Energy-saving effect evaluation system and method for intelligent energy management strategy of hybrid electric vehicles

Abstract

The present invention relates to the field of passenger vehicle energy-saving technology, and discloses a system and method for evaluating the energy-saving effect of a hybrid vehicle intelligent energy management strategy. The system comprises a map navigation simulation module, a non-intelligent hub fuel consumption test module, an intelligent hub fuel consumption test module, an oil-to-electricity energy conversion and wheel-side energy normalization module, and an energy-saving effect evaluation module. The present invention activates / deactivates the intelligent energy management strategy of a test vehicle by simulating navigation map information, and uses a chassis dynamometer for testing, eliminating the influence of factors such as the road environment, traffic conditions, and weather conditions to obtain the energy-saving effect of the intelligent energy management strategy. This method has the characteristics of high accuracy, short test cycle, and strong practicality, and can also reversely support the calibration and optimization of the intelligent energy management strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation of passenger vehicles, and in particular to a system and method for evaluating energy conservation effects of an intelligent energy management strategy for hybrid electric vehicles. Background Art

[0002] Since the development of hybrid vehicles, low carbonization has always been a primary goal, and intelligent energy management strategies are one of the key means for hybrid vehicles to achieve low carbonization. Currently, the energy-saving effects of intelligent energy management strategies are generally evaluated using outdoor actual road testing. Existing outdoor actual road testing evaluation methods require engineers to manually set up actual test roads for energy consumption testing based on their experience, determine the representativeness of the roads based on experience, and manually plan actual road test conditions. The energy-saving effects are evaluated by activating / deactivating the intelligent energy management strategy of the test vehicle and comparing the energy consumption test results of actual road driving. However, due to the uncertainty of the representativeness of the test roads, the road environment, traffic conditions, and weather conditions are difficult to standardize and repeat, resulting in large fluctuations in test results. This method cannot accurately evaluate energy-saving effects, and its long test cycle makes it difficult to support calibration optimization. The technical characteristics of these methods have certain limitations. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned problems existing in the prior art and provide a system and method for evaluating the energy-saving effect of the intelligent energy management strategy of hybrid electric vehicles. The present invention activates / disables the intelligent energy management strategy of the test vehicle by simulating navigation map information, uses a chassis dynamometer for testing, eliminates the influence of factors such as road environment, traffic conditions and weather environment, and obtains the energy-saving effect of the intelligent energy management strategy. The method has the characteristics of high accuracy, short test cycle, strong practicality, etc., and can reversely support the calibration and optimization work of the intelligent energy management strategy.

[0004] To achieve the above-mentioned object, the present invention provides a hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system, comprising: a map navigation simulation module for correcting road driving condition data using a short-trip analysis method to generate corrected map navigation information;

[0005] The non-intelligent fuel consumption test module is used to switch the hybrid vehicle to the main mode. In the main mode, the speed and time curve in the road driving condition data is combined with the average slope in the road driving condition data to obtain the fuel consumption results of the hybrid vehicle in the main mode.

[0006] The rotating hub intelligent fuel consumption test module is used to switch the hybrid vehicle to intelligent mode. In intelligent mode, the corrected map navigation information, the speed and time curve in the road driving condition data, and the average slope in the road driving condition data are combined to obtain the fuel consumption results of the hybrid vehicle in intelligent mode.

[0007] The normalization module is used to use the law of conservation of energy to perform charge correction on the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode, respectively, to obtain the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode after charge correction, and then perform wheel-side energy normalization processing on the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode after charge correction, respectively, to obtain the corrected fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode;

[0008] The energy-saving effect evaluation module is used to evaluate the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle based on the fuel consumption results of the hybrid vehicle in the main mode and the intelligent mode after correction.

[0009] Beneficial effects of the present invention:

[0010] The present invention can be directly applied to the development of correlation between energy-saving field evaluation and vehicle calibration of hybrid electric vehicles, and has the following advantages:

[0011] Improved work: This approach closes the loop on smart energy management strategy development, facilitating evaluation and improvement efforts.

[0012] Scientific Method: This method combines historical user driving data to make route planning more representative. It has fewer influencing factors and can be replicated multiple times, facilitating evaluation and improvement.

[0013] Working cycle: This design method can avoid delays caused by weather conditions and effectively shorten the test cycle without affecting the original project development cycle;

[0014] Technical cost: The technical principle of this method is simple and the technical cost is low;

[0015] Combining virtual map with chassis dynamometer: Using technical means to combine virtual map information with chassis dynamometer test conditions, it realizes indoor road test.

[0016] Scientific evaluation: Use test equipment and theoretical calculations to obtain accurate fuel consumption results under test conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the present invention;

[0018] Figure 2 is a graph of a short stroke of an embodiment of the present invention;

[0019] Figure 3 is a cumulative frequency graph of the present invention;

[0020] Figure 4 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0021] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0022] The endpoints of the ranges and any values ​​disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0023] Example 1

[0024] like Figure 1 The energy-saving effect evaluation system of the hybrid electric vehicle intelligent energy management strategy shown in the figure includes:

[0025] The map navigation simulation module is used to correct the road driving condition data using the short-trip analysis method to generate corrected map navigation information;

[0026] The non-intelligent fuel consumption test module switches the hybrid vehicle to the main mode and uses a chassis dynamometer to test the fuel consumption of the hybrid vehicle in the main mode according to the speed and time curve in the road driving condition data and the average slope in the road driving condition data.

[0027] The rotating hub intelligent fuel consumption test module is used to switch the hybrid vehicle to intelligent mode. The hybrid vehicle uses the chassis dynamometer to test the fuel consumption of the hybrid vehicle in intelligent mode based on the corrected map navigation information, the speed and time curve in the road driving condition data, and the average slope in the road driving condition data.

