Self-learning method and system for the optimal economic curve of series power generation in hybrid vehicles
The self-learning method and system dynamically adjust the engine's operating point to optimize energy consumption in series hybrid vehicles by correcting the optimal economic curve based on real-time fuel consumption rates, addressing deviations in engine and motor efficiencies.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- CHERY AUTOMOBILE CO LTD
- Filing Date
- 2025-04-15
- Publication Date
- 2026-07-07
AI Technical Summary
Conventional series hybrid vehicle models use a fixed optimal economic curve that fails to account for manufacturing variations and environmental influences, leading to suboptimal energy consumption due to deviations in engine and motor efficiency distributions.
A self-learning method and system that adjusts the engine's operating point based on real-time fuel consumption rates, determining the minimum fuel consumption rate to correct the optimal economic curve dynamically.
Ensures optimal energy consumption by continuously adapting the economic curve to actual conditions, mitigating variations in engine and motor efficiencies.
Smart Images
Figure 2026522229000001_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of power control for hybrid vehicle models, and particularly to a self-learning method and system for the optimal economic curve of series power generation in hybrid vehicle models.
Background Art
[0002] Conventional series hybrid vehicle models fit an optimal economic curve fixed based on the universal characteristic curve of the engine and the efficiency distribution of each operating point of the motor during the series power generation process, and use it as the target operating point of the engine at each required power generation power in the series mode. Usually, due to certain deviations and changes occurring in the universal characteristics of the engine and the motor drive efficiency distribution along with manufacturing variations and the influence of the external environment, the fixed optimal economic curve cannot guarantee the optimization of energy consumption.
Summary of the Invention
[0003] The present disclosure , provides a self-learning method and system for the optimal economic curve of series power generation in hybrid vehicle models. The above technical solution is as follows.
[0004] BookThe disclosure provides a self-learning method for the optimal economic curve of a series power generation system in a hybrid vehicle, which is applied to a hybrid vehicle including a generator and an engine under series operating conditions, and includes: increasing a time-varying test torque at a target torque of the engine operating at an operating point in a preset optimal economic curve, so that the engine adjusts to a time-varying test operating point on a target power line based on the final torque; obtaining the corresponding time-varying fuel consumption rate at the time-varying test operating point of the engine; determining the minimum value of the fuel consumption rate at the time-varying fuel consumption rate, further determining the test operating point of the engine corresponding to the minimum value; and performing a 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, wherein 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 rotational speed of the engine at the final torque, and the target power line is the target power generated by the generator, which is the torque change curve of the engine with respect to the rotational speed.
[0005] In one possible implementation, the range of change of the test torque is from a first torque to a second torque, and the first torque is smaller than the second torque.
[0006] In one possible implementation, the method by which the test torque changes over time includes increasing it from 0 to the second torque at a preset rate of change, then decreasing it from the second torque to the first torque at a preset rate of change, and finally increasing it from the first torque to 0 at a preset rate of change.
[0007] In one possible implementation, the first torque is -10 Nm, and the second torque is 10 Nm.
[0008] In one possible implementation, the final torque and the rotational speed of the engine at the final torque satisfy the equation EngSpd = 9550 * P / Tq, where Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque.
[0009] Book The disclosure relates to a self-learning system for the optimal economic curve of series power generation in hybrid vehicles, applicable to hybrid vehicles including a generator and engine under series operating conditions, and comprising a test module, an acquisition module, a decision module and a learning module, wherein the test module is used to increase the time-varying test torque so that the engine adjusts to a time-varying test operating point based on the final torque, at a target torque of the engine operating at the operating point in a preset optimal economic curve, the final torque being the operating torque calculated based on the target torque and the test torque, and the test operating point being the final torque and the final torque of the engine The system includes rotational speed in 100°, the target power line is the target power generated by the generator, and is a curve of the change in engine torque with respect to rotational speed, the acquisition module is used to acquire the corresponding time-varying fuel consumption rate at the time-varying test operating point of the engine, the determination module is used to determine the minimum value of the fuel consumption rate at the time-varying fuel consumption rate and further to determine the test operating point of the engine corresponding to the minimum value, and 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, in order to obtain the corrected optimal economic curve of the hybrid vehicle. Furthermore provide.
