Intelligent networked vehicle energy consumption testing method and system

Abstract

The application provides a kind of intelligent network connection car energy consumption test method and system, belong to the field of automobile energy management.The method comprises the following steps: step one, provide two identical configuration test vehicles, one is set to open intelligent energy-saving system, the other is set to close intelligent energy-saving system;Step two, on the test road, let the opening car drive in front, the closing car follows at a safe following distance, from the starting point to the end of the test route;After reaching the end, exchange the opening / closing state of the two cars, return to the starting point along the same test route;Step three, change the test route, execute step two until all the preset test routes are completed;Step four, collect the energy consumption data of each test section, calculate the energy consumption value of the two test vehicles under each state;Step five, based on the energy consumption data of the two test vehicles under the same test route, calculate the energy-saving effect of the intelligent energy-saving system.The application accurately evaluates the energy-saving effect through double-car synchronous comparison.

Description

Technical Field

[0001] This invention belongs to the field of automotive energy management, specifically relating to a method and system for testing the energy consumption of intelligent connected vehicles. Background Technology

[0002] With the rapid development of intelligent connected vehicle technology, an increasing number of vehicles are equipped with intelligent energy-saving systems. These systems integrate navigation map information, vehicle-to-everything (V2X) data, and radar / camera perception information to collaboratively optimize the powertrain, transmission, thermal management system, and driving strategies (such as predictive cruise control, economical speed planning, and intelligent energy recovery), thereby further reducing overall vehicle energy consumption on real roads. However, accurately and fairly evaluating the energy-saving performance of these systems in actual traffic environments has become a key technical challenge for the industry.

[0003] Currently, mainstream domestic and international automotive energy consumption testing methods (such as GB / T 18386.1—2021 and GB / T 19753—2021) are all based on laboratory drum test conditions (such as CLTC and WLTC). These testing methods have the following inherent drawbacks: They cannot reflect the dynamic traffic environment of real roads: laboratory test conditions are pre-set speed-time curves, which cannot reflect random factors in real-world traffic such as traffic light waiting, interference from vehicles ahead, gradient changes, and real-time navigation path planning. The core advantage of intelligent energy-saving systems lies precisely in their adaptive optimization to real dynamic environments; therefore, their true energy-saving potential cannot be fully realized in the laboratory. They lack an effective control testing mechanism: existing real-world road tests (such as GB / T 43758.1—2024 and GB / T44719—2024) mainly target autonomous driving functions or single-vehicle energy consumption records, and do not provide a control testing method that can isolate the mixed factors such as traffic environment, weather, and driving behavior. If the same vehicle travels the same route twice, the difference in energy consumption between the two tests cannot be attributed to the intelligent energy-saving system itself because traffic flow and traffic light phases cannot be replicated. There is a lack of specific evaluation indicators and correction methods for intelligent energy-saving systems: existing standards do not define the "intelligent energy-saving system" functional module, nor do they provide a calculation model for correcting fuel consumption due to changes in battery charge in hybrid mode. When the system actively maintains or charges its battery during testing, directly comparing fuel consumption will introduce significant errors.

[0004] Some companies and research institutions have attempted to evaluate performance by repeatedly running the same route with a single vehicle and taking the average value, or by using high-precision simulation software for virtual testing. However, single-vehicle repeated testing cannot address the issue of the irreproducible nature of traffic environments. Differences in traffic light waiting times and interference from other vehicles between different trips can exceed 30%, resulting in extremely poor test repeatability. While simulation testing offers a controllable environment, it cannot replace the complex interactions and sensor noise of real roads. Furthermore, it requires extensive scene data calibration, is time-consuming and costly, and the simulation results deviate significantly from actual road performance. A few solutions attempt to compare two vehicles driving simultaneously, but they lack strict guidelines for following order exchange, route cross-verification, and battery / fuel consumption correction methods. This means the test results may still be affected by systematic errors such as the positions of preceding and following vehicles, driver habits, and route differences.

[0005] In summary, existing technologies lack a standardized testing method that can effectively eliminate external interference factors and accurately quantify the independent energy-saving effect of intelligent energy-saving systems in real traffic environments. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for testing the energy consumption of intelligent connected vehicles. It adopts a technical architecture of "dual vehicles operating simultaneously on the same route, with comparative following and cross-route verification" and calculates the actual road energy-saving effect of the intelligent energy-saving system through strict control of variables, data acquisition and energy correction.

