Parameter determination method and device for hybrid system

Abstract

The application provides a parameter determination method and device for a hybrid power system, and applies to the fields of hybrid power systems and control technologies.The method comprises the following steps: acquiring attribute data of a vehicle, an operating condition evaluation index and a preset importance order of the evaluation index, determining a demand power of the vehicle according to the attribute data and the operating condition of the vehicle; based on the demand power, screening candidate hybrid power systems from a database; constructing a cost function for each candidate hybrid power system according to the evaluation index and an initial weight, and obtaining a candidate solution set of each candidate hybrid power system; based on the preset importance order of the evaluation index and the candidate solution set of each candidate hybrid power system, determining a target hybrid power system from the candidate hybrid power systems; based on simulation control results of controlling the target hybrid power system by using multiple candidate control strategies, determining a target control strategy, and taking the target hybrid power system and the target control strategy as a parameter determination result.

Description

Technical Field

[0001] This invention relates to the field of hybrid power systems and control technology, and more specifically to a method and apparatus for determining parameters of a hybrid power system. Background Technology

[0002] With the escalating global energy crisis and environmental pollution, hybrid vehicles (HEVs) have emerged as an effective solution for energy conservation and emission reduction in the transportation sector due to their ability to reduce energy consumption and emissions. However, compared to traditional single-power systems, HEVs feature multiple power sources and operating modes. This means that determining the parameters of a HEV system for different driving scenarios often relies heavily on manual calibration and repeated testing. This process not only results in a large workload and long cycle for parameter determination but also limits the efficiency and intelligence of the parameter determination process. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method and apparatus for determining parameters for hybrid power systems that improves the efficiency and intelligence of the parameter determination process.

[0004] One aspect of this disclosure provides a parameter determination method for a hybrid power system. The method includes: acquiring vehicle attribute data, vehicle operating conditions, evaluation indicators for assessing the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators; determining the vehicle's required power based on the vehicle attribute data and operating conditions; selecting multiple candidate hybrid power systems from a pre-built database based on the required power; constructing a cost function for each candidate hybrid power system based on the evaluation indicators and initial weights configured for the evaluation indicators, and adjusting the initial weights until the candidate solution set determined based on the adjusted weights and cost function satisfies preset conditions, thereby obtaining a candidate solution set for each candidate hybrid power system; determining the target solution for each candidate hybrid power system from the candidate solution set based on the preset importance ranking of the evaluation indicators, and determining the target hybrid power system from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system; determining the target control strategy from the multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, and using the target hybrid power system and the target control strategy as the parameter determination result.

[0005] According to an embodiment of the present invention, the target solution of each candidate hybrid power system is determined from the candidate solution set of each candidate hybrid power system based on a preset importance ranking of evaluation indicators, including: constructing an initial importance matrix of evaluation indicators according to the preset importance ranking; performing a consistency check on the initial importance matrix to obtain a target importance matrix; constructing an initial decision matrix from the candidate solution set of each candidate hybrid power system; normalizing the initial decision matrix to obtain a target decision matrix; obtaining a first target matrix based on the target importance matrix and the target decision matrix; and determining the target solution of the candidate hybrid power system based on the proximity between each data point in the first target matrix and a first predetermined solution.

[0006] According to an embodiment of the present invention, determining a target hybrid power system from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system includes: constructing a target solution set of the candidate hybrid power systems based on the target solutions of the candidate hybrid power systems, and normalizing the target solution set; obtaining a second target matrix based on the normalized target solution set and the target importance matrix; and determining the target hybrid power system based on the proximity between each data point in the second target matrix and a second predetermined solution.

[0007] According to an embodiment of the present invention, a target control strategy is determined from multiple candidate control strategies based on the simulation control results of a target hybrid power system controlled by multiple candidate control strategies. The determination includes: constructing a control result set according to the simulation control results of each candidate control strategy, and normalizing the control result set; obtaining a third target matrix according to the normalized control result set and the target importance matrix; and determining the target control strategy according to the proximity between each data point in the third target matrix and a third predetermined solution.

[0008] According to an embodiment of the present invention, adjusting the initial weights until the candidate solution set determined based on the adjusted weights and the cost function satisfies preset conditions, thereby obtaining a candidate solution set for each candidate hybrid power system, includes: constructing target parameters for the candidate hybrid power system, the target parameters being used to describe the operation of the candidate hybrid power system under driving conditions; performing the following operations on the adjusted weights until the obtained candidate solution set satisfies preset conditions: inputting the adjusted weights into the cost function to obtain a target cost function; processing the target parameters using a dynamic programming algorithm, and aiming to minimize the target cost function to obtain a vehicle control sequence, wherein the control sequence includes the vehicle's operating mode and component power allocation at each time step; determining the value of an evaluation index based on the vehicle's operating mode and component power allocation at each time step, and using the value of the evaluation index as the candidate solution corresponding to the adjusted weights.

[0009] According to an embodiment of the present invention, the target parameters include state variables, decision variables, and constraints; constructing target parameters for a candidate hybrid power system includes: constructing state variables based on the vehicle's battery state of charge; constructing decision variables based on the operating mode and component power of the candidate hybrid power system; and constructing constraints based on the power balance constraints, component power constraints, battery state of charge constraints, and operating mode constraints that the candidate hybrid power system needs to satisfy.

[0010] According to an embodiment of the present invention, determining the required power of a vehicle based on vehicle attribute data and operating conditions includes: determining the vehicle acceleration based on the vehicle speed at adjacent time points and the duration of adjacent time points; determining the vehicle's driving force requirement based on the vehicle's mass, vehicle acceleration, gravitational acceleration, rolling resistance coefficient, air density, air resistance coefficient, and vehicle frontal area; determining the required power based on the driving force requirement and drive transmission efficiency when the vehicle's driving force requirement is greater than or equal to a predetermined value; and determining the required power based on the driving force requirement, vehicle speed, and vehicle regenerative braking efficiency when the vehicle's driving force requirement is less than the predetermined value.

[0011] According to an embodiment of the present invention, the components of the target hybrid power system are configured with priorities, and the target solution of the target hybrid power system corresponds to a target weight; the candidate control strategy includes at least one of the following: determining the operating mode of the target hybrid power system based on the relationship between the vehicle speed, power demand, and battery state of charge of the vehicle and the respective speed threshold, power demand threshold, and battery state of charge threshold; and determining the adjustment mode of the components according to the priorities in the operating mode; constructing an equivalent cost function based on the evaluation index and the target weight corresponding to the target solution of the target hybrid power system, and allocating the operating mode and component power allocation of the target hybrid power system according to the function value of the equivalent cost function; inputting the vehicle speed, power demand, and battery state of charge of the vehicle into the control model, and outputting the operating mode and component power of the target hybrid power system.

[0012] According to an embodiment of the present invention, the database includes a configuration library and a component library. The configuration library is used to store hybrid power system configurations, and the component library is used to store hybrid power system components. Based on the required power, multiple candidate hybrid power systems are selected from the pre-built database, including: selecting candidate hybrid power system configurations from the configuration library and selecting candidate hybrid power system components from the component library according to the required power; and combining the candidate hybrid power system configurations and candidate hybrid power system components to obtain a candidate hybrid power system.

[0013] Another aspect of the present invention provides a parameter determination device for a hybrid power system. The device includes: an acquisition module, configured to acquire vehicle attribute data, vehicle operating conditions, evaluation indicators for evaluating the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators, and determine the required power of the vehicle based on the vehicle attribute data and operating conditions; a screening module, configured to screen multiple candidate hybrid power systems from a pre-built database based on the required power; a construction module, configured to construct a cost function for each candidate hybrid power system based on the evaluation indicators and initial weights configured for the evaluation indicators, and adjust the initial weights until the candidate solution set determined based on the adjusted weights and cost function satisfies preset conditions, thereby obtaining a candidate solution set for each candidate hybrid power system; a first determination module, configured to determine the target solution of each candidate hybrid power system from the candidate solution set of each candidate hybrid power system based on the preset importance ranking of the evaluation indicators, and determine the target hybrid power system from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system; and a second determination module, configured to determine the target control strategy from multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, and use the target hybrid power system and the target control strategy as the parameter determination result.

