the obtained relatively comprehensive and robust training samples to more efficiently control the hybrid vehicle.

[0064] According to an embodiment of the present invention, while keeping the driving speed sequence of a hybrid vehicle in a historical driving cycle unchanged, using a plurality of weight coefficients as decision variables of an elitist non-dominated genetic algorithm, and minimizing the weighted sum of a plurality of driving cost metrics as an optimization objective, to determine a set of driving cost metric values and a set of preferred decision sequences respectively corresponding to a plurality of sets of objective weight coefficients, including: under the constraint conditions of the plurality of weight coefficients, for each individual composed of a plurality of weight coefficients in the p-th generation population, while keeping the driving speed sequence unchanged, using the value range of each of the plurality of vehicle operating parameters as a constraint, and using minimizing the weighted sum of the plurality of driving cost metrics in the historical driving cycle as an optimization objective, to optimize the engine power of the hybrid vehicle, obtaining a decision sequence corresponding to the driving speed sequence, and the p-th set of driving cost metric values in the historical driving cycle, where p>0 and p is an integer, and the p-th set of driving cost metric values includes a plurality of p-th driving cost metric values; determining the p-th fitness of each individual according to the p-th set of driving cost metric values of each individual; based on the p-th fitness of each individual, selecting each individual to obtain the p-th sub-population; determining the (p + 1)-th generation population according to the p-th sub-population, and repeating the above operations until a preset number of iteration rounds P is reached; determining the set of driving cost metric values corresponding to each individual in the P-th sub-population as the set of driving cost metric values; and determining the P-th decision sequence corresponding to each individual in the P-th sub-population as the set of preferred decision sequences.

[0065] According to an embodiment of the present invention, the constraint conditions of the plurality of weight coefficients may include: each weight coefficient is greater than 0, and the sum of the plurality of weights is 1. To reduce the computational amount, a weight vector composed of one less weight coefficient than the plurality of weight coefficients can be used to represent an individual.

[0066] For example, in the case of having four weight coefficients, can be used as the decision variable of the elitist non-dominated genetic algorithm.

[0067] According to an embodiment of the present invention, each individual in the first generation population can be obtained by random initialization under the constraint conditions of the plurality of weight coefficients. In the p-th generation population, for each individual composed of a plurality of weight coefficients, a dynamic programming algorithm can be used. While keeping the driving speed sequence unchanged, using the value range of each of the plurality of vehicle operating parameters as a constraint, and using minimizing the weighted sum of the plurality of driving cost metrics in the historical driving cycle as an optimization objective, to optimize the engine power of the hybrid vehicle.

[0068] According to an embodiment of the present invention, by initializing the cost at the end moment, taking the weighted sum of multiple driving cost indicators as the objective function, searching backward for the optimal decision variable at each driving speed in each sampling period, and iterating until the initial moment, a globally optimal decision path corresponding to the individual is obtained. When the multiple driving cost indicators are fuel consumption, power consumption, battery degradation, and engine start-stop times in the historical driving cycle, since the dimensions of each cost are different, multiple weight coefficients can be used to normalize the multiple driving cost indicators respectively. Then the fuel consumption at each moment can be determined by formula (1):

[0069] (1);

[0070] Wherein, is the fuel consumption within time, is the sampling step, is the fuel consumption of the engine when the engine power is , and k is the serial number represented by the sampling time point.

[0071] The power consumption at each moment can be determined by formula (2):

[0072] (2);

[0073] Wherein, is the power consumption within time, is the motor power.

[0074] The battery degradation at each moment can be determined by formula (3):

[0075] (3);

[0076] Wherein, is the battery degradation amount within time, is the pre-exponential factor, is the gas constant, is the thermodynamic temperature, is the battery current rate, can be determined by formula (4):

[0077] (4);

[0078] Wherein, is the battery open-circuit voltage, is the battery capacity in Ah.

[0079] The engine start-stop times at each moment can be determined by formula (5):

[0080] (5);

[0081] Wherein, is the number of engine start / stop times within a sampling period.

