Self-learning method and system for optimal economic curve of series power generation for hybrid vehicle model

Abstract

The present disclosure relates to the technical field of hybrid vehicle model power control, and provides a self-learning method and system for the optimal economic curve of series power generation for a hybrid vehicle model. The method comprises: adding a time-varying test torque to a target torque of an engine operating at an operating point on a preset optimal economic curve, so that the engine adjusts, on the basis of a final torque, a time-varying test operating point on a target equal-power line; acquiring a time-varying fuel consumption rate corresponding to the engine at the time-varying test operating point; determining a minimum value of the fuel consumption rate from the time-varying fuel consumption rate, and determining a test operating point of the engine corresponding to the minimum value; and on the basis of the test operating point corresponding to the minimum value, performing a self-learning correction on the preset optimal economic curve, so as to obtain a corrected optimal economic curve for a hybrid vehicle. The present disclosure mitigates the technical problem in the related art of the inability of a fixed optimal economic curve to guarantee optimal energy consumption.

Description

Self-learning method and system for optimal economic curve of series power generation in hybrid electric vehicles

[0001] This disclosure claims priority to Chinese Patent Application No. 202410493246.0, filed on April 23, 2024, entitled “Self-learning method and system for optimal economic curve of series power generation of hybrid vehicle,” the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of power control for hybrid electric vehicles, and in particular to a self-learning method and system for the optimal economic curve of series power generation in hybrid electric vehicles. Background Technology

[0003] Existing series hybrid electric vehicles use a fixed optimal economic curve fitted to the universal characteristic curve of the engine and the efficiency distribution of the motor at various operating points during the series power generation process. This curve serves as the target operating point for the engine under various power generation demands in series mode. However, the universal characteristics of the engine and the efficiency distribution of the motor drive are usually subject to deviations and changes due to manufacturing variations and external environmental influences. Therefore, a fixed optimal economic curve cannot guarantee optimal energy consumption. Summary of the Invention

[0004] This disclosure provides a self-learning method and system for the optimal economic curve of series power generation in hybrid electric vehicles, which can solve the technical problems existing in related technologies. The technical solution is as follows:

[0005] In a first aspect, this disclosure provides a self-learning method for the optimal economic curve of a hybrid vehicle's series power generation, applied to a hybrid vehicle; the hybrid vehicle includes a generator and an engine operating in series; the method includes: adding a time-varying test torque to the target torque of the engine operating at a preset optimal economic curve, so that the engine adjusts its time-varying test operating point on a target isopower line based on the final torque; the final torque is the operating torque calculated based on the target torque and the test torque; the test operating point includes the final torque and the engine speed at the final torque; the target isopower line is the curve of the engine's torque versus speed when the generator generates a target power; obtaining the time-varying fuel consumption rate of the engine at the time-varying test operating point; determining the minimum value of the fuel consumption rate from the time-varying fuel consumption rates, and determining the test operating point of the engine corresponding to the minimum value; and performing self-learning correction on the preset optimal economic curve based on the test operating point corresponding to the minimum value to obtain the corrected optimal economic curve of the hybrid vehicle.

[0006] In one possible implementation, the test torque varies between a first torque and a second torque; the first torque is less than the second torque.

[0007] In one possible implementation, the test torque changes over time in the following manner: increasing from 0 to the second torque at a preset rate of change, then decreasing from the second torque to the first torque at the preset rate of change, and finally increasing from the first torque back to 0 at the preset rate of change.

[0008] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.

[0009] In one possible implementation, the final torque and the engine speed at the final torque satisfy the following formula:

[0010] EngSpd = 9550 * P / Tq; where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

[0011] Secondly, this disclosure provides a self-learning system for the optimal economic curve of series power generation in a hybrid vehicle, applied to a hybrid vehicle; the hybrid vehicle includes a generator and an engine operating in series; it includes: a testing module, an acquisition module, a determination module, and a learning module; wherein, the testing module is used to add a time-varying test torque to the target torque of the engine operating at a preset optimal economic curve operating point, so that the engine adjusts its time-varying test operating point on the target isopower line based on the final torque; the final torque is the operating torque calculated based on the target torque and the test torque; the test operating point includes the final torque and the time-varying test torque. The engine speed at the final torque is described; the target isopower line is the curve showing the change in engine torque with engine speed when the generator is generating power at the target power; the acquisition module is used to acquire the time-varying fuel consumption rate of the engine at the time-varying test operating point; the determination module is used to determine the minimum value of the time-varying fuel consumption rate and the test operating point of the engine corresponding to the minimum value; the learning module is used to perform self-learning correction on the preset optimal economic curve based on the test operating point corresponding to the minimum value to obtain the corrected optimal economic curve of the hybrid vehicle.