[0014] According to embodiments of the present invention, by acquiring vehicle attribute data, vehicle operating conditions, evaluation indicators, and a preset importance ranking, and determining the vehicle's required power based on the vehicle attribute data and operating conditions, multiple candidate hybrid power systems are selected from a pre-built database based on the required power. A cost function is constructed for each candidate hybrid power system based on the evaluation indicators and the initial weights configured for the evaluation indicators, resulting in a candidate solution set for the candidate hybrid power systems. Based on the preset importance ranking of the evaluation indicators, a target hybrid power system can be determined from the candidate hybrid power systems. The target control strategy is determined based on the simulation control results of multiple candidate control strategies controlling the target hybrid power system, and the target control strategy and the target hybrid power system are used as the parameter determination results. Because the selection and matching of candidate hybrid power systems can be automatically completed based on vehicle attributes, operating conditions, evaluation indicators, and importance ranking during the process of determining hybrid power system parameters, and the determination of the hybrid power system and control strategy can be automated without extensive manual calibration and testing, the efficiency and intelligence of the parameter determination process are significantly improved. Attached Figure Description

[0015] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0016] Figure 1An application scenario diagram of the parameter determination method for a hybrid power system according to an embodiment of the present invention is shown;

[0017] Figure 2 A flowchart of a parameter determination method for a hybrid power system according to an embodiment of the present invention is shown;

[0018] Figure 3A An architecture diagram of priority division according to an embodiment of the present invention is shown;

[0019] Figure 3B The flowcharts show the operating modes of components with different priorities;

[0020] Figure 4 A flowchart of a parameter determination method for a hybrid power system according to another embodiment of the present invention is shown;

[0021] Figure 5 An architecture diagram of a parameter determination system for a hybrid power system according to an embodiment of the present invention is shown;

[0022] Figure 6 A structural block diagram of a parameter determination device for a hybrid power system according to an embodiment of the present invention is shown;

[0023] Figure 7 A block diagram of an electronic device suitable for implementing a parameter determination method for a hybrid power system according to an embodiment of the present invention is shown. Detailed Implementation

[0024] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] 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 are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0028] With the escalating global energy crisis and environmental pollution, vehicles equipped with hybrid power systems offer an effective solution for energy conservation and emission reduction in the transportation sector. However, compared to traditional single-power systems, the multi-power characteristics of hybrid systems necessitate extensive calibration and testing for parameter determination in specific scenarios. This significantly increases the workload and cost of parameter determination. Furthermore, effectively balancing various indicators to obtain the most suitable system solution for the current scenario remains a challenge in determining hybrid power system parameters, given the diverse requirements of different scenarios. Therefore, an efficient and intelligent parameter determination method that can effectively adapt to various application scenarios is urgently needed.

[0029] In view of this, embodiments of the present invention provide a method for determining parameters of a user hybrid power system to solve the problems of low efficiency and low intelligence caused by large calibration resources, high complexity, and long cycle in the parameter determination process of existing hybrid power systems. Specifically, the method includes: acquiring vehicle attribute data, vehicle operating conditions, evaluation indicators for evaluating the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators; determining the vehicle's required power based on the vehicle attribute data and operating conditions; selecting multiple candidate hybrid power systems from a pre-built database based on the required power; constructing a cost function for each candidate hybrid power system according to the evaluation indicators and the initial weights configured for the evaluation indicators, and adjusting the initial weights until the candidate solution set determined based on the adjusted weights and cost function meets preset conditions, thereby obtaining a candidate solution set for each candidate hybrid power system; determining the target solution for each candidate hybrid power system from the candidate solution set based on the preset importance ranking of the evaluation indicators, and determining the target hybrid power system from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system; determining the target control strategy from multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, and using the target hybrid power system and the target control strategy as the parameter determination result.

[0030] Figure 1 An application scenario diagram of the parameter determination method for a hybrid power system according to an embodiment of the present invention is shown.

[0031] like Figure 1 As shown, application scenario 100 according to this embodiment may include vehicle 101, network 102, database 103, and server 104. Network 102 is used as a medium to provide a communication link between vehicle 101, database 103, and server 104. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0032] The attribute data, operating conditions, evaluation indicators, and preset importance ranking of vehicle 101 can be sent to server 104 via network 102. In one embodiment, a user can input the configuration information such as the attribute data, operating conditions, evaluation indicators, and preset importance ranking of vehicle 101 into a terminal device connected to server 104 via the network, so that the terminal device can send this input configuration information to server 104 via the network. The terminal device may include, but is not limited to, smartphones, tablets, laptops, and desktop computers.

[0033] Database 103 can be pre-built, including multiple system configurations and multiple system components required for a synthetic hybrid power system.

[0034] Server 104 can provide various services, such as attribute data, operating conditions, evaluation indicators, and preset importance rankings for vehicle 101. Based on the power demand determined by the attribute data and operating conditions, it filters suitable system configurations and system components from database 103 and combines them into multiple candidate hybrid power systems. Based on the evaluation indicators and the initial weights configured for those indicators, it obtains a candidate solution set for each candidate hybrid power system. On this basis, based on the preset importance ranking of the evaluation indicators, it determines the target hybrid power system from the candidate hybrid power systems. Based on the simulation control results of the target hybrid power system using multiple candidate control strategies, it determines the target control strategy. Finally, it outputs the target hybrid power system and the target control strategy as parameters. The output target hybrid power system and target control strategy can be used in vehicle 101. Server 104 can also provide the output target hybrid power system and target control strategy to the user who inputs configuration information via a terminal device.

[0035] It should be noted that the parameter determination method for a hybrid power system provided in this embodiment of the invention can generally be executed by server 104. Correspondingly, the parameter determination device for a hybrid power system provided in this embodiment of the invention can generally be located in server 104. The parameter determination method for a hybrid power system provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 104 and capable of communicating with vehicle 101, database 103, and / or server 104. Correspondingly, the parameter determination device for a hybrid power system provided in this embodiment of the invention can also be located in a server or server cluster that is different from server 104 and capable of communicating with vehicle 101, database 103, and / or server 104.

[0036] It should be understood that Figure 1 The number of vehicles, networks, databases, and servers shown is merely illustrative. Any number of vehicles, networks, databases, and servers can be included depending on implementation needs.

[0037] The following will be based on Figure 1 The described scene, through Figures 2-5 The parameter determination method for a hybrid power system according to an embodiment of the present invention will be described in detail.

[0038] Figure 2 A flowchart of a parameter determination method for a hybrid power system according to an embodiment of the present invention is shown.

[0039] like Figure 2 As shown, the parameter determination method for a hybrid power system in this embodiment includes operations S210 to S250.

[0040] In operation S210, the vehicle's attribute data, operating conditions, evaluation indicators for assessing the performance of the hybrid power system, and the preset importance ranking of the evaluation indicators are obtained. Based on the vehicle's attribute data and operating conditions, the required power of the vehicle is determined.

[0041] In operation of S220, multiple candidate hybrid power systems are selected from a pre-built database based on the required power.

[0042] In operation S230, a cost function is constructed for each candidate hybrid power system based on the evaluation index and the initial weights configured for the evaluation index, and the initial weights are adjusted until the candidate solution set determined based on the adjusted weights and cost function meets the preset conditions, thus obtaining the candidate solution set for each candidate hybrid power system.

[0043] In operation S240, based on the preset importance ranking of the evaluation index, the target solution of each candidate hybrid power system is determined from the candidate solution set of each candidate hybrid power system, and the target hybrid power system is determined from the candidate hybrid power system according to the target solution of each candidate hybrid power system.

[0044] In operation S250, based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, the target control strategy is determined from the multiple candidate control strategies, and the target hybrid power system and the target control strategy are used as parameters to determine the result.

[0045] In some embodiments, vehicle attribute data can describe the vehicle's physical characteristics, such as vehicle weight, frontal area, drag coefficient, rolling resistance coefficient, transmission efficiency, wheel radius, and maximum permissible speed.

[0046] Vehicle operating conditions can refer to time series data of vehicle speed changes over time under predetermined driving conditions. For example, in the case of urban road delivery, operating conditions can include characteristic driving cycle conditions for urban delivery, which consist of time, vehicle speed, and road gradient.

[0047] Evaluation metrics can be quantitative parameters used to measure the performance of a hybrid system, such as fuel consumption, energy consumption, battery life loss, speed following ability, engine start-stop frequency, and pollutant emission levels.

[0048] The preset importance ranking can be obtained by users pre-setting the order of importance of multiple evaluation indicators according to actual needs. This is used to subsequently determine the target solution and the weight allocation of each evaluation indicator in the target hybrid system. For example, based on the needs of urban delivery scenarios, the preset importance ranking can be set as follows: fuel consumption and pollutant emissions first, battery life deterioration second, and vehicle speed following capability and engine start-stop frequency third.

[0049] The required power can be the total power that the hybrid system needs to provide in order for the vehicle to operate under given conditions. This required power can be calculated based on the vehicle's attribute data and operating conditions using a vehicle longitudinal dynamics model.

[0050] The database can be pre-built, including system configurations and components for constructing a hybrid power system. System configurations may include series, parallel, and series-parallel configurations, while components may include engines, motors, and batteries. The database may also include indicator functions corresponding to various evaluation metrics, used to construct cost functions. Furthermore, the database may include a control strategy library, where control strategies are used to control the target hybrid power system.

[0051] Candidate hybrid power systems can be hybrid power systems formed by combining configurations and components that meet the power demand requirements, selected from a database based on the required power output.

[0052] The cost function can be a mathematical expression with evaluation indicators and their weights as variables, used for quantitative evaluation of multiple candidate hybrid power systems. The initial weights in the cost function can be initial allocation coefficients assigned to each evaluation indicator, serving as the starting point for multi-objective optimization.