[0028] The normalization module is used to use the law of conservation of energy to perform charge correction on the fuel consumption results of the hybrid electric vehicle when traveling in the main mode and the fuel consumption results of the hybrid electric vehicle when traveling in the smart mode, respectively, to obtain the fuel consumption results of the hybrid electric vehicle when traveling in the main mode after charge correction and the fuel consumption results of the hybrid electric vehicle when traveling in the smart mode after charge correction, and then perform wheel-side energy normalization processing on the fuel consumption results of the hybrid electric vehicle when traveling in the main mode after charge correction and the fuel consumption results of the hybrid electric vehicle when traveling in the smart mode after charge correction, respectively, to obtain the corrected fuel consumption results of the hybrid electric vehicle when traveling in the main mode and the corrected fuel consumption results of the hybrid electric vehicle when traveling in the smart mode;

[0029] The energy-saving effect evaluation module is used to evaluate the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle based on the modified fuel consumption results of the hybrid vehicle running in the main mode and the modified fuel consumption results of the hybrid vehicle running in the intelligent mode.

[0030] In the technical solution of the present invention, the method for constructing the road driving condition includes the following steps:

[0031] S1. Cutting the original driving data of the vehicle into a plurality of short trips, and obtaining a plurality of driving databases according to a preset road type and the plurality of short trips, wherein each short trip includes a running segment and an idling segment;

[0032] S2. For each of the driving databases, based on the characteristic values ​​of the running segments and the characteristic values ​​of the idling segments of each of the short trips in the corresponding database, obtaining target operating condition parameters that meet the target operating condition duration, combining target short trips in the driving database that match the target operating condition parameters to obtain multiple groups of short trip sets to be selected, and testing each group of the short trip sets to obtain an optimal short trip set;

[0033] S3. Aggregating the optimal short-trip sets of the various driving databases to obtain a road driving condition set, where the road driving condition set is used to characterize an actual driving condition of a vehicle on a road.

[0034] In the present invention, the vehicle mainly refers to a passenger vehicle for users, and the original driving data mainly refers to actual working condition data of the passenger vehicle traveling on urban roads.

[0035] Among them, the original driving data refers to the historical driving condition data of the vehicle on different types of roads. It is obtained by collecting the time, speed, slope, longitude and latitude signals in the TBOX big data of multiple (>100) vehicles with a data frequency of 1s (taking into account the accuracy of the test data and the cloud storage capacity, the accuracy of collecting data with a frequency greater than 1s will deteriorate, and the requirements for cloud storage capacity will increase if the data is collected with a frequency less than 1s); and synchronously obtaining the start / end coordinates, start / end distance, start / end estimated travel time, number of traffic lights, traffic light location, traffic light cycle, road type, road speed limit, road condition information, weather, temperature and other information corresponding to the time and longitude and latitude in the map navigation signal to form the original driving data.

[0036] Among them, the different types of roads mainly refer to roads with different speed requirements. For road types, they can be divided according to CJJ37-2012 "Urban Road Engineering Design Code". For example, according to the driving speed requirements, road types can be divided into low-speed roads, medium-speed roads and high-speed roads.

[0037] like Figure 2 As shown, the original driving data of the vehicle is cut to obtain several short trips, each of which reflects the traffic conditions experienced by the vehicle when driving on the actual road. Each short trip includes a running segment and an idle segment. The running segment refers to the running segment of the vehicle between one start and the next stop, and the idle segment refers to the idle segment of the vehicle from one stop to the next start. That is, the speeds of the starting point and the end point of the moving segment are both 0, and the speed of the entire idle segment is 0. Therefore, the moving segment and the idle segment can be smoothly spliced. That is to say, the embodiment of the present application cuts the original driving database into several short trips. The purpose is to be able to smoothly splice each short trip when obtaining a subsequent short trip set that meets the target operating condition length, thereby improving data processing efficiency.

[0038] In the present invention, multiple driving databases are obtained based on a preset road type and a number of short trips. These databases can be divided into a low-speed database, a medium-speed database, and a high-speed database based on low-speed roads, medium-speed roads, and high-speed roads. Each short trip is stored in a corresponding database based on its speed. In other words, each database includes multiple short trips. For example, the low-speed database includes short trips with a speed of less than 50 km / h, the medium-speed database includes short trips with a speed between 50 and 80 km / h, and the high-speed database includes short trips with a speed greater than 80 km / h.

[0039] Furthermore, after cutting the original driving data of the vehicle to obtain a plurality of short trips, the method further includes:

[0040] First, the data is smoothed by Kalman filtering, and then several short trips are cleaned based on preset cleaning rules. Then, the data are filtered according to the test working conditions (such as weather, temperature, road conditions, road type, etc.). The original database is constructed for each short trip obtained by screening, so that the original database can be divided into multiple driving databases according to the preset road type.

[0041] Among them, each of the short strokes includes multiple characteristic values, including but not limited to the maximum vehicle speed, average vehicle speed, average operating speed, maximum acceleration, maximum deceleration, average acceleration of the acceleration section, average deceleration of the deceleration section, relative positive acceleration, idle section ratio, acceleration ratio, deceleration section ratio, uniform speed section ratio, etc. By analyzing the above characteristic values ​​of each short stroke, it can be known whether the corresponding short stroke is a short stroke that meets the requirements, so as to facilitate subsequent calculation of the target operating condition parameters.

[0042] In the present invention, the preset cleaning rules may be as shown in Table 1:

[0043] Table 1

[0044]

[0045] For example, the total duration of the original driving data is 100,000 seconds. After filtering through the preset filtering rules, the total duration of each short trip obtained is 80,000 seconds. That is to say, the 20,000 seconds of short trip data that do not meet the requirements are filtered out.

[0046] In some embodiments, for each characteristic value of the short stroke, a corresponding target characteristic value is pre-set, and the target characteristic value refers to a theoretical characteristic value that meets the preset requirements. By calculating the degree of deviation between each characteristic value and the corresponding target characteristic value, it can be determined whether the short stroke is a short stroke that meets the requirements. For example, the maximum degree of deviation between each characteristic value and the corresponding target characteristic value is required to be -93% to 107%.

[0047] In step S2, the target driving condition duration refers to the sum of the durations of all short trips in the resulting road driving condition set. It should be noted that if the actual driving condition duration is too long, it will affect the feasibility and cost of the drum test; if it is too short, the resulting data features will not be representative.