[0082] According to an embodiment of the present invention, the weighted sum of multiple driving cost indicators is used as the objective function, and iterative calculation is performed from back to front. The iterative process of the weighted sum of multiple driving cost indicators is as shown in formula (6),

[0083] (6);

[0084] Wherein, represents the weighted sum of multiple driving cost indicators generated from the k-th sampling time node to the end of the historical driving cycle; represents the weighted sum of multiple driving cost indicators generated from the k-th sampling time node to the end of the historical driving cycle at the (k + 1)-th sampling time node. The hybrid vehicle has different working modes, and the decision variables generate different costs. Only the minimum value of the weighted sum iteration of multiple driving cost indicators at each moment is taken to determine the decision sequence corresponding to the driving speed sequence when the weighted sum of multiple driving cost indicators for the entire historical driving cycle is minimized. The decision sequence and the decision path formed by the motor power corresponding to each engine power in the decision sequence are the optimal control strategies for the state at the initial moment. Then, forward calculation is performed according to the optimal decision path determined by the initial state to obtain the optimal solution of the cycle.

[0085] Figure 3 shows a schematic diagram of the principle for determining the decision sequence according to an embodiment of the present invention.

[0086] As Figure 3 shown, in the dynamic programming process, the decision variables u(1)~u(N), and the current cost J(k), the current cost J(k) is the in the above formula (6). The corresponding relationship between them is: the decision variable, i.e., the engine power u(k), and the current state variable, i.e., the state of charge s(k), jointly determine the next state variable s(k + 1), where k belongs to 1~N, and the current cost J(k) is generated in this process.

[0087] Figure 3 shows the change of the state of charge over time, where t represents time. In dynamic programming, optimization usually involves the control of state variables. The arrows and annotations in the figure Indicates the optimization direction of the state of charge (SOC) at different time points. For example, the arrow from SOC(t + 1) to SOC(t) indicates how the decision made at time t affects the state at the next time point.

[0088] According to an embodiment of the present invention, the p-th driving cost index value set includes a plurality of p-th driving cost index values, and the plurality of p-th driving cost index values are the values of each driving cost index without weights. For each individual in the p-th population, during the process of determining the decision sequence, record the p-th driving cost index value set corresponding to the individual, so as to use the plurality of p-th driving cost index values included in the p-th driving cost index value set of each individual as the values of a plurality of objective functions in the elitist non-genetic algorithm to calculate the p-th fitness of each individual.

[0089] According to an embodiment of the present invention, based on the p-th fitness of each individual, select each individual to obtain the p-th sub-population until the population iteration number reaches the preset value P. When the population iteration number has not reached the preset value P, perform crossover and mutation on the p-th sub-population to obtain a plurality of p-th transformed individuals, and combine the plurality of p-th transformed individuals and the p-th sub-population to obtain the (p + 1)-th population.

[0090] According to an embodiment of the present invention, determine the p-th driving cost index value sets corresponding to multiple individuals in the p-th population as multiple driving cost index value sets, that is, multiple weight coefficient sets obtained by the elitist non-genetic algorithm, determine the driving cost index value sets corresponding to the multiple weight coefficient sets respectively, and multiple preferred decision sequences corresponding to the multiple weight coefficient sets respectively.

[0091] According to an embodiment of the present invention, use the driving cost index value set generated during the optimization process as the basis for calculating fitness to determine the influence of different individuals on the final result. By using multiple driving cost index values without weights to evaluate individuals, multiple driving cost indexes can be better balanced, avoiding ignoring the influence of other driving cost indexes on the final result due to overemphasizing a single objective, and improving the accuracy of the driving cost index value set and the preferred decision sequence.

[0092] According to an embodiment of the present invention, determining the target driving cost index value set from multiple driving cost index value sets includes: constructing a decision matrix using the multiple driving cost index value sets; normalizing the decision matrix to obtain a normalized matrix; determining an ideal optimal solution set and an ideal worst solution set based on the normalized matrix; calculating the relative closeness of each of the multiple driving cost index value sets according to the ideal optimal solution and the ideal worst solution; and determining the target driving cost index value set from the multiple driving cost index value sets according to the multiple relative closenesses.

[0093] According to an embodiment of the present invention, in this study, a decision matrix is constructed. The decision matrix It can be expressed by formula (7):

[0094] (7);

[0095] Wherein, represents the th driving cost index value in the th individual, is the number of individuals in the th sub-population of the P wheels, and

[0096] is the number of types of driving cost index values. According to the embodiments of the present invention, the decision matrix is normalized to obtain a normalized matrix, and the normalized matrixcan be expressed by formula (8):

[0097] (8);

[0098] Wherein, is the minimum value of the th driving cost index value among the individuals in the th sub-population of the P wheels; is the maximum value of the

[0099] th driving cost index value among the individuals in the

[0100] th sub-population of the P wheels. According to the embodiments of the present invention, in the normalized matrix, the minimum value of the driving cost index values of each category is selected as the ideal optimal solution set; in the normalized matrix, the minimum value of the driving cost index values of each category is selected as the ideal worst solution set.

