Method for testing energy efficiency of intelligent auxiliary driving mode of vehicle

Abstract

The application discloses a vehicle intelligent auxiliary driving mode energy efficiency test analysis method, comprising: testing the power consumption of an electric drive system, the average power of a DCDC under vehicle static and non-static conditions in different temperature environments when a vehicle closes an intelligent auxiliary driving system; calculating the dynamic average power of the intelligent auxiliary driving system in different temperature environments; testing the energy consumption contribution coefficient of the intelligent auxiliary driving system; calculating the dispersion coefficient of the energy consumption of the whole vehicle; calculating the working condition energy consumption of the vehicle in different temperature environments when the intelligent auxiliary driving system is started according to the power consumption of the electric drive system, the dynamic average power of the intelligent auxiliary driving system, the average power of the DCDC under vehicle static and non-static conditions, the static and non-static time, the energy consumption contribution coefficient and the dispersion coefficient of the actual working condition energy consumption. The application combines the energy consumption of the intelligent auxiliary driving system in multiple working conditions, analyzes the comprehensive energy efficiency level of the vehicle in the intelligent auxiliary driving system and realizes the comprehensive analysis of the energy efficiency of the auxiliary driving mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle test analysis, in particular to a vehicle intelligent auxiliary driving mode energy efficiency test analysis method. BACKGROUND

[0002] With the deep transformation of the automotive industry towards intelligence and electrification, intelligent auxiliary driving system (ADAS) has become the core configuration to improve vehicle safety and driving experience, and its penetration rate in passenger and commercial vehicle fields has shown explosive growth. However, the continuous operation of the auxiliary driving function relies on the cooperative work of multiple sensors (laser radar, millimeter wave radar, camera, etc.) and VCU, which leads to a significant increase in vehicle power consumption, especially in the new energy vehicle scenario, the energy efficiency level directly determines the cruising range and user cost, and becomes a key indicator to measure the comprehensive performance of the auxiliary driving system. The current industry focuses on the functional safety and scene adaptability of the auxiliary driving system, and there is no unified standard and mature method for energy efficiency test.

[0003] Currently, for vehicle energy efficiency test, the main way is to test the energy consumption level of the vehicle under standard working conditions on the drum test bench, such as CLTC-P, WLTC, etc. However, if the vehicle opens the intelligent auxiliary driving system on the drum test bench, it will detect the front obstacle and stop urgently. In addition, when driving on the test bench, the vehicle will detect the state of the built-in inertial navigation system and the state of the map positioning, so the vehicle will report an error. Therefore, it is difficult to realize the energy efficiency level test of opening the intelligent auxiliary driving on the test bench.

[0004] Under this background, it is urgent to establish an energy efficiency test analysis method for the vehicle opening the intelligent auxiliary driving, which takes into account the authenticity, systematicness and accuracy, and realizes the comprehensive analysis of the energy efficiency of the auxiliary driving mode. SUMMARY

[0005] The purpose of the present application is to overcome the deficiencies and defects of the prior art, and to provide a vehicle intelligent auxiliary driving mode energy efficiency test analysis method. Based on the vehicle standard working condition energy consumption result, combined with the hardware energy consumption of intelligent auxiliary driving in multiple working conditions, the energy efficiency contribution rate of intelligent driving control logic, and the intelligent driving energy consumption stability correction, the comprehensive energy efficiency level of the vehicle in intelligent auxiliary driving is analyzed, and the comprehensive analysis of the energy efficiency of the auxiliary driving mode is realized.

[0006] A vehicle intelligent auxiliary driving mode energy efficiency test analysis method, comprising the steps of:

[0007] Based on the test bench, the electric drive system power consumption of the vehicle under different temperature environments when the intelligent auxiliary driving system is closed and the average power of the DCDC under vehicle static and non-static conditions are tested;

[0008] Test the static average power of the intelligent auxiliary driving system in different temperature environments in the open state, and calculate the high and low temperature influence coefficient based on the static average power.

[0009] Determine the dynamic average power of the intelligent auxiliary driving system in different road conditions and normal temperature environments in the open state, calculate the dynamic average power of the intelligent auxiliary driving system in normal temperature environments based on the dynamic average power of the intelligent auxiliary driving system in different road conditions and normal temperature environments in the open state, road condition coefficient, extreme weather coefficient, and travel time coefficient, multiply the dynamic average power of the intelligent auxiliary driving system in normal temperature environments by the high and low temperature influence coefficient to obtain the dynamic average power of the intelligent auxiliary driving system in high and low temperature environments.

[0010] Test the energy consumption contribution coefficient of the intelligent auxiliary driving system.

[0011] Measure the energy consumption of the intelligent auxiliary driving system in the same condition for multiple times, and calculate the discrete coefficient of the vehicle energy consumption based on the measured energy consumption.

[0012] According to the power consumption of the electric drive system in different temperature environments, the dynamic average power of the intelligent auxiliary driving system in different temperature environments, the average power of the DCDC in the vehicle static and non-static states, the static and non-static time, the energy consumption contribution coefficient, calculate the energy consumption of the vehicle in different temperature environments with the intelligent auxiliary driving system open, and then determine the actual energy consumption according to the discrete coefficient of the vehicle energy consumption.