[0048] Since each driving database corresponds to a target operating condition duration, the sum of the target operating condition durations of each driving database is the final aggregate operating condition duration. For example, based on current regulatory requirements, the sum of the target operating condition durations is set to 1800 seconds. Of course, the sum of the target operating condition durations can also be 1600 seconds, 2000 seconds, 2100 seconds, etc., without limitation here. Then, the target operating condition durations for the low-speed database, medium-speed database, and high-speed database can each be 600-800 seconds, etc.

[0049] In some embodiments, the target operating condition duration of each driving database is determined as follows: first, the sum of the target operating condition durations is determined, for example, 1800 seconds; second, based on the total duration of the original database or the filtered original database, and the ratio of the duration of each driving database to the original database, the ratio between the target operating condition duration and the sum of the target operating condition durations is determined, and then each target operating condition duration is obtained.

[0050] In the present invention, it is more appropriate to control the sum of the durations of each target operating condition to be 0.5-1h.

[0051] In step S2, the target operating condition duration includes a first operating condition duration and a second operating condition duration, and the target operating condition parameters include a first operating condition parameter and a second operating condition parameter. Obtaining the target operating condition parameters that meet the target operating condition duration based on the characteristic values ​​of the running segments and the characteristic values ​​of the idling segments of each short trip in the corresponding database includes:

[0052] S21. Obtaining first operating condition parameters that satisfy the first operating condition duration based on the operating segment duration characteristics and operating segment frequency characteristics of each short trip in the corresponding database, where the first operating condition parameters include the number of first time periods and the sub-duration of each first time period;

[0053] In step S21, obtaining the first operating condition parameter that satisfies the first operating condition duration according to the operating segment duration characteristics and operating segment frequency characteristics of each short trip in the corresponding database includes:

[0054] S211. Arrange the running segments of each short trip in the corresponding database in ascending order according to the duration, obtain the frequency of each duration, and obtain the cumulative frequency of each duration based on each frequency (the purpose of arranging the running segments in ascending order according to the duration is to facilitate the subsequent calculation of the frequency and cumulative frequency of each duration, wherein the frequency of each duration refers to the number of times each duration of the running segment in the corresponding database appears, and the cumulative frequency of each duration is the sum of the frequencies of each duration. By calculating the frequency and cumulative frequency, each short trip can be normalized to facilitate subsequent calculations; for example, Figure 3 As shown, Figure 3 After arranging in ascending order, there is 1 1s, 2 2s, 3 3s, 4 5s, a total of 10, the probability of ST1 (1s) is 0.1, the cumulative frequency of 1s is 0.1, the probability of ST2 (2s) is 0.2, and the cumulative frequency is 0.1 + 0.2 = 0.3);

[0055] S212: Determine the target number of operating segments based on the first operating condition duration, and evenly divide the accumulated frequency according to the target number to obtain the number of first time periods that meet the first operating condition duration. (The first operating condition duration refers to the sum of the durations of all operating segments within the target operating condition duration. Therefore, based on the first operating condition duration, it is possible to determine the specific number of operating segments required to meet the first operating condition duration, i.e., the target number of operating segments.)

[0056] In the present invention, the target number of running segments and idling segments are calculated based on the same principle. Therefore, the two are described together below. After determining the target operating condition duration corresponding to each driving database, the number of running segments and the number of idling segments that constitute the target operating condition duration can be calculated as follows:

[0057] First, according to the duration of each running segment, the average running time T of the running segment is calculated. spd and the average idling time T of the idling section zero ,as follows:

[0058]

[0059] Where, T spd1 、T spd2 ,…,T spdn Represents the duration of each running segment, n represents the total number of running segments in the corresponding driving database, T zero1 、T zero2 ,…,T zero(n+1) Represents the duration of each idle segment, and n+1 represents the total number of idle segments in the corresponding driving database.

[0060] Secondly, according to the normal running time T of the running segment spd and the average idling time T of the idling section zero , calculate the number of operating segments N that constitute the target operating time st and the number of idle segments N li ,as follows:

[0061]

[0062] N li =N st +1

[0063] Where T represents the target operating time.

[0064] For example, if the cumulative frequency is 100% and the target number of the operating segments is 5, then after dividing the cumulative frequency equally according to the target number, the first period with a cumulative frequency interval of 20% is obtained. That is to say, the first operating condition duration can be constructed by the 5 first periods.

[0065] S213: Determine the sub-duration of the corresponding first time period according to the duration of each of the running segments in each of the first time periods.

[0066] Wherein, each first time period is actually each time period with a certain cumulative frequency, for example,

[0067] The frequency is 100%, and the target number of operating segments is 5. Then, each first time period is actually a frequency segment with a cumulative frequency interval of 20%. Then, each first time period actually also includes multiple operating segments. By obtaining the duration and number of each operating segment, the average duration is calculated, and the average duration is used as the sub-duration of the corresponding first time period. Of course, the median of the duration of each operating time period can also be used as the sub-duration of the corresponding first time period, which is not limited here.

[0068] In step S213, determining the sub-duration of the corresponding first time period according to the duration of each of the running segments in each of the first time periods includes:

[0069] If the total duration of each operating segment in the last of the first time period is greater than the target difference, the sub-duration of the last of the first time period is assigned to the target difference, and the target difference is the difference between the first operating condition duration and the sum of the sub-durations of other first time periods.

[0070] Among them, since the duration of each running segment is arranged in ascending order, the last

[0071] The total duration of each operating segment in the first time period may be greater than the target difference. In order to meet the first working condition duration, the last sub-duration of the first time period is directly assigned to the target difference to meet the demand.

[0072] S22. Obtain second operating condition parameters that meet the second operating condition duration based on the idle segment duration characteristics and idle segment frequency characteristics of each short trip in the corresponding database. The second operating condition parameters include the number of second time periods and the sub-duration of each second time period.

[0073] The calculation principle of the second operating condition parameters corresponding to the idle stage is the same as that of the running stage, and is not repeated here.

