Test method and device for predicted energy consumption performance of extended-range hybrid vehicle and upper computer

Abstract

The invention relates to the technical field of vehicles, in particular to a test method and device for predicting energy consumption performance of an extended-range hybrid vehicle and an upper computer, and the method comprises the steps: recognizing the working condition information of a plurality of road sections in navigation information; predicting the energy consumption of the plurality of road sections according to the working condition information of the plurality of road sections; a torque control instruction is generated according to the energy consumption of the multiple road sections and the operation information of the vehicle model of the extended-range hybrid vehicle, the torque control instruction is used for controlling the target rack to conduct a simulation experiment, and the driver model and the vehicle model on the target rack respond to the torque control instruction and simulate driving on the multiple road sections; and generating an energy consumption performance prediction result of the extended-range hybrid vehicle according to the data of the simulation experiment. Therefore, the problems that in the prior art, the accuracy of energy consumption prediction is low, and then energy management of the vehicle is affected are solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a test method, device and host computer for predicting energy consumption performance of an extended-range hybrid vehicle. Background Art

[0002] With the development of intelligent transportation systems, automakers are constantly exploring how to more effectively manage the energy use of new energy vehicles, especially for extended-range hybrid vehicles, which combine a battery-driven motor and a small internal combustion engine (i.e., a range extender) to extend the pure electric driving range and reduce emissions. However, the energy management of extended-range hybrid vehicles faces many challenges. Predicting the energy consumption of the vehicle in advance is of great significance to the energy management of the vehicle, ensuring that the vehicle has enough power to support pure electric driving when it is most needed, and using the internal combustion engine to supplement energy under appropriate conditions, thereby maximizing overall energy efficiency. Accurate energy consumption prediction can help avoid starting the range extender too early or too much, reducing unnecessary fuel consumption and emissions.

[0003] Existing technologies mainly use static or semi-static methods to predict energy consumption, that is, calculating energy consumption under different driving conditions based on historical data or theoretical formulas. However, this method is difficult to accurately reflect the complex situations in the actual driving process, resulting in low prediction accuracy and subsequent impact on vehicle energy management. Summary of the invention

[0004] The present invention provides a test method, device and host computer for predicting energy consumption performance of an extended-range hybrid vehicle, so as to solve the problem that the energy consumption prediction accuracy of the prior art is low, thereby affecting the energy management of the vehicle.

[0005] The first aspect of the present invention provides a test method for predicting the energy consumption performance of an extended-range hybrid vehicle, comprising the following steps: identifying operating condition information of multiple road sections in navigation information; predicting the energy consumption of the multiple road sections based on the operating condition information of the multiple road sections; generating torque control instructions based on the energy consumption of the multiple road sections and operating information of a vehicle model of the extended-range hybrid vehicle, and using the torque control instructions to control a target test bench to perform a simulation experiment, wherein a driver model and a vehicle model on the target test bench respond to the torque control instructions to simulate driving on multiple road sections; and generating energy consumption performance prediction results of the extended-range hybrid vehicle based on the data of the simulation experiment.

[0006] Optionally, the multiple road sections include the current road section and the future road sections, and the energy consumption of the multiple road sections is predicted based on the working condition information of the multiple road sections, including: inputting the working condition information of the future road sections into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future road sections.

[0007] Optionally, before predicting the energy consumption of multiple road sections based on the operating condition information of multiple road sections, it also includes: if the current road section is a congested section, the vehicle model of the extended-range hybrid vehicle travels in pure electric mode; if the current road section is a smooth section, predicting the energy consumption of future sections based on the operating condition information of multiple road sections.

[0008] Optionally, a torque control instruction is generated based on the energy consumption of multiple road sections and the operating information of the vehicle model of the extended-range hybrid vehicle, including: adjusting the SOC (State of Charge) balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of the future road section; adjusting the working state of the range extender based on the adjusted SOC balance point and the operating information; and generating the torque control instruction based on the adjusted SOC balance point and the working state of the range extender.

[0009] Optionally, adjusting the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of multiple road sections includes: determining the unobstructed road section in the future road section that is closest to the current road section as the target road section; predicting the supplementary power consumption of the extended-range hybrid vehicle based on the predicted energy consumption of each road section in the future road section and the target road section, wherein the calculation formula of the supplementary power consumption is: , To compensate for the power consumption, is the target section, is the penalty coefficient, is the total number of road sections, ~ is the predicted energy consumption of each section from the current section to the target section, ~ The predicted energy consumption of each section after the target section; the target incremental SOC of the battery power of the extended-range hybrid vehicle is calculated based on the supplementary power consumption, the battery self-discharge efficiency of the extended-range hybrid vehicle and the total energy; and the SOC balance point of the vehicle model of the extended-range hybrid vehicle is adjusted based on the target incremental SOC.