[0012] In one possible implementation, the test torque varies between a first torque and a second torque; the first torque is less than the second torque.

[0013] In one possible implementation, the test torque changes over time in the following manner: increasing from 0 to the second torque at a preset rate of change, then decreasing from the second torque to the first torque at the preset rate of change, and finally increasing from the first torque back to 0 at the preset rate of change.

[0014] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.

[0015] In one possible implementation, the final torque and the engine speed at the final torque satisfy the following formula:

[0016] EngSpd = 9550 * P / Tq; where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

[0017] The beneficial effects of the technical solution provided in this disclosure include at least the following:

[0018] This disclosure provides a self-learning method and system for the optimal economic curve of series power generation in hybrid electric vehicles. By increasing the test torque of the engine, the vehicle can operate on the corrected optimal economic curve at various power generation levels. This alleviates the problems of engine universal characteristic dispersion and motor efficiency distribution dispersion, as well as the technical problem that a fixed optimal economic curve cannot guarantee optimal energy consumption in related technologies.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 is a flowchart of a self-learning method for the optimal economic curve of series power generation in a hybrid vehicle provided in an embodiment of this disclosure;

[0022] Figure 2 is a schematic diagram of the curve of test torque changing over time according to an embodiment of this disclosure;

[0023] Figure 3 is a schematic diagram of a modified preset optimal economic curve provided by an embodiment of this disclosure;

[0024] Figure 4 is a schematic diagram of a self-learning system for the optimal economic curve of series power generation of a hybrid vehicle provided in an embodiment of this disclosure. Detailed Implementation

[0025] The present disclosure will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0026] The following detailed description is exemplary and intended to provide further detailed explanation of this disclosure. Unless otherwise specified, all technical terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure.

[0027] Example 1

[0028] Figure 1 is a flowchart of a self-learning method for the optimal economic curve of series power generation in a hybrid electric vehicle according to an embodiment of the present disclosure. This method is applied to a hybrid electric vehicle; wherein the hybrid electric vehicle includes a generator and an engine operating in series. As shown in Figure 1, the method specifically includes the following steps:

[0029] Step S102: At the target torque of the engine operating at the preset optimal economic curve, a test torque that varies with time is added, so that the engine adjusts the test operating point that varies with time on the target isopower line based on the final torque; the final torque is the operating torque calculated based on the target torque and the test torque; the test operating point includes the final torque and the engine speed at the final torque; the target isopower line is the curve of the engine torque changing with the speed when the generator is generating power at the target power.

[0030] In practice, when the engine is operating at the operating point on the preset optimal economic curve, the test torque is increased on the current target torque, and the final torque is determined based on the test torque and the target torque.

[0031] Since the generator's target power output remains constant, the engine speed at the final torque can be determined based on the target isoelectric line corresponding to the target power output, and then the engine can be controlled to operate at the final torque and the engine speed at the final torque.

[0032] It is understandable that the test torque is a torque that changes over time. Therefore, after the test torque is increased, the final torque will change with the change of the test torque over a period of time, and the speed at the final torque will also change accordingly, thus obtaining the test operating point that changes over time.

[0033] Step S104: Obtain the time-varying fuel consumption rate of the engine at the test operating point that varies with time.

[0034] In practice, when the engine operates at test operating points that vary over time, the fuel consumption rate that varies over time is obtained. This fuel consumption rate that varies over time includes the fuel consumption rate corresponding to each test operating point, that is, different final torques and speeds at the final torques may correspond to different fuel consumption rates.

[0035] Step S106: Determine the minimum value of the fuel consumption rate from the fuel consumption rate that changes over time, and determine the test operating point of the engine corresponding to the minimum value.

[0036] In practice, among all fuel consumption rates corresponding to the test operating point, the minimum value is determined, and then the test operating point corresponding to the minimum value is determined, that is, the final torque and speed corresponding to the minimum value are determined.

[0037] Step S108: Based on the test operating point corresponding to the minimum value, perform self-learning correction on the preset optimal economic curve to obtain the corrected optimal economic curve for the hybrid vehicle.

[0038] In practice, after determining the test operating point corresponding to the minimum value, the operating point at the target isopower line in the preset optimal economic curve can be corrected to the test operating point, thereby obtaining the corrected optimal economic curve.