[0074] After calculating the sub-duration of each first period of the operating segment and the sub-duration of each second period of each idle segment, each operating segment and each idle segment are interspersed and spliced ​​to obtain the target operating condition duration.

[0075] In step S2, the target short trips in the driving database that match the target operating condition parameters are combined to obtain multiple groups of short trip sets to be selected, including:

[0076] The target short trips in the driving database that match the target operating condition parameters are combined through Cartesian products to obtain multiple groups of short trip sets to be selected.

[0077] Furthermore, the Cartesian product, also known as the direct product, is the Cartesian product of two sets X and Y, expressed as X×Y, where the first object is a member of X and the second object is a member of all possible ordered pairs of Y. In this embodiment, the two sets can be the sub-durations of each operating segment and each sub-duration of each idle segment within the target operating duration. Using the Cartesian product, all possible combinations of short segments are enumerated and combined to form multiple sets of candidate short trip sets.

[0078] For example, the target operating condition duration of the low-speed database is 400 seconds, including 5 operating segments and 6 idling segments. The sub-duration of each operating segment and the sub-duration of each idling segment can be calculated as described above. For example, if the sub-duration of one of the operating segments is 10 seconds, then in the low-speed database, there may be 10 or even 20 groups of short-stroke operating segments that meet the 10-second requirement. Therefore, the groups of short strokes can be combined to obtain multiple groups of short-stroke sets to be selected.

[0079] In step S2, based on the above solution, each group of the short-trip sets to be selected is tested to obtain the optimal short-trip set, including:

[0080] Obtaining a speed and acceleration probability distribution for each group of the short trip sets to be selected, and obtaining a degree of deviation between the short trip sets to be selected and a target trip set of the corresponding group based on the speed and acceleration probability distributions, the target trip set including each short trip in the corresponding driving database;

[0081] In this embodiment, based on the above solution, obtaining the degree of deviation between the selected short trip set and the target trip set of the corresponding group according to the speed and acceleration probability distribution includes:

[0082] The degree of deviation between the actual value of the speed and acceleration probability distribution and the target value is obtained by a chi-square test, and the target value is the speed and acceleration probability distribution of the target stroke set.

[0083] Among them, each group of short-trip sets to be selected obtained by enumeration and combination through Cartesian product corresponds to a VT curve, that is, a vehicle speed and time curve. The probability distribution of speed and acceleration can be calculated through the VT curve. It can be understood that calculation through probability distribution is actually normalizing the data, so that the data is in a unified dimension and is feasible for mutual comparison.

[0084] The set of short-trip sets to be selected with the smallest deviation is taken as the optimal short-trip set.

[0085] It can be understood that the group of short trip sets to be selected with the smallest degree of deviation can best represent the actual driving condition of the vehicle on the road.

[0086] This embodiment obtains a plurality of driving databases by cutting the original driving data into a number of short trips, and obtains target operating condition parameters that meet the target operating condition duration of each database based on the characteristics of each short trip in each driving data. Then, a corresponding optimal trip set is obtained based on the target operating condition parameters. After aggregating the various optimal trip sets, a road driving condition set is obtained to characterize the actual driving condition of the vehicle on the road. That is, the embodiment of the present application will obtain a road driving condition set that greatly reduces the operating condition duration and can accurately characterize the actual driving condition by extracting and aggregating features from a massive amount of original driving data, thereby improving the user experience and reducing a large amount of test time and cost during actual vehicle development.

[0087] The road driving condition constructed by the above method includes data information such as time, vehicle speed, slope, longitude, latitude, starting / end point coordinates, starting / end point distance, starting / end point estimated travel time, number of traffic lights, traffic light location, traffic light cycle, road type, road speed limit, road condition information, weather, temperature, etc.; the time and vehicle speed are formed into a vehicle speed and time curve, that is, the vehicle speed and time curve in the road driving condition data is obtained.

[0088] Furthermore, to enable indoor testing of intelligent energy management strategies, the vehicle must first recognize navigation information and make decisions based on it. However, since the original data from the road driving profiles has been combined, the map information is distorted. Therefore, the road driving profile data must be corrected to generate corrected map navigation information before being sent to the test vehicle.

[0089] Among them, the specific method of using the short-trip analysis method to correct the road driving condition data includes: importing the road driving condition data into a map navigation simulation program, calculating the driving distance in km based on the speed and time curve in the road driving condition data, and using it as the distance from the starting point to the end point; then updating the estimated travel time from the starting point to the end point to the end time in the speed and time curve in the road driving condition data, in seconds; then correcting the longitude and latitude data with discontinuous signals in the road driving condition data, and finally obtaining continuous longitude and latitude data, and in the process of correcting the longitude and latitude data, synchronously correcting the signal data related to the longitude and latitude (all data are corresponding, for example, after the longitude and latitude are corrected, the final position of the end point is saved to the new longitude and latitude position); performing equivalent calculations on all the slope signal data in the road driving condition to obtain the average slope (α) (because subsequent tests are carried out on a chassis dynamometer, which cannot respond quickly to changes in slope, so the slope signal data is replaced by the average slope (α)).

[0090] In the above technical solution, the signal data related to longitude and latitude are mainly related to the position. In theory, the analog signal is used to shield the actual driving position changes, such as the coordinates of the starting point and the end point, the position of the traffic light, etc.

[0091] In the present invention, the specific method for correcting the discontinuous latitude and longitude data in the road driving condition data includes: re-segmenting the discontinuous latitude and longitude data in the road driving condition data (because the experimental conditions are spliced, there is, for example, data in Wuhan in the first section and data in Beijing in the second section, so it needs to be segmented into separate sections), forming [D1, D2, D3, ..., D g 】, then according to the latitude and longitude data at the end of D1, correct the latitude and longitude data at the start of D2, and subtract the latitude and longitude data at the end of D1 from all the latitude and longitude data in D2 to get the new D2 (for example, the latitude and longitude data at the end of the first section are 90, 90 respectively; the latitude and longitude data at the beginning of the second section are 180, 180; then subtract 90, 90 from all the latitude and longitude data in the second section at the same time); then according to the new latitude and longitude data at the end of D2, correct the latitude and longitude data at the start of D3, and subtract the new latitude and longitude data at the end of D2 from all the latitude and longitude data in D3 to get the new D3; and so on. g Also make corrections.

