Optimized point selection method, device and equipment for hybrid electric vehicle and storage medium

Abstract

The invention discloses a hybrid vehicle optimization point selection method, device and equipment and a storage medium, and relates to the technical field of vehicle control. Driving experience and fuel consumption data of an engine under different power generation powers are obtained through real vehicle testing, then a multi-target point selection function based on key performance indexes such as fuel-electricity conversion rate, noise and seat vibration is constructed, corresponding target weights are determined according to different types of working conditions, and the multi-target point selection function is constructed; and finally, solving the function by using the test data to obtain an optimal working point so as to guide the point selection of the power system of the whole vehicle. By comprehensively considering multiple performance objectives such as oil-electricity conversion rate, noise and vibration, the scheme can remarkably improve comprehensive driving experience of a hybrid vehicle model under different working conditions, optimal balance of fuel economy and driving comfort is achieved, a power system can more flexibly adapt to different driving requirements, and the driving experience of the hybrid vehicle model is greatly improved no matter medium-speed and high-speed driving or parking charging. And more optimized driving experience can be provided.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a hybrid vehicle optimization point selection method, device, equipment and storage medium. Background Art

[0002] With the severe challenge of global climate change and the increase of environmental awareness, the automotive industry is undergoing an unprecedented green transformation. As alternatives to traditional internal combustion engine vehicles, electric vehicles (EVs) and hybrid electric vehicles (HEVs) are widely welcomed by the market due to their lower carbon footprint and energy consumption.

[0003] In this green transformation, hybrid vehicles, with their advantages of combining internal combustion engines and electric motors, can significantly reduce fuel consumption and emissions while providing sufficient power and endurance. However, hybrid vehicles face technical challenges in practical applications. Specifically, the power system of hybrid vehicles needs to achieve optimal performance in different driving scenarios, and both consumers and manufacturers tend to focus on a single performance indicator, especially fuel economy, while ignoring the comprehensive consideration of multiple performance targets. This leads to poor NVH performance of the vehicle under certain working conditions, such as parking or idling, which affects driving comfort while pursuing fuel economy.

[0004] In addition, the requirements for power system performance in different driving scenarios are also different. How to dynamically adjust the working point of the power system according to different driving needs and working conditions to achieve the best overall driving experience is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The main purpose of this application is to provide a hybrid vehicle optimization point selection method, device, equipment and storage medium, aiming to solve the technical problem of how to dynamically adjust the operating point of the power system according to different driving needs and working conditions to achieve the best comprehensive driving experience.

[0006] To achieve the above objectives, the present application provides a hybrid vehicle optimization point selection method, the method comprising the following steps: Determine target operating conditions based on driving requirements; According to the target working conditions, a multi-objective point selection function is constructed; The multi-objective point selection function is solved according to the engine power relationship table to obtain the target power parameters.

[0007] Optionally, before determining the target operating condition according to the driving demand, the method further includes: Controlling the test vehicle to run at a preset power generation, the engine speed of the test vehicle gradually increases according to a preset step length to a test end point value; Acquiring driving experience data and fuel consumption data of the test vehicle at various test speeds; According to the preset power generation power, test speed, driving experience data and fuel consumption data, an engine power relationship table is obtained.

[0008] Optionally, determining the target operating condition according to the driving demand includes: According to the driving demand, the required power generation power is obtained; Determining a target vehicle speed and a driving gear according to the required power generation power; The target operating condition is determined according to the target vehicle speed and the driving gear.

[0009] Optionally, determining the target operating condition according to the target vehicle speed and the driving gear includes: If the target vehicle speed is greater than the first vehicle speed threshold, the target operating condition is a medium-high speed operating condition; If the target vehicle speed is less than the first vehicle speed threshold and greater than the second vehicle speed threshold, the target operating condition is a medium-low speed operating condition; If the target vehicle speed is less than a second vehicle speed threshold or the driving gear is a parking gear, the target operating condition is an idle operating condition.

[0010] Optionally, constructing a multi-objective point selection function according to the target operating condition includes: Determining weight coefficients of a multi-objective point selection function according to the target operating condition, wherein the weight coefficients include a noise reduction experience weight coefficient, a driving stability weight coefficient, and a fuel economy weight coefficient; According to the weight coefficients, a multi-objective point selection function is constructed.