[0039] By using the above method, the current optimal economic curve is adjusted in real time during vehicle operation, so that the optimal economic curve can always be kept in the state of optimal energy consumption according to the actual situation.

[0040] In one possible implementation, in this embodiment of the disclosure, the range of variation of the test torque is between a first torque and a second torque; the first torque is less than the second torque.

[0041] In one possible implementation, the test torque changes over time by: increasing from 0 to a second torque at a preset rate of change, then decreasing from the second torque to a first torque at a preset rate of change, and finally increasing from the first torque back to 0 at a preset rate of change.

[0042] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.

[0043] Figure 2 is a schematic diagram of the curve of test torque changing over time according to an embodiment of the present disclosure. As shown in Figure 2, the range of test torque variation is between ±10 Nm.

[0044] In practice, the preset rate of change can be a rate at which the value changes over time or a rate at which the value remains constant throughout the entire time period corresponding to the test torque. This disclosure does not limit this.

[0045] When the preset rate of change is the rate at which a value changes over time, the preset rate of change can include the rate of change over time. See Figure 2, which shows the rate of change of the test torque, i.e., the rate of change over time.

[0046] Specifically, when the hybrid vehicle operates stably under series conditions, the load on the high and low voltage power supplies remains relatively constant, the battery current is stable in real time, the engine condition is stable without any diagnostic or other factors affecting fuel economy, and the engine coolant temperature is stable. Under these conditions, it is ensured that other devices in the hybrid vehicle will not interfere with the self-learning of the optimal fuel economy curve, thereby improving the accuracy of the self-learning process.

[0047] Specific operation: The engine operates at the operating point on the original optimal economic curve (i.e. the preset optimal economic curve mentioned above). At this time, a slowly changing torque (approximately ±10 Nm) is added to the engine's target torque, as shown in Figure 2. The target power generation speed is calculated based on the current demand for power generation and the engine's final target torque, thereby ensuring that the generator's power generation remains constant.

[0048] Specifically, the final torque and the engine speed at the final torque satisfy the following formula:

[0049] EngSpd = 9550 * P / Tq;

[0050] Where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

[0051] Figure 3 is a schematic diagram illustrating the correction of a preset optimal economic curve according to an embodiment of this disclosure. As shown in Figure 3, the engine's operating point moves near the original optimal economic curve along the isopower line as the engine's torque increases or decreases. Within the boxed area of ​​Figure 3, the real-time fuel consumption rate emitted by the engine is searched and compared, and the engine operating point (engine torque and speed) at the point of minimum fuel consumption rate is recorded and stored as the next operating point of the entire vehicle under that power generation. After the optimal economic curves under each power generation are corrected and learned, the optimal economic curve shown by the solid line in Figure 3 is formed.

[0052] As described above, the present invention provides a self-learning method for the optimal economic curve of series power generation in hybrid electric vehicles. By increasing the test torque of the engine, the vehicle can operate on the corrected optimal economic curve at various power generation levels. This alleviates the problems of engine universal characteristic dispersion and motor efficiency distribution dispersion, as well as the technical problem in related technologies that a fixed optimal economic curve cannot guarantee optimal energy consumption.

[0053] Example 2

[0054] Figure 4 is a schematic diagram of a self-learning system for the optimal economic curve of series power generation in a hybrid vehicle according to an embodiment of the present disclosure. The system is applied to a hybrid vehicle; wherein the hybrid vehicle includes a generator and an engine operating in series. As shown in Figure 4, the system includes: a test module 10, an acquisition module 20, a determination module 30, and a learning module 40.

[0055] Specifically, the test module 10 is used to add a time-varying test torque to the target torque of the engine operating at the operating point on the preset optimal economic curve, so that the engine adjusts the time-varying test operating point on the target isopower line based on the final torque; the final torque is the operating torque calculated based on the target torque and the test torque; the test operating point includes the final torque and the engine speed at the final torque; the target isopower line is the curve of the engine torque changing with the speed when the generator is generating power at the target power.

[0056] The acquisition module 20 is used to acquire the time-varying fuel consumption rate of the engine at the test operating point that varies over time.

[0057] The determination module 30 is used to determine the minimum value of the fuel consumption rate in the time-varying fuel consumption rate, and to determine the test operating point of the engine corresponding to the minimum value.