[0010] Optionally, the operating information includes at least one of a current SOC and a current vehicle speed, and adjusting the operating state of the range extender based on the adjusted SOC balance point and the operating information includes: calculating a difference between the current SOC and the adjusted SOC balance point, and selecting a power generation power of the range extender on a target operating curve of the range extender based on the difference and the current vehicle speed; and determining a power generation timing of the range extender based on the power generation power, the current SOC and the adjusted SOC balance point.

[0011] A second aspect of the present invention provides a test device for predicting the energy consumption performance of an extended-range hybrid vehicle, comprising: an identification module for identifying operating condition information of multiple road sections in navigation information; a prediction module for predicting the energy consumption of multiple road sections based on the operating condition information of the multiple road sections; a first generation module for generating a torque control instruction based on the energy consumption of the multiple road sections and the operating information of a vehicle model of the extended-range hybrid vehicle, and using the torque control instruction to control a target test bench to perform a simulation experiment, wherein a driver model and a vehicle model on the target test bench respond to the torque control instruction to simulate driving on multiple road sections; a second generation module for generating a prediction result of the energy consumption performance of the extended-range hybrid vehicle based on the data of the simulation experiment.

[0012] Optionally, the multiple road sections include a current road section and a future road section, and the prediction module is further used to: input the working condition information of the future road section into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future road section.

[0013] Optionally, it also includes: a judgment module, which is used to, before predicting the energy consumption of multiple road sections based on the operating condition information of multiple road sections, if the current road section is a congested section, the vehicle model of the extended-range hybrid vehicle travels in pure electric mode; if the current road section is a smooth section, predicting the energy consumption of future sections based on the operating condition information of multiple road sections.

[0014] Optionally, the first generation module is further used to: adjust the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of the future road section; adjust the working state of the range extender based on the adjusted SOC balance point and operating information; and generate a torque control instruction based on the adjusted SOC balance point and the working state of the range extender.

[0015] Optionally, the first generation module is further used to: determine the unblocked road section in the future road section that is closest to the current road section as the target road section; predict the supplementary power consumption of the extended-range hybrid vehicle based on the predicted energy consumption of each road section in the future road section and the target road section, wherein the calculation formula of the supplementary power consumption is: , To compensate for the power consumption, is the target section, is the penalty coefficient, is the total number of road sections, ~ is the predicted energy consumption of each section from the current section to the target section, ~ The predicted energy consumption of each section after the target section; the target incremental SOC of the battery power of the extended-range hybrid vehicle is calculated based on the supplementary power consumption, the battery self-discharge efficiency of the extended-range hybrid vehicle and the total energy; and the SOC balance point of the vehicle model of the extended-range hybrid vehicle is adjusted based on the target incremental SOC.

[0016] Optionally, the operating information includes at least one of a current SOC and a current vehicle speed, and the first generating module is further used to: calculate a difference between the current SOC and an adjusted SOC balance point, and select a power generation power of the range extender on a target working curve of the range extender based on the difference and the current vehicle speed; and determine a power generation timing of the range extender based on the power generation power, the current SOC and the adjusted SOC balance point.

[0017] The third aspect of the present invention provides a host computer, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to perform a test method for predicting energy consumption performance of an extended-range hybrid vehicle as described in the above embodiment.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to perform a test method for predicting energy consumption performance of an extended-range hybrid vehicle as described in the above embodiment.

[0019] A fifth aspect of the present invention provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed, a method for testing the predicted energy consumption performance of an extended-range hybrid vehicle as described in the above embodiment is implemented.

[0020] Therefore, the present invention has at least the following beneficial effects: The embodiment of the present invention can predict the energy consumption of multiple sections according to the working condition information of multiple sections in the navigation information, and generate torque control instructions according to the energy consumption of multiple sections and the operation information of the vehicle model of the extended-range hybrid vehicle, and use the torque control instructions to control the target bench to perform simulation experiments, and then generate the energy consumption performance prediction results of the extended-range hybrid vehicle according to the data of the simulation experiment, so as to realize the prediction of the vehicle energy consumption performance, and use the target bench to simulate the real driving environment to improve the accuracy of the simulation experiment, without the need for real vehicle testing, and reduce the cost and risk of real vehicle testing. By simulating various driving conditions, the dependence on the actual vehicle is reduced, the testing cost and potential safety risks are reduced, and the accuracy of energy management optimization can be improved. Therefore, the technical problems such as the low accuracy of energy consumption prediction in the prior art, which in turn affects the energy management of the vehicle, are solved.

