Method and device for constructing pure electric vehicle driving cycle

Abstract

The application relates to a method and device for constructing typical driving conditions of a pure electric vehicle, wherein a small amount of data is used in the process of constructing the typical driving conditions of the electric vehicle, and only GPS speed data of vehicle driving can be used to construct the typical driving conditions close to actual driving energy consumption; as driving style has a great influence on the energy consumption of the electric vehicle, the application firstly determines the driving style category during the process of constructing the typical driving conditions, then performs secondary classification on the driving conditions to determine the driving speed category, and finally the constructed driving conditions can contain various driving categories of the original driving data and can better reflect the authenticity of the driving conditions; during the selection of the kinematic segment, the unit mileage energy consumption is weighted, so that the unit mileage energy consumption of the selected kinematic segment can be more close to the average value of the category.

Description

Technical Field

[0001] This application relates to the field of automotive driving condition technology, and more specifically, to a method and apparatus for constructing driving conditions for a pure electric vehicle. Background Technology

[0002] Vehicle operating conditions (VIDs) describe the speed-time curves of a specific type of vehicle in a particular region under specific traffic conditions, with a time step of 1 second. Research on VIDs is crucial for the automotive industry, as it can be used for performance evaluations such as vehicle emissions and fuel consumption, and provides a reference for matching vehicle powertrain parameters and optimizing control strategies. Especially with electric vehicles becoming an unstoppable trend in automotive development, publishing a dedicated VID for electric vehicles is particularly important, as it can provide essential references for setting calibration standards for electric vehicle range.

[0003] Currently, the more mature driving cycles include the US FTP75, the European NEDC, and the Japanese JAPAN10-15. Traffic conditions and driving patterns vary across regions, resulting in differences in vehicle driving cycles and significant discrepancies between vehicle energy consumption test results and actual local driving conditions. Summary of the Invention

[0004] To overcome at least one deficiency in the prior art, this application provides a method and apparatus for constructing driving conditions for a pure electric vehicle.

[0005] Firstly, a method for constructing typical driving conditions for pure electric vehicles is provided, including:

[0006] Acquire vehicle driving data over a period of time, including the vehicle's speed data at each moment;

[0007] The driving data is divided into multiple kinematic segments;

[0008] Determine the driving characteristic parameters for each kinematic segment; the driving characteristic parameters include the energy consumption per unit distance.

[0009] Multiple kinematic segments are classified based on driving characteristic parameters to determine the driving category of each kinematic segment; the driving category of each kinematic segment includes driving style category and driving speed category;

[0010] Determine the weight of the energy consumption ratio per unit mileage, and based on the weight of the energy consumption ratio per unit mileage and driving characteristic parameters, select multiple kinematic segments from the kinematic segments belonging to the same driving category to form a typical working condition.

[0011] Multiple kinematic segments corresponding to the typical operating conditions of each driving category are spliced ​​together to form the typical driving conditions of a pure electric vehicle.

[0012] In one embodiment, multiple kinematic segments are classified based on driving characteristic parameters to determine the driving category of each kinematic segment, including:

[0013] The driving characteristic parameters of multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment. The driving style categories include aggressive driving style and conservative driving style.

[0014] Secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

[0015] In one embodiment, the driving characteristic parameters in a single cluster include: mean absolute acceleration, standard deviation of absolute acceleration, mean absolute impact, and standard deviation of absolute impact.

[0016] In one embodiment, the driving characteristic parameters in the secondary clustering include: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, and energy consumption per unit distance.

[0017] In one embodiment, a clustering process is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving style category of each kinematic segment, including:

[0018] Determine the number of driving style categories;

[0019] Multiple initial cluster centers are determined using a population-based stochastic optimization algorithm, with the number of initial cluster centers being the same as the number of driving style categories; a crossover operator is used in the population-based stochastic optimization algorithm.

[0020] Based on multiple initial cluster centers, the k-means clustering method is used to cluster the driving characteristic parameters of multiple kinematic segments to determine the driving style category of each kinematic segment.