[0101] Wherein, is the relative proximity of the th driving cost index value set, is the distance from the th driving cost index value set to the ideal optimal solution set, and is the distance from the th driving cost index value set to the ideal worst solution set. According to the embodiments of the present invention, in the case where the relative proximity is expressed by formula (9), the multiple driving cost index value sets are sorted according to the relative proximity, and the driving cost index value set with the minimum relative proximity among the multiple driving cost index value sets is used as the target driving cost index value set.

[0102]

[0103] ​According to an embodiment of the present invention, the numerator in formula (9) can also be changed to In this case, the driving cost index value set with the largest relative proximity among the multiple driving cost index value sets is taken as the target driving cost index value set.

[0104] According to an embodiment of the present invention, a target driving cost index value set is determined from multiple driving cost index value sets by calculating relative proximity to an ideal optimal solution and an ideal worst solution, wherein the multiple driving cost index value sets are sets of pre-selected solutions that satisfy a relative balance of multiple driving cost index values. In the multiple driving cost index value sets, the target driving cost index value set is determined based on multiple relative proximity. By calculating the relative proximity, solutions that are balanced and close to the ideal in multiple dimensions can be further accurately identified, thereby improving the reliability of the target driving cost index value set, and further improving the reliability of the training samples.

[0105] According to an embodiment of the present invention, the sample construction method of the control model of a hybrid vehicle also includes: weighting each driving cost index value according to the index type of each driving cost index value in the normalized matrix to obtain a weighted normalized matrix, so as to determine an ideal optimal solution set and an ideal worst solution set based on the weighted normalized matrix.

[0106] According to an embodiment of the present invention, each driving cost index value in the normalized matrix is ​​weighted. Specifically, a weight is assigned to each driving cost index value according to the importance of each index type to construct a weighted normalized matrix. Each element in the weighted normalized matrix can be expressed by formula (10):

[0107] (10);

[0108] in, For the The driving cost index value set The importance weight of the class travel cost index value, It can be determined based on expert experience. It is the index value after weighting the driving cost index value.

[0109] According to an embodiment of the present invention, after the weighted normalization matrix is ​​determined, the ideal optimal solution set and the ideal worst solution set are calculated to obtain the maximum and minimum values ​​of different indicators, wherein the ideal optimal solution set It can be expressed by formula (11):

[0110] (11);

[0111] Ideal worst solution set It can be expressed by formula (12):

[0112] (12);

[0113] Wherein, is the maximum value of the driving cost index value m in the weighted normalization matrix, is the maximum value vector composed of the maximum values of m driving cost indexes; is the minimum value of the driving cost index value m in the weighted normalization matrix, is the minimum value vector composed of the minimum values of m indexes.

[0114] According to the embodiment of the present invention, calculate the distances from each alternative solution to the ideal optimal solution set and the ideal worst solution set; the distance from the th Pareto optimal solution to the ideal solution can be expressed by formula (13):

[0115] (13);

[0116] The th Pareto optimal solution to the ideal solution can be expressed by formula (14):

[0117] (14);

[0118] According to the embodiment of the present invention, based on the Pareto optimal solution, by constructing a decision matrix, normalizing, assigning weights, calculating the distances from the ideal solution and the worst solution, and the relative closeness, the ranking and screening of the solutions are finally realized. This can comprehensively consider multiple evaluation indexes, avoid the one-sidedness of single-index decision-making; objectively conduct quantitative evaluation according to the relationship between the index values and the ideal solution and the worst solution, reduce the interference of subjective factors; further screen based on the Pareto optimal solution, and can accurately locate the optimal solution with the best comprehensive performance among multiple excellent solutions, so as to provide a more scientific, accurate and efficient solution for decision-making, and improve the quality and effect of decision-making.

[0119] According to the embodiment of the present invention, weight each driving cost index value according to the index type of each driving cost index value in the normalization matrix to obtain a weighted normalization matrix, including: constructing a judgment matrix according to the relative importance degree of multiple index types; calculating the arithmetic mean value corresponding to each index type by using the judgment matrix; weighting each driving cost index value by using each arithmetic mean value respectively to obtain a weighted normalization matrix.

