Parameter matching method and device for extended-range hybrid power vehicle and extended-range hybrid power mine truck

Abstract

The invention relates to a parameter matching method and device for an extended-range hybrid vehicle and an extended-range hybrid mine truck, and the method can obtain the transportation working condition data matched with the extended-range hybrid vehicle, and achieves the matching of the parameters of the extended-range hybrid vehicle based on the transportation working condition data and a drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle. Establishing a target simulation platform, and based on a preset constraint condition, performing model selection on a specified component of the extended-range hybrid vehicle to obtain an initial parameter matching result of the specified component; and in the target simulation platform, performing simulation correction on the initial parameter matching result to obtain a target parameter matching result. Model selection in two dimensions of dynamic property and economical efficiency is jointly carried out based on transportation working condition data matched with an actual extended-range hybrid power vehicle, a drive-by-wire chassis energy management control strategy and working characteristics of a core specified component of the extended-range hybrid power vehicle; and the design requirements on parameter matching in the vehicle model development process can be accurately met.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a parameter matching method and device for an extended-range hybrid vehicle and an extended-range hybrid mining truck. Background Art

[0002] At present, combined with the current goal of green mines, the development of extended-range hybrid vehicles has been an important theme in recent years. Among them, extended-range hybrid vehicles are mining trucks that use extended-range hybrid technology, combining traditional fuel power and electric drive, with higher fuel efficiency and lower emissions, suitable for harsh mining environments.

[0003] In practice, it is found that when developing extended-range hybrid power systems, the parameter matching method for the system is usually targeted at basic power requirements, and the impact of other influencing factors on the matching results is rarely considered, resulting in the parameter matching goals being not comprehensive enough, and thus resulting in poor accuracy in parameter matching. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a parameter matching method and device for an extended-range hybrid vehicle and a mining truck, so as to select the vehicle in terms of power and economy, and to accurately meet the design requirements for parameter matching during the vehicle development process.

[0005] According to a first aspect of an embodiment of the present invention, a parameter matching method for an extended-range hybrid vehicle is provided, comprising:

[0006] Obtain transportation operating condition data matching the extended-range hybrid vehicle;

[0007] Establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0008] Based on the preset constraint conditions, the designated components of the extended-range hybrid vehicle are selected to obtain the initial parameter matching results of the designated components; wherein the preset constraint conditions include at least one of the following: MAP characteristic constraint, charge and discharge number constraint;

[0009] In the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result.

[0010] Preferably, before establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, the method further includes:

[0011] According to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold, the vehicle demand driving power value interval is divided; and, according to the minimum SOC threshold and the maximum SOC threshold, the SOC value interval is divided;

[0012] Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals;

[0013] For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold;

[0014] The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

[0015] Preferably, based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, a target simulation platform is established, including:

[0016] Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0017] The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

[0018] Preferably, the transport operating condition data is the vehicle driving condition data of the extended-range hybrid vehicle at various stages in the mining area; wherein, each stage includes at least one of the following: loading stage, heavy-load uphill transport stage, unloading stage, and empty-load downhill return to the loading area stage; wherein, the vehicle driving condition data includes at least one of the following: vehicle speed data, vehicle slope data, and vehicle load data.

[0019] Preferably, selecting the designated components of the extended-range hybrid vehicle based on the preset constraint conditions to obtain the initial parameter matching results of the designated components includes:

[0020] calculating a drive motor power requirement of the extended-range hybrid vehicle;

[0021] Based on the drive motor power demand and the MAP characteristic constraint, selecting the engine generator set and the drive motor of the extended-range hybrid vehicle to obtain initial engine generator set parameters and initial drive motor parameters;

[0022] Based on the initial engine generator set parameters, the initial drive motor parameters, and the charge and discharge times constraint, selecting a battery for the extended-range hybrid vehicle to obtain initial battery parameters;

[0023] The initial engine generator set parameters, the initial drive motor parameters and the initial battery parameters are determined as the initial parameter matching result.

[0024] Preferably, in the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result, including:

[0025] Determining a selection combination of designated components of the extended-range hybrid vehicle based on the initial parameter matching result;

[0026] For each selection combination, the vehicle economy index of the selection combination is calculated through the target simulation platform;

[0027] Based on the vehicle economy index of each selection combination, determining a target selection combination from the selection combinations;

[0028] Based on the target selection combination, the target parameter matching result is determined.