[0053] For the same candidate hybrid power system, the candidate solution set can include multiple candidate solutions. Each candidate solution can be a Pareto optimal solution obtained by adjusting the weights of the evaluation indicators. The preset condition can be the Pareto optimality condition, that is, without sacrificing the performance of at least one other evaluation indicator (an evaluation indicator other than the target evaluation indicator), it is impossible to further improve any target evaluation indicator. Specifically, the performance of the evaluation indicators can be controlled by the weights. Since there are multiple evaluation indicators, under the first weight allocation method, the performance of evaluation indicator A may be performance 1, and the performance of evaluation indicator B may be performance 2. After changing the first weight allocation method, the performance of evaluation indicator A improves from performance 1 to performance 3, and the performance of evaluation indicator B improves from performance 2 to performance 4. Then the first weight allocation method is not optimal. If the performance of evaluation indicator A decreases from performance 1 to performance 5, but the performance of evaluation indicator B improves from performance 2 to performance 4, this means that improving the performance of evaluation indicator B requires sacrificing the performance of evaluation indicator A. In this case, the first weight allocation method can be optimal, and correspondingly, a Pareto optimal solution is obtained.

[0054] The target solution can be a solution that meets the preset importance ranking from the candidate solution set of the candidate hybrid power system, and is selected by a multi-index decision method (such as AHP-TOPSIS) to represent the optimal performance of the candidate hybrid power system.

[0055] The target hybrid power system can be the overall optimal hybrid power system obtained by comparing the target solutions of candidate hybrid power systems and sorting them according to the preset importance of evaluation indicators.

[0056] Candidate control strategies can be various algorithmic schemes for controlling the target hybrid power system, such as rule-based control strategies, control strategies based on minimizing equivalent fuel consumption, and control strategies based on random forests. In a simulation environment, these candidate control strategies can be used to control the target hybrid power system. The simulation control results can be evaluation index data obtained by operating the target hybrid power system under the same urban delivery cycle conditions using these candidate control strategies, such as fuel consumption, energy consumption, battery life depletion, vehicle speed following ability, engine start-stop frequency, and pollutant emissions.

[0057] The target control strategy can be a control strategy that matches the preset importance ranking, selected from multiple candidate control strategies based on the degree of matching between the simulation control results of each candidate control strategy and the preset importance ranking.

[0058] The target hybrid power system and target control strategy determined by the above process can be output as parameter determination results. In one embodiment, the configuration of the target hybrid power system, components, and control logic of the target control strategy can also be output.

[0059] According to embodiments of the present invention, the above-described parameter determination method for a hybrid power system involves inputting vehicle information and operating conditions, selecting evaluation indicators, and ranking the evaluation indicators by importance. The cycle power demand is calculated based on the vehicle information and operating conditions. Candidate hybrid power systems and cost functions are constructed based on data in a database. A multi-objective global optimization algorithm is used to optimize the multi-cost function, obtaining a multi-objective globally optimal solution set (candidate solution set) for the candidate hybrid power systems. A multi-objective decision-making method is used to select the multi-objective cost weight allocation and target solution for the candidate hybrid power systems from the candidate solution set. Based on the target solution of each candidate hybrid power system, a target hybrid power system is selected from the candidate hybrid power systems using the multi-objective decision-making method. Based on the target hybrid power system, for rule-based control strategies, a multi-objective optimization algorithm is used to optimize the decision threshold; for control strategies based on minimizing equivalent fuel consumption, the cost function of the target hybrid power system is used as the equivalent fuel consumption model; for control strategies based on random forests, the globally optimal decision data of the target hybrid power system is used as the training set to train the strategy. A target control strategy is selected from the three candidate control strategies using the multi-objective decision-making method, and the target control strategy and the target hybrid power system are output as the parameter determination result.

[0060] According to embodiments of the present invention, by acquiring vehicle attribute data, vehicle operating conditions, evaluation indicators, and a preset importance ranking, and determining the vehicle's required power based on the vehicle attribute data and operating conditions, multiple candidate hybrid power systems are selected from a pre-built database based on the required power. A cost function is constructed for each candidate hybrid power system based on the evaluation indicators and the initial weights configured for the evaluation indicators, resulting in a candidate solution set for the candidate hybrid power systems. Based on the preset importance ranking of the evaluation indicators, a target hybrid power system can be determined from the candidate hybrid power systems. The target control strategy is determined based on the simulation control results of multiple candidate control strategies controlling the target hybrid power system, and the target control strategy and the target hybrid power system are used as the parameter determination results. Because the selection and matching of candidate hybrid power systems can be automatically completed based on vehicle attributes, operating conditions, evaluation indicators, and importance ranking during the process of determining hybrid power system parameters, and the determination of the hybrid power system and control strategy can be automated without extensive manual calibration and testing, the efficiency and intelligence of the parameter determination process are significantly improved.

[0061] The following describes the process of parameter determination method for a hybrid power system according to an embodiment of the present invention, taking a city delivery vehicle as an example and the vehicle's operating scenario as urban road delivery. This urban road delivery scenario is characterized by low average speed, frequent starts and stops, numerous accelerations and decelerations, and high potential for intelligent energy recovery. Vehicle attribute data may include parameters such as vehicle weight, frontal area, air resistance coefficient, rolling resistance coefficient, transmission system efficiency, wheel radius, and maximum permissible speed. Application scenario information includes characteristic urban delivery driving cycle conditions, which consist of time, vehicle speed, and road gradient, and can be represented as: ,in, This is a dataset of characteristic driving cycle conditions. For the first Each sampling time, For the first Vehicle speed at each sampling time, For the first Road slope angle at each sampling time This represents the total number of sampling points for the cyclic operating condition.

[0062] In this embodiment, fuel consumption, energy consumption, battery life deterioration, vehicle speed following capability, engine start-stop frequency, and pollutant emissions are selected as evaluation indicators. Based on the needs of urban delivery scenarios, the importance of the evaluation indicators is preset as follows: fuel consumption and pollutant emissions are prioritized, followed by battery life deterioration, and then vehicle speed following capability and engine start-stop frequency. This preset importance ranking is used to construct the indicator importance weights.

[0063] In some embodiments, the cyclic power demand can be calculated using a vehicle longitudinal dynamics model based on vehicle attribute data and operating conditions. Specifically, the process of determining the power demand may include the following steps: determining the vehicle acceleration based on the vehicle speed at adjacent time points and the duration of adjacent time points; determining the vehicle's driving force demand based on the vehicle's mass, vehicle acceleration, gravitational acceleration, rolling resistance coefficient, air density, air resistance coefficient, and vehicle frontal area; determining the power demand based on the driving force demand and drive transmission efficiency when the vehicle's driving force demand is greater than or equal to a predetermined value; and determining the power demand based on the driving force demand, vehicle speed, and vehicle regenerative braking efficiency when the vehicle's driving force demand is less than the predetermined value.

[0064] In some embodiments, the acceleration of the vehicle at each sampling time can be calculated based on vehicle attribute data and operating conditions, as shown in formula (1).

[0065] (1)

[0066] in, For the first Vehicle acceleration at each sampling time point The sampling time interval, For the first Vehicle speed at +1 sampling time, For the first Vehicle speed at each sampling time.

[0067] Based on the vehicle's longitudinal dynamics model, calculate the first... The vehicle driving force requirement at each sampling time is shown in formula (2).

[0068] (2)

[0069] in, For the driving force requirements of the vehicle, For the quality of the vehicle, It is the acceleration due to gravity. The rolling resistance coefficient, air density, The air drag coefficient, The frontal area of ​​the vehicle.

[0070] The required power of the vehicle is calculated based on the vehicle's driving force requirement and speed, as shown in formula (3), with a predetermined value of 0 as an example.

[0071] (3)

[0072] in, For the first Vehicle power demand at each sampling time. To improve drive transmission efficiency, For the vehicle's braking energy recovery efficiency. When When positive, it indicates that the vehicle requires driving power; when... When the value is negative, it indicates that the vehicle is in the braking energy recovery condition. The time-power demand sequence for the entire cycle can be obtained as shown in formula (4).

[0073] (4)

[0074] According to an embodiment of the present invention, by calculating the acceleration based on the speed change of the vehicle at adjacent moments during the process of determining the parameters of the hybrid power system, and combining multiple factors such as the vehicle's mass, drag coefficient, and aerodynamic parameters to calculate the driving force requirement, the driving conditions and braking conditions are distinguished, and different power calculation methods are adopted for each condition (considering transmission efficiency during driving and energy recovery efficiency during braking). This achieves refined and accurate calculation of the vehicle's required power in different scenarios, and improves the accuracy of the parameter determination process.

[0075] In some embodiments, candidate hybrid power systems can be determined from a database based on power demand. Specifically, the database includes a configuration library and a component library, whereby the configuration library stores hybrid power system configurations and the component library stores hybrid power system components. The process of selecting multiple candidate hybrid power systems from a pre-built database based on power demand may include the following operations: selecting candidate hybrid power system configurations from the configuration library and selecting candidate hybrid power system components from the component library according to power demand; and combining the candidate hybrid power system configurations and candidate hybrid power system components to obtain a candidate hybrid power system.