[0120] According to the embodiment of the present invention, the analytic hierarchy process is used to determine the relative importance degree of multiple index types. A judgment matrix can be constructed based on the importance cognition, and the weight vector of each target can be obtained based on the judgment matrix. After passing the consistency test, the weight vector is the importance weight of each target.

[0121] According to an embodiment of the present invention, the construction of the judgment matrix A can be expressed by formula (15):

[0122] (15);

[0123] is the driving cost index For the driving cost index of the degree of importance, its value can be determined according to the 1-9 scale method, and the weight vector is obtained by using the arithmetic mean method, then the weight vector can be expressed by formula (16):

[0124] (16);

[0125] According to an embodiment of the present invention, by performing a consistency test on the judgment matrix and the weight vector, calculating the consistency index, and calculating the consistency ratio according to the average random consistency index, if the consistency ratio is less than 0.1, it is considered that the judgment is accurate, and the arithmetic mean corresponding to each index type can be calculated by using the judgment matrix; the values of each driving cost index are weighted by using each arithmetic mean to obtain a weighted normalization matrix.

[0126] According to an embodiment of the present invention, by constructing a judgment matrix based on the relative importance degree of multiple index types, the important differences between different indexes can be systematically quantified, and the values of the driving cost indexes are weighted by using the calculated arithmetic mean, so that the final weighted normalization matrix can more accurately reflect the value weights of each index in the actual decision-making. This helps to highlight the influence of key driving cost indexes in determining multiple non-dominated Pareto optima in the problem of optimizing the driving cost of hybrid vehicles, so as to allocate resources and formulate strategies more scientifically and reasonably, and improve the accuracy and effectiveness of decision-making.

[0127] Figure 4 Fig. shows a schematic diagram of determining the target driving cost index set according to an embodiment of the present invention.

[0128] As Figure 4 shown, it is assumed that the index types include driving cost index 1, driving cost index 2, driving cost index 3, and driving cost index 4. The decision maker subjectively judges the relative importance degree of multiple index types to construct a judgment matrix for quantifying the relative importance between different types of driving cost indexes. The arithmetic mean corresponding to each index type is calculated by using the judgment matrix; the values of each driving cost index in the Pareto front are weighted by using each arithmetic mean to obtain a weighted normalization matrix.

[0129] According to the decision matrix, determine the ideal optimal solution and the ideal worst solution. Calculate the relative closeness of each alternative solution to the ideal optimal solution and the ideal worst solution for the driving cost indicators 1, 2, 3, and 4. When the relative closeness is obtained by using the formula with the numerator in formula (9) changed to the formula, determine the set of target driving cost indicator values by increasing the set of driving cost indicator values with increased relative closeness.

[0130] According to an embodiment of the present invention, the training samples include first training samples and second training samples; the vehicle driving parameter set includes motor power, vehicle power, and state of charge; according to the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed, construct training samples for training a control model of a hybrid vehicle, including: determining the driving mode of the hybrid vehicle based on the target engine power and the motor power; constructing a first training sample based on the driving mode, vehicle power, state of charge, and driving speed corresponding to the vehicle driving parameter set, where the driving mode is the label of the first training sample; constructing a second training sample corresponding to the first training sample based on the driving mode, vehicle power, state of charge, driving speed corresponding to the vehicle driving parameter set, and the target engine power corresponding to the driving speed, where the target engine power is the label of the second training sample.

[0131] According to an embodiment of the present invention, when the hybrid vehicle is only equipped with one motor for providing power for vehicle driving, the driving mode of the hybrid vehicle can be determined according to the target engine power and the motor power. Specifically, when the target engine power is not zero and the motor power is not zero, the driving mode of the hybrid vehicle is hybrid drive; when the target engine power is zero and the motor power is not zero, the driving mode of the hybrid vehicle is motor drive; when the target engine power is not zero and the motor power is zero, the driving mode of the hybrid vehicle is engine drive.

[0132] According to an embodiment of the present invention, construct a first training sample by using the vehicle power, state of charge, driving speed corresponding to the vehicle driving parameter set, and driving mode included in the vehicle driving parameter set. Among them, the vehicle power, state of charge, and driving speed corresponding to the vehicle driving parameter set can be used as the working conditions of the hybrid vehicle as input data during training, and the driving mode is the label of the first training sample. The relationship between vehicle power, engine power, and motor power is related to the configuration of the hybrid vehicle, so it is expressed by the following formula (17):

[0133] (17);

[0134] Wherein, is the battery power, is the required power of the whole vehicle, i.e., the vehicle power.