[0029] According to a second aspect of an embodiment of the present invention, there is provided a parameter matching device for a range-extended hybrid vehicle, comprising:

[0030] A data acquisition unit, used to acquire transportation operating condition data matching the extended-range hybrid vehicle;

[0031] A simulation unit, configured to establish a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0032] A selection unit, configured to select each designated component of the extended-range hybrid vehicle based on preset constraints to obtain initial parameter matching results of each designated component; wherein the preset constraints include at least one of the following: MAP characteristic constraint and charge and discharge number constraint;

[0033] The correction unit is used to perform simulation correction on the initial parameter matching result in the target simulation platform to obtain a target parameter matching result.

[0034] Preferably, the simulation unit is also used for:

[0035] Before establishing a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, the vehicle demand driving power value interval is divided according to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold; and the SOC value interval is divided according to the minimum SOC threshold and the maximum SOC threshold;

[0036] Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals;

[0037] For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold;

[0038] The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

[0039] Preferably, the simulation unit is specifically used for:

[0040] Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0041] The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

[0042] Preferably, the transport operating condition data is the vehicle driving condition data of the extended-range hybrid vehicle at various stages in the mining area; wherein, each stage includes at least one of the following: loading stage, heavy-load uphill transport stage, unloading stage, and empty-load downhill return to the loading area stage; wherein, the vehicle driving condition data includes at least one of the following: vehicle speed data, vehicle slope data, and vehicle load data.

[0043] Preferably, the selection unit is specifically used for:

[0044] calculating a drive motor power requirement of the extended-range hybrid vehicle;

[0045] Based on the drive motor power demand and the MAP characteristic constraint, selecting the engine generator set and the drive motor of the extended-range hybrid vehicle to obtain initial engine generator set parameters and initial drive motor parameters;

[0046] Based on the initial engine generator set parameters, the initial drive motor parameters, and the charge and discharge times constraint, selecting a battery for the extended-range hybrid vehicle to obtain initial battery parameters;

[0047] The initial engine generator set parameters, the initial drive motor parameters and the initial battery parameters are determined as the initial parameter matching result.

[0048] Preferably, the correction unit is specifically used for:

[0049] Determining a selection combination of designated components of the extended-range hybrid vehicle based on the initial parameter matching result;

[0050] For each selection combination, the vehicle economy index of the selection combination is calculated through the target simulation platform;

[0051] Based on the vehicle economy index of each selection combination, determining a target selection combination from the selection combinations;

[0052] Based on the target selection combination, the target parameter matching result is determined.

[0053] According to a third aspect of an embodiment of the present invention, there is provided an extended-range hybrid mining truck, which uses the above-mentioned extended-range hybrid vehicle parameter matching method to develop a vehicle model.

[0054] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0055] Through a comprehensive and detailed analysis of actual transport conditions, combined with an efficient by-wire chassis energy management control strategy, the core power components, such as engines, motors and batteries, are accurately selected, greatly improving the accuracy and efficiency of the selection work. In addition, through the subsequent simulation verification process, the actual feasibility and superiority of the solution can be ensured, thereby significantly improving the power performance and economic performance of the extended-range hybrid mining vehicle.

[0056] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0058] Figure 1 is a flow chart showing a parameter matching method for a range-extended hybrid vehicle according to an exemplary embodiment;

[0059] Figure 2 is a schematic diagram of energy management strategy control according to an exemplary embodiment;

[0060] Figure 3It is a schematic diagram of a design scheme of a mining truck electric drive system according to an exemplary embodiment;

[0061] Figure 4 It is a schematic block diagram of a parameter matching device 400 for a range-extended hybrid vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0062] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0063] Figure 1 is a flow chart of a parameter matching method for a range-extended hybrid vehicle according to an exemplary embodiment. Figure 1 As shown, including:

[0064] Step S11, acquiring transportation operating condition data matching the range-extended hybrid vehicle;

[0065] Step S12, establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0066] Step S13: based on preset constraints, select the designated components of the extended-range hybrid vehicle to obtain initial parameter matching results of the designated components; wherein the preset constraints include at least one of the following: MAP characteristic constraint, charge and discharge number constraint;

[0067] Step S14: In the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result.