[0011] Optionally, determining a weight coefficient of a multi-objective point selection function according to the target operating condition includes: When the target operating condition is a medium-high speed operating condition, the fuel economy weight coefficient is greater than the sum of the noise reduction experience weight coefficient and the driving stability weight coefficient; When the target operating condition is a medium-low speed operating condition, the fuel economy weight coefficient is equal to the noise reduction experience weight coefficient, and the fuel economy weight coefficient is greater than the driving stability weight coefficient; When the target operating condition is an idle operating condition, the driving stability weight coefficient is equal to the noise reduction experience weight coefficient, and the driving stability weight coefficient is greater than the fuel economy weight coefficient.

[0012] Optionally, solving the multi-objective point selection function according to the engine power relationship table to obtain the target power parameter includes: Determining the required power generation according to the target operating condition; Determine the speed selection interval according to the required power generation; According to the speed selection point interval and the required power generation power, a selection point data set in the engine power relationship table is obtained; Substituting the point selection data set into the multi-objective point selection function to obtain speed data that meets the preset point selection conditions; The rotation speed data and the required power generation power are used as the target power parameters.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a hybrid vehicle optimization point selection device, the hybrid vehicle optimization point selection device comprising: The working condition analysis module is used to determine the target working condition according to driving requirements; A multi-objective function construction module, used to construct a multi-objective point selection function according to the target working condition; The working point data processing module is used to solve the multi-objective point selection function according to the engine power relationship table to obtain the target power parameters.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a hybrid vehicle optimization point selection device, which includes: a memory, a processor, and a hybrid vehicle optimization point selection program stored in the memory and executable on the processor, wherein the hybrid vehicle optimization point selection program is configured to implement the steps of the hybrid vehicle optimization point selection method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, on which a hybrid vehicle optimization point selection program is stored. When the hybrid vehicle optimization point selection program is executed by a processor, the steps of the hybrid vehicle optimization point selection method described above are implemented.

[0016] This application obtains the driving experience and fuel consumption data of the engine under different power generation powers through actual vehicle testing, and then constructs a multi-objective point selection function based on key performance indicators such as oil-to-electricity conversion rate, noise and seat vibration. The corresponding target weights are determined according to different types of working conditions to construct a multi-objective point selection function; finally, the aforementioned test data is used to solve the function to obtain the optimal operating point to guide the selection of the power system of the entire vehicle.

[0017] In summary, this application comprehensively considers multiple performance targets such as oil-to-electricity conversion rate, noise and vibration. This solution can significantly improve the comprehensive driving experience of hybrid vehicles under different working conditions, achieve the best balance between fuel economy and driving comfort, and make the power system more flexibly adapt to different driving needs. Whether it is medium and high speed driving or parking charging, it can provide a more optimized driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 This is a flow chart of the first embodiment of the method for optimizing point selection for a hybrid vehicle of the present application; Figure 2 This is a schematic diagram of the weight configuration of the working condition-experience parameters of the first embodiment of the method for optimizing the selection of points for a hybrid vehicle of the present application; Figure 3 This is a flow chart of the second embodiment of the method for optimizing point selection for a hybrid vehicle of the present application; Figure 4 This is a flow chart of the third embodiment of the method for optimizing point selection for a hybrid vehicle of the present application; Figure 5 This is a schematic diagram of the functional modules of the hybrid vehicle optimization point selection device of this application; Figure 6 It is a structural diagram of a terminal device in the hardware operating environment involved in the embodiment of the present application.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a hybrid vehicle optimization point selection device, etc. The following takes the hybrid vehicle optimization point selection device as an example to illustrate this embodiment and the following embodiments.

[0025] The present application embodiment provides a hybrid vehicle optimization point selection method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the present application.

[0026] In this embodiment, the hybrid vehicle optimization point selection method includes: Step S10: Determine the target operating condition according to driving requirements.