[0076] In some embodiments, the configuration library may include hybrid power system configuration models such as series, parallel, and series-parallel configurations. For each configuration, the positional priority of each power component constituting that configuration also needs to be determined and saved in the configuration library. For example, a configuration may have three positions for placing power components: position 1 is the highest priority, position 2 is the second highest priority, and position 3 is the third highest priority. Power components from the component library are then placed in these positions to achieve power component optimization. The component library stores the performance and parameter information of each power component, such as different models of engines, different models of motors, batteries of different capacities and power levels, and corresponding power conversion component models. Components in the component library can be saved in the form of functions, which can reflect the energy conversion efficiency, power coverage, energy storage capacity, and other performance characteristics of each component.

[0077] Figure 3A An architecture diagram of priority partitioning according to an embodiment of the present invention is shown.

[0078] like Figure 3A As shown, the configuration library can also store the position priorities of each component. A system configuration connected to the wheel can connect components with multiple priorities, such as a first priority component, a second priority component, a third priority component, and an m-th priority component.

[0079] Figure 3B The flowchart shows the working modes of components with different priorities.

[0080] like Figure 3B As shown, the process may include operations S301 to S306.

[0081] In operation S301, determine whether the first priority component is working. If the first priority component is working, execute operation S302; if the first priority component is not working, execute operation S303.

[0082] When operating S302, active control of the first priority power unit is used.

[0083] In operation S303, determine whether the second priority component is working. If the second priority component is working, execute operation S304; if the second priority component is not working, execute operation S305.

[0084] When operating S304, active control of the second priority power unit is used.

[0085] When operating S305, the lowest priority power components are actively controlled.

[0086] When operating S306, other levels are passively adjusted.

[0087] Specifically, this priority can include the following information: Under the premise of meeting the system's power requirements, the power of power components at the first priority level is actively controlled, while other power components at different priorities are passively adjusted to meet the system's power requirements. When the first priority power component is not operating, the power of power components at the second priority level is actively controlled, while other power components at different priorities are passively adjusted to meet the system's power requirements. When both the first and second priority components are not operating, the power of power components at the third priority level is actively controlled, while other power components at different priorities (excluding the first, second, and third priorities) are passively adjusted to meet the system's power requirements. The processes of active power control and passive power adjustment for other priority levels (excluding the first, second, and third priorities) are then repeated accordingly.

[0088] In some embodiments, the power component priority is used to indicate the pre-defined control order of power components in the configuration library, rather than indicating whether a component will necessarily work in each operating mode, or indicating a change in priority order under different operating modes. For the same candidate configuration, the priority order of the power components is pre-stored in the configuration library.

[0089] Taking a dual-motor parallel hybrid system as an example, the system includes an engine, a first drive motor, a second drive motor, and a power battery. The engine, the first drive motor, and the second drive motor all output power to the vehicle's drive wheels. The power battery, as an energy storage component, provides electrical energy to the first and second drive motors or absorbs regenerative braking energy. The configuration library pre-sets the engine as the first priority power component, the first drive motor as the second priority power component, and the second drive motor as the third priority power component. This priority order remains consistent in engine-only drive mode, engine and first drive motor combined drive mode, engine and dual-motor combined drive mode, pure electric drive mode, and regenerative braking mode.

[0090] When the first-priority power unit is active, its output power is used as an active control variable. The output power of the second-priority and third-priority power units is passively calculated based on the vehicle's power demand, the output power of the first-priority power unit, and the power balance relationship. When the first-priority power unit is not active in a certain operating mode, its output power is zero, and the second-priority power unit becomes the currently effective control unit. When both the first-priority and second-priority power units are not active, the third-priority power unit becomes the currently effective control unit.

[0091] Therefore, priorities do not need to be reordered according to the operating mode, but are predetermined based on the candidate configuration; under different operating modes, the currently effective control component is determined only by whether the high-priority component is working.

[0092] For example, in a candidate configuration of dual-motor parallel operation, with a fixed priority order of engine as the first priority, first drive motor as the second priority, second drive motor as the third priority, and power battery as an energy storage support component:

[0093] In the engine-only drive mode, the high-priority component status is that the engine is operating; the first and second drive motors do not output drive power. The actual control and passive adjustment are as follows: engine output power is used as the active control variable, and the first and second drive motors do not participate in drive operation. The first and second drive motors are different motors.

[0094] In the operating mode of joint drive by the engine and the first drive motor, the high-priority component status is: engine is working; the first drive motor is working; the second drive motor is not working or works only as needed. The actual control and passive adjustment are as follows: the engine output power is used as the active control variable, and the first drive motor is passively adjusted according to the difference between the vehicle's required power and the engine output power; if the first drive motor has met the power difference, the second drive motor is not working.

[0095] In the combined engine and dual-motor drive mode, the high-priority component status is: engine is working; first drive motor is working; second drive motor is working. The actual control and passive adjustment are as follows: engine output power is used as the active control variable, and the first drive motor is passively adjusted based on the remaining power demand; when the first drive motor reaches its power limit, the second drive motor continues to passively adjust based on the remaining power difference.

[0096] In pure electric drive mode, the high-priority component status is: engine not working; first drive motor working; second drive motor working as needed. The actual control and passive adjustment are as follows: first priority engine output power is zero, second priority first drive motor is the currently effective control component; when the first drive motor cannot meet the power demand alone, third priority second drive motor passively compensates.

[0097] In regenerative braking mode, the high-priority components are in a state where the engine is not operating; the first and second drive motors operate according to the regeneration capacity. The actual control and passive adjustment are as follows: when the first-priority engine output power is zero, the first drive motor prioritizes regenerative braking; when the first drive motor's regeneration capacity is insufficient or limited, the second drive motor continues to regenerate the remaining braking power.

[0098] In other words, in a dual-motor parallel hybrid system, the engine is always the first-priority power component, the first drive motor is always the second-priority power component, and the second drive motor is always the third-priority power component. The pure electric drive mode and regenerative braking mode do not redefine the first or second drive motor as the first priority; rather, when the first-priority engine is not operating, the second-priority first drive motor serves as the currently effective control component. When the first drive motor is not operating or reaches its power limit, the third-priority second drive motor continues to perform the driving or energy recovery functions.

[0099] In one embodiment, the candidate hybrid power system obtained by combining system configurations in the configuration library and components in the component library may include, but is not limited to, the following three types of systems:

[0100] The first type of candidate hybrid system is a series hybrid system, which includes an engine, a generator, a battery, and a drive motor. The engine does not directly drive the wheels; instead, it powers the generator to produce electricity, and the drive motor serves as the vehicle's sole propulsion source.

[0101] The second category of candidate hybrid systems is the parallel hybrid system, which includes an engine, a drive motor, a battery, a clutch, and a transmission. Both the engine and the drive motor can output mechanical power to the wheels, and the vehicle can switch between modes such as pure electric drive, engine drive, combined engine and motor drive, and regenerative braking.

[0102] The third category of candidate hybrid power systems is the series-parallel hybrid power system, which includes an engine, a generator, a drive motor, a power battery, and a power coupling device. This system can achieve engine-generated power, engine-driven mechanical power, or a combined engine-motor drive.

[0103] For each configuration, engines, motors, and batteries of different power levels can be selected from the component library based on the required power output to form multiple candidate hybrid power systems. The set of candidate hybrid power systems can be represented as: ,in, For the candidate hybrid power system set, For the first One candidate hybrid power system, This represents the total number of candidate hybrid power systems.

[0104] In some embodiments, the indicator library may include indicator functions such as fuel consumption, electrical energy consumption, battery life depletion, vehicle speed following error, engine start-stop frequency, and pollutant emissions. For the first... The cumulative result of each evaluation indicator under the entire operating condition can be expressed as: , among which, among which, For the first The cumulative results of each indicator over time. For the first The sampling time of the first sampling moment The instantaneous function of each index, For system state variables, For system decision variables, This represents the number of sampling times.

[0105] According to an embodiment of the present invention, in the process of determining the parameters of a hybrid power system, candidate configurations are selected from the configuration library and candidate components are selected from the component library according to the required power, and the two are combined to obtain a candidate hybrid power system. This achieves automatic and efficient generation of candidate systems while ensuring that the candidate systems meet the power requirements, thereby improving the efficiency of parameter determination.

[0106] In some embodiments, based on the constructed multiple candidate hybrid power systems, a cost function can be constructed for each candidate hybrid power system according to the evaluation index and the initial weights configured for the evaluation index, and the initial weights can be adjusted until the candidate solution set determined based on the adjusted weights and cost function satisfies preset conditions, thus obtaining the candidate solution set for each candidate hybrid power system. Specifically, the process of determining the candidate solution set may include the following operations: constructing target parameters for the candidate hybrid power system, the target parameters being used to describe the operation of the candidate hybrid power system under driving conditions; performing the following operations on the adjusted weights until the obtained candidate solution set satisfies preset conditions: inputting the adjusted weights into the cost function to obtain the target cost function; processing the target parameters using a dynamic programming algorithm, and aiming to minimize the target cost function to obtain the vehicle control sequence, wherein the control sequence includes the vehicle's operating mode and component power allocation at each time step; determining the value of the evaluation index according to the vehicle's operating mode and component power allocation at each time step, and using the value of the evaluation index as the candidate solution corresponding to the adjusted weights.