[0021] In one embodiment, secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, including:

[0022] Determine the number of driving speed categories;

[0023] Multiple initial cluster centers are determined using a population-based stochastic optimization algorithm, with the number of initial cluster centers being the same as the number of driving speed categories; the population-based stochastic optimization algorithm employs a crossover operator;

[0024] Based on multiple initial cluster centers, the k-means clustering method is used to cluster the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment.

[0025] In one embodiment, the driving characteristic parameters include: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, energy consumption per unit distance, mean absolute value of acceleration, standard deviation of absolute value of acceleration, mean absolute value of impact, and standard deviation of absolute value of impact.

[0026] In one embodiment, based on the weight of the energy consumption per unit mile ratio and driving characteristic parameters, multiple kinematic segments are selected from kinematic segments belonging to the same driving category to form a typical operating condition, including:

[0027] Determine the percentage of the sum of the times of kinematic segments in each driving category in the total time of all kinematic segments;

[0028] Based on the proportion and the total time of typical working conditions, determine the typical working condition sub-time corresponding to each driving category;

[0029] For each driving category, the cumulative parameter value of each kinematic segment is calculated based on the weight of the energy consumption per unit distance of each kinematic segment and the driving characteristic parameters.

[0030] Calculate the error between the cumulative parameter value of each kinematic segment and the mean of the cumulative parameter values ​​of all kinematic segments in the driving category;

[0031] Based on the error and typical operating condition sub-time, multiple kinematic segments are selected from the kinematic segments in the driving category to form the typical operating condition.

[0032] Secondly, a device for constructing a typical driving condition for a pure electric vehicle is provided, comprising:

[0033] The driving data acquisition module is used to acquire the driving data of the vehicle over a period of time, including the vehicle's speed data at each moment.

[0034] The kinematic segmentation module is used to divide driving data into multiple kinematic segments;

[0035] The driving characteristic parameter determination module is used to determine the driving characteristic parameters for each kinematic segment; the driving characteristic parameters include the energy consumption ratio per unit distance.

[0036] The classification module is used to classify multiple kinematic segments based on driving feature parameters and determine the driving category of each kinematic segment; the driving category of each kinematic segment includes driving style category and driving speed category;

[0037] The typical operating condition segment selection module is used to determine the weight of the energy consumption ratio per unit mileage. Based on the weight of the energy consumption ratio per unit mileage and driving characteristic parameters, it selects multiple kinematic segments from the kinematic segments belonging to the same driving category to form the typical operating condition.

[0038] The typical operating condition splicing module is used to splice together multiple kinematic segments that constitute the typical operating condition for each driving category to form the typical driving condition of a pure electric vehicle.

[0039] In one embodiment, the classification module is also used for:

[0040] The driving characteristic parameters of multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment. The driving style categories include aggressive driving style and conservative driving style.

[0041] Secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

[0042] Compared with existing technologies, this application has the following advantages: It uses less data to construct typical driving conditions for electric vehicles, requiring only GPS speed data to construct typical driving conditions that closely resemble actual driving energy consumption; since driving style has a significant impact on the energy consumption of electric vehicles, this application first determines the driving style category during the construction of typical driving conditions, then performs a secondary classification of the driving conditions to determine the driving speed category. The final constructed driving conditions include all driving categories from the original driving data, better reflecting the authenticity of the driving conditions; when selecting kinematic segments, the energy consumption per unit mile is weighted, allowing the energy consumption per unit mile of the selected kinematic segments to better match the average value of their respective classes. Attached Figure Description

[0043] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings:

[0044] Figure 1 A flowchart illustrating a method for constructing driving conditions for a pure electric vehicle according to an embodiment of this application is shown.

[0045] Figure 2A structural block diagram of a construction apparatus for a typical driving condition of a pure electric vehicle according to an embodiment of this application is shown;

[0046] Figure 3 The results of classifying driving styles based on kinematic segments are shown in the diagram.