[0021] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1A flowchart of a method for testing the energy consumption performance of an extended-range hybrid vehicle provided in accordance with an embodiment of the present invention; Figure 2 A training process of a neural network energy consumption prediction model provided according to an embodiment of the present invention; Figure 3 A schematic diagram of prediction of a neural network energy consumption model provided according to an embodiment of the present invention; Figure 4 A structural diagram of a test system for predicting energy consumption performance of an extended-range hybrid vehicle provided according to an embodiment of the present invention; Figure 5 A flow chart of an energy management optimization strategy provided according to an embodiment of the present invention; Figure 6 An exemplary diagram of a test device for predicting energy consumption performance of a range-extended hybrid vehicle provided according to an embodiment of the present invention; Figure 7 The figure is a schematic diagram of the structure of a host computer provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0024] The following describes a method, device and host computer for testing the predicted energy consumption performance of an extended-range hybrid vehicle according to an embodiment of the present invention with reference to the accompanying drawings. In view of the fact that the prior art mentioned in the above background technology mainly adopts a static or semi-static method to predict energy consumption, that is, the energy consumption under different driving conditions is calculated according to historical data or theoretical formulas, but this method is difficult to accurately reflect the complex situation in the actual driving process, resulting in low prediction accuracy, which in turn affects the energy management of the vehicle. The present invention provides a test method for predicting the energy consumption performance of an extended-range hybrid vehicle. In this method, the energy consumption of multiple sections can be predicted according to the working condition information of multiple sections in the navigation information, and a torque control instruction is generated according to the energy consumption of the multiple sections and the operation information of the vehicle model of the extended-range hybrid vehicle. The torque control instruction is used to control the target bench to perform a simulation experiment, and then the energy consumption performance prediction result of the extended-range hybrid vehicle is generated according to the data of the simulation experiment, so as to realize the prediction of the vehicle energy consumption performance, and the target bench is used to simulate the real driving environment, so as to improve the accuracy of the simulation experiment, without the need for real vehicle testing, and reduce the cost and risk of real vehicle testing. By simulating various driving conditions, the dependence on the actual vehicle is reduced, the testing cost and potential safety risks are reduced, and then the accuracy of energy management optimization can be improved. This solves the problem that the existing technology has low accuracy in predicting energy consumption, which in turn affects the energy management of the vehicle.

[0025] Specifically, Figure 1 A schematic flow chart of a method for testing the energy consumption performance of an extended-range hybrid vehicle provided in an embodiment of the present invention.

[0026] like Figure 1 As shown, the test method for predicting energy consumption performance of the extended-range hybrid vehicle includes the following steps: In step S101, operating condition information of a plurality of road sections in navigation information is identified.

[0027] Among them, the multiple road sections include current road sections and future road sections; the working condition information includes road section congestion information, average vehicle speed of the road section, road section length, etc.

[0028] In step S102, energy consumption of the multiple road sections is predicted according to the working condition information of the multiple road sections.

[0029] It is understandable that the embodiment of the present invention can predict the energy consumption of multiple road sections based on the working condition information of multiple road conditions. The specific prediction method is as follows.

[0030] In an embodiment of the present invention, the energy consumption of multiple road sections is predicted based on the working condition information of the multiple road sections, including: inputting the working condition information of the future road sections into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future road sections.

[0031] It is understandable that the embodiment of the present invention can input the working condition information of the future road section into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future time period.

[0032] It should be noted that the neural network energy consumption prediction model is an existing technology. Before using the neural network energy consumption prediction model to output the predicted energy consumption in the future period, the neural network energy consumption prediction model needs to be trained through the historical information collected by the actual vehicle. The training process is as follows Figure 2 As shown in the figure, specifically, the number of input layer nodes is set to 3, and the input is the road congestion, the average speed of the road section, and the length of the road section; the hidden layer is set to two layers, each with 25 nodes; the number of output layer nodes is 1, and the output is energy consumption. Among them, the training function uses the trainlm function, and the normalization function uses the mapminmax function. The process of online prediction of the neural network energy consumption prediction model is as follows Figure 3 As shown in Figure 2, when the energy consumption of each road section is predicted online, the trained model receives the map matrix information and predicts the energy consumption matrix information of the future road section, where v i represents the average speed of the i-th road section, s i Represents the distance of the i-th road segment, m i represents the congestion information of the i-th road section, E iRepresents the predicted energy consumption information of the i-th road section.

[0033] In an embodiment of the present invention, before predicting the energy consumption of multiple road sections based on the operating condition information of multiple road sections, it also includes: if the current road section is a congested section, the vehicle model of the extended-range hybrid vehicle travels in pure electric mode; if the current road section is a smooth section, predicting the energy consumption of future sections based on the operating condition information of multiple road sections.