[0107] In some embodiments, the target parameters include state variables, decision variables, and constraints. The process of constructing target parameters for a candidate hybrid power system may include the following operations: constructing state variables based on the vehicle's battery state of charge; constructing decision variables based on the operating mode and component power of the candidate hybrid power system; and constructing constraints based on the power balance constraints, component power constraints, battery state of charge constraints, and operating mode constraints that the candidate hybrid power system needs to satisfy.

[0108] In some embodiments, for candidate hybrid systems The battery state of charge is used as the main state variable in the dynamic programming algorithm: ,in, For the first The battery state of charge at each sampling time can be the percentage of the battery's remaining capacity. The battery state of charge threshold can be a threshold value for the percentage of the battery's remaining capacity.

[0109] Using the system operating mode and the output power of the first priority power component as decision variables: ,in, For the first The system's operating mode at each sampling time. For the first The output power of the first priority power unit at each sampling time. The first priority power unit is determined by the power unit position priority rules in the configuration library.

[0110] The state transition relationship of the battery's state of charge can be expressed as: ,in, For the first Battery state of charge at each sampling time, For the first Battery power at each sampling time, The energy available to the battery. When When positive, it indicates that the battery is discharging; when... A negative value indicates that the battery is charging.

[0111] The control decisions of candidate hybrid power systems must satisfy power constraints. ≤ ≤ and battery state of charge constraints ,in, and These are the lower and upper limits of the battery's state of charge, respectively. For the first The power component is in the first Output power at each sampling time, and These are the minimum and maximum output power of the power component, respectively.

[0112] The following is the process of obtaining the candidate solution set for the candidate hybrid power system:

[0113] Perform dynamic programming global optimization on candidate hybrid power systems:

[0114] For each candidate hybrid system Dynamic programming can be performed based on cyclic demand power. The single-step cost function of the dynamic programming algorithm is composed of the weighted sum of the instantaneous functions of multiple evaluation indicators, as shown in formula (5).

[0115] (5)

[0116] in, For the first The cost per step at each sampling time. For the number of target indicators, For the first Cost weights for each indicator.

[0117] The recurrence relation in dynamic programming is: ,in, For the first The optimal residual cost at each sampling time. For the first The set of feasible decisions that satisfy the system constraints at each sampling time. For the first The optimal residual cost at +1 sampling time.

[0118] Candidate hybrid power systems are obtained using dynamic programming algorithms. Given cost weights The global optimal control trajectory is as follows: ,in, For the first Candidate systems Given a cost weight vector Below, the globally optimal control trajectory is obtained through dynamic programming algorithm. For the first The optimal state variables at each sampling time. For the first The optimal control decision variables at each sampling time point are determined, and the corresponding cyclic index results for the optimal trajectory are calculated: ,in, For the first Given a cost weight vector, candidate systems The resulting vector of cyclic indicators, The first The first to the second The cyclical results of each target indicator.

[0119] A multi-objective optimization algorithm is used to obtain the candidate solution set for the candidate hybrid power system:

[0120] Since there are interdependent relationships among the evaluation indicators, the weights of the evaluation indicators in the dynamic programming cost function can be used as optimization parameters, and a multi-objective genetic algorithm can be employed to optimize the weights. The weight vector is represented as: And satisfy: .

[0121] The multi-objective optimization problem is represented as Through the above multi-objective optimization, candidate hybrid power systems are obtained. The solution set of multi-objective optimal cost weight allocation and the corresponding set of index results: ,in, For the first A candidate solution set for a candidate hybrid power system. This represents the number of solutions in the candidate solution set. For the first The dynamic programming cost weight vector corresponding to each candidate solution. For the first The candidate system at the ... The cyclic index result vector is obtained by dynamic programming under each cost weight vector.

[0122] In some embodiments, based on the obtained candidate solution set, the target solution of each candidate hybrid power system can be determined from the candidate solution set of each candidate hybrid power system based on a preset importance ranking of the evaluation indicators. Specifically, the process may include the following operations: constructing an initial importance matrix of the evaluation indicators according to the preset importance ranking; performing a consistency check on the initial importance matrix to obtain a target importance matrix; constructing an initial decision matrix from the candidate solution set of each candidate hybrid power system; normalizing the initial decision matrix to obtain a target decision matrix; obtaining a first target matrix based on the target importance matrix and the target decision matrix; and determining the target solution of the candidate hybrid power system based on the proximity between each data point in the first target matrix and the first predetermined solution.

[0123] In some embodiments, an initial importance matrix of the evaluation indicators can be constructed using the Analytic Hierarchy Process (AHP) method based on a preset importance ranking of the evaluation indicators input by the user. ,in, This is an indicator importance judgment matrix constructed based on the AHP method. Indicates the first The first indicator is relative to the first The importance of each indicator is determined by finding the largest eigenvalue and the corresponding eigenvector of the comparison matrix, thus obtaining the indicator importance weight vector. ,in, This is the importance weight vector of the indicators obtained through the AHP method. This is the largest eigenvalue of the comparison matrix.

[0124] To ensure the rationality of the initial importance matrix, a consistency check is performed: , ,in, As a consistency indicator, As a random consistency indicator, This represents the consistency ratio.

[0125] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to evaluate candidate hybrid power systems. The decision is made based on the multi-objective optimal solution set. The candidate solution set of the candidate hybrid power system is constructed as the initial decision matrix: ,in, Denotes the initial decision matrix. For the first The candidate solution at the th... The evaluation results are under each evaluation metric, where Q represents the number of candidate solutions in the candidate solution set.

[0126] The initial decision matrix is ​​normalized as shown in formula (6).

[0127] (6)

[0128] in, This indicates that the target decision matrix is ​​obtained after normalization.

[0129] By combining the target importance matrix of the evaluation indicators obtained by AHP and the target decision matrix obtained by TOPSIS, the weighted normalized first target matrix can be obtained, as shown in formula (7).

[0130] (7)

[0131] in, Let represent the first objective matrix, and be the in the weighted normalized decision matrix. The candidate solution at the th... Weighted normalization results under each target indicator The first one obtained by the AHP method The importance weight of each target indicator Let be the objective decision matrix, representing the th... The candidate solution at the th... Normalized results under each target indicator.

[0132] In some embodiments, the first predetermined solution may include a positive ideal solution and a negative ideal solution. The positive ideal solution may be a virtual solution in which all evaluation indicators reach their optimal values, such as the solution corresponding to the minimum fuel consumption, minimum cost, and minimum emissions. The negative ideal solution may be a virtual solution in which all evaluation indicators reach their worst values, such as the solution corresponding to the maximum fuel consumption, maximum cost, and maximum emissions.

[0133] The first objective matrix can be calculated to have the [missing information]. The solution leads to the ideal solution. distance As shown in formula (8), and the first From one solution to the negative ideal solution distance As shown in formula (9), j is the index value.

[0134] (8)

[0135] (9)

[0136] Furthermore, the proximity of the q-th solution can be calculated. As shown in formula (10).

[0137] (10)

[0138] The solution with the highest approximation was selected as the candidate hybrid power system. Target solution: , Represent the objective solution. And determine the candidate system. Target cost weight vector: and target indicator results ,in, For the first The target candidate solution number corresponding to each candidate system; For the first The target cost weight vector of each candidate system; For the first The target metrics results for each candidate system.

[0139] Through the above process, the target solution corresponding to each candidate hybrid power system is obtained, namely the result of target weight allocation and evaluation index.

[0140] According to an embodiment of the present invention, in the process of determining the parameters of a hybrid power system, an initial importance matrix of evaluation indicators is constructed based on a preset importance ranking, and then transformed into an objective and reasonable target importance matrix through a consistency check. Simultaneously, the candidate solution set of the candidate system is constructed as a decision matrix and normalized. Then, the first target matrix is ​​obtained by combining the target importance matrix and the target decision matrix. Finally, the target solution of the candidate system is determined based on the closeness between each data point and the predetermined solutions (positive ideal solution and negative ideal solution). This process avoids decision biases that may be caused by subjective arbitrariness or simple weighted summation, and improves the accuracy of parameter determination.

[0141] In some embodiments, a target hybrid power system can be determined from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system. Specifically, the process may include the following operations: constructing a set of target solutions for the candidate hybrid power systems based on the target solutions of the candidate hybrid power systems, and normalizing the set of target solutions; obtaining a second target matrix based on the normalized set of target solutions and the target importance matrix; and determining the target hybrid power system based on the proximity between each data point in the second target matrix and a second predetermined solution.