[0027] It should be noted that, due to different vehicle operating conditions, the generator speed of a hybrid vehicle is limited by the optimal point curve, under which the actual fuel economy level of the vehicle will reach the theoretical optimal solution at the corresponding speed. However, it cannot be ignored that for the generator of a hybrid vehicle, basic fuel economy can only be achieved when the speed reaches a certain value. This is because the efficiency of most internal combustion engines is not consistent at all speeds, but there are one or more high-efficiency ranges. Only in these high-efficiency ranges, the engine's fuel consumption rate is the lowest, that is, the fuel economy is the best. If the actual required speed does not fall within these ranges, even if it runs according to the minimum fuel consumption curve, its fuel economy will be greatly reduced compared to the fuel-saving effect in the high-efficiency range. Therefore, the idea of ​​this application is that if the current driving demand makes the theoretically required generator speed unable to fall within the high-efficiency range, then it will seek to improve other forms of driving experience under these conditions, that is, to reduce the point selection logic with fuel economy as the first demand in medium and low speed conditions.

[0028] It is understandable that the driving experience of a vehicle includes multiple aspects, among which the vast majority of car companies prioritize fuel economy as the most important part of the driving experience. However, this strategy may affect other aspects of the driving experience. For example, when fuel economy is the top priority in parking and idling conditions, it may reduce other aspects of the driving experience, and finally the overall experience will regress. For example, in parking conditions, if the engine runs in a low speed range in pursuit of fuel economy, it may cause increased engine vibration and noise, because the engine combustion may not be smooth enough at low speeds, which will affect the comfort of passengers; in idling conditions, if the engine runs at a very low speed for a long time to save fuel, it may cause the engine temperature to be too low, affecting the efficiency of the catalytic converter, thereby increasing emission pollution. On the other hand, the low speed at idle may also affect the operation of on-board electronic equipment such as air conditioning and audio, because these devices may require the engine to be above a certain speed to obtain sufficient power supply. Some engines will also resonate at low speeds, causing the vehicle seat and body to vibrate, affecting the riding experience. Therefore, it is necessary to establish a working parameter selection method that can dynamically adjust the working point of the power system according to different driving needs and working conditions to achieve the best comprehensive driving experience.

[0029] It should be understood that the driving demand here can be either a driving demand that will occur immediately at the next moment or a driving demand within the next period of time. For example, when the vehicle arrives at the destination and needs to park immediately, the system will immediately recognize this new driving demand based on the gear change and determine that the target operating condition is the parking condition. On the other hand, for the vehicle that is about to enter certain specific sections, such as schools, hospitals, pedestrian streets, etc., which have speed limits for vehicles, the vehicle will determine in advance that the target operating condition is a medium or low speed condition based on the navigation information.

[0030] In one embodiment, determining the target operating condition according to driving demand includes: obtaining required power generation according to the driving demand; determining a target vehicle speed and a driving gear according to the required power generation; and determining the target operating condition according to the target vehicle speed and the driving gear.

[0031] It should be noted that the required power generation is obtained based on driving demand. This step is to identify and quantify the driver's immediate demand for vehicle power. For example, if the driver needs to accelerate quickly, the system will calculate the required power generation so that the motor or engine can provide sufficient power. This required power generation may be determined based on the position of the accelerator pedal, the vehicle's acceleration requirements, the current state of the battery, or other relevant factors. In addition, it is also related to navigation data and external sensor information, which is used to analyze environmental factors such as traffic conditions, weather conditions, and road conditions to correct the required power generation.

[0032] It is understandable that after determining the required power generation, the system will further analyze the target vehicle speed and driving gear corresponding to this power level. Even if the power generation is the same, there may be different target vehicle speeds and driving gears, and the aforementioned driving requirements will guide the selection in this step.

[0033] It should be understood that since different gears and vehicle speeds correspond to different driving experience priorities, the selection of target operating conditions can be roughly divided into three categories: medium and high speed operating conditions with priority on fuel economy, medium and low speed operating conditions with a balance between fuel economy and noise reduction experience and vibration reduction experience, and idling conditions (parking conditions) with priority on noise reduction experience and vibration reduction experience and fuel economy at the end. In medium and high speed operating conditions, such as cruising on highways or driving on urban expressways, it is necessary for the vehicle control system to give priority to minimizing fuel consumption, because the engine usually runs in its high efficiency range at this speed, using its higher thermal efficiency to provide power. In medium and low speed operating conditions such as urban roads or suburban driving, the system needs to find a balance between fuel economy and driving comfort. For example, when driving at low speed, the system may adjust the engine's operating point multiple times to keep the noise and vibration within a certain range to reduce the impact of noise and vibration. In idling conditions, such as parking or waiting for traffic lights, the main goal of the system is to provide the best comfort and the lowest noise and vibration levels.