[0092] After correcting the relevant signals, the map navigation simulation program recombines all signal data including the corrected signals (time, vehicle speed, average slope, corrected continuous longitude and latitude, corrected coordinates of the starting point and end point, corrected distance from the starting point to the end point, corrected estimated travel time from the starting point to the end point, number of traffic lights, corrected traffic light positions, traffic light cycle, road type, road speed limit, road condition information, weather, temperature and other signals) to generate corrected map navigation information.

[0093] In the present invention, since the map navigation information needs to be closed-loop, and because the working conditions are recombined, it may happen that the start is not the start segment in the acquisition working condition, and the end is not the end segment in the acquisition working condition, so it is necessary to reset and correct this information to make the map navigation information reasonable.

[0094] Furthermore, in the present invention, the non-intelligent fuel consumption test module of the rotating hub is used to adjust the hybrid vehicle to the balance point (the hybrid vehicle maintains the battery near a certain SOC value through the engine, then the SOC is the balance point, the balance point is a description of the battery SOC, that is, the test can obtain the SOC starting point of the balance state) (the balance point can be provided by the manufacturer or determined through experiments, and it is difficult for hybrid vehicles to achieve complete balance considering the battery characteristics and signal accuracy. Therefore, the balance point in the present invention refers to the situation where the change in electric energy measured by the power analyzer is ≤±4% of the cycle energy according to the national standard), and the vehicle's electric balance state is collected by the power analyzer (the electric balance state is the data collected by the power analyzer, so that the battery power of the vehicle is balanced from the beginning to the end. The main energy comes from the engine. The balance point is controlled by the vehicle itself through calibration. For example, the manufacturer sets the balance point SOC to 15%. In reality, it will fluctuate around 15% according to the working conditions, and specific tests need to be carried out to confirm it). The vehicle is placed on a chassis dynamometer (the entire vehicle is driven onto the chassis dynamometer's rotating hub, and then the vehicle is fixed to the chassis dynamometer through a fixing device to ensure that the wheels can move but the entire vehicle is fixed in space). The speed and time curve in the road driving condition data is imported into the chassis dynamometer, the average slope (α) is loaded into the chassis dynamometer, the hybrid vehicle is switched to the main mode (the main mode is the mode corresponding to the time when it rolls off the factory as defined by regulations, generally referring to the driving mode + the drive mode), and the map navigation simulation program does not send the corrected map navigation information to the hybrid vehicle. The driver drives according to the speed and time curve in the road driving condition data (the actual vehicle speed is controlled within the range of the target value ±2km / h. If the actual vehicle speed deviation is too large, the speed and time curve in the road driving condition data constructed above is meaningless, which is also the national standard requirement; the driving time is determined according to the time of the speed and time curve in the loaded road driving condition data); chassis dynamometer detection is used (according to GB / T 19753-2021 corresponding energy consumption regulations test) to obtain the fuel consumption results of hybrid vehicles in the main mode; and record the electric balance state collected by the power analyzer during driving, the actual driving distance of the hybrid vehicle, and the actual driving speed and time curve of the driver.

[0095] In the present invention, the hub intelligent fuel consumption test module is used to adjust the hybrid vehicle to the vicinity of the balance point, and collect the vehicle's electrical balance state through a power analyzer, then fix the hybrid vehicle on a chassis dynamometer, import the vehicle speed and time curve in the road driving condition data into the chassis dynamometer, load the average slope (α) to the chassis dynamometer, switch the hybrid vehicle to the intelligent mode (the intelligent mode is a mode equipped with an intelligent energy management strategy), and the map navigation simulation program communicates with the vehicle network through the on-board U disk interface and the CAN (the U disk interface is an external interface, and the signal is transmitted through the CAN communication) of the vehicle network. The corrected map navigation information is sent to the hybrid vehicle (interactively) and read through the vehicle's built-in map software (the map information generally does not send the slope to the vehicle, and the slope signal is not accurate enough). The driver then drives according to the speed and time curve in the road driving condition data (the actual speed is controlled within the range of the target value ±2km / h), and the chassis dynamometer is used to calculate the fuel consumption results of the hybrid vehicle in intelligent mode; and the power balance status collected by the power analyzer during driving, the actual driving distance of the hybrid vehicle, and the actual speed and time curve of the driver are recorded.

[0096] Furthermore, since the electrical balance state of the vehicle will have a significant impact on the fuel consumption result, and the present invention is mainly intended to evaluate the energy-saving effect of the intelligent energy management strategy, the electrical balance state needs to be corrected.

[0097] The calculation formula for the power correction of the fuel consumption results of the hybrid vehicle driving in the main mode and the fuel consumption results of the hybrid vehicle driving in the smart mode is:

[0098] fuel1=F1-E1 / 100×K_fuel / S1×100

[0099] fuel2=F2-E2 / 100×K_fuel / S2×100

[0100] Wherein, fuel1 represents the fuel consumption of the hybrid vehicle in main mode after charge correction, in L / 100km; fuel2 represents the fuel consumption of the hybrid vehicle in smart mode after charge correction, in L / 100km; F1 represents the fuel consumption of the hybrid vehicle in main mode, in L / 100km; F2 represents the fuel consumption of the hybrid vehicle in smart mode, in L / 100km (both F1 and F2 are obtained by testing using a chassis dynamometer in accordance with national standards); E1 represents the energy discharged by the battery when the hybrid vehicle is in main mode, and E2 represents the energy discharged by the battery when the hybrid vehicle is in smart mode (E1 and E2 are obtained by integrating the current and voltage of a power analyzer, with charging as positive and discharging as negative, both in Wh / km, according to national standards); K_fuel represents the fuel-to-electricity conversion coefficient, in Wh / km; S1 represents the actual distance traveled by the hybrid vehicle in main mode; S2 represents the actual distance traveled by the hybrid vehicle in smart mode (both S1 and S2 are obtained by testing using a chassis dynamometer, both in km).