[0058] Learning module 40 is used to perform self-learning correction on the preset optimal economic curve based on the test operating point corresponding to the minimum value, so as to obtain the corrected optimal economic curve of the hybrid vehicle.

[0059] In one possible implementation, the test torque varies between a first torque and a second torque; the first torque is less than the second torque.

[0060] In one possible implementation, the test torque changes over time by: increasing from 0 to a second torque at a preset rate of change, then decreasing from the second torque to a first torque at a preset rate of change, and finally increasing from the first torque back to 0 at a preset rate of change.

[0061] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.

[0062] In one possible implementation, the final torque and the engine speed at the final torque satisfy the following formula:

[0063] EngSpd = 9550 * P / Tq;

[0064] Where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

[0065] As is known from common technical knowledge, this disclosure can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the above-disclosed embodiments are merely illustrative in all respects and are not the only ones. All changes within the scope of this disclosure or equivalent to this disclosure are included in this disclosure.

[0066] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit them. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this disclosure. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this disclosure should be covered within the protection scope of the claims of this disclosure.

Claims

1. A self-learning method for the optimal economic curve of series power generation in hybrid electric vehicles, characterized in that, Applied to hybrid vehicles; the hybrid vehicle includes a generator and an engine operating in series; including: At the target torque of an engine operating at its preset optimal economic curve, a time-varying test torque is added to adjust the engine's time-varying test operating point on the target isopower line based on the final torque. The final torque is the operating torque calculated based on the target torque and the test torque. The test operating point includes the final torque and the engine speed at the final torque. The target isopower line is the curve showing the engine's torque versus speed when the generator is generating power at the target output. Obtain the time-varying fuel consumption rate of the engine at the time-varying test operating point; Among the fuel consumption rates that vary over time, a minimum value of the fuel consumption rate is determined, and the test operating point of the engine corresponding to the minimum value is determined; Based on the test operating point corresponding to the minimum value, the preset optimal economic curve is self-learned and corrected to obtain the corrected optimal economic curve of the hybrid vehicle.

2. The method according to claim 1, characterized in that, The range of the test torque is between a first torque and a second torque; the first torque is less than the second torque.

3. The method according to claim 2, characterized in that, The test torque changes over time in the following manner: increasing from 0 to the second torque at a preset rate of change, then decreasing from the second torque to the first torque at the preset rate of change, and finally increasing from the first torque back to 0 at the preset rate of change.

4. The method according to claim 2 or 3, characterized in that, The first torque is -10 Nm, and the second torque is 10 Nm.

5. The method according to claim 1, wherein The final torque and the engine speed at the final torque satisfy the following formula: EngSpd=9550*P / Tq; Where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

6. A self-learning system for the optimal economic curve of series power generation in hybrid electric vehicles, characterized in that, Applied to hybrid vehicles; the hybrid vehicle includes a generator and an engine operating in series; comprising: a testing module, an acquisition module, a determination module, and a learning module; wherein, The testing module is used to add a time-varying test torque to the target torque of the engine operating at its operating point on a preset optimal economic curve, so that the engine adjusts its time-varying test operating point on the target isopower line based on the final torque; the final torque is the operating torque calculated based on the target torque and the test torque; the test operating point includes the final torque and the engine speed at the final torque; the target isopower line is the curve showing the change of the engine torque with speed when the generator is generating power at the target output power. The acquisition module is used to acquire the time-varying fuel consumption rate of the engine at the time-varying test operating point. The determining module is used to determine the minimum value of the fuel consumption rate among the fuel consumption rates that change over time, and to determine the test operating point of the engine corresponding to the minimum value. The learning module is used to perform self-learning correction on the preset optimal economic curve based on the test operating point corresponding to the minimum value, so as to obtain the corrected optimal economic curve of the hybrid vehicle.

7. The system according to claim 6, characterized in that, The range of the test torque is between a first torque and a second torque; the first torque is less than the second torque.

8. The system according to claim 7, characterized in that, The test torque changes over time in the following manner: increasing from 0 to the second torque at a preset rate of change, then decreasing from the second torque to the first torque at the preset rate of change, and finally increasing from the first torque back to 0 at the preset rate of change.

9. The system according to claim 7 or 8, characterized in that, The first torque is -10 Nm, and the second torque is 10 Nm.

10. The system according to claim 6, characterized in that, The final torque and the engine speed at the final torque satisfy the following formula: EngSpd=9550*P / Tq; Where Tq is the final torque, P is the target power generation, and EngSpd is the engine speed at the final torque.

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