[0034] It can be understood that, before predicting the energy consumption of multiple road sections according to the working condition information of multiple road sections, the embodiment of the present invention can first determine whether the current road section is a congested road section. If the current road section is a congested road section, since congested road sections in cities usually mean frequent starts and stops and low-speed driving, in this case, the internal combustion engine (i.e., the range extender) has low working efficiency, poor fuel economy, and high emissions. Then, directly making the extended-range hybrid vehicle drive in pure electric mode can avoid the internal combustion engine from working under unfavorable conditions, thereby reducing fuel consumption and pollutant emissions, while providing a quieter and smoother driving experience, and the battery electric drive system has a higher efficiency at low speeds, so it is more suitable for dealing with traffic jams; if the current road section is a smooth road section, that is, when the road conditions are good and the vehicle can maintain a high average speed, the internal combustion engine can operate in a more efficient working range, so the navigation information is used to predict the energy consumption of future road sections, so that it can help determine the best time to start the range extender for charging, and ensure that there is enough power to support the congested road sections that may be encountered later to continue driving in pure electric mode.

[0035] In step S103, a torque control instruction is generated according to the energy consumption of multiple road sections and the operating information of the vehicle model of the extended-range hybrid vehicle, and the torque control instruction is used to control the target test bench to perform a simulation experiment, wherein the driver model and the vehicle model on the target test bench respond to the torque control instruction and simulate driving on multiple road sections.

[0036] The operation information includes dynamic response information such as the current SOC of the battery and the current vehicle speed; the target test bench may be a HIL (Hardware-in-the-Loop) test bench.

[0037] It can be understood that the embodiments of the present invention can generate torque control instructions based on the energy consumption of multiple road sections and the operating information of the vehicle model of the extended-range hybrid vehicle, and use the torque control instructions to control the target test bench to perform simulation experiments and simulate driving on multiple road sections.

[0038] In an embodiment of the present invention, a torque control instruction is generated according to energy consumption of multiple road sections and operating information of a vehicle model of an extended-range hybrid vehicle, including: adjusting the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of future road sections; adjusting the working state of the range extender based on the adjusted SOC balance point and the operating information; and generating the torque control instruction based on the adjusted SOC balance point and the working state of the range extender.

[0039] Among them, the working status of the range extender includes the power generation power and the power generation timing of the range extender.

[0040] It can be understood that the embodiment of the present invention can adjust the SOC balance point according to the energy consumption of the future road section, and adjust the working state of the range extender based on the adjusted balance point and the operating information of the extended-range hybrid vehicle, and then generate a torque control instruction based on the adjusted SOC balance point and the working state of the range extender to ensure that the vehicle maintains optimal energy consumption efficiency throughout the entire driving process.

[0041] In an embodiment of the present invention, the SOC balance point of a vehicle model of an extended-range hybrid vehicle is adjusted according to the energy consumption of multiple road sections, including: determining a smooth road section in a future road section that is closest to a current road section as a target road section; predicting the supplementary power consumption of the extended-range hybrid vehicle based on the predicted energy consumption of each road section in the future road section and the target road section; calculating a target incremental SOC of the battery power of the extended-range hybrid vehicle based on the supplementary power consumption, the battery self-discharge efficiency and the total energy of the extended-range hybrid vehicle, and adjusting the SOC balance point of the vehicle model of the extended-range hybrid vehicle based on the target incremental SOC.

[0042] The future road section includes at least one road section.

[0043] It can be understood that the embodiment of the present invention can determine the unobstructed section in the future section that is closest to the current section as the target section, and then predict the supplementary power consumption of the future congested section based on the predicted energy consumption of each section in the future section and the target section, calculate the target incremental SOC of the battery power of the extended-range hybrid vehicle based on the supplementary power consumption, the battery self-discharge efficiency and total energy of the extended-range hybrid vehicle, and adjust the SOC balance point of the vehicle model of the extended-range hybrid vehicle based on the target incremental SOC.

[0044] The calculation formula for supplementary power consumption is: , To compensate for the power consumption, is the target section, is the penalty coefficient, is the total number of road sections, ~ is the predicted energy consumption of each section from the current section to the target section, ~ Predicted energy consumption for each segment after the target segment.

[0045] It should be noted that section k is the unobstructed section closest to the current section. The purpose of the embodiment of the present invention is to ensure that the vehicle can travel in pure electric mode on congested sections. Therefore, only the energy consumption required for the short section and the section behind section k needs to be considered.

[0046] Specifically, the process of dynamically adjusting the SOC balance point is: First, mark the unobstructed road section closest to the current road section as road section k (i.e., the target road section), and make an estimated power replenishment energy consumption prediction based on road section k. , the specific algorithm is as follows: ; is the penalty coefficient, The size of depends on the ratio of the number of unobstructed sections in the subsequent sections of section k to the total number of subsequent sections. , specifically, The expression is as follows: ; If all subsequent sections are unblocked, =1, then , that is, at this time, the prediction of future power replenishment demand energy consumption only considers the congested sections before section k; if all subsequent sections are congested sections, that is ,at this time =0.5, at this time, the power replenishment demand of the current section should not only meet the energy consumption of the congested section in front of section k, but also appropriately supplement a part of the energy consumption of the congested section behind section k to prevent the actual power replenishment of section k from failing to meet the power replenishment demand of subsequent congested road conditions.