[0013] Preferably, the high and low temperature influence coefficient includes a high temperature influence coefficient and a low temperature influence coefficient, the high temperature influence coefficient = high temperature static average power / normal temperature static average power, and the low temperature influence coefficient = low temperature static average power / normal temperature static average power.

[0014] Preferably, the dynamic average power of the intelligent auxiliary driving system in normal temperature environments is calculated as follows:

[0015] W 22-normaltemp =K 22-1 ×K 22-4 ×W 22-1 +K 22-1 ×K 22-5 ×W 22-2 +K 22-2 ×K 22-4 ×W 22-3 +K 22-2 ×K 22-5 ×W 22-4 +K 22-3 ×K 22-4 ×W 22-5 +K 22-3 ×K 22-5 ×W22-6 +K 22-6 × W 22-7 +K 22-6 ×W 22-8 +K 22-6 ×W 22-9 ;

[0016] wherein, K 22-1 is a vehicle coefficient for urban roads, K 22-2 is a vehicle coefficient for suburban roads, K 22-3 is a vehicle coefficient for highways, K 22-4 is a vehicle coefficient for daytime, K 22-5 is a vehicle coefficient for nighttime, K 22-6 is a vehicle coefficient for extreme weather, W 22-1 , W 22-2 , W 22-3 , W 22-4 , W 22-5 , W 22-6 , W 22-7 , W 22-8 , W 22-9 are, in turn, the dynamic average power of the intelligent auxiliary driving system in a normal temperature environment under urban road daytime, urban road nighttime, suburban road daytime, suburban road nighttime, highway daytime, highway nighttime, urban road extreme weather, suburban road extreme weather, and highway extreme weather working conditions.

[0017] Preferably, the dynamic average power of the intelligent auxiliary driving system in an open state under different road conditions and in a normal temperature environment is obtained through typical road testing and / or calculated based on determined coefficients after obtaining part of the test results.

[0018] Preferably, the dynamic average power of the intelligent auxiliary driving system in a normal temperature environment under suburban road nighttime, highway nighttime, suburban road extreme weather, and highway extreme weather working conditions is W 22-4 , W 22-6 , W 22-8 , W 22-9 , which is calculated by the following formula:

[0019] W 22-4 = W 22-3 × K night , the nighttime coefficient K night = W 22-2 / W 22-1 ;

[0020] W 22-6 = W 22-5 × K night ,

[0021] W 22-8 = Kbadwether XW 22-3 , extreme weather coefficient K badwether = W 22-7 / W 22-1 ;

[0022] W 22-9 = K badwether XW 22-5 .

[0023] Preferably, the energy consumption contribution coefficient of the intelligent auxiliary driving system is obtained by dividing the energy consumption of the electric drive system of the vehicle tested under the intelligent auxiliary driving system enabled in different road conditions by the energy consumption of the electric drive system tested on the test bench by artificial driving under the intelligent auxiliary driving system closed in different road conditions of the simulated traffic scene, and then adding the weighted sum; the energy consumption contribution coefficient of the intelligent auxiliary driving system is represented as K3;

[0024] K3 = k 31 X E 31-11 / E 32-11 + k 32 X E 31-12 / E 32-12 + k 33 X E 31-13 / E 32-13 ;

[0025] wherein, E 31-11 , E 31-12 , E 31-13 are the energy consumptions of the electric drive system of the vehicle in the city, suburban and high-speed conditions tested under the typical conditions with the intelligent auxiliary driving system enabled;

[0026] E 32-11 , E 32-12 , E 32-13 are the energy consumptions of the electric drive system of the vehicle in the city, suburban and high-speed conditions tested on the test bench with the intelligent auxiliary driving system closed;

[0027] k 31 , k 32 , k 33 are coefficients, and the sum of the three is 1.

[0028] Preferably, the simulated traffic scene is constructed according to the road state, traffic flow information and traffic light information recorded during the testing process under the typical conditions with the intelligent auxiliary driving system enabled.

[0029] Preferably, the dispersion coefficient of the vehicle energy consumption is obtained by selecting a typical road condition and continuously testing the vehicle energy consumption under the intelligent auxiliary driving system n times to obtain the energy average E4 and the standard deviation S4, including:

[0030] CV= S4 / E4;

[0031] E4= (E 41 +E 42 +……+E 4n ) / n;

[0032] S4= ;

[0033] wherein, E 41 , E 42 ……E 4n are the vehicle energy consumptions under n times of starting the intelligent auxiliary driving system, E 4i represents the vehicle energy consumption measured for the i-th time, i takes the value of 1 to n.