[0034] In one embodiment, the target operating condition is determined based on the target vehicle speed and the driving gear, including: if the target vehicle speed is greater than a first vehicle speed threshold, the target operating condition is a medium-high speed condition; if the target vehicle speed is less than the first vehicle speed threshold and greater than a second vehicle speed threshold, the target operating condition is a medium-low speed condition; if the target vehicle speed is less than the second vehicle speed threshold or the driving gear is a parking gear, the target operating condition is an idle condition.

[0035] It should be noted that, taking conventional road traffic standards as an example, the first vehicle speed threshold is between 60km / h and 80km / h, and the second vehicle speed threshold is between 15km / h and 30km / h. Considering that the impact of the gear position on the target operating condition is different for vehicles of different configurations, this embodiment only points out special restrictions on the target operating condition corresponding to the parking gear position.

[0036] Step S20: constructing a multi-objective point selection function according to the target working condition.

[0037] It should be noted that the multiple objectives in the multi-objective point selection function in this embodiment include at least three aspects of driving experience objectives, namely, noise reduction experience, driving stability and fuel economy. The preferred scheme can be more but not less. Among them, the parameter corresponding to the noise reduction experience is the 1-meter noise sound pressure level data, the driving stability corresponds to the seat vibration acceleration data, and the fuel economy corresponds to the oil-to-electricity conversion rate.

[0038] In one embodiment, the multi-performance objective function is as follows:

[0039] Among them, the , is a multi-objective performance function; is the weight of the oil-to-electricity conversion performance indicator, is the maximum oil-to-electricity conversion rate under this working power, It is the minimum value of oil-to-electricity conversion rate. is the noise performance index weight, is the maximum noise value under this working power, is the minimum value of noise. is the seat vibration performance index weight, is the maximum seat vibration value under the working power, This is the minimum seat vibration.

[0040] It is understandable that since the larger the three data are, the greater the impact on the driving experience, the comprehensive experience is optimal only when the overall value of the multi-objective point selection function is the smallest within the corresponding point selection range.

[0041] In one embodiment, the weight coefficient of the multi-objective point selection function is determined according to the target operating condition, including: when the target operating condition is a medium-high speed condition, the fuel economy weight coefficient is greater than the sum of the noise reduction experience weight coefficient and the driving stability weight coefficient; when the target operating condition is a medium-low speed condition, the fuel economy weight coefficient is equal to the noise reduction experience weight coefficient, and the fuel economy weight is greater than the driving stability weight coefficient; when the target operating condition is an idle condition, the driving stability weight coefficient is equal to the noise reduction experience weight coefficient, and the driving stability weight coefficient is greater than the fuel economy weight coefficient.

[0042] like Figure 2 As shown, Figure 2 It is a schematic diagram of the weight configuration of the working condition-experience parameters of the first embodiment of the hybrid vehicle optimization point selection method of the present application.

[0043] Step S30: solving the multi-objective point selection function according to the engine power relationship table to obtain the target power parameters.

[0044] It should be noted that first, based on the power generation required by driving needs, the possible speed range is determined in the engine power relationship table, and then a data set containing multiple potential operating points is created, and the operating point with the best comprehensive driving experience is calculated through a multi-objective point selection function. Finally, these calculated optimal speed and power generation are set as target power parameters, and the vehicle is controlled in real time to achieve the best balance between fuel economy, power performance and ride comfort in different driving scenarios.

[0045] This application obtains the driving experience and fuel consumption data of the engine under different power generation powers through actual vehicle testing, and then constructs a multi-objective point selection function based on key performance indicators such as oil-to-electricity conversion rate, noise and seat vibration. The corresponding target weights are determined according to different types of working conditions to construct a multi-objective point selection function; finally, the aforementioned test data is used to solve the function to obtain the optimal operating point to guide the selection of the power system of the entire vehicle.