[0068] Among them, the executor of this embodiment can be an electronic device such as a terminal device or a server, which can obtain transportation condition data matching the extended-range hybrid vehicle, and perform data processing and simulation modeling on these transportation condition data, and finally calculate and select the parameters of designated components matching the extended-range hybrid vehicle to complete the entire calculation process of parameter matching. Since the transportation condition data is taken as the basis for selection and combined with the selection strategy of simulation and simulation correction, parameter matching can be achieved more accurately.

[0069] Specifically, in actual driving scenarios, the extended-range hybrid vehicle may need to travel in a complex and changeable transportation environment. In this regard, when designing the parameters of the extended-range hybrid vehicle, it is necessary to first obtain the transportation condition data of the transportation environment in which the extended-range hybrid vehicle will be mainly used in the future. The transportation condition data may include commonly used vehicle speed data, road slope data, vehicle load requirement data, road lifting angle data, etc., which are not limited in this embodiment.

[0070] Afterwards, a target simulation platform is established based on the transportation condition data and the energy management control strategy of the drive-by-wire chassis of the extended-range hybrid vehicle. Among them, the drive-by-wire chassis energy management control strategy is a strategy for multiple aspects such as battery pack management, motor control, brake energy recovery, and vehicle motion control in the field of new energy vehicles. Specifically, an initial simulation platform can be established based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, and then the transportation condition data and various data of other dimensions (body model data, driver model data, and transmission system model data, etc.) are imported into the initial simulation platform to obtain the final target simulation platform. Among them, the target simulation platform can be used to combine the drive-by-wire chassis energy management control strategy to simulate the control of the operation of the extended-range hybrid vehicle under specific transportation conditions, calculate the vehicle fuel consumption and other parameters at this time, and assist in the parameter selection of the extended-range hybrid vehicle in the design stage based on the vehicle fuel consumption and other parameters.

[0071] Specifically, for the parameter matching design of the extended-range hybrid vehicle, the demand analysis can be first performed based on the output demand of different power sources to establish the constraints of the designated components of the extended-range hybrid vehicle. Among them, the designated components may include engines, ISG generators, drive motors, power batteries, etc., which are not limited in this embodiment. Among them, the constraints may include MAP characteristic constraints, charge and discharge times constraints, expected cost constraints, etc., which are not limited in this embodiment. For MAP (i.e., map) characteristics, Map provides a mapping relationship, in which the elements are stored in the form of key-value pairs, which can realize fast search for values ​​according to keys. MAP characteristics can be any characteristics of core components such as engines, ISG generators, drive motors, power batteries, etc., and the characteristics can be, for example, the power and torque of the generator, which can be determined according to user needs. After the constraints are established, the designated components can be selected in combination with the constraints and the resource data of the designated components in practice to obtain the initial parameter matching results.

[0072] Afterwards, the initial parameter matching results are verified in the target simulation platform. During the verification process, the initial parameter matching results are continuously fed back and corrected to complete the entire simulation correction process and obtain the final target parameter matching results.

[0073] It can be seen that the parameter matching method for an extended-range hybrid vehicle provided by this scheme can be based on the actual use requirements of the extended-range hybrid vehicle, combined with the matching transportation condition data, the energy management control strategy of the drive-by-wire chassis, and the working characteristics of the core designated components of the extended-range hybrid vehicle, to jointly select and match the target parameter matching results. At the same time, it takes into account the power and economy in the selection process of the core designated components, adapts to the actual transportation conditions, selects the parameters that best meet the requirements, and can accurately meet the design requirements for parameter matching in the vehicle model development process.

[0074] Preferably, before establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, the method further includes:

[0075] According to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold, the vehicle demand driving power value interval is divided; and, according to the minimum SOC threshold and the maximum SOC threshold, the SOC value interval is divided;

[0076] Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals;

[0077] For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold;

[0078] The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

[0079] Among them, this scheme can divide each vehicle demand driving power value interval according to the vehicle demand driving power of the extended-range hybrid vehicle under specific transportation conditions; and divide each SOC value interval according to the SOC value range of the extended-range hybrid vehicle. This embodiment does not limit the number of intervals of specific vehicle demand driving power value intervals and SOC value intervals. Afterwards, each vehicle demand driving power value interval and each SOC value interval are combined to obtain each power and SOC combination interval, and for each power and SOC combination interval, a corresponding engine management control strategy is set, and the engine management control strategy is used as part of the drive-by-wire chassis energy management control strategy.