[0010] In one possible implementation, the range of change of the test torque is from a first torque to a second torque, and the first torque is smaller than the second torque.
[0011] In one possible implementation, the method by which the test torque changes over time includes increasing it from 0 to the second torque at a preset rate of change, then decreasing it from the second torque to the first torque at a preset rate of change, and finally increasing it from the first torque to 0 at a preset rate of change.
[0012] In one possible implementation, the first torque is -10 Nm, and the second torque is 10 Nm.
[0013] In one possible implementation, the final torque and the rotational speed of the engine at the final torque satisfy the equation EngSpd = 9550 * P / Tq, where Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque. ru.
[0014] above The general descriptions and detailed descriptions below are illustrative and explanatory only and should be understood not to limit this disclosure. [Brief explanation of the drawing]
[0015] To further illustrate the technical concepts in the embodiments of this disclosure, the drawings used in the description of the embodiments are briefly introduced below. Clearly, the drawings in the following description represent only a few embodiments of this disclosure, and those skilled in the art can obtain further drawings based on these without any creative work. [Figure 1] This is a flowchart of the self-learning method for the optimal economic curve of series power generation in hybrid vehicles provided by the embodiments of this disclosure. [Figure 2] This is a schematic diagram of the curve showing how the test torque provided by the embodiments of this disclosure changes over time. [Figure 3] This is a schematic diagram of the correction of a pre-set optimal economic curve provided by the embodiments of this disclosure. [Figure 4]This is a schematic diagram of a self-learning system for the optimal economic curve of series power generation in hybrid vehicles provided by the embodiments of this disclosure. [Modes for carrying out the invention]
[0016] The present disclosure will be described in detail below with reference to the drawings and in conjunction with embodiments. It should be noted that, to the extent that they do not contradict each other, the embodiments and features described herein can be combined with each other.
[0017] The following detailed descriptions are all illustrative and are intended to provide further information regarding this disclosure. Unless otherwise specified, all technical terms used in this disclosure have the same meaning as those commonly understood by those skilled in the art. The terms used in this disclosure are for illustrative purposes only and are not intended to limit the exemplary embodiments provided herein.
[0018] Example 1 Figure 1 is a flowchart of a self-learning method for the optimal economic curve of series power generation in a hybrid vehicle provided by an embodiment of the present disclosure, which is applied to a hybrid vehicle including a generator and engine under series operating conditions. As shown in Figure 1, the method specifically includes the following steps:
[0019] In step S102, the test torque is increased over time to adjust the test operating point, which changes over time, to the target torque of the engine operating at the operating point in a preset optimal economic curve, based on the final torque, where 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 rotational speed of the engine at the final torque, and the target power line is the curve of change of engine torque with rotational speed, where the target power generated by the generator is the target power generated by the generator.
[0020] In implementation, when the engine operates at an operating point on a preset optimal economic curve, the test torque is added to the current target torque, and the final torque is determined based on the test torque and the target torque.
[0021] Since the target power generation power of the generator does not change, based on the final torque, the rotational speed at the final torque is determined on the target equal-power line corresponding to the target power generation power, and then the engine can be controlled to operate at the final torque and the rotational speed at the final torque.
[0022] As can be understood, since the test torque is a torque that changes over time, within a certain period of time after increasing the test torque, all of the final torques change with the change of the test torque, and the rotational speed at the final torque also changes accordingly, thereby obtaining a test operating point that changes over time.
[0023] In step S104, the corresponding fuel consumption rate that changes over time at the test operating point of the engine that changes over time is obtained.
