Method and device for evaluating energy saving effect of vehicle predictive energy management system

Abstract

This application relates to the field of vehicle energy-saving testing technology, and in particular to a method and apparatus for evaluating the energy-saving effect of a vehicle predictive energy management system (PEM). The method includes: determining the main test condition and secondary test condition of the PEM; collecting energy-saving effect values ​​of the vehicle under the main and secondary test conditions; validating the dataset of energy-saving effect values ​​composed of these values; and calculating the final energy-saving effect of the PEM based on the validity test results. This solves the problems of current methods for evaluating the energy-saving effect of PEMs, such as the susceptibility to targeted calibration under purely fixed conditions, large dispersion of test results under purely random conditions, a disconnect between test conditions and actual PEM usage scenarios, low reliability of energy-saving effect evaluation due to interdependence in data validity testing, and high testing costs.

Description

Technical Field

[0001] This application relates to the field of vehicle energy-saving testing technology, and in particular to a method and apparatus for evaluating the energy-saving effect of a vehicle predictive energy management system. Background Technology

[0002] With the rapid development of intelligent connected vehicle technology, Predictive Energy Management (PEM) has become an important direction for automotive energy-saving technology. PEM systems optimize vehicle energy management strategies by predicting map information (slope, curvature, speed limits, etc.) and traffic information (congestion index, traffic light phases, etc.) of the road ahead, thereby achieving overall vehicle energy savings.

[0003] Currently, the testing and evaluation methods for the energy-saving effect of PEM systems have several problems, including: purely fixed operating conditions are easily calibrated for specific purposes; purely random operating conditions result in large dispersion of test results; the test conditions are out of touch with the actual use scenarios of PEM; data validity verification is interdependent, leading to low reliability of energy-saving effect evaluation and high testing costs. Summary of the Invention

[0004] This application provides a method and apparatus for evaluating the energy-saving effect of a vehicle predictive energy management system (PEM), in order to solve the problems of current methods for evaluating the energy-saving effect of PEM, such as the ease of targeted calibration under purely fixed operating conditions, large dispersion of test results under purely random operating conditions, disconnect between test conditions and actual PEM usage scenarios, interdependence of data validity verification leading to low reliability of energy-saving effect evaluation, and high testing costs.

[0005] This application provides a method for evaluating the energy-saving effect of a vehicle predictive energy management system, comprising the following steps: determining the main test condition and the secondary test condition of the vehicle predictive energy management system; collecting energy-saving effect values ​​of the vehicle under the main test condition and the secondary test condition, and performing a validity check on the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the secondary test condition; and calculating the final energy-saving effect of the vehicle predictive energy management system based on the validity check results of the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the secondary test condition.

[0006] Optionally, the main test conditions include a first test condition in which the vehicle predictive energy management system is inactive and a second test condition in which it is active, and the secondary test conditions include a third test condition in which the vehicle predictive energy management system is inactive and a fourth test condition in which it is active.

[0007] Optionally, collecting the energy-saving effect values ​​of the vehicle under the main test condition and the sub-test condition includes: controlling the vehicle to perform the first test condition twice to obtain a first energy consumption value and a second energy consumption value; controlling the vehicle to perform the second test condition twice to obtain a third energy consumption value and a fourth energy consumption value; controlling the vehicle to perform the third test condition twice to obtain a fifth energy consumption value and a sixth energy consumption value; controlling the vehicle to perform the fourth test condition twice to obtain a seventh energy consumption value and an eighth energy consumption value; obtaining the first energy-saving effect value and the second energy-saving effect value under the main test condition based on the first energy consumption value, the second energy consumption value, the third energy consumption value, and the fourth energy consumption value; and obtaining the third energy-saving effect value and the fourth energy-saving effect value under the sub-test condition based on the fifth energy consumption value, the sixth energy consumption value, the seventh energy consumption value, and the eighth energy consumption value.

[0008] Optionally, the validity of the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the secondary test condition is verified, including: verifying the validity of the energy-saving effect value dataset composed of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value; calculating a first average value of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculating a standard deviation based on the first average value; determining the maximum value and the minimum value among the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculating the difference between the maximum value and the minimum value; and determining the effective energy-saving effect value based on the difference and the standard deviation.

[0009] Optionally, when determining the effective energy-saving effect value based on the difference and the standard deviation, the method includes: if the difference is less than or equal to the standard deviation, then the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all determined to be effective energy-saving effect values; if the difference is greater than the standard deviation, then the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, are selected as the effective energy-saving effect value, or the effectiveness is verified again after retesting the main test condition and the sub-test condition.

[0010] Optionally, the final energy-saving effect of the vehicle predictive energy management system is calculated based on the validity test results of the energy-saving effect value dataset composed of the energy-saving effect values ​​of the vehicle under the main test condition and the auxiliary test condition. This includes: if the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all valid energy-saving effect values, then the final energy-saving effect of the vehicle predictive energy management system is calculated based on the valid energy-saving effect values ​​using a preset weighted calculation method; if the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value is used as the valid energy-saving effect value, then the final energy-saving effect value of the vehicle predictive energy management system is calculated based on the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, using a preset weighted calculation method.

[0011] Optionally, based on the effective energy-saving effect value, the final energy-saving effect of the vehicle predictive energy management system is calculated using a preset weighted calculation method, including: calculating the second average value of the first energy-saving effect value and the second energy-saving effect value; calculating the third average value of the third energy-saving effect value and the fourth energy-saving effect value; calculating the first product of the second average value and the corresponding first weight value; calculating the second product of the third average value and the corresponding second weight value; calculating the comprehensive energy-saving effect of the vehicle predictive energy management system based on the first product value and the second product value; and calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0012] Optionally, the final energy-saving effect value of the vehicle predictive energy management system is calculated using a preset weighted calculation method based on the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value. This includes: calculating the third product of the smaller of the first energy-saving effect value and the second energy-saving effect value with the corresponding first weight value; calculating the fourth product of the smaller of the third energy-saving effect value and the fourth energy-saving effect value with the corresponding second weight value; calculating the comprehensive energy-saving effect of the vehicle predictive energy management system based on the third product value and the fourth product value; and calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0013] Optionally, before calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect, the method includes: determining the current configuration level of the vehicle predictive energy management system; and determining the pre-configured user usage frequency coefficient based on the current configuration level of the vehicle predictive energy management system.

[0014] This application embodiment also provides an energy-saving effect evaluation device for a vehicle predictive energy management system, comprising: a working condition generation module for determining a main test working condition and a secondary test working condition for the vehicle predictive energy management system; a validity verification module for collecting energy-saving effect values ​​of the vehicle under the main test working condition and the secondary test working condition, and performing validity verification on the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test working condition and the secondary test working condition; and an energy-saving effect evaluation module for calculating the final energy-saving effect of the vehicle predictive energy management system based on the validity verification results of the energy-saving effect value dataset composed of the energy-saving effect values ​​of the vehicle under the main test working condition and the secondary test working condition.

[0015] Optionally, the main test conditions include a first test condition in which the vehicle predictive energy management system is inactive and a second test condition in which it is active, and the secondary test conditions include a third test condition in which the vehicle predictive energy management system is inactive and a fourth test condition in which it is active.