[0080] Please also read Figure 2 , Figure 2 is a schematic diagram of an energy management strategy control according to an exemplary embodiment. Figure 2 As shown, the above vehicle demand driving power value interval is divided into 3, that is, less than the minimum vehicle demand driving power threshold P req_low The value range of the minimum vehicle required driving power threshold P req_low and the maximum vehicle required driving power threshold P req_hi The value range between is greater than the maximum vehicle required driving power threshold P req_hi The above SOC value interval is also divided into 3, namely, the value interval less than the minimum SOC threshold SOC_min, the value interval between the minimum SOC threshold SOC_min and the maximum SOC threshold SOC_max, and the value interval greater than the maximum SOC threshold SOC_max. Afterwards, the 3 vehicle demand driving power value intervals and the 3 SOC value intervals are combined to obtain 9 power and SOC combination intervals. For each power and SOC combination interval, set the corresponding engine management control strategy. For example, when the SOC value exceeds the set maximum threshold SOC_max, and the vehicle demand driving power is less than the minimum power P req_low The engine will remain off.

[0081] Preferably, based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, a target simulation platform is established, including:

[0082] Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0083] The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

[0084] Among them, when establishing the target simulation platform, the wire-controlled chassis energy management control strategy of the extended-range hybrid vehicle can be analyzed first, and based on the wire-controlled chassis energy management control strategy, the initial simulation platform of the extended-range hybrid vehicle can be established; wherein, the initial simulation platform can perform selection simulation of different specified component parameters based on the wire-controlled chassis energy management control strategy. After that, the pre-acquired transportation condition data, body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the final target simulation platform. Among them, the target simulation platform can simulate the actual transportation condition according to the transportation condition data, and simulate the transportation process of the actual extended-range hybrid vehicle based on the body model, driver model, and transmission system model, perform energy management control on the extended-range hybrid vehicle based on the wire-controlled chassis energy management control strategy, and perform selection simulation of different specified component parameters to simulate the operating conditions of the actual extended-range hybrid vehicle in the actual operating environment, obtain corresponding indicator data, and assist in the parameter selection of the core specified components.

[0085] Preferably, the transport operating condition data is the vehicle driving condition data of the extended-range hybrid vehicle at various stages in the mining area; wherein, each stage includes at least one of the following: loading stage, heavy-load uphill transport stage, unloading stage, and empty-load downhill return to the loading area stage; wherein, the vehicle driving condition data includes at least one of the following: vehicle speed data, vehicle slope data, and vehicle load data.

[0086] If the extended-range hybrid vehicle is an extended-range hybrid mining truck, the transportation operating condition data may be vehicle driving operating condition data of the extended-range hybrid vehicle at various stages in the mining area.

[0087] Preferably, selecting the designated components of the extended-range hybrid vehicle based on the preset constraint conditions to obtain the initial parameter matching results of the designated components includes:

[0088] calculating a drive motor power requirement of the extended-range hybrid vehicle;

[0089] Based on the drive motor power demand and the MAP characteristic constraint, selecting the engine generator set and the drive motor of the extended-range hybrid vehicle to obtain initial engine generator set parameters and initial drive motor parameters;

[0090] Based on the initial engine generator set parameters, the initial drive motor parameters, and the charge and discharge times constraint, selecting a battery for the extended-range hybrid vehicle to obtain initial battery parameters;

[0091] The initial engine generator set parameters, the initial drive motor parameters and the initial battery parameters are determined as the initial parameter matching result.