[0046] In summary, this application comprehensively considers multiple performance targets such as oil-to-electricity conversion rate, noise and vibration. This solution can significantly improve the comprehensive driving experience of hybrid vehicles under different working conditions, achieve the best balance between fuel economy and driving comfort, and make the power system more flexibly adapt to different driving needs. Whether it is medium and high speed driving or parking charging, it can provide a more optimized driving experience.

[0047] Reference Figure 3 , Figure 3 This is a flow chart of the second embodiment of the method for optimizing point selection for a hybrid vehicle of the present application. Based on the above-mentioned first embodiment, the second embodiment of the method for optimizing point selection for a hybrid vehicle of the present application is proposed.

[0048] In this embodiment, before step S10, the following steps are further included: Step S001: Controlling the test vehicle to run at a preset power generation, the engine speed of the test vehicle is gradually increased according to a preset step length to a test end point value.

[0049] It should be noted that the test vehicle and the vehicle to which the method is applied are of the same model. The purpose of the test is to obtain driving experience data corresponding to various speeds under various required power generation powers. Specifically in this embodiment, the three items are oil-to-electricity conversion rate, noise and seat vibration.

[0050] Step S002: Acquire driving experience data and fuel consumption data of the test vehicle at each test speed.

[0051] It is understandable that the test will first anchor the required power generation, and then gradually increase the speed from the vehicle's allowed speed to the speed limit in a certain step length. For example, when the power generation is anchored at 50KW, the speed range is: , where P is 50KW anchored here, Tmax is the maximum torque of the engine, is the maximum engine speed. Within this range, the driving experience data and fuel consumption data at each speed value are obtained one by one with a unit step of 100 rpm.

[0052] It should be noted that noise and seat vibration correspond to driving experience data, and oil-to-electricity conversion rate corresponds to fuel consumption data. In addition, driving experience data can further add some other items that affect driving experience, such as power response speed, or vehicle stability data during turning, lane changing, etc., including roll angle, steering response, etc. Fuel consumption data can also include instantaneous fuel consumption, comprehensive fuel consumption, or engine operating status, that is, the multi-objective function in the above formula can further expand more goals.

[0053] Step S003: Obtain an engine power relationship table according to the preset power generation power, test speed, driving experience data and fuel consumption data.

[0054] It should be noted that when all the driving experience data and fuel consumption data under all feasible speeds and power generation are obtained, it is equivalent to knowing the actual performance of the vehicle under any possible working conditions, and all the data are counted as an engine power relationship table. After the corresponding weights are configured in the subsequent table, these actual data can be used to obtain the multi-objective point selection function.

[0055] The present embodiment provides a method for optimizing point selection for a hybrid vehicle. The present embodiment first controls a test vehicle to run at a preset power generation power, and the engine speed of the test vehicle gradually increases to a test endpoint value according to a preset step length; obtains driving experience data and fuel consumption data of the test vehicle at each test speed; and obtains an engine power relationship table based on the preset power generation power, test speed, driving experience data, and fuel consumption data.

[0056] In summary, by conducting real vehicle tests on the same model of test vehicles at different preset power generation and gradually increasing engine speeds, key performance indicator data such as oil-to-electricity conversion rate, noise, and seat vibration are collected. On this basis, the scope of data collection is further expanded to include power response speed, handling stability, and instantaneous fuel consumption, etc., to comprehensively evaluate the driving experience. By integrating these data, a detailed engine power relationship table is constructed, and then using a multi-objective point selection function, the working point of the power system is optimized according to the driving experience data and fuel consumption data, which can ensure that hybrid vehicles achieve the best balance between fuel economy and driving comfort under various driving conditions, improve the responsiveness of the power system and vehicle handling stability, and at the same time, by comprehensively considering multiple performance indicators, the comprehensiveness of the driving experience is enhanced, meeting the expectations of modern drivers for efficient, environmentally friendly and comfortable driving experience.