[0016] Optionally, the validity verification module is configured to: control the vehicle to perform the first test condition twice to obtain a first energy consumption value and a second energy consumption value; control the vehicle to perform the second test condition twice to obtain a third energy consumption value and a fourth energy consumption value; control the vehicle to perform the third test condition twice to obtain a fifth energy consumption value and a sixth energy consumption value; control the vehicle to perform the fourth test condition twice to obtain a seventh energy consumption value and an eighth energy consumption value; obtain a first energy-saving effect value and a second energy-saving effect value under the main test condition based on the first energy consumption value, the second energy consumption value, the third energy consumption value, and the fourth energy consumption value; and obtain a third energy-saving effect value and a fourth energy-saving effect value under the sub-test condition based on the fifth energy consumption value, the sixth energy consumption value, the seventh energy consumption value, and the eighth energy consumption value.

[0017] Optionally, the validity verification module is further configured to: perform validity verification based on the energy-saving effect value dataset composed of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value; calculate a first average value of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculate a standard deviation based on the first average value; determine the maximum value and the minimum value among the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculate the difference between the maximum value and the minimum value; and determine the effective energy-saving effect value based on the difference and the standard deviation.

[0018] Optionally, the validity verification module is further configured to: if the difference is less than or equal to the standard deviation, determine that the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all valid energy-saving effect values; if the difference is greater than the standard deviation, select the smaller value among the first energy-saving effect value and the second energy-saving effect value, select the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, and use the smaller value among the first energy-saving effect value and the second energy-saving effect value, and the smaller value among the third energy-saving effect value and the fourth energy-saving effect value as the valid energy-saving effect value, or retest the main test condition and the sub-test condition and perform the validity verification again.

[0019] Optionally, the energy-saving effect evaluation module is configured to: calculate the final energy-saving effect of the vehicle predictive energy management system based on the effective energy-saving effect values ​​using a preset weighted calculation method when the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all effective energy-saving effect values; and calculate the final energy-saving effect value of the vehicle predictive energy management system based on the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value, using a preset weighted calculation method when the effective energy-saving effect value is the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value.

[0020] Optionally, the energy-saving effect evaluation module is further configured to: calculate a second average of the first energy-saving effect value and the second energy-saving effect value; calculate a third average of the third energy-saving effect value and the fourth energy-saving effect value; calculate a first product of the second average value and the corresponding first weight value; calculate a second product of the third average value and the corresponding second weight value; calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the first product value and the second product value; and calculate the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0021] Optionally, the energy-saving effect evaluation module is further configured to: calculate the third product of the smaller of the first energy-saving effect value and the second energy-saving effect value with the corresponding first weight value; calculate the fourth product of the smaller of the third energy-saving effect value and the fourth energy-saving effect value with the corresponding second weight value; calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the third product value and the fourth product value; and calculate the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0022] Optionally, before calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect, the energy-saving effect evaluation module is further configured to: determine the current configuration level of the vehicle predictive energy management system; and determine the pre-configured user usage frequency coefficient based on the current configuration level of the vehicle predictive energy management system.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for evaluating the energy-saving effect of a vehicle predictive energy management system according to an embodiment of this application; Figure 2 This is a flowchart of an energy-saving effect evaluation method for a vehicle predictive energy management system according to a specific embodiment of this application; Figure 3 This is an example diagram of an energy-saving effect evaluation device for a vehicle predictive energy management system according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following description, with reference to the accompanying drawings, describes a method and apparatus for evaluating the energy-saving effect of a vehicle predictive energy management system (PEM) according to embodiments of this application. Addressing the problems mentioned in the background art regarding current testing and evaluation methods for the energy-saving effect of PEMs (Predictive Energy Management Systems), such as the ease of targeted calibration under purely fixed operating conditions, large dispersion of test results under purely random operating conditions, disconnect between test conditions and actual PEM usage scenarios, interdependence in data validity checks leading to low reliability of energy-saving effect evaluation, and high testing costs, this application provides a method for evaluating the energy-saving effect of a PEM. In this method, a primary test condition and a secondary test condition for the PEM are determined; energy-saving effect values ​​of the vehicle under the primary and secondary test conditions are collected, and the validity of the energy-saving effect value dataset composed of these values ​​is verified; the final energy-saving effect of the PEM is calculated based on the validity verification results of the energy-saving effect value dataset. This solves the problems of current testing and evaluation methods for the energy-saving effect of vehicle predictive energy management systems, such as the ease of targeted calibration under purely fixed operating conditions, large dispersion of test results under purely random operating conditions, disconnect between test conditions and actual PEM usage scenarios, low reliability of energy-saving effect evaluation due to interdependence of data validity verification, and high testing costs. It ensures that the evaluation results reflect the true energy-saving capability of the system, ensures the stability and reliability of test evaluation results, and significantly reduces testing costs.

[0027] Currently, the main methods for testing and evaluating the energy-saving performance of PEM systems include the following categories: (1) Actual road testing method The energy-saving performance test of the PEM system was primarily conducted on actual roads. The vehicle accessed the map API (Application Programming Interface) based on its current latitude and longitude geographic location information to obtain road information ahead and activate the predictive energy management strategy. The test evaluated the energy-saving effect by comparing energy consumption differences when the PEM system was on and off.

[0028] (2) Indoor drum test method based on GNSS (Global Navigation Satellite System) signal simulation Existing technologies disclose a scheme for achieving standardized indoor testing by simulating geographic location signals using GNSS signal simulation equipment and loading a vehicle speed curve containing a map of the road ahead and traffic information onto a drum test rig. This scheme replaces real GPS signals with simulated geographic location signals, enabling vehicles to still acquire navigation information during indoor drum tests, thereby activating the PEM system.

[0029] (3) Evaluation method based on random combination of standard working conditions Existing technologies also disclose a scheme for acquiring multiple target standard operating conditions (urban areas, suburbs, and highways), randomly combining them to generate test traffic conditions, converting the data format to obtain map traffic data, and conducting multiple sets of tests on a rotary drum test bench. This scheme simulates the unpredictability of actual roads through the random combination of standard operating conditions.

[0030] (4) Test method based on fixed operating condition curve Existing technologies also disclose a scheme that converts vehicle speed curve characteristics into traffic information, couples it with map information, and writes it into a dedicated program to send map and traffic information at fixed intervals in vehicle drum mode. This scheme sends map and traffic information directly to the vehicle through dedicated equipment, enabling the vehicle to receive road information ahead and activate the PEM system during indoor testing.

[0031] However, all of the above-mentioned existing technical solutions have varying degrees of drawbacks: (1) Deficiencies of the pure fixed working condition scheme While testing using a fixed combination of urban-suburban-highway operating conditions yields good repeatability and comparability, the completely fixed and predictable test conditions allow the PEM system to optimize its control strategy under specific conditions through targeted calibration, resulting in an artificially inflated energy-saving performance evaluation. This evaluation reflects the PEM system's "test-taking" ability under known conditions, rather than its true energy-saving level in real-world usage scenarios.

[0032] (2) Deficiencies of the purely random operating condition scheme While the method of generating test conditions through random combinations of standard operating conditions avoids the problem of targeted calibration, different random combinations can lead to significant dispersion in test results. Specifically, the energy-saving effect test results for the same vehicle and the same PEM system may differ significantly under different random operating condition combinations, resulting in a large discrepancy between the results of formal tests and spot checks; the comparability of two rounds of test results for the same PEM system is poor. This dispersion makes the test evaluation results lack stability and repeatability, making it difficult to use as a standardized evaluation basis for effective technical evaluation and management.