[0092] Among them, when matching the initial parameters of the designated components based on the preset constraints, the power demand of the drive motor of the extended-range hybrid vehicle can be calculated first. Specifically, the power demand of the drive motor under the maximum vehicle speed, maximum climbing grade and actual climbing grade index can be calculated based on the theoretical dynamics equation of the vehicle. The calculation formula is as follows:

[0093]

[0094] W mc =v i / 3.6 / r*30 / π (2)

[0095] T mc =F wt r / i0 / η / 8 (3)

[0096] P mc =T mc W mc / 9549 (4)

[0097] Among them, F wt Indicates the driving force required at the wheel, in N; m indicates the vehicle mass, in kg; g indicates the acceleration due to gravity, in m / s / s; f r represents the rolling resistance coefficient; α represents the road slope; C D Indicates the drag coefficient; A indicates the windward area, in m 2 ;v i Indicates the current vehicle speed in km / h; W mc represents the motor speed in rpm; r represents the wheel radius in m; T mc represents the motor torque in Nm; i0 represents the wheel reducer speed ratio; η represents the wheel reducer efficiency; P mc Indicates the wheel reducer power in kW.

[0098] Furthermore, after calculating the power demand of the drive motor, the engine generator set and the drive motor of the programmed hybrid vehicle can be selected in combination with the MAP characteristic constraints and the existing engine generator set and drive motor product resources to obtain the initial engine generator set parameters and the initial drive motor parameters. In the selection process, you can first select an engine that meets the conditions according to the MAP characteristic constraints and the existing engine product resources, and use the parameters of the engine that meets the conditions as the initial engine set parameters. Further considering that when the engine works in the high efficiency area, the generator needs to work in the high efficiency area to cooperate with the engine. Based on this, combined with the charge and discharge constraints, further select a generator that meets the conditions, and use its parameters as the initial generator set parameters. Among them, the above-mentioned initial engine generator set parameters include initial engine set parameters and initial generator set parameters. Further based on the power demand of the drive motor, select a drive motor that meets the conditions, and use its parameters as the initial drive motor parameters.

[0099] Furthermore, the battery life can be calculated based on the number of battery cycle life, the total daily power consumption of the hybrid system, and the power of the power battery; wherein the battery life calculation formula is as follows:

[0100] Life bat =Num bat / (Sum bat / E bat ) (5)

[0101] Among them, Life bat Indicates battery life in days; Num bat Indicates the number of battery cycle life; Sum bat Indicates the total power consumption of the hybrid system in a single day, in kWh; E bat Indicates the power of the power battery in kWh.

[0102] Among them, based on the auxiliary power unit and the battery allocation principle, the battery power demand can be determined and used as a battery selection condition. Specifically, the matching battery power demand can be determined in combination with the above-mentioned initial engine generator set parameters, initial drive motor parameters, and preset battery allocation principles. In addition, considering the discharge depth of the power battery under a single-trip operation, the above-mentioned battery life is calculated, and the battery selection is carried out together with the preset battery charge and discharge times constraint to obtain the final initial battery parameters.

[0103] Preferably, in the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result, including:

[0104] Determining a selection combination of designated components of the extended-range hybrid vehicle based on the initial parameter matching result;

[0105] For each selection combination, the vehicle economy index of the selection combination is calculated through the target simulation platform;

[0106] Based on the vehicle economy index of each selection combination, determining a target selection combination from the selection combinations;

[0107] Based on the target selection combination, the target parameter matching result is determined.

[0108] Among them, after obtaining the initial parameter matching result, the various parameters in the initial parameter matching result can be further combined to obtain various selection combinations. For each selection combination, the designated component parameters corresponding to the selection combination are imported into the target simulation platform, so that the target simulation platform can simulate the actual transportation process on the basis of matching the actual transportation conditions, combined with the selection of designated components and the vehicle parameters of the extended-range hybrid vehicle (external characteristics, MAP, voltage-SOC correspondence, etc.), and calculate the corresponding vehicle economy indicators, such as calculating vehicle fuel consumption. Based on the vehicle economy indicators, the best combination is selected from each selection combination as the target selection combination, and the parameters of the designated components in the target selection combination are used as the final target parameter matching result. For example, the combination with the least vehicle fuel consumption is selected as the target selection combination.