[0047] Secondly, after obtaining the energy consumption required for replenishment of power in future congested sections, the energy consumption is converted into battery power. (Target incremental SOC): ; in, is the battery discharge efficiency, is the total energy of the battery.

[0048] The calculation formula for dynamically adjusting the SOC balance point is as follows: ; in, The original SOC balance point is the preset original SOC balance point. The original SOC balance point is based on the test calibration default or user-defined power conservation threshold. is the adjusted SOC balance point.

[0049] In addition, the SOC balance point will be adjusted according to driving conditions, driving modes, user needs, and environmental factors, and the adjusted SOC balance point in the embodiment of the present invention takes into account the energy consumption requirements of future road sections and is optimized based on the energy management strategy.

[0050] In an embodiment of the present invention, adjusting the working state of the range extender based on the adjusted SOC balance point and the operating information includes: calculating the difference between the current SOC and the adjusted SOC balance point, and selecting the power generation power of the range extender on the target working curve of the range extender based on the difference and the current vehicle speed; and determining the power generation timing of the range extender based on the power generation power, the current SOC and the adjusted SOC balance point.

[0051] Among them, the target working curve is a preset optimal working curve of the range extender; the working state of the range extender includes the power generation and the power generation timing.

[0052] It is understandable that the embodiment of the present invention can calculate the difference between the current SOC and the adjusted SOC balance point, and select the power generation of the range extender on the target working curve of the range extender based on the difference and the current vehicle speed; and determine the power generation timing of the range extender based on the power generation, the current SOC and the adjusted SOC balance point. Specifically, the process of adjusting the working state of the range extender is: Calculate the balance point between the current SOC and the adjusted SOC ( ) : ; Combined with the difference between the current SOC and the adjusted balance point and the current vehicle speed, the charging power is selected on the preset optimal working curve of the range extender. (Also known as power generation), where the power generation can be directly obtained by querying the pre-calibrated table. A three-dimensional table is constructed based on the required power, current vehicle speed and SOC deviation to jointly determine the power generation (it is also possible to query the two-dimensional table without considering the required power of the whole vehicle). , is the power demand of the vehicle at the current moment.

[0053] At the same time, the selection of power generation timing should satisfy the following formula: ; in, t is the estimated time to pass the current section, is the charging power, is the total battery energy, To supplement the power.

[0054] In other words, the time required to replenish the battery to the adjusted SOC balance point is calculated as , ; The estimated passing time of the current road section can be expressed as: ; in, is the total length of the current section, is the average speed of the current road section, The time spent traveling on the current road segment.

[0055] In order to ensure sufficient battery power when entering congested sections and cope with random changes in future working conditions, a charging buffer time is defined. , generally, The value range of is (5, 10). With the dynamic update of map information, when the following formula is satisfied, the range extender performs the charging operation. When the following formula is not satisfied, it looks for the next unobstructed road section to perform the charging operation.

[0056] .

[0057] In step S104, an energy consumption performance prediction result of the extended-range hybrid vehicle is generated based on the data of the simulation experiment.

[0058] It can be understood that the embodiments of the present invention can generate energy consumption performance prediction results of extended-range hybrid vehicles based on the data of simulation experiments, realize the prediction of vehicle energy consumption performance, and use the HIL test bench to simulate the real driving environment. The neural network energy consumption prediction model is used to predict the vehicle energy consumption status in real time and with high precision, thereby improving the accuracy of simulation experiments, reducing the cost and risk of actual vehicle testing, and reducing dependence on actual vehicles by simulating various driving conditions, thereby reducing testing costs and potential safety risks.

[0059] It should be noted that the test method for predicting energy consumption performance of an extended-range hybrid vehicle of the present invention is performed on a test system. The specific structure of the overall test system is as follows: Figure 4 As shown in the figure, it includes the vehicle navigation system, VCU (Vehicle Control Unit), HIL test bench and host computer. Generally speaking, the vehicle navigation system sends map information to VCU. The map information includes congestion information, average vehicle speed, section length, etc. of the current section and the N sections ahead, and is dynamically updated with a step length of ∆t; the HIL test bench simulates the driver's behavior pattern and vehicle dynamic response, including battery SOC, vehicle speed, power demand, engine torque, etc.; VCU calculates the dynamic SOC balance point based on the map information and the vehicle dynamic information provided by the HIL test bench and selects the range extender working point for torque control. In this way, the entire system greatly improves the accuracy of the simulation experiment, while also reducing the risks and costs brought by the real vehicle road acquisition experiment.

[0060] The method for testing the predicted energy consumption performance of a range-extended hybrid vehicle according to an embodiment of the present invention specifically comprises the following steps: S1: Obtain the working condition information of the next N road sections through navigation information (such as Gaode API (Application Programming Interface)): road congestion, average vehicle speed, road distance, etc.