[0047] Figure 4 The results of classifying driving speeds based on kinematic segments are shown in the figure;

[0048] Figure 5 A schematic diagram of typical driving conditions for a pure electric vehicle is shown. Detailed Implementation

[0049] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0050] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution according to this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0051] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0052] Research on constructing urban vehicle driving conditions based on the city's own vehicle driving data is becoming increasingly urgent. The aim is to construct driving conditions that closely match the city's actual vehicle driving conditions, ideally representing them completely. Given that electric vehicles are becoming a major future trend, constructing a reasonable and reliable typical driving condition can effectively guide the calibration of electric vehicle range. Therefore, this application places greater emphasis on reflecting the true energy consumption when constructing typical electric vehicle driving conditions.

[0053] Figure 1 A flowchart illustrating a method for constructing driving conditions for a pure electric vehicle according to an embodiment of this application is shown. See also... Figure 1 The methods include:

[0054] Step S110: Obtain the vehicle's driving data over a period of time, including the vehicle's speed data at each moment;

[0055] In this step, the driving data is GPS data. After acquiring the raw GPS data, preprocessing is performed to address issues such as missing data and data anomalies. Specific preprocessing steps may include:

[0056] Handling of missing GPS signals: For data missing within 5 seconds, linear interpolation is used to fill in the missing data; data missing for more than 5 seconds is discarded.

[0057] Handling of abnormal data regarding maximum speed and acceleration / deceleration: According to the "Regulations for the Implementation of the Road Traffic Safety Law of the People's Republic of China," data with a speed exceeding 120 km / s is considered abnormal and should be discarded. Generally, the maximum acceleration for electric vehicles is set at 6 m / s². 2 The maximum deceleration during emergency braking is 7.5 m / s². 2 Up to 8m / s 2 The magnitude of acceleration and deceleration is calculated for each time interval. Acceleration and deceleration values ​​outside the acceptable range are directly discarded to improve the accuracy of the original data. Abnormal acceleration data are then smoothed using the smooth function according to assumed standards.

[0058] For data processing related to prolonged parking: Idle time exceeding 180 seconds is generally considered abnormal; therefore, idle times longer than 180 seconds are treated as 180 seconds. Due to prolonged traffic jams or parking, the collected data does not meet the requirements. Intermittent vehicle movement with a maximum speed less than 10 km / h is considered parking.

[0059] Filtering: Due to external factors, the driving data contains abnormal noise interference, resulting in errors. A moving average filtering algorithm is used to filter the original data.

[0060] Step S120: Divide the driving data into multiple kinematic segments;

[0061] In this step, a kinematic segment refers to the period of a vehicle's movement from one idle speed to the next. A typical kinematic segment includes four motion states: acceleration, deceleration, constant speed, and idle, and generally lasts no less than 20 seconds. Vehicles will have different kinematic segments at different times. The vehicle's speed-time curve can generally be considered as a combination of several kinematic segments, and vehicles will obtain different kinematic segments under different traffic conditions, on different road sections, and at different times.

[0062] The definitions of various driving states of a car are as follows:

[0063] (1) Idle state: The process in which the speed is equal to zero;

[0064] (2) Acceleration state: The velocity is not equal to zero and the acceleration is greater than or equal to 0.15 m / s². 2 The continuous operation process;

[0065] (3) Deceleration state: The velocity is not equal to zero and the acceleration is less than or equal to -0.15m / s². 2 The continuous operation process;

[0066] (4) Uniform state: The velocity is not equal to zero and the absolute value of the acceleration is less than or equal to 0.15 m / s². 2 The continuous operation process.