[0101] K_fuel is obtained through regulatory tests (energy consumption regulatory tests corresponding to GB / T19753-2021).

[0102] Furthermore, wheel-side energy normalization processing is performed on the fuel consumption results of the hybrid vehicle in the main mode after the power correction and the fuel consumption results of the hybrid vehicle in the smart mode after the power correction (the normalization of wheel-side energy is mainly to correct the impact of different driver speeds in each test). The specific method for obtaining the corrected fuel consumption results of the hybrid vehicle in the main mode and the corrected fuel consumption results of the hybrid vehicle in the smart mode includes:

[0103] The theoretical wheel energy (i.e., the wheel energy when the hybrid vehicle is driven completely according to the speed and time curve in the road driving condition data) is calculated using the following formula:

[0104]

[0105] Where Q represents the theoretical wheel energy; t start Indicates the starting time of the vehicle speed and time curve in the road driving condition data, in seconds; t end Indicates the end time of the vehicle speed and time curve in the road driving condition data, in seconds; F i represents the traction force of the hybrid vehicle from time i-1 to time i, in N; v i Indicates that the hybrid vehicle is i The target vehicle speed at time t (i.e., the vehicle speed in the road driving condition data and the time curve in the road driving condition data is ti The corresponding vehicle speed at that time is km / h; v i-1 Indicates that the hybrid vehicle is i-1 The target vehicle speed at time t (the speed and time curve in the road driving condition data is t i-1 The corresponding vehicle speed at the time of 0.100 km / h is expressed in km / h. TM represents the test mass, which is calculated based on the curb weight and fully loaded mass. It is the input parameter of the chassis dynamometer (same as the definition of the national standard fuel consumption test) and is expressed in kg. i Indicates time (here is cumulative calculation, when t i When t is 1s, i-1 is 0s; when t i When t is 100s, i-1 99s), unit is s; f0, f1, f2 represent the road load coefficient of the hybrid vehicle determined according to TM, unit is N, N / km / h, N / (km / h) respectively 2 .

[0106] The wheel energy of a hybrid vehicle running in the main mode is calculated as follows:

[0107]

[0108] Where Q1 represents the wheel energy of the hybrid vehicle in the main mode; F i_1 represents the traction force of the hybrid vehicle from time i-1 to time i when driving in the main mode, in N; v i_1 Indicates that the hybrid vehicle is in the main mode at t i_1 The actual vehicle speed at that time, in km / h; v i_1-1 Indicates that the hybrid vehicle is in the main mode at t i_1-1 The actual vehicle speed at time t is km / h; i_1 Indicates the driving time in the main mode, in seconds;

[0109] The wheel energy of a hybrid vehicle running in smart mode is calculated using the following formula:

[0110]

[0111] Where Q2 represents the wheel energy of the hybrid vehicle running in smart mode; F i_2 represents the traction force of the hybrid vehicle from time i-1 to time i when driving in the main mode, in N; v i_2 Indicates that the hybrid vehicle is in t when driving in smart mode. i_2 The actual vehicle speed at that time, in km / h; v i_2-1 Indicates that the hybrid vehicle is in t when driving in smart mode. i_2-1The actual vehicle speed at time t is km / h; i_2 Indicates the time of driving in smart mode, in seconds.

[0112] The above theoretical wheel-side energy, the wheel-side energy of a hybrid vehicle running in main mode, and the wheel-side energy of a hybrid vehicle running in smart mode are all calculated in accordance with the national standard GB / T 18356.2-2016.

[0113] The fuel consumption results of the hybrid vehicle in the main mode after the power correction and the fuel consumption results of the hybrid vehicle in the smart mode after the power correction are corrected respectively. The calculation formula is:

[0114] Fuel1=fuel1 / Q1×Q

[0115] Fuel2=fuel2 / Q2×Q

[0116] In the formula, Fuel1 represents the modified fuel consumption of the hybrid vehicle in the main mode, in L / 100km; Fuel2 represents the modified fuel consumption of the hybrid vehicle in the smart mode, in L / 100km.

[0117] In the present invention, the energy conservation law is used to evaluate the energy-saving effect of the intelligent energy management strategy of the hybrid electric vehicle using the following calculation formula:

[0118] K=1-Fuel2 / Fuel1

[0119] Where K represents the coefficient for evaluating the energy-saving effect of the intelligent energy management strategy. When K ≥ 5%, it means that the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle is qualified. When K < 5%, the intelligent energy management strategy of the hybrid vehicle needs to be optimized.

[0120] When K is less than 5%, since it is necessary to optimize the intelligent energy management strategy of the hybrid vehicle, the hybrid vehicle intelligent energy management strategy energy-saving effect evaluation system of the present invention also includes an optimization module, which compares and analyzes the vehicle power domain (the power domain can be collected using digital acquisition or DBC) and navigation data (navigation data refers to data sent by the navigation simulation module) collected when the hybrid vehicle is driving in the main mode and the hybrid vehicle is driving in the intelligent mode (data analysis is performed based on the internal vehicle signals collected in reality), focuses on the SOC changes during the entire driving process, and proposes relevant optimization strategies, such as the control strategy and logic of EMS and PDCU (when K ≥ 5%, since the energy-saving effect of the intelligent energy management strategy is already qualified, this operation is not required).

[0121] Furthermore, relevant optimization strategies include:

[0122] First, analyze the engine shutdown rate while waiting at traffic lights. Because idling power generation has poor fuel efficiency, the engine shutdown rate while waiting at traffic lights needs to be greater than 95%. If this indicator is not met, it is necessary to appropriately increase the power generation capacity at low and medium speeds to ensure that the engine is shut down when the vehicle stops.

[0123] Secondly, analyze the engine's average specific fuel consumption. Since the engine operating point can be adjusted in advance based on predictable operating conditions, the average engine specific fuel consumption needs to be less than the minimum specific fuel consumption × (1.1-1.2). If this indicator is not achieved, the engine operating point needs to be adjusted to focus on the high-efficiency zone (the area with higher engine energy).