[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for testing the energy consumption of intelligent connected vehicles, the method comprising the following steps: Step 1: Provide two test vehicles with identical configurations, one of which is set to be the vehicle with the intelligent energy-saving system turned on, and the other is set to be the vehicle with the intelligent energy-saving system turned off. Step 2: On the test road, the vehicle that is activated drives ahead, while the vehicle that is deactivated follows the vehicle at a safe following distance, from the starting point of the test route to the destination, completing the outbound test; after reaching the destination, the activation / deactivation status of the two vehicles is switched, and they return to the starting point along the same test route, completing the return test; Step 3: Change the test route and repeat Step 2 until all test routes are completed; Step 4: Collect energy consumption data for each test route and calculate the energy consumption values ​​of the two test vehicles under each condition. Step 5: Calculate the energy-saving effect of the intelligent energy-saving system based on the energy consumption data of the two test vehicles under the same test route, time, and traffic conditions.

[0008] Furthermore, in step one, the initial states of the two test vehicles are consistent, including: both test vehicles have driven no less than 300km on actual roads within 7 days before the test, and the cumulative mileage difference does not exceed 500km; the two test vehicles have the same load weight, the same tire pressure, and the initial SOC value difference does not exceed 1%.

[0009] Furthermore, in step two, the two test vehicles start simultaneously at an agreed time using an onboard high-precision clock module, with a time synchronization error of less than or equal to 10 milliseconds; the driving trajectory of the vehicle that is turned on is recorded in real time by the onboard integrated navigation system and a reference path is generated, and the vehicle that is turned off performs path tracking control based on the reference path.

[0010] Furthermore, in step two, vehicle position, speed, altitude, power battery voltage, current, SOC, engine / motor speed, torque, instantaneous fuel consumption rate, and cumulative mileage data are recorded synchronously at a sampling frequency of not less than 1Hz.

[0011] Furthermore, in step two, the test data is valid if the deviation between the two test vehicles along the entire route is less than or equal to 1m.

[0012] Furthermore, the number of test routes in step three is at least three, and each test route has an actual mileage of more than 15km and includes no less than 5 traffic light intersections.

[0013] Furthermore, in step four, the test vehicle is a pure electric vehicle or a plug-in hybrid electric vehicle, and the energy consumption value of the test vehicle includes electricity consumption and fuel consumption. The formula for calculating power consumption is:

[0014] in, This refers to electricity consumption. This refers to the change in energy of a rechargeable energy storage system. This refers to the actual mileage traveled on a single test route. The fuel consumption figure refers to the fuel consumption of the test vehicle when it is a plug-in hybrid electric vehicle, and the calculation formula is as follows:

[0015] in, Fuel consumption The instantaneous fuel consumption rate is given by ρ, where ρ is the fuel density. This is the correction value for fuel change, where t,0 represents the start time point and t,end represents the end time point.

[0016] Furthermore, step four also includes a fuel consumption correction step, which includes: after the completion of a data evaluation unit test, if the energy change of the rechargeable energy storage system of the same test vehicle exceeds 0.1 kWh, a correction formula is used for correction, as follows:

[0017] in, For the power battery power, η is the engine's average effective fuel consumption rate, ρ is the fuel density, and η is the engine's charging and energy storage efficiency.

[0018] Furthermore, the operation of calculating the energy-saving effect of the intelligent energy-saving system in step five includes: The energy-saving effect of a test route is calculated using the following formula:

[0019] in, To measure the energy-saving effect of the i-th test route, To test the energy consumption of the vehicle by turning off the intelligent energy-saving system, To reduce the energy consumption of vehicles with intelligent energy-saving systems; The minimum value among all the energy-saving effect calculation results of the test routes is taken as the final energy-saving effect.

[0020] A second aspect of the present invention provides a vehicle intelligent energy-saving system testing system for implementing the method, the system comprising: Two identical test vehicles, each equipped with the intelligent energy-saving system to be tested; A high-precision clock module is used to ensure that the time synchronization error between the two vehicles is less than or equal to 10 milliseconds; In-vehicle integrated navigation system is used to record the driving trajectory of the vehicle in front in real time and generate a reference path; The data acquisition module is used to synchronously record vehicle energy consumption and status data at a frequency of not less than 1Hz; The data processing module is used to perform energy consumption calculations, fuel consumption corrections, and energy-saving effect calculations.

[0021] The beneficial effects of this invention are: The tests were conducted on public roads, covering real-world scenarios such as traffic lights, slopes, and traffic flow. The results closely reflect the actual user experience, avoiding overfitting to laboratory conditions.