[0067] Step S130: Determine the driving characteristic parameters for each kinematic segment;

[0068] Specifically, the driving characteristic parameters are shown in Table 1:

[0069] Table 1 Driving Characteristic Parameters

[0070] Serial Number Eigenvalue symbol describe unit 1 T Kinematic Fragment Time s 2 <![CDATA[V m ]]> Average speed (including idle speed) m / s 3 <![CDATA[V mr ]]> Average speed (excluding idle speed) m / s 4 S Running distance m 5 <![CDATA[V max ]]> Maximum speed m / s 6 <![CDATA[A max ]]> Maximum acceleration <![CDATA[m / s 2 ]]> 7 <![CDATA[D max ]]> Maximum deceleration <![CDATA[m / s 2 ]]> 8 <![CDATA[A m ]]> average acceleration <![CDATA[m / s 2 ]]> 9 <![CDATA[D m ]]> Average deceleration <![CDATA[m / s 2 ]]> 10 <![CDATA[P i ]]> Idle time ratio ％ 11 <![CDATA[P a ]]> Acceleration time ratio ％ 12 <![CDATA[P d ]]> Deceleration time ratio ％ 13 <![CDATA[P e ]]> Uniform speed time ratio ％ 14 <![CDATA[|J| m ]]> Mean of absolute value of impact <![CDATA[m / s 3 ]]> 15 <![CDATA[S J ]]> Standard deviation of absolute value of impact <![CDATA[m / s 2 ]]> 16 <![CDATA[W s ]]> Energy consumption per unit distance J / (KG*KM) 17 <![CDATA[|a| m ]]> Mean absolute value of acceleration <![CDATA[m / s 2 ]]> 18 <![CDATA[S a ]]> Standard deviation of absolute acceleration <![CDATA[m / s 2 ]]>

[0071] Step S140: Classify multiple kinematic segments based on driving feature parameters to determine the driving category of each kinematic segment; the driving category of each kinematic segment includes a driving style category and a driving speed category. Here, after classification, each kinematic segment corresponds to a driving style category and a driving speed category.

[0072] Step S150: Determine the weight of the energy consumption ratio per unit mileage. Based on the weight of the energy consumption ratio per unit mileage and the driving characteristic parameters, select multiple kinematic segments from the kinematic segments belonging to the same driving category to form a typical working condition.

[0073] Step S160: The multiple kinematic segments that constitute the typical operating conditions corresponding to each driving category are spliced ​​together to form the typical driving conditions of a pure electric vehicle.

[0074] The embodiments described above use relatively little data to construct typical driving conditions for electric vehicles. Only GPS speed data of the vehicle is needed to construct typical driving conditions that closely resemble actual driving energy consumption. Since driving style has a significant impact on the energy consumption of electric vehicles, this application first determines the driving style category during the construction of typical driving conditions, and then performs a secondary classification of the driving conditions to determine the driving speed category. The final constructed driving conditions can include all driving categories of the original driving data, thus better reflecting the authenticity of the driving conditions. When selecting kinematic segments, the energy consumption per unit mileage is weighted, which makes the energy consumption per unit mileage of the selected kinematic segments more closely match the average value of their respective categories.

[0075] In one embodiment, since driving style has a significant impact on the energy consumption of electric vehicles, driving style is taken into account during the classification process. Multiple kinematic segments are classified based on driving characteristic parameters to determine the driving category of each kinematic segment, including:

[0076] The driving characteristic parameters of multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment. The driving style categories include aggressive driving style and conservative driving style.

[0077] Specifically, in this step, the driving characteristic parameters in a single cluster include: mean absolute acceleration, standard deviation of absolute acceleration, mean absolute impact, and standard deviation of absolute impact.

[0078] Secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

[0079] Specifically, the driving characteristic parameters in the secondary clustering are 14 of the characteristic parameters in Table 1, including: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, and energy consumption per unit distance.

[0080] The above embodiments of this application first classify driving styles, and then classify driving conditions based on the classification of driving styles. This can fully classify the original data, and the final constructed driving conditions can include all categories of the original driving data, thus better reflecting the authenticity of the driving conditions.

[0081] In one embodiment, the driving characteristic parameters of multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment, including:

[0082] Step S210: Determine the number of driving style categories; in this embodiment, the driving style categories include aggressive driving style and conservative driving style, and the number of driving style categories is 2.

[0083] Step S220: A population-based stochastic optimization (PSO) algorithm is used to determine multiple initial cluster centers. The number of initial cluster centers is the same as the number of driving style categories. The crossover operator is used in the population-based stochastic optimization algorithm.