[0135] According to an embodiment of the present invention, based on the vehicle power, state of charge, driving mode, driving speed corresponding to the vehicle driving parameter set, and target engine power corresponding to the driving speed included in the vehicle driving parameter set, a second training sample corresponding to the first training sample is constructed, wherein the target engine power is the label of the second training sample, and the vehicle power, state of charge, driving speed corresponding to the vehicle driving parameter set, and driving mode are used as input data during training.

[0136] According to an embodiment of the present invention, the driving mode of the hybrid vehicle is determined by using the target engine power and the motor power included in the vehicle driving parameter set, so as to construct the first training sample and the second training sample. The driving mode is used as an intermediate result, so as to use this intermediate variable of the driving mode to more specifically assist the control model in predicting the target engine power, improve the accuracy of the control model, and thus improve the accuracy of controlling the hybrid vehicle.

[0137] According to an embodiment of the present invention, the value ranges of multiple vehicle operating parameters respectively include an engine power range, a state of charge range, and a motor power range. The engine power range and the motor power range are preset; the state of charge range is determined by the following method: according to the (k + 1)th state of charge and the engine power range, the kth state of charge range corresponding to the kth target engine power is determined, where K≥2 and K is an integer, k belongs to [1, K - 1], and k is an integer; the smaller of the maximum value in the preset state of charge range and the second maximum value in the kth state of charge range is used as the target maximum value; the larger of the minimum value in the preset state of charge range and the second minimum value in the kth state of charge range is used as the target minimum value; the above steps are repeatedly executed until k = 1; the state of charge range is determined based on the target minimum value and the target maximum value.

[0138] According to an embodiment of the present invention, multiple vehicle operating parameters may include engine power, state of charge, and motor power. The value ranges of multiple vehicle operating parameters respectively include an engine power range, a state of charge range, and a motor power range. The engine power range and the motor power range are preset. The value ranges of multiple vehicle operating parameters can be represented by formula (18):

[0139] (18);

[0140] wherein, is the minimum limit value of the state of charge, is the maximum limit value of the state of charge, is the engine power, Minimum power limit of the engine Maximum power limit of the engine is the motor power Minimum power limit of the motor is the maximum power limit of the motor. In this embodiment, a single motor is taken as an example, which can cover multiple motors.

[0141] According to an embodiment of the present invention, the state of charge of the battery is discretized as a discrete state variable is at time , and a state transition equation is established. The discretized engine power is used as a discrete decision variable , is the engine power at time is the sampling time. The complete driving cycle is composed of "time - vehicle speed", and the time range can be 0 to K.

[0142] According to an embodiment of the present invention, the state transition at each moment is realized through decision variables. The state transition can be represented by formula (19):

[0143] (19);

[0144] wherein is the open - circuit voltage of the battery is the internal resistance of the battery is the capacitance of the battery is the sampling step. The internal resistance and capacitance of the battery are fixed parameters.

[0145] According to an embodiment of the present invention, the change boundary of is set to realize the constraint on , and ensure that is near 0.6 at the end of the working condition. In the reverse optimization process, by determining the maximum and minimum powers of the battery generated in the previous - moment decision process, and based on the state transition equation, the minimum and maximum values of the battery at the previous moment are deduced, and backward calculation is performed until the maximum and minimum values reach the boundaries of 0.3 and 0.8. The minimum value boundary can be represented by formula (20):

[0146] (20);

[0147] wherein is the lowest limit value at time , is Moment The lowest limit value of is the power change obtained at the moment under the decision variable and can be understood in combination with Formula (17) and Formula (19). The power change can be expressed by Formula (21):

[0148] (21);

[0149] The maximum value boundary can be expressed by Formula (22):

[0150] (20);

[0151] Wherein is the highest limit value of the moment and is the highest limit value of the moment and is the power change obtained at the moment under the decision variable .

[0152] The state of charge of the battery in the last discrete state , the discrete decision variable .

[0153] According to the embodiments of the present invention, it is possible to dynamically define the state of charge interval by combining the engine power range and the state of charge at different stages. Compared with simply using a preset state of charge interval, it can more accurately reflect the reasonable range of the state of charge of the vehicle during actual operation. When determining the target engine power, a more accurate state of charge interval means that the determination of the target engine power can better match the current energy state of the vehicle, reducing the unreasonable setting of the target engine power caused by inaccurate estimation of the value range of the state of charge, thereby improving the reliability and accuracy of the training samples.