[0022] By adopting a control architecture in which two vehicles follow each other on the same route simultaneously, the test repeatability error is reduced from ±15% to within ±5% by ensuring that the two vehicles experience the exact same time, position, traffic light phase and weather.

[0023] By exchanging system states for outbound and return trips, system errors introduced by manufacturing tolerances, tire wear, and differences in front and rear positions between the two vehicles can be offset, thereby improving the fairness and accuracy of the results.

[0024] For plug-in hybrid electric vehicles, a fuel consumption correction formula based on battery energy changes and engine average fuel consumption rate is proposed to avoid fuel consumption misinterpretation caused by SOC strategy adjustments.

[0025] Using the minimum value of the energy-saving effect of multiple routes as the final result, rather than the average value, meets the requirements of vehicle certification and consumer rights protection, avoids exaggerating the system's capabilities, and reflects rigor and engineering practicality. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0027] This invention employs two identical vehicles, operating simultaneously on the same road. One vehicle activates its intelligent energy-saving system while the other deactivates it, with the activated vehicle leading and the deactivated vehicle following, ensuring a completely consistent external traffic environment. This method includes the following steps (see attached diagram). Figure 1 (Test flowchart shown) Step 1: Test Preparation (1) Vehicle preparation Two identical test vehicles (with identical hardware and software versions) were provided by the car manufacturer, both equipped with the intelligent energy-saving system to be tested.

[0028] Both vehicles were driven on actual roads for no less than 300km within 7 days prior to the test, so that the intelligent energy-saving system could complete self-learning and be in a fully activated state, and the cumulative mileage of the two vehicles did not differ by more than 500km.

[0029] The two vehicles have the same load weight (occupants and counterweight) and the tire pressure is set to the same value according to GB / T 19753—2021.

[0030] Initial settings: Based on the external environment, set the target temperature, air volume, and air outlet mode of the air conditioning in both vehicles to be the same; select the same driving mode; if they are rechargeable vehicles, the initial SOC values ​​of the two vehicles should not differ by more than 1%.

[0031] (2) Test road The testing organization shall provide no less than 10 actual road test routes that meet the requirements of GB / T 44719, and the manufacturer shall select at least 3 routes from them.

[0032] Each route is more than 15km in actual length and includes no fewer than 5 traffic light intersections. The start and end points of the routes are clearly marked.

[0033] (3) Vehicle infotainment system settings Both vehicles were set to the exact same navigation route, and the navigation could not be changed or exited during the test.

[0034] Set one car as the "on car" (with the intelligent energy-saving system on) and the other as the "off car" (with the intelligent energy-saving system off, but retaining basic power control functions).

[0035] (4) Pre-test Preferably, the on / off states of the two vehicles can be switched once on each of the round trips along the pre-test route to familiarize the route with the route and check whether the data recording equipment is working properly.

[0036] Step 2: Single-route test To ensure strict synchronization between the two vehicles during the test, both vehicles were checked before starting and then put into standby mode. The two vehicles were started simultaneously at the agreed time using on-board high-precision clock modules, ensuring that the time synchronization error between the two vehicles was ≤10 milliseconds.

[0037] The driving trajectory of the vehicle in front (the vehicle with the engine on) is recorded in real time by the in-vehicle integrated navigation system and a reference path is generated. The autonomous driving system or driver assistance system of the vehicle behind (the vehicle with the engine off) performs path tracking control based on this reference path and adopts a following vehicle approach.

[0038] After the test, the test data is considered valid if the deviation between the two vehicles along the entire route is ≤1m.

[0039] Its operation includes: (1) Selecting a route (2) First test journey: Drive from the starting point to the destination along the first selected route. At this time, vehicle A activates the intelligent network assistance function, while vehicle B deactivates it. To ensure a high degree of consistency in the external traffic environment, the vehicle with the activation function drives ahead, while the vehicle with the deactivation function maintains a safe following distance behind (to avoid mutual interference or danger). The safe following distance refers to the minimum longitudinal distance under any driving conditions at which the following vehicle can ensure that the two vehicles do not collide when the preceding vehicle decelerates at maximum speed (emergency braking), while also avoiding significant interference from the rear airflow of the preceding vehicle on the energy consumption of the following vehicle.