[0033] (3) The evaluation method did not consider the impact of strategy perfection on the actual energy-saving effect. In existing technologies, energy-saving effect evaluations typically use the arithmetic mean or simple difference of multiple test results, without considering the impact of the PEM system's strategy completeness on actual energy-saving performance. The level of strategy completeness varies significantly among different PEM systems: some systems can only activate the strategy when navigation is enabled, while others can activate it even on familiar routes without navigation. Systems with higher strategy completeness show activation frequency and energy-saving effects closer to the ideal values ​​in laboratory tests during actual use. Existing evaluation methods fail to take this crucial factor into account, leading to discrepancies between evaluation results and actual user energy-saving experiences.

[0034] (4) The test logic has the defect of interdependence. In existing technologies, the test result verification logic of some solutions has an interdependent problem: repeatability verification depends on the activation energy consumption data of the main test condition, and range verification depends on the energy-saving effect data of the secondary test condition. When the test results of the main test condition itself have problems, repeatability verification cannot effectively identify them; when the test results of the secondary test condition itself have problems, the benchmark of range verification will also become invalid. This interdependent verification logic has inherent flaws and may lead to distorted evaluation results.

[0035] (5) High testing costs In existing technologies, to ensure the reliability of test results, it is usually necessary to repeat each test multiple times, resulting in long testing cycles and high costs.

[0036] Specifically, Figure 1 This is a flowchart illustrating an energy-saving effect evaluation method for a vehicle predictive energy management system provided in an embodiment of this application.

[0037] like Figure 1 As shown, the energy-saving performance evaluation method of the vehicle predictive energy management system includes the following steps: In step S101, the main test condition and the secondary test condition of the vehicle predictive energy management system are determined.

[0038] In the embodiments of this application, the main test conditions include a first test condition in which the vehicle predictive energy management system is inactive and a second test condition in which it is active, and the sub-test conditions include a third test condition in which the vehicle predictive energy management system is inactive and a fourth test condition in which it is active.

[0039] It is understandable that both the main test condition and the auxiliary test condition include the vehicle predictive energy management system being in an active and inactive state. The main test condition includes two types of conditions: the first test condition and the second test condition. The first test condition is the vehicle test condition in which the vehicle predictive energy management system is in an inactive (OFF) state; the second test condition is the vehicle test condition in which the vehicle predictive energy management system is in an active (ON) state.

[0040] The secondary test conditions include two types: the third test condition and the fourth test condition. The third test condition is the vehicle test condition in which the vehicle predictive energy management system is in an inactive (OFF) state; the fourth test condition is the vehicle test condition in which the vehicle predictive energy management system is in an active (ON) state.

[0041] Among them, the full-scenario parameters of the first and second test conditions are the same; the full-scenario parameters of the two tests in the third test condition may be the same or different; the full-scenario parameters of the two tests in the fourth test condition may be the same or different.

[0042] The main test condition is generated as follows: The main test conditions meet the following constraints: the starting point of the test is the urban test; the ending point of the test is the urban test; the middle part includes any combination of two highway test conditions and two suburban test conditions.

[0043] Under the premise of satisfying the above constraints, the six possible combinations of the main test conditions are limited to those shown in Table 1: Table 1

[0044] Before each test, the testers randomly select one of the above six possible combinations as the main test condition, and the selection remains unchanged after selection.

[0045] In the main test conditions, the first test condition was tested twice, and the second test condition was tested twice to obtain the energy consumption values ​​under the first and second test conditions.

[0046] The secondary test conditions are generated as follows: Multiple standard operating condition segments are acquired, including urban operating conditions, suburban operating conditions, and highway operating conditions. From these standard operating condition segments, two urban operating conditions, two suburban operating conditions, and two highway operating conditions are randomly selected, for a total of six operating condition segments. These segments are then randomly arranged and combined to generate two sets of sub-test operating conditions. The full-scene parameters of these two sets of sub-test operating conditions may be the same or different.

[0047] In the secondary test conditions, the third test condition was tested twice, and the fourth test condition was tested twice to obtain energy consumption data for the two secondary test conditions.

[0048] The random combination of the 6 working condition segments of the secondary test condition, after deduplication, has a total of 89 possible combinations (excluding the main test condition).

[0049] In step S102, the energy-saving effect values ​​of the vehicle under the main test condition and the sub-test condition are collected, and the validity of the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the sub-test condition is verified.

[0050] The test conditions of this invention are divided into two parts: main test conditions and secondary test conditions. The entire process is completed on an indoor test bench, and road slope, curvature, traffic conditions, etc. are injected through cloud platform access programs or map simulation equipment.

[0051] In this embodiment of the application, the energy-saving effect values ​​of the vehicle under the main test condition and the secondary test condition are collected, including: controlling the vehicle to perform two first test conditions to obtain a first energy consumption value and a second energy consumption value; controlling the vehicle to perform two second test conditions to obtain a third energy consumption value and a fourth energy consumption value; controlling the vehicle to perform two third test conditions to obtain a fifth energy consumption value and a sixth energy consumption value; controlling the vehicle to perform two fourth test conditions to obtain a seventh energy consumption value and an eighth energy consumption value; obtaining the first energy-saving effect value and the second energy-saving effect value under the main test condition based on the first energy consumption value, the second energy consumption value, the third energy consumption value and the fourth energy consumption value; and obtaining the third energy-saving effect value and the fourth energy-saving effect value under the secondary test condition based on the fifth energy consumption value, the sixth energy consumption value, the seventh energy consumption value and the eighth energy consumption value.

[0052] After determining the first, second, third, and fourth test conditions, execute the first, second, third, and fourth test conditions. 1. First test condition (PEM not activated): Control the vehicle to perform two complete tests continuously and independently, and collect the first energy consumption value E in each test. Main,OFF,1 Second energy consumption value E Main,OFF,2 .

[0053] 2. Second test condition (PEM activation): Control the vehicle to perform two complete tests continuously and independently, and collect the third energy consumption value E respectively. Main,ON,1 and the fourth energy consumption value E Main,ON,2 .

[0054] 3. Third test condition (PEM not activated): Control the vehicle to perform two complete tests continuously and independently, and collect the fifth energy consumption value E respectively. Sub,OFF,1 and the sixth energy consumption value E Sub,OFF,2 .

[0055] 4. Fourth Test Condition (PEM Activation): Control the vehicle to perform two complete tests continuously and independently, and collect the seventh energy consumption value E respectively.Sub,ON,1 And the eighth energy consumption value E Sub,ON,2 .

[0056] Furthermore, the calculation methods for the first and second energy-saving effect values ​​under the main test conditions are as follows: Using the first energy consumption value E Main,OFF,1 Second energy consumption value E Main,OFF,2 Subtract the third energy consumption value E respectively Main,ON,1 Fourth energy consumption value E Main,ON,2 The first energy-saving effect value ΔE under the main test condition was obtained. Main,1 Second energy saving effect value ΔE Main,2 This represents the energy-saving effect of PEM in two independent tests under the main test condition, i.e., the first energy-saving effect value ΔE. Main,1 =E Main,OFF,1 -E Main,ON,1 Second energy saving effect value ΔE Main,2 =E Main,OFF,2 -E Main,ON,2 .