[0034] Preferably, the vehicle operating condition energy consumption under starting the intelligent auxiliary driving system at different temperature environments comprises:

[0035] normal temperature operating condition energy consumption E 5-normaltemp =E 12-normaltemp ×K3+ ((W 14_1-normaltemp +W 21-normaltemp )×T 14_1 +(W 14_2- normaltemp +W 22-normaltemp )×T 14_2 ) / K DCDC-normaltemp ;

[0036] wherein, K DCDC-normaltemp is the efficiency of DCDC under normal temperature operating condition; E 12-normaltemp is the vehicle electrical drive system power consumption under normal temperature operating condition, W 14_1-normaltemp is the average power of DCDC under normal temperature operating condition when the vehicle is stationary, W 21-normaltemp is the static average power of the intelligent auxiliary driving system under normal temperature operating condition, T 14_1 is the stationary time, W 14_2-normaltemp is the average power of DCDC under normal temperature operating condition when the vehicle is not stationary, W 22-normaltemp is the dynamic average power of the intelligent auxiliary driving system under normal temperature operating condition, T 14_2 is the non-stationary time;

[0037] low temperature operating condition energy consumption E 5-lowtemp =E 12-lowtemp ×K3+ ((W 14_1-lowtemp +W 21-lowtemp )×T 14_1 +(W 14_2-lowtemp +W 22-lowtemp )× T 14_2 ) / K DCDC-lowtemp +E 17-lowtemp +E18-lowtemp ;

[0038] E 12-lowtemp is the power consumption of the electric drive system of the vehicle under low-temperature conditions, W 14_1-lowtemp is the average power of the DCDC under low-temperature conditions for the vehicle to remain stationary, W 21-lowtemp is the static average power of the intelligent auxiliary driving system under low-temperature conditions, T 14_1 is the stationary time, W 14_2-lowtemp is the average power of the DCDC under low-temperature conditions for the vehicle to be non-stationary, W 22-lowtemp is the dynamic average power of the intelligent auxiliary driving system under low-temperature conditions, T 14_2 is the non-stationary time; K DCDC-lowtemp is the efficiency of the DCDC under low-temperature conditions, E 17-lowtemp is the energy consumption of the air conditioner compressor under low-temperature conditions, E 18-lowtemp is the energy consumption of the heating component PTC under low-temperature conditions;

[0039] E 5-hightemp is the energy consumption under high-temperature conditions, E 12-hightemp = E 14_1-hightemp × K3+ ((W 21-hightemp + W 14_1 )× T 14_2- hightemp +(W 22-hightemp + W 14_2 )× T DCDC-hightemp ) / K 17-hightemp +E 12-hightemp ;

[0040] E 12-hightemp is the power consumption of the electric drive system of the vehicle under low-temperature conditions, W 14_1-hightemp is the average power of the DCDC under high-temperature conditions for the vehicle to remain stationary, W 21-hightemp is the static average power of the intelligent auxiliary driving system under high-temperature conditions, T 14_1 is the stationary time, W 14_2-hightemp is the average power of the DCDC under high-temperature conditions for the vehicle to be non-stationary, W 22-hightemp is the dynamic average power of the intelligent auxiliary driving system under normal-temperature conditions, T 14_2 is the non-stationary time; K DCDC-hightemp is the efficiency of the DCDC under high-temperature conditions, E 17-hightemp is the energy consumption of the air conditioner compressor under high-temperature conditions.

[0041] Preferably, the actual working condition energy consumption is obtained by multiplying the working condition energy consumption of the vehicle under different temperature environments with the intelligent auxiliary driving system turned on and the dispersion coefficient.

[0042] The vehicle intelligent auxiliary driving mode energy efficiency test analysis method provided by the application can solve the problem of vehicle intelligent auxiliary driving energy consumption test by combining bench test and road test. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flowchart of the vehicle intelligent auxiliary driving mode energy efficiency test analysis method of the application. DETAILED DESCRIPTION

[0044] The application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0045] As shown in Figure 1 , the embodiment provides a vehicle intelligent auxiliary driving mode energy efficiency test analysis method, which comprises the following steps:

[0046] S1. Based on the test bench, the electric power consumption of the electric drive system of the vehicle under different temperature environments when the intelligent auxiliary driving system is turned off and the average power of the DCDC under the conditions of vehicle static and non-static are tested;

[0047] S2. Test the static average power under different temperature environments when the intelligent auxiliary driving system is turned on, and calculate the high and low temperature influence coefficient based on the static average power;

[0048] S3. Determine the dynamic average power under different road conditions and normal temperature environments when the intelligent auxiliary driving system is turned on, calculate the dynamic average power of the intelligent auxiliary driving system under normal temperature environments based on the dynamic average power of the intelligent auxiliary driving system under different road conditions and normal temperature environments, road condition coefficient, extreme weather coefficient and travel time coefficient, and multiply the dynamic average power of the intelligent auxiliary driving system under normal temperature environments by the high and low temperature influence coefficient to obtain the dynamic average power of the intelligent auxiliary driving system under high and low temperature environments;

[0049] S4. Test the energy consumption contribution coefficient of the intelligent auxiliary driving system;

[0050] S5. Continuously use the intelligent auxiliary driving system to drive under the same conditions for energy consumption measurement, and calculate the dispersion coefficient of the vehicle energy consumption based on the measured energy consumption;

[0051] S6. Calculate the working condition energy consumption of the vehicle under different temperature environments when the intelligent auxiliary driving system is turned on according to the power consumption of the electric drive system under different temperature environments, the dynamic average power of the intelligent auxiliary driving system under different temperature environments, the average power and static time and non-static time of the DCDC under the vehicle static and non-static, and the energy consumption contribution coefficient, and then determine the actual working condition energy consumption according to the discrete coefficient of the vehicle energy consumption.