[0040] (3) Return Trip Test: To offset the differences between the vehicles and the influence of their positions, a return trip is performed. After reaching the destination, the on / off states of the intelligent network functions of the two vehicles are switched during the return trip, with the vehicle that was originally on becoming the vehicle that was originally off, and the vehicle that was originally off becoming the vehicle that was originally on (i.e., vehicle A is off and vehicle B is on); the vehicles then return to the starting point along the same route. This step can offset the minor differences between the two vehicles (such as inconsistent tire rolling resistance) and the aerodynamic effects caused by the positions of the vehicles in front and behind.

[0041] (4) Remote test confirmation: Record the data of this round trip and complete the full test cycle of a single route.

[0042] During the test, the following data will be recorded synchronously at a sampling frequency of not less than 1Hz (optional methods include remote recording, OBD interface, on-board power analyzer or fuel consumption meter, etc., to record vehicle location, speed, altitude, power battery voltage, current, SOC, engine / motor speed, torque, instantaneous fuel consumption rate under operating conditions (mg / s), cumulative mileage, etc.).

[0043] Step 3: Route cross-verification (iterative control) (1) Route iteration judgment: The system judges the number of routes completed at present.

[0044] (2) Execute in a loop: If the number of test routes is less than 3, return to "Step 2" and change to a new route to continue the "outbound + return" test; if the number of routes is greater than or equal to 3, end the loop and enter the data processing stage.

[0045] Step 4: Data Acquisition and Energy Consumption Calculation Summarize test data for all routes and all conditions. Perform energy consumption calculations (including electricity or fuel consumption), as follows: (1) Calculation of electricity consumption (applicable to pure electric vehicles or plug-in hybrid electric vehicles that use electricity as the main power source) Calculate the power consumption of each test segment (route i, vehicle j, state s) according to the following formula (1). (Unit: kWh / 100km): For pure electric vehicles or plug-in hybrid electric vehicles that use electricity as the main energy source during the test, calculate the electricity consumption in each test section.

[0046] (1) In the formula: 1 represents the power consumption of the j-th car in state s when testing the i-th route. State s mainly has two states: intelligent energy-saving system on and off. The unit is kWh / 100km. This represents the change in energy of the test vehicle's Rechargeable Energy Storage System (REESS), expressed in kWh. This represents the actual mileage traveled by the test vehicle on a single route test, in km.

[0047] (2) Calculation of fuel oil consumption For plug-in hybrid electric vehicles, fuel consumption in each test section should be calculated according to formula (2).

[0048] (2) In the formula: 2 represents the fuel consumption of the j-th vehicle in state s when testing the i-th route for a plug-in hybrid electric vehicle. State s mainly has two states: intelligent energy-saving system on and off. The unit is L / 100km. t,0 represents the start time of data recording for the test segment (i.e., the time when the vehicle departs from the starting point of the route), and t,end represents the end time of data recording for the test segment (i.e., the time when the vehicle arrives at the end point of the route). This represents the instantaneous fuel consumption rate of the j-th car in state s when testing the i-th route, expressed in mg / s. This represents the density of the fuel added to the test vehicle, expressed in kg / L. This represents the actual mileage traveled by the test vehicle on a single route test, in km. This represents the correction value for changes in fuel consumption of the test vehicle, expressed in liters (L).

[0049] Step 5: Fuel Consumption Correction Two test vehicles complete a round trip on a fixed route as a data evaluation unit. After the unit test is completed, the energy change is judged. If the energy change of the rechargeable energy storage system (REESS) of the same vehicle exceeds 0.1 kWh, the fuel consumption needs to be corrected according to formula (3): (3) In the formula: The power of the battery of the j-th car in state s when the i-th segment of the test route is tested is represented by kW. The average effective fuel consumption rate of the engine during the actual operation during the test is represented by g / kWh and is provided by the car manufacturer. If not provided, the current industry average of 220 g / kWh is used. This represents the energy efficiency of the engine in charging the generator and storing the energy in the battery. It is provided by the car manufacturer; if not provided, the value is 0.9.

[0050] The correction ends and the process moves to the next step when the energy change of the REESS (Renewable Energy Storage System) exceeds 0.1 kWh.

[0051] Step Six: Calculation of Energy Saving Effect (1) Calculate the energy-saving effect based on (corrected) energy consumption data.