[0057] The calculation methods for the third and fourth energy-saving effect values ​​under the secondary test conditions are as follows: Utilizing the fifth energy consumption value E Sub,OFF,1 and the sixth energy consumption value E Sub,OFF,2 Subtract the seventh energy consumption value E respectively Sub,ON,1 The eighth energy consumption value E Sub,ON,2 The third energy-saving effect value ΔE under the secondary test conditions was obtained. Sub,1 Fourth energy-saving effect value ΔE Sub,2 That is, the third energy-saving effect value ΔE Sub,1 =E Sub,OFF,1 -E Sub,ON,1 Fourth energy-saving effect value ΔE Sub,2 =E Sub,OFF,2 -E Sub,ON,2 .

[0058] In this embodiment of the application, the validity of the energy-saving effect value dataset composed of energy-saving effect values ​​under the main test condition and the secondary test condition is verified, including: verifying the validity of the energy-saving effect value dataset composed of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value; calculating the first average value of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculating the standard deviation based on the first average value; determining the maximum value and the minimum value among the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculating the difference between the maximum value and the minimum value; and determining the valid energy-saving effect value based on the difference and the standard deviation.

[0059] Understandably, based on the first, second, third, and fourth energy-saving effect values, a first average value is calculated, and then the corresponding standard deviation σ is calculated based on this first average value to define a reasonable range for data dispersion. Simultaneously, the maximum and minimum values ​​from the four sets of energy-saving effect values ​​are extracted, and the difference ΔE between the maximum and minimum values ​​is calculated. 节能,max .

[0060] The difference ΔE 节能,max By comparing with the standard deviation σ, the number of effective energy-saving effect values ​​among the first, second, third, and fourth energy-saving effect values ​​is determined.

[0061] Wherein, the standard deviation σ = 0.063 × Ē 节能 Ē 节能 The first average of the four energy-saving performance values ​​is expressed in L / 100km.

[0062] In this embodiment of the application, when determining the effective energy-saving effect value based on the difference and the standard deviation, the method includes: if the difference is less than or equal to the standard deviation, then the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all determined to be effective energy-saving effect values; if the difference is greater than the standard deviation, then the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value are selected, and the effective energy-saving effect value is based on the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, or the effectiveness is verified again after retesting the main test condition and the sub-test condition.

[0063] Specifically, if ΔE 节能,max If the value is less than or equal to the standard deviation σ, the first, second, third, and fourth energy-saving effect values ​​are determined to be effective energy-saving effect values. In other words, there are two effective energy-saving effect values ​​under the main test condition and two effective energy-saving effect values ​​under the secondary test condition.

[0064] If ΔE 节能,max If the standard deviation σ indicates that the first, second, third, and fourth energy-saving effect values ​​have failed the validity test, then the smaller value of the first and second energy-saving effect values, and the smaller value of the third and fourth energy-saving effect values, are selected as the valid energy-saving effect values ​​for subsequent calculations.

[0065] As an alternative, if the first, second, third, and fourth energy-saving effect values ​​fail the validity test, all the main and secondary test conditions can be retested, and then the complete validity test process can be repeated (i.e., the standard deviation of all energy consumption values ​​is recalculated, the range is compared, and the effective energy-saving effect value is selected based on the test results).

[0066] In step S103, the final energy-saving effect of the vehicle predictive energy management system is calculated based on the validity test results of the energy-saving effect values ​​of the vehicle under the main test condition and the sub-test condition.

[0067] In this embodiment, the final energy-saving effect of the vehicle predictive energy management system is calculated based on the validity test results of the energy-saving effect value dataset composed of energy-saving effect values ​​of the vehicle under the main test condition and the secondary test condition. This includes: if the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all valid energy-saving effect values, then the final energy-saving effect of the vehicle predictive energy management system is calculated based on the valid energy-saving effect values ​​using a preset weighted calculation method; if the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value is used as the valid energy-saving effect value, then the final energy-saving effect value of the vehicle predictive energy management system is calculated based on the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, using a preset weighted calculation method.

[0068] In this embodiment, the final energy-saving effect of the vehicle predictive energy management system is calculated based on the effective energy-saving effect value using a preset weighted calculation method. This includes: calculating the second average of the first energy-saving effect value and the second energy-saving effect value; calculating the third average of the third energy-saving effect value and the fourth energy-saving effect value; calculating the first product of the second average value and the corresponding first weight value; calculating the second product of the third average value and the corresponding second weight value; calculating the comprehensive energy-saving effect of the vehicle predictive energy management system based on the first product value and the second product value; and calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0069] Specifically, based on the effective energy-saving effect values ​​in the main test condition and the sub-test condition obtained after validity verification, this application uses a corresponding weighted calculation formula with a fixed ratio of 60% for the first weight of the effective energy-saving effect value in the main test condition and 40% for the second weight of the effective energy-saving effect value in the sub-test condition to calculate the comprehensive energy-saving effect of the vehicle predictive energy management system. The specific classification and calculation methods are as follows: When the first, second, third, and fourth energy-saving effect values ​​are all effective energy-saving effect values, that is, when there are 2 effective energy-saving effect values ​​under the main test condition and 2 effective energy-saving effect values ​​under the secondary test condition: First, the average of the first and second energy-saving effect values ​​under the main test conditions is taken as the second average value, thus obtaining the effective average value of the main test conditions. At the same time, the average of the third and fourth energy-saving effect values ​​under the two sub-test conditions is taken as the third average value, thus obtaining the effective average value of the sub-test conditions.

[0070] Then, according to the weighting ratio, the overall energy saving effect in this case is calculated as follows: effective average value of the main test condition × 60% + effective average value of the secondary test condition × 40%.

[0071] Finally, based on the calculated overall energy-saving effect, the final energy-saving effect of the vehicle predictive energy management system is calculated by multiplying the pre-configured user usage frequency coefficient by the overall energy-saving effect, as follows: E 最终 =E 综合 ×k; Where k is the user usage frequency coefficient of the PEM system, which is determined based on the completeness of the predictive energy management strategy. The higher the completeness of the strategy, the closer the user usage frequency coefficient k is to 1.

[0072] In this embodiment of the application, the effective energy-saving effect value is based on the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value. The calculation includes: calculating the third product of the smaller of the first energy-saving effect value and the second energy-saving effect value and the corresponding first weight value; calculating the fourth product of the smaller of the third energy-saving effect value and the fourth energy-saving effect value and the corresponding second weight value; calculating the comprehensive energy-saving effect of the vehicle predictive energy management system based on the third product value and the fourth product value; and calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0073] Specifically, if the validity test fails, the smaller of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are used. Based on the first weighting value of 60% and the second weighting value of 40%, the third product value can be calculated as the smaller energy-saving effect value under the main test condition multiplied by 60%, and the fourth product value is the smaller energy-saving effect value under the sub-test condition multiplied by 40%.

[0074] Therefore, the overall energy saving effect under this condition = 60% of the smaller energy saving effect value under the main test condition + 40% of the smaller energy saving effect value under the secondary test condition.