[0052] In the embodiments of the application, the different temperature environments include normal temperature, low temperature and high temperature environments, such as a typical normal temperature environment of 23℃, a low temperature environment of -7℃ and a high temperature environment of 35℃.

[0053] In step S1, when the vehicle is turned off in the intelligent auxiliary driving mode, the standard working condition vehicle energy consumption E of the vehicle under different temperature environments such as normal temperature, low temperature and high temperature is tested on the drum bench. 1-normaltemp 、E 1-lowtemp 、E 1-hightemp , as the reference energy consumption, including the power consumption and average power of the electric drive system, the output power and average power of the DCDC, the power consumption of the air conditioner compressor and the power consumption of the heating component PTC. Taking the normal temperature condition as an example, the normal temperature condition test does not start the air conditioner compressor and PTC, so only the standard working condition vehicle energy consumption needs to be tested, and the vehicle power battery net discharge power E 11-normaltemp and the average power W 11-normaltemp , the power consumption E 12-normaltemp and the average power W 12-normaltemp of the electric drive system, the output power E 14-normaltemp and the average power W 14-normaltemp of the DCDC are tested. 14_1-normaltemp and the time T 14_1 , and the average power W 14_2-normaltemp and the corresponding time T 14_2 of the vehicle when it is not static.

[0054] When testing the component energy consumption of the intelligent auxiliary driving system, if the vehicle is equipped with an insurance box, a shunt current sensor can be inserted from the insurance box to directly test the energy consumption of each component of the intelligent auxiliary driving system. However, since some vehicles cancel the traditional insurance box, each low-voltage component is directly powered through the domain controller, it is difficult to directly distinguish the power supply circuit of the intelligent driving system components. In addition, the intelligent driving component structure is compact, and there is almost no space to install a current clamp. In addition, direct installation is prone to omissions. Therefore, it is necessary to use the vehicle low-voltage connection circuit diagram to test the power consumption and power by installing current clamps one by one according to the circuit diagram to test the energy consumption of each component of the intelligent auxiliary driving system.

[0055] In different environments, the sensors activated by the intelligent auxiliary driving system and the controller computing power of the intelligent auxiliary driving system are different, which results in a relatively large difference in energy consumption in different scenarios, so it needs to be tested statically and dynamically. In the embodiment of the application, when testing the static average power of the intelligent auxiliary driving system in different temperature environments in the on state, in order to obtain accurate data, and the outdoor temperature environment is uncontrollable, therefore, this test needs to be performed in the environment warehouse. Let the vehicle first stand in the environment warehouse for a period of time, such as 6h, and then start the intelligent auxiliary driving mode. Continuously test for a certain period of time, such as 1 hour, in normal temperature, low temperature and high temperature environments, and record the static energy consumption E 21-lowtemp 21-normaltemp 21-hightemp and the static average power W 21-lowtemp 21-normaltemp 21-hightemp .

[0056] In the embodiment of the application, the high and low temperature influence coefficients are calculated based on the static energy consumption test results, including the high temperature influence coefficient K hightemp and the low temperature influence coefficient K lowtemp , the high temperature influence coefficient K hightemp = high temperature static average power / normal temperature static average power; the low temperature influence coefficient K lowtemp = low temperature static average power / normal temperature static average power, that is, K lowtemp = W 21-lowtemp / W 21-normaltemp ; K hightemp = W 21-hightemp / W 21-normaltemp .

[0057] In the embodiment of the application, the dynamic average power of the intelligent auxiliary driving system in the on state under different road conditions and normal temperature environments is obtained by typical road testing and / or calculated based on the determined coefficient after obtaining part of the test results.

[0058] In the embodiment of the application, the dynamic average power is obtained by dynamic energy consumption testing in the on state of the intelligent auxiliary driving system. Dynamic energy consumption testing can be performed on social open roads. Because the actual road temperature is difficult to control, the temperature is selected to be within 20-28℃ as a normal temperature condition. Typical urban road sections, suburban road sections and highway sections are selected and distributed in the daytime, nighttime and extreme weather to start testing. Each road section is tested multiple times, and the dynamic average power and energy consumption of the intelligent auxiliary driving system components in different conditions are counted. The variables of the intelligent auxiliary driving system components counted are shown in Table 1.

[0059] Table 1

[0060] Average power of intelligent co-pilot system Road Environment Remarks [WC 22-1 ]]> Urban road Day Measured [WC 22-2 ]]> Urban road Night Measured [WC 22-3 ]]> Suburban road Day Measured [WC 22-4 ]]> Suburban road Night Calculated [WC 22-5 ]]> Highway Day Measured [WC 22-6 ]]> Highway Night Calculated [WC 22-7 ]]> Urban road Extreme weather Measured [WC 22-8 ]]> Suburban road Extreme weather Calculated [WC 22-9 ]]> Highway Extreme weather Calculated ​​​​

[0061] To reduce the amount of testing, calculate the nighttime factor and extreme operating condition factor, including the nighttime factor K. night =W 22-2 / W 22-1 W was calculated 22-4 =W 22-3 ×K night W 22-6 =W 22-5 ×K night Extreme weather coefficient K badwether =W 22-7 / W 22-1 W was calculated 22-8 =K badwether ×W 22-3 W 22-9 =K badwether ×W 22-5 In this way, by measuring the average power of the intelligent driving assistance system during the day and night on urban roads, suburban roads, and highways, the dynamic average power under other road conditions can be calculated using nighttime and extreme condition coefficients. Alternatively, all power can be obtained through actual testing. Through the above methods, the dynamic average power of the intelligent driving assistance system under different road conditions can be obtained.