[0142] The objective solutions of each candidate hybrid power system are combined into a set of objective solutions for the candidate hybrid power systems. ,in, For candidate hybrid power systems The target index results are obtained after multi-objective optimization and multi-objective decision-making. The target solution set is then normalized, and the normalization process can be referred to formula (6).

[0143] Using each candidate hybrid power system as the decision-making scheme, and fuel consumption, electricity consumption, battery life loss, vehicle speed following ability, engine start-stop frequency, and pollutant emissions as evaluation indicators, the AHP and TOPSIS methods are used again to make decisions at the candidate hybrid power system level. For example, based on the verified target importance matrix and target solution set, a second target matrix can be obtained, and this process can refer to formula (7).

[0144] The candidate hybrid power system with the highest similarity in the second objective matrix is ​​selected as the target hybrid power system. ,in, For the target hybrid power system, For the first The degree of closeness of each candidate hybrid power system in the decision-making process at the candidate hybrid power system level. The process of determining the degree of closeness can be shown by formulas (8) to (10).

[0145] In some embodiments, when determining the target hybrid power system Then, a control strategy algorithm library can be invoked to generate three types of candidate control strategies suitable for the target hybrid power system. These candidate control strategies can include rule-based control strategies, control strategies based on minimizing equivalent fuel consumption, and control strategies based on random forests.

[0146] Rule-based control strategy: Based on the relationship between vehicle speed, power demand, and battery state of charge and the respective thresholds for vehicle speed, power demand, and battery state of charge, the operating mode of the target hybrid system is determined, and the adjustment method of components is determined according to priority in the operating mode.

[0147] This strategy uses vehicle speed, vehicle power demand, and battery state of charge as mode switching conditions, and the mode switching threshold as the parameter to be optimized. The parameter vector of the rule strategy can be expressed as formula (11):

[0148] (11)

[0149] in, The first vehicle speed threshold is used to distinguish between low-speed and medium-speed operating conditions; and This is the second vehicle speed threshold, used to distinguish between medium-speed and high-speed operating conditions; The first required power threshold is used to distinguish between low power demand and medium power demand; This is the second demand power threshold, used to distinguish between medium and high power demand; and The SOC threshold is used to distinguish between low, medium, and high battery levels. A multi-objective genetic algorithm can be used to optimize the rule-based strategy threshold, as shown in formula (12).

[0150] (12)

[0151] in, Represents the threshold parameter vector of the rule policy To optimize variables, multi-objective optimization is performed. This indicates that the rule strategy is based on the threshold parameter. The multi-objective evaluation function vector, This indicates that the rule strategy is based on the threshold parameter. Next The cumulative results of each target indicator over a cycle.

[0152] The control strategy based on minimizing equivalent fuel consumption is as follows: Based on the evaluation index and the target solution of the target hybrid system, an equivalent cost function is constructed, and the operating mode and component power allocation of the target hybrid system are assigned according to the function value of the equivalent cost function.

[0153] This strategy uses the target cost weights obtained by the target hybrid power system in the dynamic programming and multi-objective decision-making process as the weights of the equivalent cost function, and selects the operating mode and power allocation with the minimum instantaneous equivalent cost at each sampling time, as shown in formula (13):

[0154] (13)

[0155] in, For the first The optimal control decision variable that minimizes the instantaneous equivalent cost at each sampling time. The first objective determined by the multi-objective decision-making process of the target system. Cost weighting of each indicator; For the first The target indicator in the first The instantaneous equivalent cost function at each sampling time.

[0156] Random forest-based control strategy: Input the vehicle speed, power demand, and battery state of charge into the control model, and output the operating mode and component power of the target hybrid system.

[0157] This strategy uses the globally optimal control trajectory of the target hybrid power system obtained in dynamic programming as training data. The training dataset is represented by formula (14):

[0158] (14)

[0159] Among them, vehicle speed Power Requirement and battery state of charge This is the input to the random forest model. and These represent the optimal system operating mode and the power of the first priority power component obtained through dynamic programming, respectively. The random forest control strategy after training can be expressed as formula (15):

[0160] (15)

[0161] in, This is the trained random forest model. and These represent the operating mode and the power of the first priority power component output by the random forest model, respectively.

[0162] In some embodiments, for multiple candidate control strategies, a target control strategy can be determined from the multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using the multiple candidate control strategies. Specifically, the process may include the following operations: constructing a control result set based on the simulation control results of each candidate control strategy, and normalizing the control result set; obtaining a third target matrix based on the normalized control result set and the target importance matrix; and determining the target control strategy based on the proximity between each data point in the third target matrix and a third predetermined solution.

[0163] In some embodiments, rule-based control strategies, equivalent fuel consumption minimization-based control strategies, and random forest-based control strategies can be applied to the target hybrid power system, respectively. Simulation calculations were performed under the same urban delivery cycle conditions to obtain the results of indicators such as fuel consumption, power consumption, battery life loss, vehicle speed following ability, engine start-stop frequency and pollutant emissions corresponding to the three control strategies.

[0164] Using the three control strategies as decision-making schemes and the above indicator results as decision indicators, the target control strategy is selected by ranking the importance of the indicators input by the user and using the AHP and TOPSIS methods as formula (16):

[0165] (16)

[0166] in, For target control strategy, For the first One candidate control strategy For the first The closeness of each candidate control strategy.

[0167] Based on the obtained target control strategy, the target hybrid power system can be used as parameters to determine the output structure. Specifically, the output may include the target hybrid power system configuration, target component model or component parameters, target hybrid power system operating mode set, target control strategy type, target control strategy parameters, and comprehensive index results of the target hybrid power system under characteristic driving cycle conditions.

[0168] The parameter determination method for hybrid power systems provided in this invention can automatically complete the generation of candidate hybrid power systems, global optimization of candidate hybrid power systems, multi-objective decision-making of candidate hybrid power systems, selection of target hybrid power systems, generation of control strategies, and selection of target control strategies, provided that only vehicle information, characteristic driving cycle operating conditions, and importance ranking of indicators are input. Compared with methods that rely on human experience for configuration selection and control strategy calibration, this invention can reduce the workload of repetitive modeling, repetitive calibration, and multi-scheme comparison in the process of determining hybrid power system parameters, improve the matching degree between system solutions and specific application scenarios, and increase the automation level of the hybrid power system parameter determination process.

[0169] Figure 4 A flowchart of a parameter determination method for a hybrid power system according to another embodiment of the present invention is shown.

[0170] like Figure 4 As shown, the parameter determination method for a hybrid power system in this embodiment may include operations S410 to S470.

[0171] When operating S410, input the vehicle's attribute data, operating conditions, evaluation indicators, and the preset importance ranking of the evaluation indicators.

[0172] When operating the S420, the required power is determined using the vehicle's longitudinal dynamics model.

[0173] When operating the S430, candidate hybrid power systems are assembled based on the configuration library and component library.

[0174] In operating S440, a multi-objective dynamic programming algorithm is used to obtain the optimal index results of the global optimal decision dataset for the candidate hybrid power system. The weights of each index in the weighted cost function of the objective index are used as the cost function of the dynamic programming algorithm. A multi-objective genetic algorithm is used to optimize the weights of the cost function to obtain the candidate solution set.

[0175] In operating S450, the importance of indicators is combined with the analytic hierarchy process to calculate the importance weights of the indicators, and these weights are used to establish a weighted normalized decision matrix to select the target solution from the candidate solution set.

[0176] In operation S460, the target hybrid system is determined from multiple candidate hybrid systems by obtaining the target solution for each candidate hybrid system.

[0177] In operating S470, rule-based, optimization-based, and learning-based strategies are selected from the control strategy library. Based on the target hybrid power system, a multi-objective optimization algorithm is used to determine the decision threshold of the rule-based strategy. The system's objective cost function is used as the cost function of the optimization-based strategy. A random forest model is trained using globally optimal data. The simulation control results of the target hybrid power system are controlled based on the three candidate control strategies, and the target control strategy is selected from the three candidate control strategies.

[0178] Figure 5 An architecture diagram of a parameter determination system for a hybrid power system according to an embodiment of the present invention is shown.

[0179] like Figure 5 As shown, the architecture may include an input terminal 501, a target hybrid power system determination module 502, a target control strategy determination module 503, and an output terminal 504.

[0180] Input terminal 501 allows input of vehicle attribute data, operating conditions, evaluation indicators, and a preset importance ranking of the evaluation indicators. By inputting vehicle attribute data into the vehicle's longitudinal dynamics model and combining the output of the longitudinal dynamics with the operating conditions, the vehicle's required power can be obtained. Based on the evaluation indicators, the weighted cost function for each indicator can be retrieved from the indicator library. Based on the preset importance ranking, the importance weight of each indicator can be determined using the analytic hierarchy process (AHP).

[0181] In the target hybrid power system determination module 502, multiple candidate hybrid power systems can be determined based on the configuration library and component library. For each candidate hybrid power system, dynamic programming is performed based on the demand power and cost function, and candidate solutions are determined based on the multi-objective genetic algorithm. The target solution is determined based on the TOPSIS decision method, and the target hybrid power system is determined based on the TOPSIS decision method.