[0075] Finally, based on the calculated overall energy-saving effect, E 最终 =E 综合 The final energy-saving effect of the vehicle predictive energy management system is obtained by calculating ×k.

[0076] Alternatively, you can choose to retest all main and secondary test conditions, conduct repeatability checks on the retest data, and process the repeatability check results in the same way as described above.

[0077] In both scenarios, whether all energy-saving effect values ​​are valid or only the smaller value of the energy-saving effect value of the main test condition and the secondary test condition can be selected, the calculation of the comprehensive energy-saving effect is realized, ensuring that each evaluation result has both baseline stability and scenario authenticity.

[0078] In summary, the classification and processing rules for the validity of the results determined based on the number of effective energy-saving values ​​in the main test condition and the secondary test condition are shown in Table 2: Table 2

[0079] In this embodiment of the application, before calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect, the process includes: determining the current configuration level of the vehicle predictive energy management system; and determining the pre-configured user usage frequency coefficient based on the current configuration level of the vehicle predictive energy management system.

[0080] Specifically, the pre-configured user usage frequency coefficient k is a core correction parameter used to correct the energy-saving effect of ideal bench tests and to match the real-world energy-saving experience of users on roads. It is used to quantitatively characterize the policy completeness, scenario activation capability, and actual usable coverage of the PEM system. The smaller the k value, the more severely the actual activation scenarios of the system are limited and the lower the user usage frequency, and the energy-saving effect in laboratory tests needs to be weakened; the closer the k value is to 1, the more actual activation scenarios of the system are and the higher the user usage frequency, and the energy-saving effect in laboratory tests needs to be strengthened.

[0081] The specific rules for determining the value of k are as follows: Based on the policy activation conditions, scenario adaptability, and autonomous perception and learning capabilities of the PEM system, it is divided into three configuration levels: basic, advanced, and perfect. Each level corresponds to a strict range of k values.

[0082] 1. Basic Level Judgment criteria: The PEM strategy is activated solely by external navigation map signals. The system has no autonomous ability to recognize road conditions, remember historical routes, or predict the environment. The energy optimization strategy can only be activated when the vehicle actively starts the navigation task. After navigation is turned off, the system completely exits the predictive energy management logic.

[0083] User frequency coefficient selection rule: keep it relatively small, for example, k≤0.5.

[0084] 2. Advanced level Judgment criteria: In addition to supporting strategy activation when navigation is enabled, the system has the ability to recognize familiar routes and remember historical routes. Without navigation enabled, it can rely on the local historical driving route database and the results of repeated commuting route recognition to autonomously trigger the PEM predictive energy management strategy to achieve energy-saving control in the absence of navigation (the energy-saving effect reaches 70% or more of the energy-saving effect when navigation is enabled).

[0085] User usage frequency coefficient value rules: Higher values, for example, 0.9>k≥0.7.

[0086] 3. Perfect Level Judgment criteria: Based on navigation enabled and recognition of familiar routes without navigation, it also has the ability to perceive the real-time road environment, predict unfamiliar road conditions, and adaptively activate in multiple scenarios. It can rely on onboard sensing devices, cloud-based road condition data, and real-time road features to complete strategy activation (the energy-saving effect reaches 50% or more of the energy-saving effect under navigation enabled), without relying on fixed navigation tasks and historical routes, and the actual road activation coverage is extremely high.

[0087] User frequency coefficient values ​​are determined by the following rules: k should be close to the full value, for example, 1 ≥ k ≥ 0.9. The higher the system strategy completeness, the more comprehensive the activation scenarios, and the higher the actual user activation rate, the closer the k value will be to 1, with a maximum value not exceeding 1.0.

[0088] This embodiment uses the energy-saving effect test and evaluation of the PEM system of a hybrid vehicle as an example to describe the method of the present invention in detail. Energy consumption is expressed in L / 100km.

[0089] (a) Examples of successful validity tests: Assume the four energy-saving performance values ​​obtained from the main test condition and the secondary test condition are as shown in Table 3: Table 3

[0090] Then, the validity test is as follows: Ē 节能 =(0.65+0.63+0.60+0.62) / 4=0.625L / 100km; σ=0.063×0.625=0.0394L / 100km; ΔE 节能,max =0.63-0.60=0.03L / 100km; Due to ΔE 节能,max=0.03, 0.03 < 0.0394, therefore, through the validity test of the four energy-saving effect values, the average energy-saving effect value of the main test condition is selected as (0.61 + 0.63) / 2 = 0.62 L / 100km, and the average energy-saving effect value of the secondary test condition is selected as (0.60 + 0.62) / 2 = 0.61 L / 100km. The effective value of the main test condition is 0.62 L / 100km; the effective average value of the secondary test condition is 0.61 L / 100km. E 综合 =60%×0.62+40%×0.61=0.378+0.244=0.616L / 100km; The final evaluation result is obtained after correction by the user usage frequency coefficient k.

[0091] (ii) Examples that failed the validity test (1) Construction and execution of the main test conditions From the six main test condition combinations that meet the constraints, one is randomly selected, for example, the combination of "urban area-suburbs-highway-highway-suburbs-urban area". Each test condition segment uses the corresponding segment of the CLTC test condition.

[0092] The PEM was not activated (OFF) twice, and the energy consumption E was measured. Main,OFF,1 =6.50L / 100km, E Main,OFF,2 =6.55L / 100km; The PEM activation (ON) test was performed twice, and the energy consumption E was measured. Main,ON,1 =5.85L / 100km, E Main,ON,2 =5.82L / 100km.

[0093] Calculate the energy-saving effect under the main test conditions: First energy-saving effect value ΔE Main,1 =6.50-5.85=0.65L / 100km.

[0094] Second energy-saving effect value ΔE Main,2 =6.55-5.82=0.73L / 100km.

[0095] (2) Construction and execution of secondary test conditions Six test segments were selected from the CLTC (China Light-duty vehicle Test Cycle): two urban segment, two suburban segment, and two highway segment. These segments were randomly arranged and combined using a computer program to generate two sets of sub-test segments, which were not identical.

[0096] The PEM was not activated (OFF) twice, and the energy consumption E was measured. Sub,OFF =6.45L / 100km, E Sub,OFF =6.49L / 100km.

[0097] The PEM activation (ON) test was performed twice, and the energy consumption E was measured. Sub,ON,1 =5.90L / 100km, E Sub,ON,2 =5.88L / 100km.

[0098] Calculate the energy-saving effect under the secondary test conditions: Third energy-saving effect value ΔE Sub,1 =6.45-5.90=0.55L / 100km; Fourth energy saving effect value ΔE Sub,2 =6.49-5.88=0.61L / 100km.

[0099] (3) Summary of the four energy-saving effect values.

[0100] Table 4

[0101] (4) Validity test The average of the four energy-saving performance values: Ē 节能 =(0.65+0.73+0.55+0.61) / 4=0.635L / 100km.

[0102] Standard deviation: σ=0.063×0.635=0.04L / 100km.

[0103] The difference between the maximum and minimum values: ΔE 节能,max =0.73-0.55=0.18L / 100km.

[0104] Due to ΔE 节能,max =0.18, 0.18>0.04, therefore, the validity test of the four energy-saving effect values ​​was not passed. The smaller value of the energy-saving effect value of the main test condition and the secondary test condition was selected for subsequent calculations. The selected energy-saving effect value of the main test condition is 0.65L / 100km, and the energy-saving effect value of the secondary test condition is 0.55L / 100km.