[0024] In implementation, when the engine operates at a test operating point that changes over time, the fuel consumption rate that changes over time is obtained, and the fuel consumption rate that changes over time includes the fuel consumption rates corresponding to each test operating point, that is, different final torques and rotational speeds at the final torque may correspond to different fuel consumption rates.
[0025] In step S106, the minimum value of the fuel consumption rate is determined from the fuel consumption rate that changes over time, and further, the test operating point of the engine corresponding to the minimum value is determined.
[0026] In implementation, among all the fuel consumption rates corresponding to the test operating points, the minimum value thereof is determined, and then the test operating point corresponding to the minimum value is determined, that is, the final torque and rotational speed corresponding to the minimum value are determined.
[0027] In step S108, self-learning correction is performed on the preset optimal economic curve based on the test operating point corresponding to the minimum value, and the corrected optimal economic curve for the hybrid vehicle is obtained.
[0028] In the implementation, after determining the test operating point corresponding to the minimum value, the operating point on the target power line in the pre-set optimal economic curve is corrected to that test operating point, thereby obtaining the corrected optimal economic curve.
[0029] By using the method described above, the current optimal economic curve can be corrected in real time during the vehicle's operation, thereby maintaining the optimal economic curve at all times in the state of optimal energy consumption according to the actual situation.
[0030] In one possible embodiment, in the embodiment of the present disclosure, the range of change of the test torque is from a first torque to a second torque, where the first torque is smaller than the second torque.
[0031] In one possible implementation, a method for changing the test torque over time includes increasing it from 0 to a second torque at a predetermined rate of change, decreasing it from the second torque to a first torque at a predetermined rate of change, and finally increasing it from the first torque to 0 at a predetermined rate of change.
[0032] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.
[0033] Figure 2 is a schematic diagram of the curve over which the test torque provided by the embodiment of this disclosure changes over time. As shown in Figure 2, the range of change in the test torque is between ±10 Nm.
[0034] In implementation, the preset rate of change may be a rate at which the value changes over time throughout the entire time period corresponding to the test torque, or a rate at which the value does not change, and the embodiments of this disclosure are not limited thereto.
[0035] If the preset rate of change is a rate at which the value changes over time, the preset rate of change may include the rate at which the value changes over time. Refer to Figure 2, which shows the rate of change of the test torque, i.e., the rate at which the value changes over time.
[0036] Specifically, when the entire hybrid vehicle is operating stably under series operation conditions, the load on the high and low voltage power supplies of the entire vehicle does not change significantly, the real-time current of the battery is stable, the engine condition is stable, there are no diagnostic or other factors that affect fuel efficiency, and the engine water temperature is stable. In the above scenario, it is ensured that other devices in the hybrid vehicle do not interfere with the self-learning of the optimal economic curve, thereby improving the accuracy of the self-learning of the optimal economic curve.
[0037] The specific operation is as follows: When the engine is operating at the operating point of the original optimal economic curve (i.e., the preset optimal economic curve mentioned above), the torque is slowly increased (approximately ±10 Nm) to the engine's target torque. As shown in Figure 2, the rotational speed for the target power generation is calculated based on the current demand for power generation and the engine's final target torque, thereby ensuring that the generator's power output remains unchanged.
[0038] Specifically, the final torque and engine rotational speed at the final torque satisfy the equation EngSpd = 9550 * P / Tq, where Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque.
[0039] Figure 3 is a schematic diagram of the correction of a preset optimal economic curve provided by an embodiment of this disclosure. As shown in Figure 3, the engine's operating point moves near the original optimal economic curve in accordance with changes in engine torque on the isopower lines. For example, the real-time fuel consumption rate emitted by the engine is searched and compared within the block region of Figure 3, and the engine operating point (engine torque, rotational speed) at the minimum value of the fuel consumption rate is recorded and stored, and this is used as the operating point for the next generation of power for the entire vehicle. After each optimal economic curve for each generation of power has been corrected and learned, the optimal economic curve shown by the solid line in Figure 3 is formed.