[0052] For the i-th route segment, calculate the energy-saving effect of the intelligent energy-saving system. In this invention, the energy-saving effect calculated for two test vehicles traveling in tandem, under the premise of the same time, the same road segment, and consistent traffic conditions, is valid. The calculation formula is as follows: (4) In the formula: This represents the energy-saving effect of the i-th route segment, expressed as a percentage, and rounded to three significant figures. Energy consumption (electricity or fuel) of vehicles with closed-loop intelligent energy-saving systems, expressed in kWh / 100km or L / 100km. This represents the energy consumption (electricity or fuel) of a vehicle with its intelligent energy-saving system turned off, expressed in kWh / 100km or L / 100km.

[0053] (2) Multi-route data integration: Calculate the average energy saving effect of each test route.

[0054] (3) Select the minimum value among all route calculation results as the final energy-saving effect output, using the following formula: S=min( (5) S represents the final energy-saving effect of the intelligent connected vehicle, expressed as a percentage, with three significant figures.

[0055] The method of the present invention will be further described in detail using the test process of a specific method of synchronous following of two vehicles and cross-verification of routes as an example.

[0056] This test employed a dual-vehicle synchronous following and route cross-verification method. On two opposite routes (A→B and B→A), the energy consumption performance of the same PHEV was tested with the intelligent driving function enabled and disabled. The test aimed to isolate environmental and driving habit differences and accurately evaluate the real-world fuel-saving effect of the intelligent driving system. The specific implementation details are shown in the table below:

[0057] Example 1: This example is a test of route A→B. Basic conditions: The distance of route A→B is approximately 40.5km (on) / 40.0km (off), and the initial SOC of both vehicles is 35.0%.

[0058] The battery energy changes as follows: With intelligent driving enabled: SOC drops from 35.0% to 23.4%, and the battery system energy change value ΔEREESS = -2.378 kWh (meaning 2.378 kWh of electricity was consumed).

[0059] With intelligent driving off: SOC drops from 35.0% to 21.7%, ΔEREESS = -2.727 kWh (consuming more electricity).

[0060] The battery energy difference is corrected to equivalent fuel consumption: -0.721 L in the on state and -0.826 L in the off state (meaning that the consumed electricity is equivalent to about 0.7 to 0.8 liters of gasoline).

[0061] Check the engine fuel consumption. With the engine on: the cumulative instantaneous fuel injection is 1.48 L; with the engine off: the cumulative instantaneous fuel injection is 1.47 L (the two are very close).

[0062] A comprehensive fuel consumption correction is performed, adding the engine fuel consumption and battery energy correction values ​​to obtain the corrected total fuel consumption: 2.20 L / 100km with the engine on and 2.30 L / 100km with the battery off. This translates to 5.43 L / 100km with the engine on and 5.74 L / 100km with the battery off.

[0063] On this section of road, fuel consumption was reduced by 6.61% when intelligent driving was turned on compared to when it was turned off.

[0064] Example 2: This example shows the test results for the route B→A segment (return trip). Basic conditions: Route B→A distance approximately 37.0km (on) / 36.2km (off). Due to the significant power consumption on the outbound journey, the initial SOC on the return journey is low, at 25.1% with on and 25.0% with off.

[0065] The battery energy changes as follows: Intelligent driving enabled: SOC increases from 25.1% to 29.3%, ΔEREESS = +0.861 kWh (meaning the battery is being charged and energy is being recovered).

[0066] With intelligent driving off: SOC increased from 25.0% to 29.0%, ΔEREESS = +0.820 kWh (energy was also recovered, but slightly less).

[0067] Equivalent fuel consumption correction: +0.261 L when on, +0.248 L when off (meaning the recovered energy is equivalent to saving about 0.25 liters of gasoline).

[0068] The engine fuel consumption was checked and found to be as follows: With the engine on, the cumulative instantaneous fuel injection was 2.03 L; With the engine off, the cumulative instantaneous fuel injection was 2.13 L (lower with the engine on).

[0069] After adjusting for combined fuel consumption, the total fuel consumption is 1.77 L / 100km with the power on and 1.88 L / 100km with the power off. This translates to 4.78 L / 100km with the power on and 5.20 L / 100km with the power off.

[0070] On this section of road, fuel consumption was improved by 7.90% when intelligent driving was turned on compared to when it was turned off.

[0071] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the foregoing description is only a preferred option and is not restrictive.