[0154] With the development of machine learning technology, learning-based energy management strategies are expected to achieve online optimal control of hybrid vehicles. In machine learning algorithms, the random forest algorithm has received attention due to its effectiveness in dealing with complex data and multi-objective optimization problems. Through these advanced technologies and strategies, hybrid vehicles can be managed and optimized more effectively to achieve more efficient and environmentally friendly energy utilization.

[0155] A second aspect of the present invention provides a control method for a hybrid vehicle, including: determining vehicle power based on the collected driving speed and the power transmission model of the hybrid vehicle; inputting the driving speed, the vehicle power, and the collected state of charge into a control model to output the target engine power of the hybrid vehicle, where the control model is trained using a plurality of training samples corresponding to a plurality of driving speeds; determining the target motor power of the hybrid vehicle according to the target engine power, the vehicle power, and the power transmission model; and controlling the operation of the hybrid vehicle based on the target motor power and the target motor power.

[0156] According to an embodiment of the present invention, a training sample for training the control model of the hybrid vehicle can also be constructed by the following method: constructing a training sample based on the vehicle power, the state of charge, the driving speed corresponding to the vehicle driving parameter set, and the target engine power corresponding to the driving speed included in the vehicle driving parameter set, where the target engine power is the label of the training sample.

[0157] According to an embodiment of the present invention, in order to increase the number of training samples, the sample construction method of the above-mentioned control model of the hybrid vehicle can be used to process a plurality of historical driving cycles respectively to obtain a larger number of training samples.

[0158] According to an embodiment of the present invention, taking the collected driving speed as a variable, the vehicle required power at this driving speed, that is, the vehicle power, is calculated using the power transmission model of the hybrid vehicle, and the collected driving speed, the collected state of charge, and the vehicle power are input into the control model to output the target engine power of the hybrid vehicle.

[0159] According to an embodiment of the present invention, the target engine power and the vehicle power are input into the power transmission model to calculate the target motor power of the hybrid vehicle using the power transmission model; and the operation of the hybrid vehicle is controlled based on the target motor power and the target motor power.

[0160] According to an embodiment of the present invention, parameters such as fuel injection and ignition timing of the engine can be adjusted through the engine control unit to achieve the target motor power. The motor controller needs to adjust the speed and torque of the motor according to the target motor power to achieve the target motor power.

[0161] According to an embodiment of the present invention, the driving speed of a hybrid vehicle in a historical driving cycle is taken as a fixed value. While keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, the power ratio between the engine and the motor in the historical driving cycle is optimized by using the minimum weighted value of multiple driving cost indicators to determine a target decision sequence. Then, the vehicle driving parameter set corresponding to the driving speed is determined by using the driving speed of the hybrid vehicle and the target decision sequence. Furthermore, based on the corresponding relationship among the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed, the corresponding relationship between the driving state and the power ratio of the hybrid vehicle is constructed. Based on this corresponding relationship, training samples for training a control model are constructed to train the control model by using multiple training samples, so that the control model can make decisions that are beneficial to optimizing energy management and improving the overall performance during the driving process of the hybrid vehicle.

[0162] According to an embodiment of the present invention, the control model includes: a first control module and a second control module. Among them, the first control module is used to output the driving mode of the hybrid vehicle according to the driving speed, the state of charge, and the vehicle power; the second control module is used to output the target engine power according to the driving mode, the driving speed, the state of charge, and the vehicle power.

[0163] According to an embodiment of the present invention, the first control module is responsible for outputting the driving mode of the hybrid vehicle according to the driving speed of the vehicle and the state of charge and the vehicle power in the vehicle driving parameter set. The first control module can be trained by using the first training samples, and the second control module is trained by using the second training samples corresponding to the first training samples. Based on the driving mode determined by the first control module, the second control module further outputs the target engine power according to the driving speed, the state of charge, and the vehicle power. Thus, it is ensured that the power output of the engine matches the driving mode to achieve better fuel efficiency and performance.

[0164] Figure 5 The flowchart of the control method for a hybrid vehicle according to an embodiment of the present invention is shown.