[0057] Reference Figure 4 , Figure 4 This is a flow chart of the third embodiment of the method for optimizing point selection for a hybrid vehicle of the present application. Based on the above-mentioned first embodiment, the third embodiment of the method for optimizing point selection for a hybrid vehicle of the present application is proposed.

[0058] In this embodiment, the step S30 includes: Step S301: Determine the required power generation power according to the target operating condition.

[0059] It should be noted that, since the aforementioned steps are to obtain the target operating condition through driving demand, it is equivalent to having confirmed the unique required power generation power corresponding to the target operating condition.

[0060] Step S302: Determine a rotation speed selection interval according to the required power generation power.

[0061] It is understandable that, since the speed corresponding to the generated power is limited by the maximum torque and the maximum speed, the available speed can be indirectly determined, and these optional speeds together constitute the speed selection range.

[0062] Step S303: obtaining a selection point data set in the engine power relationship table according to the speed selection point interval and the required power generation power.

[0063] It should be noted that since the construction process of the engine power relationship table has been introduced in the previous steps, when the combination of speed and power generation is determined, the real experience data under this selection point combination can be determined accordingly. Combined with the working conditions corresponding to the combination of speed and power generation and the weights of each experience data corresponding to the working conditions, the comprehensive experience data under each working point combination can be calculated. This part of the working point combination and the corresponding real experience data are summarized to form the selection data set under the current driving demand.

[0064] Step S304: Substitute the point selection data set into the multi-objective point selection function to obtain rotation speed data that meets the preset point selection conditions.

[0065] It should be noted that the preset site selection conditions here refer to the various work site combinations that can achieve the best overall experience, that is, the work site combination with the lowest calculation result obtained in the above formula is the work site combination that meets the preset site selection conditions.

[0066] It can be understood that according to the above multi-objective formula, due to the normalization processing and the selection of parameter types, the work point combination with the lowest function calculation result is the work point combination that meets the preset point selection conditions. Correspondingly, if the construction method of the multi-objective function is different from that in the above example, the specific selection method is also different, but the basic idea of ​​​​point selection is to select the work point parameters that can achieve the best comprehensive experience among various work point combinations.

[0067] Step S305: taking the rotation speed data and the required power generation power as the target power parameters.

[0068] It can be understood that after determining the working point combination, the working points under this combination are used as power parameters to control the vehicle, so as to achieve the best comprehensive experience of vehicle operation under the driving requirements.

[0069] The present embodiment provides a method for optimizing point selection for a hybrid vehicle. The present embodiment first determines the required power generation according to the target operating condition; determines a speed point selection interval according to the required power generation; obtains a point selection data set in the engine power relationship table according to the speed point selection interval and the required power generation; substitutes the point selection data set into the multi-objective point selection function to obtain speed data that meets preset point selection conditions; and uses the speed data and the required power generation as the target power parameters.

[0070] In summary, by analyzing the data set through the multi-objective point selection function, the optimal speed and power generation combination is found, which is used as the target power parameter to guide vehicle control. The beneficial effects of this solution include improving fuel economy, reducing noise and vibration, improving driving responsiveness, and enhancing vehicle handling stability, thereby providing drivers with a more comfortable and efficient driving experience while ensuring environmental performance.

[0071] Reference Figure 5 The present application also provides a hybrid vehicle optimization point selection device, the hybrid vehicle optimization point selection device comprising: The operating condition analysis module 10 is used to determine the target operating condition according to the driving demand; A multi-objective function construction module 20, used to construct a multi-objective point selection function according to the target working condition; The work point data processing module 30 is used to solve the multi-objective point selection function according to the engine power relationship table to obtain the target power parameters.

[0072] In one embodiment, the multi-objective function construction module 20 is also used to control the test vehicle to operate under a preset power generation power, and the engine speed of the test vehicle is gradually increased to the test end value according to a preset step size; the driving experience data and fuel consumption data of the test vehicle at each test speed are obtained; and the engine power relationship table is obtained according to the preset power generation power, test speed, driving experience data and fuel consumption data.

[0073] In one embodiment, the operating condition analysis module 10 is further used to obtain the required power generation according to the driving demand; determine the target vehicle speed and driving gear according to the required power generation; and determine the target operating condition according to the target vehicle speed and driving gear.