[0062] To facilitate the calculation of dynamic average power, this embodiment of the application uses user vehicle usage big data to statistically analyze the proportion of vehicle usage time in various traffic scenarios and calculates the coefficient for each operating condition to calculate the dynamic average power. The operating condition data is divided into three dimensions: road conditions, travel time, and extreme weather. Specific parameters include: K. 22-1 K represents the urban road vehicle usage coefficient. 22-2 K represents the vehicle usage coefficient for suburban roads. 22-3 K represents the vehicle usage coefficient for highways. 22-4 K represents the daytime vehicle usage coefficient. 22-5 For nighttime vehicle usage coefficient, K 22-6 This is a vehicle usage coefficient for extreme weather conditions.

[0063] Based on the operating condition coefficient and the dynamic average power of the intelligent driver assistance system under normal temperature conditions obtained from the test, the dynamic average power W of the intelligent driver assistance system under normal temperature conditions can be calculated. 22-normaltemp .

[0064] W 22-normaltemp =K 22-1 ×K 22-4 ×W 22-1 +K 22-1 × K 22-5 ×W 22-2 +K 22-2 ×K 22-4 × W22-3 +K 22-2 ×K 22-5 ×W 22-4 +K 22-3 × K 22-4 ×W 22-5 +K 22-3 ×K 22-5 ×W 22-6 +K 22-6 ×W 22-7 +K 22-6 ×W 22-8 +K 22-6 ×W 22-9 .

[0065] Wherein, as shown in Table 1, W 22-1 , W 22-2 , W 22-3 , W 22-4 , W 22-5 , W 22-6 , W 22-7 , W 22-8 , W 22-9 are the dynamic average power of the intelligent auxiliary driving system in the normal temperature environment under the urban road daytime, urban road nighttime, suburban road daytime, suburban road nighttime, highway daytime, highway nighttime, urban road extreme weather, suburban road extreme weather, highway extreme weather working conditions, respectively.

[0066] In the embodiments of the present application, based on the dynamic average power of the intelligent auxiliary driving system under the normal temperature working condition obtained in the foregoing, and according to the high and low temperature influence coefficient calculated in the foregoing, the dynamic average power of the intelligent auxiliary system under the high temperature state and the low temperature condition can be obtained:

[0067] W 22-lowtemp =K lowtemp × W 22-normaltemp .

[0068] W 22-hightemp =K hightemp × W 22-normaltemp .

[0069] Through the above, the dynamic average power of the intelligent auxiliary system under different temperature environments for subsequent calculation can be calculated.

[0070] In the embodiments of the present application, the energy consumption contribution coefficient of the intelligent auxiliary driving system is obtained by dividing the energy consumption of the electric drive system of the vehicle under the condition of enabling the intelligent auxiliary driving system under different road conditions by the energy consumption of the electric drive system under the condition of manually driving on the test bench under the condition of closing the intelligent auxiliary driving system, and then adding the weighted sum of each. Preferably, the simulation traffic scene is constructed according to the road state, traffic flow information and traffic light information recorded during the test process under the condition of enabling the intelligent auxiliary driving system under the typical working condition.

[0071] Specifically, when testing the energy consumption contribution coefficient of the intelligent auxiliary driving system, the intelligent auxiliary driving system is enabled to drive once under the typical working condition, and the energy consumption of the electric drive system is recorded. In addition, the road state, traffic flow information and traffic light information are recorded during the test process of the intelligent auxiliary driving system, which are used to construct the traffic scene. Then, the electric drive system energy consumption during manual driving is tested by driving the vehicle on the test bench through manual driving under the same scene constructed by the constructed traffic scene, so as to calculate the energy consumption contribution coefficient of the intelligent auxiliary driving system.

[0072] Specifically, the typical road city working condition, suburban working condition and high-speed working condition are selected, the intelligent auxiliary driving is enabled to drive, and the energy consumption E 31-11 , E 31-12 , E 31-13 of the electric drive system of the vehicle under the road city working condition, suburban working condition and high-speed working condition is tested. 32-11 32-12 32-13 Then, the recorded road state, traffic flow information and traffic light information are used to construct the traffic scene by using the simulation software, and then the energy consumption E 32-11 , E 32-12 , E 32-13 of the electric drive system under the road city working condition, suburban working condition and high-speed working condition on the test bench under the condition of closing the intelligent auxiliary driving system is tested, respectively.

[0073] K3=k 31 × E 31-11 / E 32-11 + k 32 × E 31-12 / E 32-12 + k 33 × E 31-13 / E 32-13 ;

[0074] In the formula, k 31 , k 32 , k 33 are coefficients, and the sum of the three is 1.