[0182] In the target control strategy determination module 503, rule-based control strategies, control strategies based on minimizing equivalent fuel consumption, and control strategies based on random forests are queried from the control strategy library.

[0183] For rule-based control strategies, multi-objective optimization of the threshold is performed. For control strategies based on minimizing equivalent fuel consumption, the target system cost function is used. For random forest-based control strategies, training is conducted using the target system's globally optimal decision data. Based on the simulation control results of the target hybrid power system under each strategy, the TOPSIS decision method can be used to determine the target control strategy.

[0184] Output terminal 504 can output the target hybrid power system and the target control strategy.

[0185] In some embodiments, the indicator library mentioned in the above system may include a first type of indicator, a second type of indicator, a third type of indicator, and a fourth type of indicator. The first type of indicator may include fuel consumption, battery consumption, and battery life deterioration, etc. The second type of indicator may include maximum output power and acceleration / deceleration response, etc. The third type of indicator may include particulate matter emissions, nitrogen oxide emissions, etc. The fourth type of indicator may include driving smoothness, range, etc.

[0186] The configuration library mentioned in the above system can include series configuration, parallel configuration, series-parallel configuration, power split configuration, etc.

[0187] The component libraries mentioned in the above system may include engine component libraries, motor component libraries, and battery component libraries, etc.

[0188] The control strategy library mentioned in the above system may include rule-based control strategies, control strategies based on minimizing equivalent fuel consumption, and control strategies based on random forests.

[0189] According to embodiments of the present invention, by modularizing the design content in the hybrid power system design and development process, an index library, a configuration library, a component library, and a control strategy library are constructed to form candidate schemes for hybrid power systems and control strategies. By inputting vehicle attribute data, operating conditions, evaluation indicators, and a preset importance ranking of the evaluation indicators, and combining multi-objective dynamic programming algorithms, multi-objective optimization algorithms, and multi-objective decision-making methods, a reasonable target hybrid power system structure and target control strategy are automatically selected from the candidate schemes. The embodiments of the present invention fully utilize the modular characteristics of hybrid power systems and the collaborative relationships between components. Only vehicle attribute data, operating conditions, evaluation indicators, and a preset importance ranking of the evaluation indicators are required to automatically determine system parameters, eliminating the complex system selection and strategy calibration work in the traditional hybrid power system parameter determination process, effectively improving the convenience and rationality of system parameter determination.

[0190] Based on the above-described parameter determination method for hybrid power systems, this invention also provides a parameter determination device for hybrid power systems. The following will be combined with... Figure 6 The device is described in detail.

[0191] Figure 6 A structural block diagram of a parameter determination device for a hybrid power system according to an embodiment of the present invention is shown.

[0192] like Figure 6As shown, the parameter determination device 600 for a hybrid power system in this embodiment includes an acquisition module 610, a screening module 620, a construction module 630, a first determination module 640, and a second determination module 650.

[0193] The acquisition module 610 is used to acquire vehicle attribute data, vehicle operating conditions, evaluation indicators for assessing the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators, and to determine the vehicle's required power based on the vehicle attribute data and operating conditions.

[0194] The screening module 620 is used to select multiple candidate hybrid power systems from a pre-built database based on the required power.

[0195] The construction module 630 is used to construct a cost function for each candidate hybrid power system based on the evaluation index and the initial weights configured for the evaluation index, and adjust the initial weights until the candidate solution set determined based on the adjusted weights and cost function meets the preset conditions, thereby obtaining the candidate solution set for each candidate hybrid power system.

[0196] The first determining module 640 is used to determine the target solution of each candidate hybrid power system from the candidate solution set of each candidate hybrid power system based on the preset importance ranking of the evaluation index, and to determine the target hybrid power system from the candidate hybrid power system according to the target solution of each candidate hybrid power system.

[0197] The second determining module 650 is used to determine the target control strategy from multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, and to determine the result by using the target hybrid power system and the target control strategy as parameters.

[0198] According to an embodiment of the present invention, the first determining module 640 may include a first building unit, a verification unit, a construction unit, a first processing unit, a first determining unit, and a second determining unit.

[0199] The first building unit is used to construct an initial importance matrix of evaluation indicators according to a preset importance ranking.

[0200] The verification unit is used to perform consistency checks on the initial importance matrix to obtain the target importance matrix.

[0201] The construction unit is used to construct the initial decision matrix from the candidate solution set of each candidate hybrid power system.

[0202] The first processing unit is used to normalize the initial decision matrix to obtain the target decision matrix.

[0203] The first determining unit is used to obtain the first target matrix based on the target importance matrix and the target decision matrix.

[0204] The second determining unit is used to determine the target solution of the candidate hybrid power system based on the proximity between each data point in the first target matrix and the first predetermined solution.

[0205] According to an embodiment of the present invention, the first determining module 640 may further include a second constructing unit, a third determining unit, and a fourth determining unit.

[0206] The second construction unit is used to construct a set of target solutions for the candidate hybrid power system based on the target solution of the candidate hybrid power system, and to normalize the set of target solutions.

[0207] The third determining unit is used to obtain the second objective matrix based on the normalized objective solution set and the objective importance matrix.

[0208] The fourth determining unit is used to determine the target hybrid power system based on the proximity between each data point in the second target matrix and the second predetermined solution.

[0209] According to an embodiment of the present invention, the second determining module 650 may include a third constructing unit, a fifth determining unit, and a sixth determining unit.

[0210] The third building unit is used to construct a set of control results based on the simulation control results of each candidate control strategy, and to normalize the set of control results.

[0211] The fifth determining unit is used to obtain the third target matrix based on the normalized control result set and the target importance matrix.

[0212] The sixth determining unit is used to determine the target control strategy based on the proximity between each data point in the third target matrix and the third predetermined solution.

[0213] According to an embodiment of the present invention, the construction module 630 may include a fourth construction unit, an input unit, a second processing unit, and a seventh determination unit.

[0214] The fourth building unit is used to build target parameters for the candidate hybrid system. The target parameters are used to describe the operation of the candidate hybrid system under driving conditions.

[0215] The input unit is used to input the adjusted weights into the cost function to obtain the target cost function.

[0216] The second processing unit is used to process the target parameters using a dynamic programming algorithm and to obtain the vehicle's control sequence with the goal of minimizing the target cost function. The control sequence includes the vehicle's operating mode and component power allocation at each time step.

[0217] The seventh determining unit is used to determine the value of the evaluation index based on the vehicle's working mode and component power distribution at each time, and to use the value of the evaluation index as the candidate solution corresponding to the adjusted weight.

[0218] According to an embodiment of the present invention, the fourth building unit may include a first building subunit, a second building subunit, and a third building subunit.

[0219] The first construction subunit is used to construct state variables based on the vehicle's battery state of charge.

[0220] The second construction subunit is used to construct decision variables based on the operating mode and component power of the candidate hybrid power system.

[0221] The third construction subunit is used to construct constraint conditions based on the power balance constraints, component power constraints, battery state of charge constraints, and operating mode constraints that the candidate hybrid power system needs to meet.

[0222] According to an embodiment of the present invention, the acquisition module 610 may include an eighth determining unit, a ninth determining unit, a tenth determining unit, and an eleventh determining unit.

[0223] The eighth determining unit is used to determine the vehicle acceleration based on the vehicle speed at adjacent time points and the duration of adjacent time points.

[0224] The ninth determining unit is used to determine the driving force requirements of the vehicle based on the vehicle's mass, vehicle acceleration, gravitational acceleration, rolling resistance coefficient, air density, air resistance coefficient, and vehicle frontal area.

[0225] The tenth determining unit is used to determine the required power based on the driving force requirement and the driving transmission efficiency when the driving force requirement of the vehicle is greater than or equal to a predetermined value.

[0226] The eleventh determining unit is used to determine the required power based on the driving force demand, the vehicle speed, and the vehicle's braking energy recovery efficiency when the vehicle's driving force demand is less than a predetermined value.

[0227] According to an embodiment of the present invention, the components of the target hybrid power system are configured with priorities, and the target solution of the target hybrid power system corresponds to a target weight; the candidate control strategy includes at least one of the following: determining the operating mode of the target hybrid power system based on the relationship between the vehicle speed, power demand, and battery state of charge of the vehicle and the respective speed threshold, power demand threshold, and battery state of charge threshold; and determining the adjustment mode of the components according to the priorities in the operating mode; constructing an equivalent cost function based on the evaluation index and the target weight corresponding to the target solution of the target hybrid power system, and allocating the operating mode and component power allocation of the target hybrid power system according to the function value of the equivalent cost function; inputting the vehicle speed, power demand, and battery state of charge of the vehicle into the control model, and outputting the operating mode and component power of the target hybrid power system.

[0228] According to an embodiment of the present invention, the filtering module 620 may include a first filtering unit and a second filtering unit.

[0229] The first screening unit is used to select candidate hybrid power system configurations from the configuration library and candidate hybrid power system components from the component library based on the required power.