[0165] As Figure 5 shown, the driving speed, the state of charge, and the vehicle power are input into the first control module 501 to output the driving mode of the hybrid vehicle; the driving mode, the driving speed, the state of charge, and the vehicle power are input into the second control module to output the target engine power of the hybrid vehicle.

[0166] According to an embodiment of the present invention, the first control module and the first control module can be trained based on a random forest algorithm as the basic algorithm model. During the training process, if the driving mode is correct, further check the parameters and algorithms related to engine power calculation in the second control module; if the driving mode is incorrect, the judgment logic of the first control module can be optimized. This modular feedback and adjustment mechanism can more efficiently improve the accuracy of the output result of the entire system.

[0167] According to an embodiment of the present invention, the driving mode, as an intermediate result, contains important information about the vehicle operating state. Since the calculation method and influencing factors of the target engine power are different under different driving modes, using the driving mode output by the first control module as one of the inputs of the second control module can make the prediction of the target engine power better adapt to different driving scenarios, enabling the second control module to perform power prediction more targeted and improving the accuracy of the target engine power.

[0168] Figure 6 The block diagram of an electronic device for implementing a method for constructing a sample of a control model suitable for a hybrid vehicle according to an embodiment of the present invention is shown.

[0169] As Figure 6 shown, the electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The processor 601 can include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 601 can also include on-board memory for caching purposes. The processor 601 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0170] In the RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to an embodiment of the present invention by executing the programs in the ROM 602 and / or the RAM 603. It should be noted that the program can also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 can also perform various operations of the method flow according to an embodiment of the present invention by executing the programs stored in one or more memories.

[0171] According to an embodiment of the present invention, the electronic device 600 may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAP card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. The driver 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the driver 610 as needed so that a computer program read therefrom can be installed into the storage portion 608 as needed.

[0172] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present invention is implemented.

[0173] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the above-described ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603.

[0174] An embodiment of the present invention further includes a computer program product, which includes a computer program, and the computer program includes program codes for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program codes are used to cause the computer system to implement the method for constructing a sample of the control model of a hybrid vehicle provided by the embodiments of the present invention.

[0175] When the computer program is executed by the processor 601, the above functions defined in the system / apparatus of the embodiments of the present invention are executed. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0176] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program can also be transmitted and distributed in the form of signals on a network medium, and be downloaded and installed through the communication part 609, and / or be installed from the removable medium 611. The program code included in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0177] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or be installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiments of the present invention are executed. According to an embodiment of the present invention, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0178] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0180] Those skilled in the art will appreciate that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0181] The embodiments of the present invention have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments have been described separately above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. A sample construction method for a control model of a hybrid vehicle, characterized in that: The method comprises: Under the condition that the driving speed sequence of hybrid vehicles in the historical driving cycle remains unchanged, multiple weight coefficients are used as decision variables of the elite non-dominated genetic algorithm, and the weighted sum of multiple driving cost indicators is minimized as the optimization goal, and the driving cost indicator value sets and the optimal decision sequence corresponding to the multiple target weight coefficient sets are determined; determining a target driving cost index value set from the plurality of driving cost index value sets; Determining the preferred decision sequence corresponding to the target driving cost index value set as a target decision sequence; wherein the target decision sequence includes target engine powers corresponding to various driving speeds; Determining a vehicle driving parameter set corresponding to each of the driving speeds according to the driving speed sequence and the plurality of target engine powers; A training sample for training a control model of the hybrid vehicle is constructed based on the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed.

2. The method according to claim 1, characterized in that The method comprises: taking multiple weight coefficients as decision variables of an elite non-dominated genetic algorithm while keeping the driving speed sequence of the hybrid vehicle in the historical driving cycle unchanged, minimizing the weighted sum of multiple driving cost indicators as the optimization target, and determining driving cost indicator value sets and optimal decision sequences corresponding to the multiple target weight coefficient sets respectively. Under the constraint conditions of the plurality of weight coefficients, for each individual in the p-th wheel population composed of the plurality of weight coefficients, while keeping the driving speed sequence unchanged, taking the value ranges of the plurality of vehicle operating parameters as constraints, minimizing the weighted sum of the plurality of driving cost indices in the historical driving cycle as an optimization target, optimizing the engine power of the hybrid vehicle, and obtaining a decision sequence corresponding to the driving speed sequence and a p-th driving cost index value set in the historical driving cycle, wherein p>0, and p is an integer, and the p-th driving cost index value set includes a plurality of p-th driving cost index values; Determining the pth fitness of each individual according to the pth travel cost index value set of each individual; Based on the p-th fitness of each individual, each individual is selected to obtain a p-th wheel population; Determine the (p+1)th round population according to the pth round sub-population, and repeat the above operation until a preset iteration round P is reached; Determine the Pth driving cost index value set corresponding to each individual in the Pth wheel subpopulation as a plurality of the driving cost index value sets; The P-th decision sequence corresponding to each individual in the P-th wheel sub-population is determined as a plurality of the preferred decision sequences.