[0074] In one embodiment, the operating condition analysis module 10 is also used to: if the target vehicle speed is greater than a first vehicle speed threshold, then the target operating condition is a medium-high speed condition; if the target vehicle speed is less than the first vehicle speed threshold and greater than a second vehicle speed threshold, then the target operating condition is a medium-low speed condition; if the target vehicle speed is less than the second vehicle speed threshold or the driving gear is a parking gear, then the target operating condition is an idle condition.

[0075] In one embodiment, the multi-objective function construction module 20 is also used to determine the weight coefficients of the multi-objective point selection function according to the target operating conditions, the weight coefficients including the noise reduction experience weight coefficient, the driving stability weight coefficient and the fuel economy weight coefficient; and construct the multi-objective point selection function according to the weight coefficients.

[0076] In one embodiment, the multi-objective function construction module 20 is also used for, when the target operating condition is a medium-high speed condition, the fuel economy weight coefficient is greater than the sum of the noise reduction experience weight coefficient and the driving stability weight coefficient; when the target operating condition is a medium-low speed condition, the fuel economy weight coefficient is equal to the noise reduction experience weight coefficient, and the fuel economy weight is greater than the driving stability weight coefficient; when the target operating condition is an idle condition, the driving stability weight coefficient is equal to the noise reduction experience weight coefficient, and the driving stability weight coefficient is greater than the fuel economy weight coefficient.

[0077] In one embodiment, the working point data processing module 30 is also used to determine the required power generation according to the target working condition; determine the speed selection point interval according to the required power generation; obtain the selection point data set in the engine power relationship table according to the speed selection point interval and the required power generation; substitute the selection point data set into the multi-objective selection point function to obtain the speed data that meets the preset selection point conditions; and use the speed data and the required power generation as the target power parameters.

[0078] An embodiment of the present application also provides a hybrid vehicle optimization point selection device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the hybrid vehicle optimization point selection method in the above-mentioned embodiment 1.

[0079] Reference below Figure 6, which shows a schematic diagram of the structure of a hybrid vehicle optimization point selection device suitable for implementing the embodiment of the present application. The hybrid vehicle optimization point selection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The hybrid vehicle optimization point selection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0080] like Figure 6 As shown, the hybrid vehicle optimization point selection device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the hybrid vehicle optimization point selection device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the hybrid vehicle optimization point selection device to communicate wirelessly or wired with other devices to exchange data. Although the hybrid vehicle optimization point selection device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0081] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0082] The hybrid vehicle optimization point selection device provided by the present application adopts the hybrid vehicle optimization point selection method in the above embodiment, which can solve the technical problem of how to dynamically adjust the working point of the power system according to different driving needs and working conditions to achieve the best comprehensive driving experience. Compared with the prior art, the beneficial effects of the hybrid vehicle optimization point selection device provided by the present application are the same as the beneficial effects of the hybrid vehicle optimization point selection method provided by the above embodiment, and the other technical features of the hybrid vehicle optimization point selection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0083] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0084] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0085] The present application also provides a storage medium, on which a hybrid vehicle optimization point selection program is stored. When the hybrid vehicle optimization point selection program is executed by a processor, the steps of the hybrid vehicle optimization point selection method described in any one of the above are implemented.

[0086] The storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: RandomAccess Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0087] The above-mentioned storage medium may be included in the hybrid vehicle optimization point selection device; or it may exist independently without being assembled into the hybrid vehicle optimization point selection device.

[0088] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the hybrid vehicle optimization point selection device, the hybrid vehicle optimization point selection device: determines the target operating condition according to driving requirements; constructs a multi-objective point selection function according to the target operating condition; solves the multi-objective point selection function according to the engine power relationship table to obtain the target power parameters.

[0089] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0091] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0092] The storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned hybrid vehicle optimization point selection method, and can solve the technical problem of how to dynamically adjust the working point of the power system according to different driving needs and working conditions to achieve the best comprehensive driving experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the hybrid vehicle optimization point selection method provided in the above-mentioned embodiment, and will not be repeated here.