[0040] As can be seen from the above description, the embodiments of this disclosure provide a self-learning method for the optimal economic curve of series power generation in hybrid vehicles, and by increasing the test torque to the engine, it is possible to ensure that the operating point of each generated power in the entire vehicle operates on the corrected optimal economic curve, mitigating the problems of variations in engine universal characteristics and motor efficiency distribution, and also mitigating the technical problem that the fixed optimal economic curves existing in related technologies cannot guarantee optimal energy consumption.
[0041] Example 2 Figure 4 is a schematic diagram of a self-learning system for the optimal economic curve of series power generation in a hybrid vehicle provided by an embodiment of the present disclosure. The system is applied to a hybrid vehicle including a generator and engine under series operating conditions. As shown in Figure 4, the system includes a test module 10, an acquisition module 20, a decision module 30, and a learning module 40.
[0042] Specifically, the test module 10 is used to increase the test torque over time so that the engine changes over time to a target power line based on the final torque, at a target torque of the engine operating at the operating point in a preset optimal economic curve. 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 rotational speed of the engine at the final torque, and the target power line is the curve of change of the engine torque with respect to the rotational speed, with respect to the target power generated by the generator.
[0043] The acquisition module 20 is used to acquire the corresponding time-varying fuel consumption rate at the time-varying test operating point of the engine.
[0044] The determination module 30 is used to determine the minimum value of the fuel consumption rate in a time-varying fuel consumption rate, and further to determine the test operating point of the engine corresponding to the minimum value.
[0045] The learning module 40 is used to obtain the corrected optimal economic curve for a hybrid vehicle by performing self-learning corrections on a preset optimal economic curve based on the test operating point corresponding to the minimum value.
[0046] In one possible implementation, the range of change in the test torque is from a first torque to a second torque, where the first torque is smaller than the second torque.
[0047] In one possible implementation, a method for changing the test torque over time includes increasing it from 0 to a second torque at a predetermined rate of change, decreasing it from the second torque to a first torque at a predetermined rate of change, and finally increasing it from the first torque to 0 at a predetermined rate of change.
[0048] In one possible implementation, the first torque is -10 Nm and the second torque is 10 Nm.
[0049] In one possible implementation, the final torque and the engine's rotational speed at the final torque satisfy the equation EngSpd = 9550 * P / Tq, where Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque.
[0050] As is common technical knowledge, this disclosure can be implemented by other embodiments that do not deviate from its spiritual substance or essential features. Accordingly, the embodiments disclosed above are all illustrative and not unique in all aspects. All modifications within the scope of this disclosure or equivalent scope are included in this disclosure.
[0051] Those skilled in the art will understand that embodiments of the Disclosure can be provided as methods, systems, or computer program products. Accordingly, the Disclosure may take the form of complete hardware embodiments, complete software embodiments, or embodiments of a combination of software and hardware. The Disclosure may also take the form of one or more computer program products implemented on a computer-compatible storage medium (including, but not limited to, magnetic disk memory, CD-ROM, optical memory, etc.) containing computer-compatible program code.
[0052] This disclosure will be described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that computer program instructions can realize each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a dedicated computer, an embedded processor, or other programmable data processing device to generate a device in which the instructions executed by the processor of the computer or other programmable data processing device generate a device for realizing one or more flows in a flowchart and / or one or more blocks in a block diagram.
[0053] These computer program instructions may be stored in computer-readable memory that can operate a computer or other programmable data processing device in a particular way, thereby generating a product that includes an instruction unit that implements the functions specified in one or more flows of a flowchart and / or one or more blocks of a block diagram.
[0054] These computer program instructions may be loaded into a computer or other programmable data processing device, thereby executing a series of operational steps on the computer or other programmable device to generate processing to be implemented by the computer, and the instructions executed on the computer or other programmable device thereby provide steps to implement a function specified in one or more flows of a flowchart and / or one or more blocks of a block diagram.