[0061] S2: Build an intelligent energy management model, including offline training of BP (Back Propagation Neural Network) neural network and online application for energy consumption prediction.

[0062] S3: Build a complete driver model and vehicle model on the HIL bench.

[0063] The driver model is designed to simulate the behavior of a real driver, including reaction time, acceleration and deceleration pedal opening, etc. The vehicle model includes a dynamic model, an electric drive system model, a battery management system model, a brake system model, a tire model, an aerodynamic model, and a thermal management model. The data signals interact based on the energy transmission relationship between the models. The vehicle model is used to simulate the various characteristics of a real vehicle.

[0064] S4: Energy consumption prediction and dynamic adjustment.

[0065] The operating condition information matrix in S1 is input into the BP neural network of S2. The neural network outputs the predicted energy consumption matrix information and inputs it into the energy management module to adjust the dynamic SOC balance point and the working state of the range extender. S2 sends the torque control command to the vehicle model in S3 for control. At the same time, S3 feeds back the dynamic information of the vehicle to the intelligent energy management system of S2 in real time. Through the HIL bench simulation experiment, the optimization results of driving energy consumption are reflected more accurately.

[0066] Specifically, the specific process of the energy management optimization strategy of the embodiment of the present invention is as follows: Figure 5 As shown, including: Receive online map information and determine whether the current road section is a congested section. If it is a congested section, maintain pure electric mode; if it is an unobstructed section, enter the smart range-extended mode to calculate the dynamic SOC balance point and determine the working status of the range extender.

[0067] First, mark the unobstructed road section closest to the current road section as road section k, and make an estimated power replenishment energy consumption prediction based on road section k , the specific algorithm is as follows: ; is the penalty coefficient, The size of depends on the ratio of the number of unobstructed sections in the subsequent sections of section k to the total number of subsequent sections. , specifically, The expression is as follows: ; If all subsequent sections are unblocked, =1, then , that is, at this time, the prediction of future power replenishment demand energy consumption only considers the congested sections before section k; if all subsequent sections are congested sections, that is ,at this time =0.5, at this time, the power replenishment demand of the current section should not only meet the energy consumption of the congested section in front of section k, but also appropriately supplement a part of the energy consumption of the congested section behind section k to prevent the actual power replenishment of section k from failing to meet the power replenishment demand of subsequent congested road conditions.

[0068] Secondly, after obtaining the energy consumption required for replenishment of power in future congested sections, the energy consumption is converted into battery power. : ; in, is the battery discharge efficiency, is the total energy of the battery.

[0069] The calculation formula for dynamically adjusting the SOC balance point is as follows: ; in, It is the preset original SOC balance point.

[0070] Calculate the difference between SOC and SOC balance point : ; Combined with the difference between the current SOC and the balance point and the current vehicle speed, the charging efficiency is selected on the preset optimal working curve of the range extender. At the same time, the selection of power generation timing should satisfy the following formula: ; in, t is the estimated time to pass the current section, is the charging power.

[0071] As map information is continuously updated, the present invention can dynamically acquire map information and adjust and optimize the SOC balance point on the HIL test bench. On the premise that the remaining SOC does not meet the remaining mileage of pure electric driving and ensures the NVH of the vehicle, the SOC balance point can be dynamically changed, and the range extender's high-efficiency area can be used for power replenishment in unobstructed sections of the road, and pure electric driving can be achieved in congested low-speed sections, so as to achieve the effect of energy conservation and emission reduction, and realize dynamic regulation of the range extender's working timing and working time, so as to achieve the effect of optimizing energy consumption.

[0072] According to the test method for predicting the energy consumption performance of an extended-range hybrid vehicle proposed in an embodiment of the present invention, the energy consumption of multiple road sections can be predicted according to the operating condition information of the multiple road sections in the navigation information, and a torque control instruction can be generated according to the energy consumption of the multiple road sections and the operating information of the vehicle model of the extended-range hybrid vehicle. The torque control instruction is used to control a target test bench to perform a simulation experiment, and then the energy consumption performance prediction result of the extended-range hybrid vehicle is generated according to the data of the simulation experiment, so as to realize the prediction of the vehicle's energy consumption performance, and the target test bench is used to simulate the real driving environment to improve the accuracy of the simulation experiment. There is no need for real vehicle testing, which reduces the cost and risk of real vehicle testing. By simulating various driving conditions, the dependence on the actual vehicle is reduced, and the testing cost and potential safety risks are reduced, thereby improving the accuracy of energy management optimization.

[0073] Next, a test device for predicting energy consumption performance of an extended-range hybrid vehicle proposed in accordance with an embodiment of the present invention will be described with reference to the accompanying drawings.

[0074] Figure 6 It is a block diagram of a testing device for predicting energy consumption performance of an extended-range hybrid vehicle according to an embodiment of the present invention.