[0084] In this step, the population-based stochastic optimization algorithm incorporates a crossover operator during the particle swarm optimization process (i.e., determining multiple initial cluster centers) to determine multiple initial cluster centers. Here, adding a crossover operator to the population-based stochastic optimization algorithm to accelerate the particle swarm optimization process is a method that exists in the present.

[0085] In the particle swarm optimization (PSO) process, n particles are randomly generated. Each particle contains multi-dimensional coordinate information of k cluster centers. It's important to note that the 8-dimensional coordinates of the n generated particles differ from the 4-dimensional coordinates of the kinematic segments. In driving style clustering, each kinematic segment contains four feature parameters, representing its coordinate information in a 4-dimensional coordinate system. However, in PSO, the generated particles use an 8-dimensional coordinate system, with the first four coordinates representing the first cluster center and the last four representing the second. The purpose of PSO is to find the optimal locations of the two cluster centers for subsequent k-means clustering.

[0086] In this embodiment, a crossover operator is added during the particle swarm optimization process, where particle i is at its current position X. i The position coordinates of (t) and the individual optimal position pbest of particle i at the previous time step. i Discretize and cross the coordinates of (t-1) so that the particles can move to the optimal position, which speeds up the optimization process and also speeds up the process of determining multiple initial cluster centers.

[0087] Step S230: Based on multiple initial cluster centers, the k-means clustering method is used to cluster the driving feature parameters of multiple kinematic segments to determine the driving style category of each kinematic segment.

[0088] In one embodiment, secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, including:

[0089] Step S310: Determine the number of driving speed categories; here, driving speed categories include low speed, medium speed and high speed; in this step, based on the experience of existing technology, the number of categories is set to 3 during secondary clustering, representing low speed, medium speed and high speed respectively.

[0090] Step S320: A population-based stochastic optimization algorithm is used to determine multiple initial cluster centers. The number of initial cluster centers is the same as the number of driving speed categories. The population-based stochastic optimization algorithm uses a crossover operator.

[0091] In this step, the method for determining multiple initial cluster centers is the same as that for determining multiple initial cluster centers in step S220. The only difference is the number of initial cluster centers and the dimension of the cluster centers. In this step, the number of initial cluster centers is 3.

[0092] Step S330: Based on multiple initial cluster centers, the k-means clustering method is used to cluster the driving feature parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment.

[0093] In this step, before using the k-means clustering method to cluster the driving characteristic parameters of multiple kinematic segments, principal component analysis can be used to reduce the dimensionality of the driving characteristic parameters of multiple kinematic segments to ensure better clustering results.

[0094] In one embodiment, based on the weight of the energy consumption per unit mile ratio and driving characteristic parameters, multiple kinematic segments are selected from kinematic segments belonging to the same driving category to form a typical operating condition, including:

[0095] Step S410: Determine the percentage of the sum of the times of kinematic segments in each driving category in the total time of all kinematic segments;

[0096] Step S420: Determine the typical operating condition sub-time for each driving category based on the proportion and the total time of typical operating conditions;

[0097] In this step, the total time for typical operating conditions is preset, specifically 3000 seconds. By multiplying the time percentage of each driving category by the total time of typical operating conditions, the sub-time of typical operating conditions corresponding to each driving category can be calculated.

[0098] Step S430: For each driving category, calculate the cumulative parameter value for each kinematic segment based on the weight of the energy consumption ratio per unit mileage and the driving characteristic parameters; the specific implementation process can be as follows:

[0099] First, the driving characteristic parameters are converted into dimensionless constants:

[0100]

[0101] z ij,k =(x ij,k -minx j,k ) / (maxx j,k -minx j,k )