[0093] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A hybrid vehicle optimization point selection method, characterized in that: The hybrid vehicle optimization point selection method comprises: Determine target operating conditions based on driving requirements; According to the target working conditions, a multi-objective point selection function is constructed; The multi-objective point selection function is solved according to the engine power relationship table to obtain the target power parameters.

2. The hybrid vehicle optimization point selection method according to claim 1, characterized in that: Before determining the target operating condition according to the driving demand, the method further includes: Controlling the test vehicle to run at a preset power generation, the engine speed of the test vehicle gradually increases according to a preset step length to a test end point value; Obtaining driving experience data and fuel consumption data of the test vehicle at various test speeds; According to the preset power generation power, test speed, driving experience data and fuel consumption data, an engine power relationship table is obtained.

3. The hybrid vehicle optimization point selection method according to claim 1, characterized in that: Determining the target operating condition according to the driving demand includes: According to the driving demand, the required power generation is obtained; Determining a target vehicle speed and a driving gear according to the required power generation power; The target operating condition is determined according to the target vehicle speed and the driving gear.

4. The hybrid vehicle optimization point selection method according to claim 3, characterized in that: The determining the target operating condition according to the target vehicle speed and the driving gear position includes: If the target vehicle speed is greater than the first vehicle speed threshold, the target operating condition is a medium-high speed operating condition; If the target vehicle speed is less than the first vehicle speed threshold and greater than the second vehicle speed threshold, the target operating condition is a medium-low speed operating condition; If the target vehicle speed is less than a second vehicle speed threshold or the driving gear is a parking gear, the target operating condition is an idle operating condition.

5. The hybrid vehicle optimization point selection method according to claim 1, characterized in that: The step of constructing a multi-objective point selection function according to the target working condition comprises: According to the target operating condition, determining weight coefficients of a multi-objective point selection function, wherein the weight coefficients include a noise reduction experience weight coefficient, a driving stability weight coefficient, and a fuel economy weight coefficient; According to the weight coefficients, a multi-objective point selection function is constructed.

6. The hybrid vehicle optimization point selection method according to claim 5, characterized in that: Determining the weight coefficient of the multi-objective point selection function according to the target working condition includes: When the target operating condition is a medium-high speed operating condition, the fuel economy weight coefficient is greater than the sum of the noise reduction experience weight coefficient and the driving stability weight coefficient; When the target operating condition is a medium-low speed operating condition, the fuel economy weight coefficient is equal to the noise reduction experience weight coefficient, and the fuel economy weight coefficient is greater than the driving stability weight coefficient; When the target operating condition is an idle operating condition, the driving stability weight coefficient is equal to the noise reduction experience weight coefficient, and the driving stability weight coefficient is greater than the fuel economy weight coefficient.

7. The hybrid vehicle optimization point selection method according to claim 1, characterized in that: Solving the multi-objective point selection function according to the engine power relationship table to obtain the target power parameter includes: Determining the required power generation power according to the target operating condition; Determine the speed selection interval according to the required power generation; According to the speed selection point interval and the required power generation power, a selection point data set in the engine power relationship table is obtained; Substituting the point selection data set into the multi-objective point selection function to obtain speed data that meets the preset point selection conditions; The rotation speed data and the required power generation power are used as the target power parameters.

8. A hybrid vehicle optimization point selection device, characterized in that: The hybrid vehicle optimization point selection device comprises: The working condition analysis module is used to determine the target working condition according to driving requirements; A multi-objective function construction module, used to construct a multi-objective point selection function according to the target working condition; The working point data processing module is used to solve the multi-objective point selection function according to the engine power relationship table to obtain the target power parameters.

9. A hybrid vehicle optimization point selection device, characterized in that: The hybrid vehicle optimization point selection device includes: a memory, a processor, and a hybrid vehicle optimization point selection program stored in the memory and executable on the processor, wherein the hybrid vehicle optimization point selection program is configured to implement the steps of the hybrid vehicle optimization point selection method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a hybrid vehicle optimization point selection program, and when the hybrid vehicle optimization point selection program is executed by the processor, the steps of the hybrid vehicle optimization point selection method according to any one of claims 1 to 7 are implemented.

Images