[0075] like Figure 6 As shown, the test device 10 for predicting the energy consumption performance of an extended-range hybrid vehicle includes: an identification module 100 , a prediction module 200 , a first generation module 300 and a second generation module 400 .

[0076] Among them, the identification module 100 is used to identify the working condition information of multiple road sections in the navigation information; the prediction module 200 is used to predict the energy consumption of multiple road sections according to the working condition information of multiple road sections; the first generation module 300 is used to generate torque control instructions according to the energy consumption of multiple road sections and the operation information of the vehicle model of the extended-range hybrid vehicle, and use the torque control instructions to control the target test bench to perform simulation experiments, wherein the driver model and the vehicle model on the target test bench respond to the torque control instructions and simulate driving on multiple road sections; the second generation module 400 is used to generate energy consumption performance prediction results of the extended-range hybrid vehicle according to the data of the simulation experiment In an embodiment of the present invention, the multiple road sections include a current road section and a future road section, and the prediction module 200 is further used to: input the working condition information of the future road section into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future road section.

[0077] In the embodiment of the present invention, the test device 10 for predicting energy consumption performance of an extended-range hybrid vehicle according to the embodiment of the present invention further includes: a judgment module.

[0078] Among them, the judgment module is used to predict the energy consumption of multiple road sections according to the operating condition information of multiple road sections. If the current road section is a congested section, the vehicle model of the extended-range hybrid vehicle travels in pure electric mode; if the current road section is a smooth section, the energy consumption of future sections is predicted according to the operating condition information of multiple road sections.

[0079] In an embodiment of the present invention, the first generation module 300 is further used to: adjust the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of the future road section; adjust the working state of the range extender based on the adjusted SOC balance point and operating information; and generate a torque control instruction based on the adjusted SOC balance point and the working state of the range extender.

[0080] In the embodiment of the present invention, the first generation module 300 is further used to: determine the unblocked road section in the future road section that is closest to the current road section as the target road section; predict the supplementary power consumption of the extended-range hybrid vehicle based on the predicted energy consumption of each road section in the future road section and the target road section, wherein the calculation formula of the supplementary power consumption is: , To compensate for the power consumption, is the target section, is the penalty coefficient, is the total number of road sections, ~ is the predicted energy consumption of each section from the current section to the target section, ~ The predicted energy consumption of each section after the target section; the target incremental SOC of the battery power of the extended-range hybrid vehicle is calculated based on the supplementary power consumption, the battery self-discharge efficiency of the extended-range hybrid vehicle and the total energy; and the SOC balance point of the vehicle model of the extended-range hybrid vehicle is adjusted based on the target incremental SOC.

[0081] In the embodiment of the present invention, the operating information includes at least one of the current SOC and the current vehicle speed. The first generating module 300 is further used to: calculate the difference between the current SOC and the adjusted SOC balance point, and select the power generation power of the range extender on the target working curve of the range extender based on the difference and the current vehicle speed; determine the power generation timing of the range extender based on the power generation power, the current SOC and the adjusted SOC balance point.

[0082] It should be noted that the above explanation of the embodiment of the test method for predicting the energy consumption performance of an extended-range hybrid vehicle is also applicable to the test device for predicting the energy consumption performance of an extended-range hybrid vehicle in this embodiment, and will not be repeated here.

[0083] According to the test device for predicting the energy consumption performance of an extended-range hybrid vehicle proposed in an embodiment of the present invention, the energy consumption of multiple road sections can be predicted according to the operating condition information of the multiple road sections in the navigation information, and a torque control instruction can be generated according to the energy consumption of the multiple road sections and the operating information of the vehicle model of the extended-range hybrid vehicle. The torque control instruction is used to control a target test bench to perform a simulation experiment, and then the energy consumption performance prediction result of the extended-range hybrid vehicle is generated according to the data of the simulation experiment, so as to realize the prediction of the vehicle's energy consumption performance, and the target test bench is used to simulate the real driving environment to improve the accuracy of the simulation experiment. There is no need for real vehicle testing, which reduces the cost and risk of real vehicle testing. By simulating various driving conditions, the dependence on the actual vehicle is reduced, and the testing cost and potential safety risks are reduced, thereby improving the accuracy of energy management optimization.

[0084] Figure 7 A schematic diagram of the structure of a host computer provided in an embodiment of the present invention. The host computer may include: A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0085] When the processor 702 executes the program, the test method for predicting the energy consumption performance of the extended-range hybrid vehicle provided in the above embodiment is implemented.

[0086] Furthermore, the host computer also includes: The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0087] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0088] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0089] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0091] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0092] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the above-mentioned test method for predicting energy consumption performance of an extended-range hybrid vehicle is implemented.

[0093] An embodiment of the present invention further provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned test method for predicting energy consumption performance of an extended-range hybrid vehicle.