[0105] (5) Weighted comprehensive calculation E 综合 =60%×0.65+40%×0.55=0.39+0.22=0.61L / 100km.

[0106] (6) Correction of user usage frequency coefficient The PEM system in the vehicle can only activate the policy when navigation is turned on, which is considered a basic level of policy completeness.

[0107] After review and confirmation, the user usage frequency coefficient k=0.45 was determined.

[0108] E 最终 =0.61×0.45=0.2745L / 100km.

[0109] The final energy-saving effect evaluation result of the PEM system of this model is 0.2745L / 100km.

[0110] (III) Examples of predicting user usage frequency coefficients for energy management systems at different levels of perfection: This embodiment illustrates the method for determining the value of the user frequency coefficient k of the predictive energy management system under different levels of perfection.

[0111] Scenario A: Basic Level A certain PEM system only uses basic navigation information, and navigation must be enabled to activate the strategy.

[0112] The user frequency coefficient is determined to be k=0.4.

[0113] Final energy-saving effect evaluation result: E 最终 =E 综合 ×0.4.

[0114] Scene B: Advanced Level A certain PEM system can not only activate the strategy when navigation is enabled, but also activate the strategy based on the historical path database when navigation is not enabled on familiar routes, and the actual energy saving effect reaches 80% of the energy saving effect when navigation is enabled.

[0115] The user frequency coefficient is determined to be k=0.75.

[0116] Final energy-saving effect evaluation result: E 最终 =E 综合 ×0.75.

[0117] Scenario C: Perfection Level A certain PEM system possesses comprehensive self-activation capabilities: It can be activated when navigation is on; it can be activated based on the historical route database on familiar routes without navigation; it can be activated based on real-time environmental perception (road slope ahead, traffic lights, etc.) on unfamiliar routes, and the actual energy saving effect is 57% of the energy saving effect of turning on navigation; actual user data shows that the actual activation time of the strategy accounts for more than 85% of the total time in a year.

[0118] The user frequency coefficient is determined to be k=0.9.

[0119] Final energy-saving effect evaluation result: E 最终 =E 综合 ×0.9.

[0120] (iv) Application examples of pure electric vehicles: This embodiment illustrates the application of the present invention in pure electric vehicles.

[0121] Replace the hybrid vehicle in Example 1 with a pure electric vehicle, and change the energy consumption unit from L / 100km to kWh / 100km.

[0122] Suppose the test results of a certain pure electric vehicle are as follows: Table 5

[0123] The constant coefficient in the standard deviation formula for validity testing can be adjusted from 0.063 to 0.056 according to the power consumption test standard (example value, actual value shall be subject to the standard specification): σ=0.056×Ē 节能 ; The remaining data processing rules, weighted calculations (60% primary + 40% secondary), and coefficient correction methods are consistent with the above methods.

[0124] To enable those skilled in the art to further understand the energy-saving effect evaluation method of the vehicle predictive energy management system according to the embodiments of this application, the following detailed description is provided in conjunction with specific embodiments, such as... Figure 2 As shown.

[0125] Step 1: Construct the main test case.

[0126] One of the six finite random driving combinations was selected as the main test condition. All six combinations consisted of urban, suburban, and highway sections. The main test condition had a fixed combination format to ensure that the test benchmark could be reproduced.

[0127] Step 2: Execute the main test condition.

[0128] The experiment was conducted by performing two PEM off tests and two PEM on tests. After the experiment was completed, two energy-saving effect values ​​corresponding to the main test conditions were obtained.

[0129] Step 3: Construct secondary test cases.

[0130] Remove the main test conditions and generate two sets of secondary test conditions from the remaining 89 completely random combinations. Each of the two sets of test conditions contains two urban segments, two suburban segments, and two highway segments, with the six test condition segments randomly arranged and combined. The secondary test conditions are completely random (the two sets of secondary test conditions may be the same or different) to avoid targeted calibration.

[0131] Step 4: Perform the secondary test conditions.

[0132] The experiment was conducted by performing two PEM off tests and two PEM on tests. After the experiment was completed, two energy-saving effect values ​​corresponding to the sub-test conditions were obtained.

[0133] Step 5: Summarize the four energy-saving performance values.

[0134] The two energy-saving effect values ​​of the main test condition and the two energy-saving effect values ​​of the secondary test condition are combined to form a total of four data samples to be tested.

[0135] Step Six: Validity Verification The validity of the four energy-saving effect values ​​obtained from the summary is verified, and then the judgment is made: Branch 1: Determine if the validity test is passed, calculate the average of the two effective energy-saving effect values ​​under the main test condition, the average of the two effective energy-saving effect values ​​under the secondary test condition, and proceed to step seven.

[0136] Branch 2: If the validity test fails, the smaller of the two valid energy-saving effect values ​​for the main test condition and the smaller of the two valid energy-saving effect values ​​for the sub-test condition will be selected, and the process will proceed to step seven.

[0137] Step 7: Weighted Comprehensive Calculation The overall energy-saving effect is calculated according to the weighted rule: 60% of the effective mean of the main test condition + 40% of the effective mean of the secondary test condition.

[0138] Step 8: Correction of User Usage Frequency Coefficient Based on the completeness of the vehicle predictive energy management system strategy, the user usage frequency coefficient k is determined, and the final energy-saving effect is calculated: E 最终 =E 综合 ×k.

[0139] After the corrections are completed, the final energy-saving effect is output, and the entire evaluation process is finished.

[0140] It should be noted that, apart from the above-mentioned preferred scheme (main test condition: OFF executed 2 times + ON executed 2 times, secondary test condition: OFF executed 2 times + ON executed 2 times), the number of tests for the main test condition and the secondary test condition can vary within a certain range, as long as the final effective energy-saving effect value can meet the settlement conditions in the result validity classification and processing rules.

[0141] Possible combined test conditions include, but are not limited to: Table 6

[0142] The number of data points varies for different test configurations, but they all have one thing in common: after the test is completed, the effective energy-saving effect value must be judged according to the "Result Validity Classification and Processing Rules". Only when the settlement conditions are met (i.e., the validity test is passed) can the weighted comprehensive calculation be performed based on the data values ​​in the entire energy-saving effect dataset. If the conditions are not met, the smaller value of the energy-saving effect value of the main test condition or / and the sub-test condition must be selected, or all tests must be retested and the validity test must be performed again until the settlement conditions are met.

[0143] The standard operating condition segments can be selected from WLTC, CLTC, or other national or regional standard operating conditions, or typical user operating conditions generated based on clustering of actual road data.

[0144] The user frequency coefficient k can be determined by comprehensively scoring different dimensions of strategy completeness, such as scoring according to dimensions such as navigation activation capability, non-navigation activation capability, and environmental awareness activation capability, and then weighting the scores.

[0145] Weight w of the main test condition Main The weight of the secondary test condition can be adjusted within the range of 60% to 90%. Sub It can be adjusted within the range of 40% to 10%, but the weight of the main test condition must be greater than the weight of the secondary test condition, and the sum of the two must be 100%.