[0075] In the embodiments of the present application, the discrete coefficient of the vehicle energy consumption is obtained by selecting a typical road condition, continuously performing n times of vehicle energy consumption test under the intelligent auxiliary driving system, and obtaining the energy average value E4 and the standard deviation S4. Specifically, when calculating the discrete coefficient of the vehicle energy consumption, the same condition is selected, the intelligent auxiliary driving mode is continuously used for driving for multiple times, the discrete coefficient of the vehicle energy consumption is calculated, and is denoted as CV.

[0076] Generally, the vehicle energy consumption under the intelligent auxiliary driving is tested for a certain number n of times, generally not less than 10 times, on a typical comprehensive road, and the vehicle energy consumption is E 41 , E 42 , …, E 4n .

[0077] The energy average value E4 is calculated as E 41 +E 42 +…+E 4n / n.

[0078] The standard deviation S4 is calculated as .

[0079] In the formula, E 4i represents the vehicle energy consumption measured for the ith time, and i takes a value from 1 to n.

[0080] The discrete coefficient CV of the vehicle energy consumption is calculated as CV = S4 / E4.

[0081] In the embodiments of the present application, the vehicle condition energy consumption E5 under the intelligent auxiliary driving mode is the vehicle condition energy consumption E5 under the intelligent auxiliary driving system driving at different temperature environments, including the normal temperature condition energy consumption E 5-normaltemp , the low temperature condition energy consumption E 5-lowtemp , and the high temperature condition energy consumption E 5-hightemp .

[0082] The normal temperature condition energy consumption E 5-normaltemp is calculated as E 12-normaltemp ×K3+ ( (W 14_1-normaltemp +W 21-normaltemp ) ×T 14_1 + (W 14_2- normaltemp +W 22-normaltemp ) ×T 14_2 ) / K DCDC-normaltemp .

[0083] In the formula, K DCDC-normaltemp is the efficiency of the DCDC under the normal temperature condition; E 12-normaltemp is the power consumption of the vehicle electric drive system under the normal temperature condition; W 14_1-normaltemp is the average power of the vehicle under the normal temperature condition; and W 21-normaltemp is the average power of the vehicle under the normal temperature condition.T is the static average power of the intelligent auxiliary driving system under normal temperature conditions, T 14_1 W is the static time, W 14_2-normaltemp T is the average power of the DCDC under normal temperature conditions when the vehicle is not static, W 22-normaltemp T is the dynamic average power of the intelligent auxiliary driving system under normal temperature conditions, T 14_2 T is the non-static time;

[0084] Low-temperature condition energy consumption E 5-lowtemp =E 12-lowtemp × K3+ ((W 14_1-lowtemp +W 21-lowtemp )× T 14_1 +(W 14_2-lowtemp +W 22-lowtemp )× T 14_2 ) / K DCDC-lowtemp +E 17-lowtemp +E 18-lowtemp ;

[0085] Among them, E 12-lowtemp is the power consumption of the electric drive system of the vehicle under low temperature conditions, W 14_1-lowtemp is the average power of the DCDC under low temperature conditions when the vehicle is static, W 21-lowtemp is the static average power of the intelligent auxiliary driving system under low temperature conditions, T 14_1 W is the static time, W 14_2-lowtemp is the average power of the DCDC under low temperature conditions when the vehicle is not static, W 22-lowtemp T is the dynamic average power of the intelligent auxiliary driving system under low temperature conditions, T 14_2 T is the non-static time; K DCDC-lowtemp is the efficiency of the DCDC under low temperature conditions, E 17-lowtemp is the energy consumption of the air conditioner compressor under low temperature conditions, E 18-lowtemp is the energy consumption of the heating component PTC under low temperature conditions;

[0086] High-temperature condition energy consumption E 5-hightemp =E 12-hightemp × K3+ ((W 14_1-hightemp +W 21-hightemp )× T 14_1 +(W 14_2- hightemp +W 22-hightemp )× T 14_2 ) / K DCDC-hightemp +E 17-hightemp ;

[0087] Among them, E 12-hightemp is the power consumption of the electric drive system of the vehicle under high temperature conditions, W 14_1-hightemp is the average power of the DCDC under high temperature conditions when the vehicle is static, W 21-hightempT is the static average power of the intelligent auxiliary driving system under high-temperature working conditions, W 14_1 W is the static time 14_2- hightemp T is the average power of the DCDC under high-temperature working conditions when the vehicle is not static, W 22-hightemp T is the dynamic average power of the intelligent auxiliary driving system under normal-temperature working conditions, W 14_2 K is the non-static time DCDC-hightemp E is the efficiency of the DCDC under high-temperature working conditions 17-hightemp P is the energy consumption of the air conditioner compressor under high-temperature working conditions

[0088] Considering that the intelligent auxiliary driving system has different energy efficiencies in actual working conditions, preferably, the actual working condition energy consumption is obtained by multiplying the working condition energy consumption of the vehicle under different temperature environments when the intelligent auxiliary driving system is started and the discrete coefficient, and specifically, in the embodiment of the present application, the discrete coefficient CV is introduced to represent the energy consumption value E 5-1 under actual conditions, and the calculation method is as follows:

[0089] E 5-1 =E5x (1±CV).