[0094] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 representations of the above terms do 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 N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0095] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0096] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in 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.

[0097] 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 embodiment, the N 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 or a combination of multiple of the following technologies known in the art: 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.

[0098] 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.

Claims

1. A method for predicting energy consumption performance of an extended-range hybrid vehicle, characterized in that: The following steps are involved: Identify the working condition information of multiple road sections in the navigation information; predicting energy consumption of the plurality of road sections according to the operating condition information of the plurality of road sections; generating a torque control instruction according to the energy consumption of the plurality of road sections and the operation information of the vehicle model of the extended-range hybrid vehicle, and controlling a target test bench to perform a simulation experiment using the torque control instruction, wherein a driver model and a vehicle model on the target test bench respond to the torque control instruction and simulate driving on the plurality of road sections; The energy consumption performance prediction result of the extended-range hybrid vehicle is generated based on the data of the simulation experiment.

2. The method for predicting energy consumption performance of an extended-range hybrid vehicle according to claim 1, characterized in that: The multiple road sections include a current road section and a future road section, and predicting the energy consumption of the multiple road sections according to the working condition information of the multiple road sections includes: The working condition information of the future road section is input into a pre-trained neural network energy consumption prediction model, and the neural network energy consumption prediction model outputs the energy consumption of the future road section.

3. The method for predicting energy consumption performance of an extended-range hybrid vehicle according to claim 2, characterized in that: Before predicting the energy consumption of the multiple road sections according to the working condition information of the multiple road sections, the method further includes: If the current road section is a congested road section, the vehicle model of the extended-range hybrid vehicle travels in a pure electric mode; If the current road section is a smooth road section, the energy consumption of the future road section is predicted according to the working condition information of the multiple road sections.

4. The method for testing the predicted energy consumption performance of an extended-range hybrid vehicle according to claim 2, characterized in that: The generating of the torque control instruction according to the energy consumption of the plurality of road sections and the operation information of the vehicle model of the extended-range hybrid vehicle comprises: Adjusting the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of the future road section; adjusting the operating state of the range extender based on the adjusted SOC balance point and the operating information; A torque control command is generated based on the adjusted SOC balance point and the operating state of the range extender.

5. The method for testing the predicted energy consumption performance of an extended-range hybrid vehicle according to claim 4, characterized in that: The step of adjusting the SOC balance point of the vehicle model of the extended-range hybrid vehicle according to the energy consumption of the plurality of road sections includes: Determine the unblocked road section in the future road section that is closest to the current road section as the target road section; The supplementary power consumption of the extended-range hybrid vehicle is predicted based on the predicted energy consumption of each section in the future section and the target section, wherein the calculation formula of the supplementary power consumption is: , To compensate for the power consumption, is the target section, is the penalty coefficient, is the total number of road sections, ~ is the predicted energy consumption of each section from the current section to the target section, ~ The predicted energy consumption of each road section after the target road section; A target incremental SOC of the battery power of the extended-range hybrid vehicle is calculated based on the supplementary power consumption, the battery self-discharge efficiency and the total energy of the extended-range hybrid vehicle, and an SOC balance point of the vehicle model of the extended-range hybrid vehicle is adjusted based on the target incremental SOC.

6. The method for testing the predicted energy consumption performance of an extended-range hybrid vehicle according to claim 4, characterized in that: The operation information includes at least one of a current SOC and a current vehicle speed, and the adjusting the working state of the range extender based on the adjusted SOC balance point and the operation information includes: Calculating a difference between a current SOC and the adjusted SOC balance point, and selecting a power generation power of the range extender on a target operating curve of the range extender based on the difference and a current vehicle speed; The power generation timing of the range extender is determined based on the power generation power, the current SOC and the adjusted SOC balance point.

7. A test device for predicting energy consumption performance of an extended-range hybrid vehicle, characterized in that: include: An identification module, used to identify working condition information of multiple road sections in navigation information; A prediction module, configured to predict energy consumption of the plurality of road sections according to working condition information of the plurality of road sections; A first generating module is used to generate a torque control instruction according to the energy consumption of the plurality of road sections and the operation information of the vehicle model of the extended-range hybrid vehicle, and use the torque control instruction to control a target test bench to perform a simulation experiment, wherein the driver model and the vehicle model on the target test bench respond to the torque control instruction to simulate driving on the plurality of road sections; The second generation module is used to generate the energy consumption performance prediction result of the extended-range hybrid vehicle according to the data of the simulation experiment.

8. A host computer, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for testing the energy consumption performance of an extended-range hybrid vehicle as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: The computer program or instruction is executed by a processor to implement the test method for predicting energy consumption performance of an extended-range hybrid vehicle as described in any one of claims 1-6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the method for testing the predicted energy consumption performance of an extended-range hybrid vehicle as described in any one of claims 1 to 6 is implemented.

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