[0230] The second screening unit is used to combine candidate hybrid system configurations and candidate hybrid system components to obtain candidate hybrid systems.

[0231] According to embodiments of the present invention, any plurality of modules among the acquisition module 610, screening module 620, construction module 630, first determination module 640, and second determination module 650 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the acquisition module 610, screening module 620, construction module 630, first determination module 640, and second determination module 650 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in hardware or firmware, or in any one of software, hardware, and firmware implementations, or in a suitable combination of any of these. Alternatively, at least one of the acquisition module 610, the filtering module 620, the construction module 630, the first determination module 640, and the second determination module 650 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0232] Figure 7 A block diagram of an electronic device suitable for implementing a parameter determination method for a hybrid power system according to an embodiment of the present invention is shown.

[0233] like Figure 7 As shown, an electronic device 700 according to an embodiment of the present invention includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may 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.

[0234] Random access memory (RAM) 703 stores various programs and data required for the operation of electronic device 700. Processor 701, read-only memory (ROM) 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present invention by executing programs in read-only memory (ROM) 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than read-only memory (ROM) 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0235] According to an embodiment of the present invention, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0236] 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 it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0237] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, a computer-readable storage medium may include the read-only memory (ROM) 702 and / or random access memory (RAM) 703 described above and / or one or more memories other than read-only memory (ROM) 702 and random access memory (RAM) 703.

[0238] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of the present invention.

[0239] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0240] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0241] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, it performs the functions defined in the system of this embodiment of the invention. According to embodiments of the invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0242] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0243] 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 a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0244] Those skilled in the art will understand 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, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0245] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for determining parameters in a hybrid power system, characterized in that, The method includes: The system acquires vehicle attribute data, vehicle operating conditions, evaluation indicators for assessing the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators. Based on the vehicle attribute data and the operating conditions, it determines the required power of the vehicle. Based on the required power, multiple candidate hybrid power systems are selected from a pre-built database; Based on the evaluation index and the initial weights configured for the evaluation index, a cost function is constructed for each candidate hybrid power system, and the initial weights are adjusted until the candidate solution set determined based on the adjusted weights and the cost function meets the preset conditions, thereby obtaining the candidate solution set for each candidate hybrid power system. Based on the preset importance ranking of the evaluation indicators, the target solution of each candidate hybrid power system is determined from the candidate solution set of each candidate hybrid power system, and the target hybrid power system is determined from the candidate hybrid power system according to the target solution of each candidate hybrid power system. Based on the simulation control results of the target hybrid power system using multiple candidate control strategies, a target control strategy is determined from the multiple candidate control strategies, and the target hybrid power system and the target control strategy are used as parameters to determine the result.

2. The method according to claim 1, characterized in that, The step of determining the target solution for each candidate hybrid power system from the candidate solution set of each candidate hybrid power system based on the preset importance ranking of the evaluation index includes: Based on the preset importance ranking, construct an initial importance matrix for the evaluation indicators; A consistency check is performed on the initial importance matrix to obtain the target importance matrix; Construct an initial decision matrix from the candidate solution set of each candidate hybrid power system; The initial decision matrix is ​​normalized to obtain the target decision matrix; Based on the target importance matrix and the target decision matrix, the first target matrix is ​​obtained; The target solution of the candidate hybrid power system is determined based on the proximity between each data point in the first target matrix and the first predetermined solution. The first predetermined solution includes a positive ideal solution and a negative ideal solution. The positive ideal solution is a virtual solution in which all evaluation indicators reach their optimal values, including the solution corresponding to the minimum fuel consumption, minimum cost, and minimum emissions. The negative ideal solution is a virtual solution in which all evaluation indicators reach their worst values, including the solution corresponding to the maximum fuel consumption, maximum cost, and maximum emissions.

3. The method according to claim 2, characterized in that, The step of determining the target hybrid power system from the candidate hybrid power systems based on the target solutions of each candidate hybrid power system includes: Based on the target solution of the candidate hybrid power system, a target solution set of the candidate hybrid power system is constructed, and the target solution set is normalized. The second objective matrix is ​​obtained based on the normalized objective solution set and the objective importance matrix; The target hybrid power system is determined based on the proximity between each data point in the second target matrix and the second predetermined solution.

4. The method according to claim 2, characterized in that, The step of determining the target control strategy from the multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies includes: Based on the simulation control results of each candidate control strategy, a set of control results is constructed, and the set of control results is normalized. Based on the normalized control result set and the target importance matrix, the third target matrix is ​​obtained; The target control strategy is determined based on the proximity between each data point in the third target matrix and the third predetermined solution.

5. The method according to claim 1, characterized in that, The initial weights are adjusted until the candidate solution set determined based on the adjusted weights and the cost function satisfies preset conditions, thereby obtaining a candidate solution set for each candidate hybrid power system, including: Target parameters are constructed for the candidate hybrid power system, and the target parameters are used to describe the operation of the candidate hybrid power system under driving conditions; Perform the following operations on the adjusted weights until the resulting candidate solution set meets the preset conditions: The adjusted weights are input into the cost function to obtain the target cost function; The target parameters are processed using a dynamic programming algorithm, and the minimization of the target cost function is the objective to obtain the vehicle control sequence, wherein the control sequence includes the vehicle's operating mode and component power allocation at each time step. Based on the vehicle's operating mode and component power allocation at each time point, the values ​​of the evaluation indicators are determined, and the values ​​of the evaluation indicators are used as candidate solutions corresponding to the adjusted weights.

6. The method according to claim 5, characterized in that, The target parameters include state variables, decision variables, and constraints. The step of constructing target parameters for the candidate hybrid power system includes: The state variables are constructed based on the battery state of charge of the vehicle; The decision variables are constructed based on the operating mode and component power of the candidate hybrid power systems; The constraints are constructed based on the power balance constraints, component power constraints, battery state of charge constraints, and operating mode constraints that the candidate hybrid power system needs to satisfy.

7. The method according to claim 1, characterized in that, Determining the required power of the vehicle based on its attribute data and operating conditions includes: The vehicle acceleration is determined based on the vehicle speed at adjacent time points and the duration of adjacent time points. The driving force requirement of the vehicle is determined based on the vehicle's mass, acceleration, gravitational acceleration, rolling resistance coefficient, air density, air resistance coefficient, and frontal area. If the driving force requirement of the vehicle is greater than or equal to a predetermined value, the required power is determined based on the driving force requirement and the driving transmission efficiency. If the driving force requirement of the vehicle is less than the predetermined value, the required power is determined based on the driving force requirement, the vehicle speed, and the vehicle's braking energy recovery efficiency.

8. The method according to claim 1, characterized in that, The components of the target hybrid power system are configured with priorities, and the target solution of the target hybrid power system corresponds to a target weight. The candidate control strategies include at least one of the following: Based on the relationship between the vehicle speed, the required power, and the vehicle's battery state of charge and the respective thresholds for vehicle speed, required power, and battery state of charge, the operating mode of the target hybrid system is determined, and in the operating mode, the adjustment method of the components is determined according to the priority. Based on the evaluation indicators and the target solution of the target hybrid system, corresponding target weights are assigned. An equivalent cost function is constructed, and the operating mode and component power allocation of the target hybrid system are allocated according to the function value of the equivalent cost function. The vehicle speed, the required power, and the vehicle's battery state of charge are input into the control model, which outputs the operating mode and component power of the target hybrid system.

9. The method according to claim 1, characterized in that, The database includes a configuration library and a component library. The configuration library is used to store hybrid power system configurations, and the component library is used to store hybrid power system components. Based on the required power, several candidate hybrid power systems are selected from a pre-built database, including: Based on the required power, candidate hybrid power system configurations are selected from the configuration library, and candidate hybrid power system components are selected from the component library; The candidate hybrid power system configuration and the candidate hybrid power system components are combined to obtain the candidate hybrid power system.

10. A parameter determination device for a hybrid power system, characterized in that, The device includes: The acquisition module is used to acquire vehicle attribute data, vehicle operating conditions, evaluation indicators for assessing the performance of the hybrid power system, and a preset importance ranking of the evaluation indicators, and to determine the required power of the vehicle based on the vehicle attribute data and the operating conditions. A screening module is used to select multiple candidate hybrid power systems from a pre-built database based on the required power. A construction module is used to construct a cost function for each candidate hybrid power system based on the evaluation index and the initial weights configured for the evaluation index, and adjust the initial weights until the candidate solution set determined based on the adjusted weights and the cost function meets the preset conditions, thereby obtaining the candidate solution set for each candidate hybrid power system. The first determining module is used to determine the target solution of each candidate hybrid power system from the candidate solution set of each candidate hybrid power system based on the preset importance ranking of the evaluation index, and to determine the target hybrid power system from the candidate hybrid power system according to the target solution of each candidate hybrid power system. The second determining module is used to determine a target control strategy from the multiple candidate control strategies based on the simulation control results of controlling the target hybrid power system using multiple candidate control strategies, and to determine the target hybrid power system and the target control strategy as parameters.

Images