[0146] In summary, compared with the prior art, the present invention has the following beneficial effects: 1. Effectively prevents targeted calibration, resulting in more realistic evaluation results: This invention uses a combination design of "limited randomization of main test conditions + completely randomization of secondary test conditions." On the one hand, it retains the benchmark and repeatability of fixed test conditions. On the other hand, the randomly generated secondary test conditions prevent the enterprise developers of the tested PEM system from predicting all test conditions. This effectively prevents the problem of obtaining inflated energy-saving effect evaluations through targeted calibration, and makes the evaluation results more realistically reflect the actual energy-saving capabilities of the PEM system.

[0147] 2. Stable and reliable test results with good repeatability: This invention effectively constrains the fluctuations in results caused by random operating conditions by setting a main test condition that accounts for up to 60% of the evaluation benchmark. Simultaneously, through main test condition tests (including two tests with PEM in the ON state and two tests with PEM in the OFF state) and secondary test condition tests (including two tests with PEM in the ON state and two tests with PEM in the OFF state), four energy-saving effect data points are ultimately obtained. This provides a more robust statistical basis for validity verification, resulting in evaluation results with high stability and repeatability.

[0148] 3. Independent and self-consistent verification logic eliminates interdependence: This invention employs a unified validity verification method to independently verify all energy-saving effect data points, without relying on mutual verification between the main test condition and the sub-test condition. The main test condition and the sub-test condition each provide two independent data points; a problem with the data in either condition type will not affect the validity judgment of the other condition type. The verification logic is clear and self-consistent.

[0149] 4. Closer to Real-World PEM Usage Scenarios: The main test scenario of this invention uses urban areas as the starting and ending point, including highways and suburban roads in between. This aligns with the typical travel scenario of a vehicle departing from the city, traveling via urban expressways / highways, and finally returning to the city, representing a classic usage scenario for PEM systems. Simultaneously, the limited randomness of the main test scenario and the complete randomness of the sub-test scenarios introduce appropriate uncertainty, simulating the real-world operating state of the PEM system facing unknown road conditions in actual use.

[0150] 5. The evaluation results fully reflect the impact of strategy completeness on actual energy-saving effects: This invention introduces a user usage frequency coefficient k based on strategy completeness, enabling the evaluation results to be differentiated according to the activation capability of the PEM system in actual usage scenarios. Systems whose strategies can only be activated when navigation is enabled have a usage coefficient of no more than 0.5, while systems that can be activated autonomously on familiar routes have a usage coefficient as high as 0.7. Systems with higher strategy completeness have usage coefficients closer to 1. This mechanism effectively distinguishes the actual energy-saving value of systems with different strategy completeness, making the evaluation results closer to the user's actual energy-saving experience.

[0151] 6. Low testing cost and high fault tolerance: This invention obtains 4 energy-saving effect data points with a total of 8 tests, resulting in high testing efficiency. When some data points do not meet the validity verification requirements, the flexible data selection principle allows for a valid evaluation based on the test results; otherwise, all tests must be retested, avoiding the practice of simply "data manipulation." The validity verification mechanism, which continues after all tests are retested, ensures that the data used for final settlement meets statistical consistency requirements.

[0152] 7. Wide range of applications: This invention is applicable to both hybrid electric vehicles (energy consumption in L / 100km) and pure electric vehicles (energy consumption in kWh / 100km). The number of tests for the main and auxiliary test conditions can be flexibly adjusted according to actual needs, as long as the validity classification and processing rules are met. It has good versatility and scalability.

[0153] Next, referring to the accompanying drawings, an energy-saving effect evaluation device for a vehicle predictive energy management system according to an embodiment of this application is described.

[0154] Figure 3 This is a block diagram of the energy-saving effect evaluation device of the vehicle predictive energy management system according to an embodiment of this application.

[0155] like Figure 3 As shown, the energy-saving effect evaluation device 10 of the vehicle predictive energy management system includes: a working condition generation module 100, an effectiveness verification module 200, and an energy-saving effect evaluation module 300.

[0156] The system includes a working condition generation module 100, which determines the main test working condition and the secondary test working condition of the vehicle predictive energy management system; a validity verification module 200, which collects the energy-saving effect values ​​of the vehicle under the main test working condition and the secondary test working condition, and performs validity verification on the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test working condition and the secondary test working condition; and an energy-saving effect evaluation module 300, which calculates the final energy-saving effect of the vehicle predictive energy management system based on the validity verification results of the energy-saving effect value dataset composed of the energy-saving effect values ​​of the vehicle under the main test working condition and the secondary test working condition.

[0157] In the embodiments of this application, the main test conditions include a first test condition in which the vehicle predictive energy management system is inactive and a second test condition in which it is active, and the sub-test conditions include a third test condition in which the vehicle predictive energy management system is inactive and a fourth test condition in which it is active.

[0158] In this embodiment, the validity verification module 200 is used to: control the vehicle to perform two first test conditions to obtain a first energy consumption value and a second energy consumption value; control the vehicle to perform two second test conditions to obtain a third energy consumption value and a fourth energy consumption value; control the vehicle to perform two third test conditions to obtain a fifth energy consumption value and a sixth energy consumption value; control the vehicle to perform two fourth test conditions to obtain a seventh energy consumption value and an eighth energy consumption value; obtain a first energy-saving effect value and a second energy-saving effect value under the main test condition based on the first energy consumption value, the second energy consumption value, the third energy consumption value, and the fourth energy consumption value; and obtain a third energy-saving effect value and a fourth energy-saving effect value under the sub-test condition based on the fifth energy consumption value, the sixth energy consumption value, the seventh energy consumption value, and the eighth energy consumption value.

[0159] In this embodiment of the application, the validity verification module 200 is further configured to: perform validity verification on the energy-saving effect value dataset composed of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value; calculate the first average value of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculate the standard deviation based on the first average value; determine the maximum value and the minimum value among the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculate the difference between the maximum value and the minimum value; and determine the valid energy-saving effect value based on the difference and the standard deviation.

[0160] In this embodiment of the application, the validity verification module 200 is further configured to: if the difference is less than or equal to the standard deviation, determine that the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all valid energy-saving effect values; if the difference is greater than the standard deviation, select the smaller value among the first energy-saving effect value and the second energy-saving effect value, select the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, and use the smaller value among the first energy-saving effect value and the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value as the valid energy-saving effect value, or retest the main test condition and the sub-test condition and perform the validity verification again.

[0161] In this embodiment, the energy-saving effect evaluation module 300 is used to: calculate the final energy-saving effect of the vehicle predictive energy management system based on the effective energy-saving effect values ​​when the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all effective energy-saving effect values; and calculate the final energy-saving effect value of the vehicle predictive energy management system based on the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value, using the preset weighted calculation method when the effective energy-saving effect value is the smaller of the first energy-saving effect value and the second energy-saving effect value, the smaller of the third energy-saving effect value and the fourth energy-saving effect value.

[0162] In this embodiment, the energy-saving effect evaluation module 300 is further configured to: calculate the second average of the first energy-saving effect value and the second energy-saving effect value; calculate the third average of the third energy-saving effect value and the fourth energy-saving effect value; calculate the first product of the second average value and the corresponding first weight value; calculate the second product of the third average value and the corresponding second weight value; calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the first product value and the second product value; and calculate the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0163] In this embodiment, the energy-saving effect evaluation module 300 is further configured to: calculate the third product of the smaller of the first energy-saving effect value and the second energy-saving effect value with the corresponding first weight value; calculate the fourth product of the smaller of the third energy-saving effect value and the fourth energy-saving effect value with the corresponding second weight value; calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the third product value and the fourth product value; and calculate the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect.