[0124] Finally, analyze the proportion of pure electric time in traffic jams (the proportion of pure electric time refers to the proportion of pure electric time in all operating time, for example, 1800s, 1000s of driving, 600s of pure electric time, 400s of hybrid time, and the proportion of pure electric time is 60%). Due to poor fuel consumption when driving at low speeds, the proportion of pure electric time in traffic jams is >80%. If this indicator is not achieved, it is necessary to appropriately increase the power generation power of the car when driving at medium and low speeds to ensure the proportion of pure electric time in traffic jams.

[0125] The above optimization strategy provides guiding direction suggestions through mathematical analysis of the collected data (this optimization strategy only provides general guiding suggestions, and specific situations still require specific analysis; the reason why it mainly starts from the engine shutdown rate when waiting for traffic lights, the average engine fuel consumption, and the proportion of pure electric time in traffic jams is because these are the values ​​that have a greater direct or indirect impact on fuel consumption). The optimization results can be integrated into the calibration of the vehicle controller, which has good engineering application value and application prospects for work in the field of energy conservation; and using the data during the experimental process, forward analysis can be used to draw optimization conclusions to support actual development work.

[0126] Example 2

[0127] A method for evaluating energy-saving effects of an intelligent energy management strategy for a hybrid electric vehicle comprises the following steps:

[0128] Step 1: Using a short-trip analysis method to correct the road driving condition data and generate corrected map navigation information;

[0129] Step 2: Switch the hybrid vehicle to the main mode, and use a chassis dynamometer to measure the fuel consumption of the hybrid vehicle in the main mode according to the vehicle speed and time curve in the road driving condition data and the average slope in the road driving condition data;

[0130] Step 3: Switching the hybrid vehicle to the smart mode, the hybrid vehicle uses a chassis dynamometer to measure fuel consumption of the hybrid vehicle in the smart mode based on the corrected map navigation information, the speed-time curve in the road driving condition data, and the average slope in the road driving condition data;

[0131] Step 4: Using the law of conservation of energy, perform charge correction on the fuel consumption results of the hybrid vehicle when traveling in the main mode and the fuel consumption results of the hybrid vehicle when traveling in the smart mode, respectively, to obtain the charge-corrected fuel consumption results of the hybrid vehicle when traveling in the main mode and the charge-corrected fuel consumption results of the hybrid vehicle when traveling in the smart mode; then, perform wheel-side energy normalization processing on the charge-corrected fuel consumption results of the hybrid vehicle when traveling in the main mode and the charge-corrected fuel consumption results of the hybrid vehicle when traveling in the smart mode, respectively, to obtain the corrected fuel consumption results of the hybrid vehicle when traveling in the main mode and the corrected fuel consumption results of the hybrid vehicle when traveling in the smart mode;

[0132] Step 5: Evaluate the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle based on the modified fuel consumption results of the hybrid vehicle in the main mode and the modified fuel consumption results of the hybrid vehicle in the intelligent mode.

[0133] Example 3

[0134] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0135] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited thereto. Within the technical concept of the present invention, various simple variations of the technical solution of the present invention may be made, including combining the various technical features in any other appropriate manner. These simple variations and combinations should also be regarded as disclosed in the present invention and fall within the scope of protection of the present invention.

Claims

1. A hybrid electric vehicle intelligent energy management strategy energy saving effect evaluation system, characterized in that: include: The map navigation simulation module is used to correct the road driving condition data using the short-trip analysis method to generate corrected map navigation information; The non-intelligent fuel consumption test module is used to switch the hybrid vehicle to the main mode. In the main mode, the speed and time curve in the road driving condition data is combined with the average slope in the road driving condition data to detect the fuel consumption result of the hybrid vehicle in the main mode. The rotating hub intelligent fuel consumption test module is used to switch the hybrid vehicle to intelligent mode. In intelligent mode, the module combines the corrected map navigation information, the speed and time curve in the road driving condition data, and the average slope in the road driving condition data to detect the fuel consumption of the hybrid vehicle in intelligent mode. The normalization module is used to use the law of conservation of energy to perform charge correction on the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode, respectively, to obtain the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode after charge correction, and then perform wheel-side energy normalization processing on the fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode after charge correction, respectively, to obtain the corrected fuel consumption results of the hybrid vehicle when driving in the main mode and the smart mode; The energy-saving effect evaluation module is used to evaluate the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle based on the fuel consumption results of the hybrid vehicle in the main mode and the intelligent mode after correction.

2. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 1 is characterized in that: The specific method of using the short-trip analysis method to correct the road driving condition data includes: importing the road driving condition data into a map navigation simulation program, calculating the driving distance based on the speed and time curve in the road driving condition data, and using it as the distance from the starting point to the end point; then updating the estimated travel time from the starting point to the end point to the end time in the speed and time curve in the road driving condition data; then correcting the longitude and latitude data with discontinuous signals in the road driving condition data to finally obtain continuous longitude and latitude data, and in the process of correcting the longitude and latitude data, synchronously correcting the signal data related to the longitude and latitude; and performing equivalent calculation on the slope signal in the road driving condition data to obtain the average slope.

3. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 2 is characterized in that: The specific method for correcting the latitude and longitude data with discontinuous signals in the road driving condition data includes: re-segmenting the latitude and longitude data with discontinuous signals in the road driving condition data to form [D1, D2, D3, ..., D g 】, then correct the latitude and longitude data at the beginning of D2 according to the latitude and longitude data at the end of D1, and subtract the latitude and longitude data at the end of D1 from all the latitude and longitude data in D2 to get the new D2; then correct the latitude and longitude data at the beginning of D3 according to the new latitude and longitude data at the end of D2, and subtract the latitude and longitude data at the end of D2 from all the latitude and longitude data in D3 to get the new D3; and so on. g Also make corrections.

4. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 1, characterized in that: The calculation formula for the power correction of the fuel consumption results of the hybrid vehicle in the main mode and the smart mode is: fuel1=F1-E1 / 100×K_fuel / S1×100 fuel2=F2-E2 / 100×K_fuel / S2×100 Wherein, fuel1 represents the fuel consumption of the hybrid vehicle in main mode after charge correction; fuel2 represents the fuel consumption of the hybrid vehicle in smart mode after charge correction; F1 represents the fuel consumption of the hybrid vehicle in main mode; F2 represents the fuel consumption of the hybrid vehicle in smart mode; E1 represents the energy charged and discharged by the battery when the hybrid vehicle is in main mode; E2 represents the energy charged and discharged by the battery when the hybrid vehicle is in smart mode; K_fuel represents the oil-to-electricity conversion coefficient; S1 represents the actual distance traveled by the hybrid vehicle in main mode, and S2 represents the actual distance traveled by the hybrid vehicle in smart mode.

5. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 1 is characterized in that: The specific method of performing wheel-side energy normalization processing on the fuel consumption results of the hybrid vehicle in the main mode and the smart mode after the power correction to obtain the corrected fuel consumption results of the hybrid vehicle in the main mode and the smart mode includes: Calculate the theoretical wheel energy using the following formula: Where Q represents the theoretical wheel energy; t start Indicates the starting time of the vehicle speed and time curve in the road driving condition data; t end Indicates the end time of the vehicle speed and time curve in the road driving condition data; F i represents the traction force of the hybrid vehicle from time i-1 to time i; v i Indicates that the hybrid vehicle is i Target vehicle speed at t; v i-1 Indicates that the hybrid vehicle is i-1 The target vehicle speed at t; TM represents the test mass; t i represents time; f0, f1, f2 represent the road load coefficients of the hybrid vehicle determined according to TM; The wheel energy of a hybrid vehicle running in the main mode is calculated as follows: Where Q1 represents the wheel energy of the hybrid vehicle in the main mode; F i_1 represents the traction force of the hybrid vehicle from time i-1 to time i when driving in the main mode; v i_1 Indicates that the hybrid vehicle is in the main mode at t i_1 The actual vehicle speed at time v i_1-1 Indicates that the hybrid vehicle is in the main mode at t i_1-1 The actual vehicle speed at t i_1 Indicates the time in main mode driving; The wheel energy of a hybrid vehicle running in smart mode is calculated using the following formula: Where Q2 represents the wheel energy of the hybrid vehicle running in smart mode; F i_2 represents the traction force of the hybrid vehicle from time i-1 to time i when driving in the main mode; v i_2 Indicates that the hybrid vehicle is in t when driving in smart mode. i_2 The actual vehicle speed at time v i_2-1 Indicates that the hybrid vehicle is in t when driving in smart mode. i_2-1 The actual vehicle speed at t i_2 Indicates the time in smart mode driving; The fuel consumption results of the hybrid vehicle in the main mode and the smart mode after power correction are corrected respectively. The calculation formula is: Fuel1=fuel1 / Q1×Q Fuel2=fuel2 / Q2×Q Where Fuel1 represents the fuel consumption of the hybrid vehicle in the main mode after correction; Fuel2 represents the fuel consumption of the hybrid vehicle in the smart mode after correction.

6. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 5, characterized in that: The calculation formula for evaluating the energy-saving effect of the intelligent energy management strategy of hybrid vehicles is: K=1-Fuel2 / Fuel1 Where K represents the coefficient for evaluating the energy-saving effect of the intelligent energy management strategy. When K ≥ 5%, it means that the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle is qualified. When K < 5%, the intelligent energy management strategy of the hybrid vehicle needs to be optimized.

7. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 6, characterized in that: The hybrid vehicle intelligent energy management strategy energy-saving effect evaluation system also includes an optimization module, which compares and analyzes the vehicle power domain and navigation data collected when the hybrid vehicle is driving in the main mode and the hybrid vehicle is driving in the intelligent mode, and proposes relevant optimization strategies.

8. The hybrid electric vehicle intelligent energy management strategy energy-saving effect evaluation system according to claim 7 is characterized in that: Relevant optimization strategies include: The engine shutdown rate while waiting at traffic lights must be greater than 95%. If this indicator is not met, the power generation capacity of the vehicle at low and medium speeds needs to be increased to ensure shutdown when the vehicle stops. Average engine specific fuel consumption < minimum specific fuel consumption × (1.1-1.2); if this indicator is not achieved, the engine operating point needs to be adjusted to focus on the high-efficiency zone; The proportion of pure electric time in traffic jams is >80%. If this indicator is not achieved, it is necessary to increase the power generation capacity of the car when driving at low speeds to ensure the proportion of pure electric time in traffic jams.

9. A method for evaluating the energy-saving effect of an intelligent energy management strategy for a hybrid electric vehicle, characterized in that: It includes the following steps: Using the short-trip analysis method to correct the road driving condition data and generate corrected map navigation information; Switching the hybrid vehicle to a main mode, and in the main mode, combining a vehicle speed-time curve in the road driving condition data with an average slope in the road driving condition data to detect a fuel consumption result of the hybrid vehicle in the main mode; Switch the hybrid vehicle to smart mode, and in the smart mode, combine the corrected map navigation information, the speed and time curve in the road driving condition data, and the average slope in the road driving condition data to detect the fuel consumption result of the hybrid vehicle in the smart mode; Using the law of conservation of energy, the fuel consumption results of the hybrid vehicle in the main mode and the smart mode are respectively corrected for electric quantity, and the fuel consumption results of the hybrid vehicle in the main mode and the smart mode after electric quantity correction are obtained. Then, the fuel consumption results of the hybrid vehicle in the main mode and the smart mode after electric quantity correction are respectively normalized by wheel-side energy, and the fuel consumption results of the hybrid vehicle in the main mode and the smart mode after correction are obtained. Based on the modified fuel consumption results of the hybrid vehicle in main mode and intelligent mode, the energy-saving effect of the intelligent energy management strategy of the hybrid vehicle is evaluated.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 9 are implemented.

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