[0090] Among them, the energy consumption value E 5-1 under actual conditions is also divided into the energy consumption E 5-1-normaltemp under actual conditions under normal-temperature working conditions, the energy consumption E 5-1-lowtemp under actual conditions under low-temperature working conditions, and the energy consumption E 5-1-hightemp under actual conditions under high-temperature working conditions.

[0091] The basic principles and main features of the present application and the advantages of the present application are shown and described above, and it is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or basic characteristics of the present application;

[0092] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application.

[0093] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description manner of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that those skilled in the art can understand.

Claims

1. A method for analyzing energy efficiency of a vehicle intelligent assisted driving mode, characterized in that, The method comprises the steps of: Testing the power consumption of the electric drive system of the vehicle in different temperature environments when the intelligent auxiliary driving system is turned off, and the average power of the DCDC in the vehicle at rest and in the vehicle in motion; Testing the static average power in different temperature environments when the intelligent auxiliary driving system is turned on, and calculating the high and low temperature influence coefficient based on the static average power; Determining the dynamic average power in different road conditions and normal temperature environments when the intelligent auxiliary driving system is turned on, calculating the dynamic average power of the intelligent auxiliary driving system in the normal temperature environment based on the dynamic average power of the intelligent auxiliary driving system in different road conditions and normal temperature environments, the road condition coefficient, the extreme weather coefficient, and the travel time coefficient, and multiplying the dynamic average power of the intelligent auxiliary driving system in the normal temperature environment by the high and low temperature influence coefficient to obtain the dynamic average power of the intelligent auxiliary driving system in the high and low temperature environment; Testing the energy consumption contribution coefficient of the intelligent auxiliary driving system; Measuring the energy consumption of the vehicle in the same condition for multiple times, and calculating the discrete coefficient of the vehicle energy consumption based on the measured energy consumption; According to the power consumption of the electric drive system in different temperature environments, the dynamic average power of the intelligent auxiliary driving system in different temperature environments, the average power of the DCDC in the vehicle at rest and in the vehicle in motion, the rest time and the non-rest time, and the energy consumption contribution coefficient, the working condition energy consumption of the vehicle in different temperature environments is calculated when the intelligent auxiliary driving system is turned on, and then the actual working condition energy consumption is determined according to the discrete coefficient of the vehicle energy consumption.

2. The method of claim 1, wherein, The high and low temperature influence coefficient includes a high temperature influence coefficient and a low temperature influence coefficient, the high temperature influence coefficient = high temperature static average power / normal temperature static average power, and the low temperature influence coefficient = low temperature static average power / normal temperature static average power.

3. The method of claim 1, wherein, The dynamic average power of the intelligent auxiliary driving system in the normal temperature environment is calculated as follows: W 22-normaltemp = K 22-1 × K 22-4 × W 22-1 + K 22-1 × K 22-5 × W 22-2 + K 22-2 × K 22-4 × W 22-3 + K 22-2 × K 22-5 × W 22-4 + K 22-3 × K 22-4 × W 22-5 + K 22-3 × K 22-5 × W 22-6 + K 22-6 × W 22-7 + K 22-6 × W 22-8 + K 22-6 × W 22-9 ; wherein K 22-1 is a coefficient for urban road use, K 22-2 is a coefficient for suburban road use, K 22-3 is a coefficient for highway road use, K 22-4 is a coefficient for daytime use, K 22-5 is a coefficient for nighttime use, K 22-6 is a coefficient for extreme weather use, W 22-1 , W 22-2 , W 22-3 , W 22-4 , W 22-5 , W 22-6 , W 22-7 , W 22-8 , W 22-9 are, in sequence, the dynamic average power of the intelligent auxiliary driving system in a normal temperature environment under urban road daytime, urban road nighttime, suburban road daytime, suburban road nighttime, highway road daytime, highway road nighttime, urban road extreme weather, suburban road extreme weather, and highway road extreme weather working conditions.

4. The method of claim 1, wherein the vehicle intelligent auxiliary driving mode energy efficiency test analysis method is characterized by, The dynamic average power of the intelligent auxiliary driving system in different road conditions and normal temperature environments when the intelligent auxiliary driving system is turned on is obtained through typical road testing and / or calculated based on the determined coefficient after obtaining part of the test results.

5. The method of claim 3, wherein the vehicle intelligent auxiliary driving mode energy efficiency test analysis method is characterized by, The intelligent auxiliary driving system has a dynamic average power W in the following working conditions: suburban road at night in normal temperature environment, highway at night in normal temperature environment, suburban road in extreme weather, and highway in extreme weather 22-4 , W 22-6 , W 22-7 , W 22-8 , W 22-9 The calculation is obtained by the following formula: W 22-4 =W 22-3 ×K night , night coefficient K night =W 22-2 / W 22-1 ; W 22-6 = W 22-5 ×K night ; W 22-8 =K badwether ×W 22-3 , extreme weather coefficient K badwether =W 22-7 / W 22-1 ; W 22-9 =K badwether ×W 22-5 .