[0164] In this embodiment of the application, before calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of the pre-configured user usage frequency coefficient and the comprehensive energy-saving effect, the energy-saving effect evaluation module 300 is further used to: determine the current configuration level of the vehicle predictive energy management system; and determine the pre-configured user usage frequency coefficient based on the current configuration level of the vehicle predictive energy management system.

[0165] It should be noted that the explanation of the above-mentioned embodiment of the energy-saving effect evaluation method of the vehicle predictive energy management system also applies to the energy-saving effect evaluation device of the vehicle predictive energy management system in this embodiment, and will not be repeated here.

[0166] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0167] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0168] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0169] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any non-volatile computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "non-volatile computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of non-volatile computer-readable storage media include: an electrical connection having one or N wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the non-volatile computer-readable storage medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0170] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0171] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0172] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a non-volatile computer-readable storage medium.

[0173] The aforementioned non-volatile computer-readable storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the energy-saving effect of a vehicle predictive energy management system, characterized in that, Includes the following steps: Determine the primary and secondary test conditions for the vehicle predictive energy management system; The energy-saving effect values ​​of the vehicle under the main test condition and the sub-test condition are collected, and the validity of the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the sub-test condition is verified. The final energy-saving effect of the vehicle predictive energy management system is calculated based on the validity test results of the energy-saving effect value dataset, which consists of the energy-saving effect values ​​of the vehicle under the main test condition and the auxiliary test condition.

2. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 1, characterized in that, The main test conditions include a first test condition in which the vehicle predictive energy management system is inactive and a second test condition in which it is active. The secondary test conditions include a third test condition in which the vehicle predictive energy management system is inactive and a fourth test condition in which it is active.

3. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 2, characterized in that, The energy-saving performance values ​​of the vehicle under the main test condition and the secondary test condition are collected, including: The vehicle is controlled to perform the first test condition twice to obtain the first energy consumption value and the second energy consumption value. The vehicle is controlled to perform the second test condition twice to obtain the third energy consumption value and the fourth energy consumption value. The vehicle is controlled to perform the third test condition twice to obtain the fifth energy consumption value and the sixth energy consumption value. The vehicle is controlled to perform the fourth test condition twice to obtain the seventh energy consumption value and the eighth energy consumption value. The first energy-saving effect value and the second energy-saving effect value under the main test condition are obtained based on the first energy consumption value, the second energy consumption value, the third energy consumption value and the fourth energy consumption value under the sub-test condition are obtained based on the fifth energy consumption value, the sixth energy consumption value, the seventh energy consumption value and the eighth energy consumption value.

4. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 3, characterized in that, The validity of the energy-saving effect value dataset, composed of the energy-saving effect values ​​under the main test condition and the sub-test condition, is verified, including: The validity of the data set of energy-saving effect values, consisting of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, is verified. Calculate the first average value of the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value, and calculate the standard deviation based on the first average value; Determine the maximum and minimum values ​​among the first, second, third, and fourth energy-saving effect values, and calculate the difference between the maximum and minimum values; The effective energy-saving effect value is determined based on the difference and the standard deviation.

5. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 4, characterized in that, When determining the effective energy-saving effect value based on the difference and the standard deviation, the following steps are included: If the difference is less than or equal to the standard deviation, then the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all determined to be effective energy-saving effect values. If the difference is greater than the standard deviation, then the smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value, is selected. The smaller value among the first energy-saving effect value and the second energy-saving effect value, the smaller value among the third energy-saving effect value and the fourth energy-saving effect value is used as the effective energy-saving effect value. Alternatively, the effectiveness is verified again after retesting the main test condition and the sub-test condition.

6. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 5, characterized in that, The final energy-saving effect of the vehicle predictive energy management system is calculated based on the validity test results of the energy-saving effect value dataset composed of the vehicle's energy-saving effect values ​​under the main test condition and the auxiliary test condition, including: If the first energy-saving effect value, the second energy-saving effect value, the third energy-saving effect value, and the fourth energy-saving effect value are all effective energy-saving effect values, then the final energy-saving effect of the vehicle predictive energy management system is calculated based on the effective energy-saving effect values ​​using a preset weighted calculation method. When the smaller of the first energy-saving effect value and the second energy-saving effect value, the third energy-saving effect value and the fourth energy-saving effect value are taken as the effective energy-saving effect value, the final energy-saving effect value of the vehicle predictive energy management system is calculated using a preset weighted calculation method based on the smaller of the first energy-saving effect value and the second energy-saving effect value, the third energy-saving effect value and the fourth energy-saving effect value.

7. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 6, characterized in that, Based on the effective energy-saving effect value, the final energy-saving effect of the vehicle predictive energy management system is calculated using a preset weighted calculation method, including: Calculate the second average of the first energy-saving effect value and the second energy-saving effect value, and calculate the third average of the third energy-saving effect value and the fourth energy-saving effect value; Calculate the first product of the second average value and the corresponding first weight value, calculate the second product of the third average value and the corresponding second weight value, and calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the first product value and the second product value; The final energy-saving effect of the vehicle predictive energy management system is calculated by multiplying the pre-configured user usage frequency coefficient with the overall energy-saving effect.

8. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 6, characterized in that, The final energy-saving effect value of the vehicle predictive energy management system is calculated using a preset weighted calculation method based on the smaller of the first energy-saving effect value and the second energy-saving effect value, and the smaller of the third energy-saving effect value and the fourth energy-saving effect value, including: Calculate the third product of the smaller of the first energy-saving effect value and the second energy-saving effect value with the corresponding first weight value; calculate the fourth product of the smaller of the third energy-saving effect value and the fourth energy-saving effect value with the corresponding second weight value; and calculate the comprehensive energy-saving effect of the vehicle predictive energy management system based on the third product value and the fourth product value. The final energy-saving effect of the vehicle predictive energy management system is calculated by multiplying the pre-configured user usage frequency coefficient with the overall energy-saving effect.

9. The energy-saving effect evaluation method of the vehicle predictive energy management system according to claim 7, characterized in that, Before calculating the final energy-saving effect of the vehicle predictive energy management system based on the product of a pre-configured user usage frequency coefficient and the overall energy-saving effect, the following steps are included: Determine the current configuration level of the vehicle predictive energy management system; The pre-configured user usage frequency coefficient is determined based on the current configuration level of the vehicle predictive energy management system.

10. An energy-saving effect evaluation device for a vehicle predictive energy management system, characterized in that, include: The test condition generation module is used to determine the main test conditions and sub-test conditions of the vehicle predictive energy management system. The validity verification module is used to collect the energy-saving effect values ​​of the vehicle under the main test condition and the sub-test condition, and to perform validity verification on the energy-saving effect value dataset composed of the energy-saving effect values ​​under the main test condition and the sub-test condition. The energy-saving effect evaluation module is used to calculate the final energy-saving effect of the vehicle predictive energy management system based on the validity test results of the energy-saving effect value dataset composed of the energy-saving effect values ​​of the vehicle under the main test condition and the secondary test condition.

Images