[0055] Finally, it should be noted that the above embodiments are used solely to illustrate the technical concepts of the Disclosure and are not intended to limit them. While the 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 specific embodiments of the Disclosure, and any modifications or equivalent substitutions that do not deviate from the spirit and scope of the Disclosure should be within the scope of protection provided by the claims of the Disclosure.
[0056] This disclosure claims priority to the Chinese patent application filed on April 23, 2024, with application number 202410493246.0 and title "Self-learning method and system for the optimal economic curve of series power generation for hybrid vehicle," all of which are incorporated herein by reference.
Claims
1. A self-learning method for the optimal economic curve of series power generation in hybrid vehicles, applicable to hybrid vehicles including a generator and engine under series operation conditions. In a target torque of an engine operating at the operating point in a preset optimal economic curve, the test torque is increased over time so that the engine adjusts to a test operating point that changes over time to the target power line based on the final torque. To obtain the corresponding time-varying fuel consumption rate at the time-varying test operating point of the engine, In the aforementioned fuel consumption rate that changes over time, the minimum value of the fuel consumption rate is determined, and further, the test operating point of the engine corresponding to the minimum value is determined. This includes performing self-learning corrections on the preset optimal economic curve based on the test operating point corresponding to the minimum value, and obtaining the corrected optimal economic curve for the hybrid vehicle. The final torque is an operating torque calculated based on the target torque and the test torque, the test operating point includes the final torque and the rotational speed of the engine at the final torque, and the target power line is the target power generated by the generator and is a curve of change in the engine's torque with respect to rotational speed. method.
2. The range of change of the test torque is from a first torque to a second torque, and the first torque is smaller than the second torque. The method according to claim 1.
3. The method for changing the test torque over time is characterized by increasing the torque from 0 to the second torque at a preset rate of change, decreasing it from the second torque to the first torque at a preset rate of change, and finally increasing it from the first torque to 0 at a preset rate of change. The method according to claim 2.
4. The first torque is -10 Nm, and the second torque is 10 Nm. The method according to claim 2 or 3.
5. The aforementioned final torque and the engine are such that the rotational speed at the aforementioned final torque is The equation EngSpd = 9550 * P / Tq is satisfied, Here, Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque. The method according to claim 1.
6. A self-learning system for the optimal economic curve of series power generation in hybrid vehicles, applicable to hybrid vehicles including a generator and engine under series operating conditions, and comprising a test module, acquisition module, decision module and learning module. The test module is used to increase the time-varying test torque so that the engine adjusts to a time-varying test operating point on a target power line based on the final torque, at a target torque of the engine operating at the operating point in a preset optimal economic curve. The acquisition module is used to acquire the corresponding time-varying fuel consumption rate at the time-varying test operating point of the engine. The determination module is used to determine the minimum value of the fuel consumption rate in the time-varying fuel consumption rate, and further to determine the test operating point of the engine corresponding to the minimum value. The learning module is used to perform self-learning corrections on the preset optimal economic curve based on the test operating point corresponding to the minimum value, in order to obtain the corrected optimal economic curve of the hybrid vehicle. The final torque is an operating torque calculated based on the target torque and the test torque, the test operating point includes the final torque and the rotational speed of the engine at the final torque, and the target power line is the target power generated by the generator and is a curve of change in the engine's torque with respect to rotational speed. system.
7. The range of change of the test torque is from a first torque to a second torque, and the first torque is smaller than the second torque. The system according to claim 6.
8. The method for changing the test torque over time is characterized by increasing the torque from 0 to the second torque at a preset rate of change, decreasing it from the second torque to the first torque at a preset rate of change, and finally increasing it from the first torque to 0 at a preset rate of change. The system according to claim 7.
9. The first torque is -10 Nm, and the second torque is 10 Nm. The system according to claim 7 or 8.
10. The aforementioned final torque and the engine are such that the rotational speed at the aforementioned final torque is The equation EngSpd = 9550 * P / Tq is satisfied, Here, Tq is the final torque, P is the target power generation, and EngSpd is the rotational speed of the engine at the final torque. The system according to claim 6.