3. The method according to claim 1, characterized in that The step of determining a target driving cost index value set from the plurality of driving cost index value sets comprises: constructing a decision matrix using a plurality of the driving cost index value sets; Normalizing the decision matrix to obtain a normalized matrix; Determining an ideal optimal solution set and an ideal worst solution set based on the normalized matrix; Calculating the relative proximity of each of the plurality of driving cost index value sets according to the ideal optimal solution and the ideal worst solution; The target driving cost index value set is determined from a plurality of the driving cost index value sets according to the plurality of relative proximity degrees.

4. The method according to claim 3, characterized in that The method further comprises: Each driving cost index value in the normalized matrix is ​​weighted according to its index type to obtain a weighted normalized matrix, so as to determine an ideal optimal solution set and an ideal worst solution set based on the weighted normalized matrix.

5. The method according to claim 4, characterized in that The step of weighting each driving cost index value according to the index type of each driving cost index value in the normalized matrix to obtain a weighted normalized matrix includes: Constructing a judgment matrix according to the relative importance of the multiple indicator types; Calculate the arithmetic mean values ​​corresponding to each indicator type by using the judgment matrix; Each of the driving cost index values ​​is weighted using each of the arithmetic mean values ​​to obtain the weighted normalized matrix.

6. The method according to claim 1, characterized in that The training samples include a first training sample and a second training sample; the vehicle driving parameter set includes motor power, vehicle power and state of charge; The step of constructing a training sample for training a control model of the hybrid vehicle according to the driving speed, the target engine power corresponding to the driving speed, and the vehicle driving parameter set corresponding to the driving speed comprises: determining a driving mode of the hybrid vehicle based on the target engine power and the motor power; constructing a first training sample based on the driving mode, the vehicle power, the state of charge, and the driving speed corresponding to the vehicle driving parameter set, wherein the driving mode is a label of the first training sample; Based on the driving mode, the vehicle power, the state of charge, the driving speed corresponding to the vehicle driving parameter set, and the target engine power corresponding to the driving speed, a second training sample corresponding to the first training sample is constructed, wherein the target engine power is a label of the second training sample.

7. The method according to claim 2, characterized in that The respective value ranges of the plurality of vehicle operating parameters include an engine power interval, a state of charge interval and a motor power interval, and the engine power interval and the motor power interval are pre-set; The state of charge interval is determined by the following method: Determine a kth state of charge interval corresponding to the kth target engine power according to the (k+1)th state of charge and the engine power interval, wherein K≥2, K is an integer, k belongs to [1, K-1], and k is an integer; The maximum value of the first maximum value in the preset state of charge interval and the second maximum value in the kth state of charge interval, whichever has a smaller value, is used as the target maximum value; The first minimum value in the preset state of charge interval and the second minimum value in the kth state of charge interval, the minimum value with a larger value is used as the target minimum value; Repeat the above steps until k=1; The state of charge range is determined based on the target minimum value and the target maximum value.

8. A control method for a hybrid vehicle, characterized in that: The method comprises: determining vehicle power based on the acquired driving speed and a powertrain model of the hybrid vehicle; Input the driving speed, the vehicle power and the collected state of charge into a control model, and output a target engine power of the hybrid vehicle, wherein the control model is trained based on training samples corresponding to a plurality of driving speeds, and the training samples are constructed according to the sample construction method of the control model of the hybrid vehicle in claims 1 to 7; determining a target motor power of a hybrid vehicle according to the target engine power, the vehicle power and the powertrain model; The hybrid vehicle is controlled to operate based on the target motor power and the target motor power.

9. The method according to claim 8, characterized in that The control model includes: a first control module and a second control module, wherein the first control module is used to output the driving mode of the hybrid vehicle according to the driving speed, the state of charge, and the vehicle power; the second control module is used to output the target engine power according to the driving mode, the driving speed, the state of charge, and the vehicle power.

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