Claims

1. A method for testing the energy consumption of intelligent connected vehicles, characterized in that, The method includes the following steps: Step 1: Provide two test vehicles with identical configurations, one of which is set to be the vehicle with the intelligent energy-saving system turned on, and the other is set to be the vehicle with the intelligent energy-saving system turned off. Step 2: On the test road, the vehicle that is activated drives ahead, while the vehicle that is deactivated follows the vehicle at a safe following distance, from the starting point of the test route to the destination, completing the outbound test; after reaching the destination, the activation / deactivation status of the two vehicles is switched, and they return to the starting point along the same test route, completing the return test; Step 3: Change the test route and repeat Step 2 until all test routes are completed; Step 4: Collect energy consumption data for each test route and calculate the energy consumption values ​​of the two test vehicles under each condition. Step 5: Calculate the energy-saving effect of the intelligent energy-saving system based on the energy consumption data of the two test vehicles under the same test route, time, and traffic conditions.

2. The energy consumption testing method for intelligent connected vehicles according to claim 1, characterized in that, In step one, the initial state of the two test vehicles is consistent, including: both test vehicles have driven no less than 300km on actual roads within 7 days before the test, and the cumulative mileage difference does not exceed 500km; the two test vehicles have the same load weight, the same tire pressure, and the initial SOC value difference does not exceed 1%.

3. The energy consumption testing method for intelligent connected vehicles according to claim 1, characterized in that, In step two, the two test vehicles start simultaneously at an agreed time using an onboard high-precision clock module, with a time synchronization error of less than or equal to 10 milliseconds; the driving trajectory of the vehicle with the engine on is recorded in real time by the onboard integrated navigation system and a reference path is generated, and the vehicle with the engine off performs path tracking control based on the reference path.

4. The energy consumption testing method for intelligent connected vehicles according to claim 1 or 3, characterized in that, In step two, vehicle position, speed, altitude, power battery voltage, current, SOC, engine / motor speed, torque, instantaneous fuel consumption rate, and cumulative mileage data are recorded synchronously at a sampling frequency of not less than 1Hz.

5. The energy consumption testing method for intelligent connected vehicles according to claim 4, characterized in that, In step two, the test data is valid if the deviation between the two test vehicles along the entire route is ≤1m.

6. The energy consumption testing method for intelligent connected vehicles according to claim 1, characterized in that, The number of test routes in step three shall be at least three, and each test route shall have an actual mileage of more than 15km and include no less than 5 traffic light intersections.

7. The energy consumption testing method for intelligent connected vehicles according to claim 1, characterized in that, In step four, the test vehicle is a pure electric vehicle or a plug-in hybrid electric vehicle, and the energy consumption value of the test vehicle includes electricity consumption and fuel consumption. The formula for calculating power consumption is: in, This refers to electricity consumption. This refers to the change in energy of a rechargeable energy storage system. This refers to the actual mileage traveled on a single test route. The fuel consumption figure refers to the fuel consumption of the test vehicle when it is a plug-in hybrid electric vehicle, and the calculation formula is as follows: in, Fuel consumption The instantaneous fuel consumption rate is given by ρ, where ρ is the fuel density. This is the correction value for fuel change, where t,0 represents the start time point and t,end represents the end time point.

8. The energy consumption testing method for intelligent connected vehicles according to claim 7, characterized in that, Step four also includes a fuel consumption correction step, which includes: after a data evaluation unit test is completed, if the energy change of the rechargeable energy storage system of the same test vehicle exceeds 0.1 kWh, a correction formula is used for correction, as follows: in, For the power battery power, η is the engine's average effective fuel consumption rate, ρ is the fuel density, and η is the engine's charging and energy storage efficiency.

9. The energy consumption testing method for intelligent connected vehicles according to claim 1, characterized in that, The operation of calculating the energy-saving effect of the intelligent energy-saving system in step five includes: The energy-saving effect of a test route is calculated using the following formula: in, To determine the energy-saving effect of the i-th test route, To test the energy consumption of the vehicle by turning off the intelligent energy-saving system, To reduce the energy consumption of vehicles with intelligent energy-saving systems enabled; The minimum value among all the energy-saving effect calculation results of the test routes is taken as the final energy-saving effect.

10. A vehicle intelligent energy-saving system testing system, used to implement the method according to any one of claims 1 to 9, characterized in that, The system includes: Two identical test vehicles, each equipped with the intelligent energy-saving system to be tested; A high-precision clock module is used to ensure that the time synchronization error between the two vehicles is less than or equal to 10 milliseconds; In-vehicle integrated navigation system is used to record the driving trajectory of the vehicle in front in real time and generate a reference path; The data acquisition module is used to synchronously record vehicle energy consumption and status data at a frequency of not less than 1Hz; The data processing module is used to perform energy consumption calculations, fuel consumption corrections, and energy-saving effect calculations.

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