[0109] Please also read Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a design scheme of a mining truck electric drive system according to an exemplary embodiment. Figure 3 As shown, in the scenario where the extended-range hybrid vehicle is an extended-range hybrid mining truck, the mining area transportation conditions can be analyzed first to obtain the transportation condition data. After that, the energy management strategy characteristics are analyzed to obtain the corresponding wire-controlled chassis energy management control strategy. After that, the engine generator set selection, drive motor selection, and battery selection can be carried out according to the power source MAP characteristic constraints, charging and discharging constraints, and expected cost constraints of the extended-range hybrid mining truck, and the forward selection of the core components of the extended-range hybrid mining truck can be completed. Then, the results of these forward selections are input into the target simulation platform to perform simulation verification of power and economy. During the simulation verification process, the parameters are continuously matched for feedback correction to obtain the final selection result, that is, the core component parameter matching result that meets the requirements of power and economy is obtained.

[0110] Based on the same inventive concept, Figure 4 is a schematic block diagram of a parameter matching device 400 for a range-extended hybrid vehicle according to an exemplary embodiment. Figure 4As shown, the device 400 includes:

[0111] The data acquisition unit 401 is used to acquire the transportation condition data matching the extended-range hybrid vehicle;

[0112] A simulation unit 402, configured to establish a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0113] The selection unit 403 is used to select each designated component of the extended-range hybrid vehicle based on preset constraints to obtain initial parameter matching results of each designated component; wherein the preset constraints include at least one of the following: MAP characteristic constraint, charge and discharge number constraint;

[0114] The correction unit 404 is used to perform simulation correction on the initial parameter matching result in the target simulation platform to obtain a target parameter matching result.

[0115] Preferably, the simulation unit 402 is further used for:

[0116] Before establishing a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, the vehicle demand driving power value interval is divided according to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold; and the SOC value interval is divided according to the minimum SOC threshold and the maximum SOC threshold;

[0117] Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals;

[0118] For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold;

[0119] The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

[0120] Preferably, the simulation unit 402 is specifically used for:

[0121] Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle;

[0122] The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

[0123] Preferably, the transport operating condition data is the vehicle driving condition data of the extended-range hybrid vehicle at various stages in the mining area; wherein, each stage includes at least one of the following: loading stage, heavy-load uphill transport stage, unloading stage, and empty-load downhill return to the loading area stage; wherein, the vehicle driving condition data includes at least one of the following: vehicle speed data, vehicle slope data, and vehicle load data.

[0124] Preferably, the selection unit 403 is specifically used for:

[0125] calculating a drive motor power requirement of the extended-range hybrid vehicle;

[0126] Based on the drive motor power demand and the MAP characteristic constraint, selecting the engine generator set and the drive motor of the extended-range hybrid vehicle to obtain initial engine generator set parameters and initial drive motor parameters;

[0127] Based on the initial engine generator set parameters, the initial drive motor parameters, and the charge and discharge times constraint, selecting a battery for the extended-range hybrid vehicle to obtain initial battery parameters;

[0128] The initial engine generator set parameters, the initial drive motor parameters and the initial battery parameters are determined as the initial parameter matching result.

[0129] Preferably, the correction unit 404 is specifically used for:

[0130] Determining a selection combination of designated components of the extended-range hybrid vehicle based on the initial parameter matching result;

[0131] For each selection combination, the vehicle economy index of the selection combination is calculated through the target simulation platform;

[0132] Based on the vehicle economy index of each selection combination, determining a target selection combination from the selection combinations;

[0133] Based on the target selection combination, the target parameter matching result is determined.

[0134] In the embodiment of the present invention, based on the actual use requirements of the extended-range hybrid vehicle, combined with the matching transportation condition data, the drive-by-wire chassis energy management control strategy, and the working characteristics of the core designated components of the extended-range hybrid vehicle, selection can be carried out together, and the target parameter matching results can be obtained by matching. At the same time, the power and economy of the core designated components are taken into consideration during the selection process, and the actual transportation conditions are adapted to select the parameters that best meet the requirements, which can accurately meet the design requirements for parameter matching during the vehicle model development process.

[0135] The implementation methods and beneficial effects of each module in this embodiment can refer to the introduction of the corresponding method steps in the above embodiment, and will not be repeated in this embodiment.

[0136] According to an extended-range hybrid mining truck shown in an exemplary embodiment, the above-mentioned parameter matching method for extended-range hybrid vehicles is used to develop a vehicle model.