6. The method of claim 1, wherein, The energy consumption contribution coefficient of the intelligent auxiliary driving system is obtained by weighting and adding the energy consumption of the electric drive system of the vehicle in different road conditions when the intelligent auxiliary driving system is turned on, and the energy consumption of the electric drive system in different road conditions in the simulation traffic scene when the intelligent auxiliary driving system is turned off, and the energy consumption contribution coefficient of the intelligent auxiliary driving system is represented as K3. K3=k 31 x E 31-11 / E 32-11 + k 32 x E 31-12 / E 32-12 + k 33 x E 31-13 / E 32-13 ; wherein E 31-11 , E 31-12 , E 31-13 is to enable intelligent auxiliary driving system driving in typical working conditions to test the energy consumption of the vehicle electric drive system in road urban working conditions, suburban working conditions and high-speed working conditions; E 32-11 、E 32-12 、E 32-13 To close the intelligent auxiliary driving system driving on the test bench to test the energy consumption of the vehicle electric drive system in the road city working condition, suburban working condition and high speed working condition; k 31 , k 32 , k 33 are coefficients, the sum of which is 1.

7. The method of claim 6, wherein the vehicle intelligent auxiliary driving mode energy efficiency test analysis method is characterized by, The simulation traffic scene is constructed based on the recorded road state, traffic flow information, and traffic light information during the testing process of the intelligent auxiliary driving system in the on state and in the typical condition.

8. The method of claim 1, wherein the vehicle intelligent auxiliary driving mode energy efficiency test analysis method is characterized by, The discrete coefficient of the vehicle energy consumption is obtained by selecting a typical road condition and continuously testing the vehicle energy consumption when the intelligent auxiliary driving system is turned on for n times, including: CV = S4 / E4; E4 = (E 41 + E 42 +... + E 4n ) / n; S4= ； Wherein, E 41 , E 42 … E 4n is the energy consumption of the whole vehicle under the n-time opening of the intelligent auxiliary driving system, E 4i refers to the energy consumption of the whole vehicle measured for the ith time, and i takes the value of 1 to n.

9. The method of claim 6, wherein the vehicle intelligent auxiliary driving mode energy efficiency test analysis method is characterized by, The working condition energy consumption of the vehicle in different temperature environments when the intelligent auxiliary driving system is turned on includes: Energy consumption E under normal temperature condition 5-normaltemp =E 12-normaltemp ×K3+((W 14_1-normaltemp +W 21-normaltemp )×T 14_1 +(W 14_2- normaltemp +W 22-normaltemp )×T 14_2 ) / K DCDC-normaltemp ; K DCDC-normaltemp is the efficiency of the DCDC under normal temperature conditions; E 12-normaltemp is the power consumption of the vehicle electric drive system under normal temperature conditions, W 14_1-normaltemp is the average power of the vehicle under normal temperature conditions, W 21-normaltemp is the average power of the intelligent auxiliary driving system under normal temperature conditions, T 14_1 is the static time, W 14_2-normaltemp is the average power of the vehicle under normal temperature conditions, W 22-normaltemp is the average power of the intelligent auxiliary driving system under normal temperature conditions, T 14_2 is the non-static time; Low-temperature working condition energy consumption E 5-lowtemp = E 12-lowtemp × K3+ (W 14_1-lowtemp + W 21-lowtemp ) × T 14_1 + (W 14_2-lowtemp + W 22-lowtemp ) × T 14_2 ) / K DCDC-lowtemp + E 17-lowtemp + E 18-lowtemp ; Wherein, E 12-lowtemp is the power consumption of the vehicle electric drive system under low temperature conditions, W 14_1-lowtemp is the average power of DCDC under low temperature conditions for the vehicle to remain stationary, W 21-lowtemp is the static average power of the intelligent auxiliary driving system under low temperature conditions, T 14_1 is the stationary time, W 14_2-lowtemp is the average power of DCDC under low temperature conditions for the vehicle to be non-stationary, W 22-lowtemp is the dynamic average power of the intelligent auxiliary driving system under low temperature conditions, T 14_2 is the non-stationary time; K DCDC-lowtemp is the efficiency of DCDC under low temperature conditions, E 17-lowtemp is the energy consumption of the air conditioner compressor under low temperature conditions, E 18-lowtemp is the energy consumption of the heating assembly PTC under low temperature conditions; High-temperature working condition energy consumption E 5-hightemp = E 12-hightemp × K3+ (W 14_1-hightemp + W 21-hightemp ) × T 14_1 + (W 14_2 -hightemp + W 22-hightemp ) × T 14_2 ) / K DCDC-hightemp + E 17-hightemp ; Wherein, E 12-hightemp is the power consumption of the vehicle electric drive system under high temperature working condition, W 14_1-hightemp is the average power of DCDC under high temperature working condition when the vehicle is stationary, W 21-hightemp is the static average power of the intelligent auxiliary driving system under high temperature working condition, T 14_1 is the stationary time, W 14_2- hightemp is the average power of DCDC under high temperature working condition when the vehicle is not stationary, W 22-hightemp is the dynamic average power of the intelligent auxiliary driving system under normal temperature working condition, T 14_2 is the non-stationary time; K DCDC-hightemp is the efficiency of DCDC under high temperature working condition, E 17-hightemp is the energy consumption of the air conditioner compressor under high temperature working condition.

10. The method of claim 1, wherein, The actual working condition energy consumption is obtained by multiplying the working condition energy consumption of the vehicle under different temperature environments and the intelligent auxiliary driving system being started and driving and a dispersion coefficient.

Images