[0137] The computer-readable storage medium disclosed in this embodiment includes, but is not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0138] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0139] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0140] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes other implementations in which the functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0141] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0143] Herein, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0144] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0145] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0146] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A parameter matching method for a range-extended hybrid vehicle, characterized in that: include: Obtain transportation operating condition data matching the extended-range hybrid vehicle; Establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle; Based on the preset constraints, the designated components of the extended-range hybrid vehicle are selected to obtain the initial parameter matching results of the designated components; wherein the preset constraints include at least one of the following: map characteristic constraints, charge and discharge times constraints; In the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result.

2. The method according to claim 1, characterized in that Before establishing a target simulation platform based on the transportation condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, the method further includes: According to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold, the vehicle demand driving power value interval is divided; and according to the minimum state of charge SOC threshold and the maximum SOC threshold, the SOC value interval is divided; Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals; For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold; The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

3. The method according to claim 1, characterized in that Based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, a target simulation platform is established, including: Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle; The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

4. The method according to claim 1, characterized in that: The transport operating condition data are the vehicle driving condition data of the extended-range hybrid vehicle at various stages in the mining area; wherein, each stage includes at least one of the following: loading stage, heavy-load uphill transport stage, unloading stage, and empty-load downhill return to the loading area stage; wherein, the vehicle driving condition data includes at least one of the following: vehicle speed data, vehicle slope data, and vehicle load data.

5. The method according to claim 1, characterized in that The selecting of the designated components of the extended-range hybrid vehicle based on the preset constraint conditions to obtain the initial parameter matching results of the designated components includes: calculating a drive motor power requirement of the extended-range hybrid vehicle; Based on the drive motor power demand and the MAP characteristic constraint, selecting the engine generator set and the drive motor of the extended-range hybrid vehicle to obtain initial engine generator set parameters and initial drive motor parameters; Based on the initial engine generator set parameters, the initial drive motor parameters, and the charge and discharge times constraint, selecting a battery for the extended-range hybrid vehicle to obtain initial battery parameters; The initial engine generator set parameters, the initial drive motor parameters and the initial battery parameters are determined as the initial parameter matching result.

6. The method according to claim 1, characterized in that In the target simulation platform, the initial parameter matching result is simulated and corrected to obtain a target parameter matching result, including: Determining a selection combination of designated components of the extended-range hybrid vehicle based on the initial parameter matching result; For each selection combination, the vehicle economy index of the selection combination is calculated through the target simulation platform; Based on the vehicle economy index of each selection combination, determining a target selection combination from the selection combinations; Based on the target selection combination, the target parameter matching result is determined.

7. A parameter matching device for a range-extended hybrid vehicle, characterized in that: include: A data acquisition unit, used to acquire transportation operating condition data matching the extended-range hybrid vehicle; A simulation unit, configured to establish a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle; A selection unit, configured to select each designated component of the extended-range hybrid vehicle based on preset constraints to obtain initial parameter matching results of each designated component; wherein the preset constraints include at least one of the following: MAP characteristic constraint and charge and discharge number constraint; The correction unit is used to perform simulation correction on the initial parameter matching result in the target simulation platform to obtain a target parameter matching result.

8. The device according to claim 7, characterized in that The simulation unit is also used for: Before establishing a target simulation platform based on the transport condition data and the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle, dividing the vehicle demand driving power value range according to the minimum vehicle demand driving power threshold and the maximum vehicle demand driving power threshold; And, dividing the SOC value interval according to the minimum SOC threshold and the maximum SOC threshold; Combining the vehicle required driving power value interval and the SOC value interval to form various power and SOC combination intervals; For each power and SOC combination interval, a matching engine management control strategy is determined; wherein the engine management control strategy includes at least one of the following: on, off, and hold; The drive-by-wire chassis energy management control strategy is generated based on each power and SOC combination interval and the engine management control strategy that matches each power and SOC combination interval.

9. The device according to claim 7, characterized in that The simulation unit is specifically used for: Establishing an initial simulation platform based on the drive-by-wire chassis energy management control strategy of the extended-range hybrid vehicle; The transport condition data, vehicle body model, driver model, and transmission system model are imported into the initial simulation platform to obtain the target simulation platform.

10. An extended-range hybrid mining truck, characterized in that: The parameter matching method of the extended-range hybrid vehicle described in any one of claims 1 to 6 is used to develop vehicle models.

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