[0102] i = 1, 2, ..., m k j = 1, 2, ... p, k = 1, 2, ... n

[0103] Where, x ij,k For the j-th driving feature parameter of the i-th kinematic segment in the k-th driving category, maxx j,k minx is the maximum value of the j-th driving feature parameter of all kinematic segments in the k-th driving category. j,k z is the minimum value of the j-th driving feature parameter of all kinematic segments in the k-th driving category. ij,k W is the dimensionless constant of the j-th driving characteristic parameter of the i-th kinematic segment in the k-th driving category. s(i,k) MaxW represents the energy consumption per unit distance for the i-th kinematic segment within the k-th driving category. s(k) minW represents the maximum energy consumption per unit distance for all kinematic segments in the k-th driving category. s(k) This represents the minimum energy consumption per unit distance for all kinematic segments in the k-th driving category. m is a dimensionless constant representing the energy consumption per unit distance for the i-th kinematic segment in the k-th driving category; k p represents the number of kinematic segments in the k-th driving category, p represents the number of driving characteristic parameters (excluding energy consumption per unit distance) for each kinematic segment, and n represents the number of driving categories.

[0104] Then, the cumulative parameter value z for each kinematic segment is calculated. i,k The following formula is used:

[0105]

[0106] Among them, z i,k This is the cumulative parameter value of the i-th kinematic segment in the k-th driving category, where μ represents the weight of the energy consumption per unit distance (μ>1), for example, μ can be 3;

[0107] Step S440: Calculate the error between the cumulative parameter value of each kinematic segment and the mean of the cumulative parameter values ​​of all kinematic segments in the driving category;

[0108] In this step, the mean of the cumulative parameter values ​​of all kinematic segments in the driving category is calculated using the following formula:

[0109]

[0110] Among them, y k This is the average of the accumulated parameter values ​​of all kinematic segments in the k-th driving category.

[0111] The error between the cumulative parameter value of each kinematic segment and the mean of the cumulative parameter values ​​of all kinematic segments in the driving category is:

[0112] R i,k =|zi,k -y k |

[0113] Among them, R i,k This is the error between the cumulative parameter value of the i-th kinematic segment in the k-th driving category and the mean of the cumulative parameter values ​​of all kinematic segments in the k-th driving category.

[0114] Step S450: Based on the error and typical operating condition sub-time, select multiple kinematic segments from the kinematic segments in the driving category to form the typical operating condition.

[0115] In this step, for each driving category, the kinematic segments are sorted in ascending order of error, and the top N kinematic segments with smaller errors are selected as the kinematic segments that constitute the typical working condition. Here, N is determined based on the typical working condition sub-time. When the sum of the times of the selected kinematic segments constituting the typical working condition is greater than or equal to the typical working condition sub-time, the number of kinematic segments selected to constitute the typical working condition is N.

[0116] Based on the same inventive concept as the method for constructing typical driving conditions for pure electric vehicles in this application, this application also provides an apparatus for constructing typical driving conditions for pure electric vehicles. Figure 2 A structural block diagram of a construction apparatus for a pure electric vehicle under typical driving conditions according to an embodiment of this application is shown. The apparatus includes:

[0117] The driving data acquisition module 510 is used to acquire the driving data of the vehicle over a period of time, including the speed data of the vehicle at each moment.

[0118] The kinematic segmentation module 520 is used to divide driving data into multiple kinematic segments; driving characteristic parameters include energy consumption per unit distance.

[0119] The driving characteristic parameter determination module 530 is used to determine the driving characteristic parameters of each kinematic segment;

[0120] The classification module 540 is used to classify multiple kinematic segments based on driving feature parameters and determine the driving category of each kinematic segment; the driving category of each kinematic segment includes driving style category and driving speed category;

[0121] The typical operating condition segment selection module 550 is used to determine the weight of the energy consumption ratio per unit mileage. Based on the weight of the energy consumption ratio per unit mileage and driving characteristic parameters, it selects multiple kinematic segments from the kinematic segments belonging to the same driving category to form the typical operating condition.

[0122] The typical operating condition splicing module 560 is used to splice together multiple kinematic segments that constitute the typical operating condition for each driving category to form the typical driving condition of a pure electric vehicle.

[0123] The apparatus for constructing typical driving conditions of a pure electric vehicle in this embodiment has the same specific functions as the method for constructing typical driving conditions of a pure electric vehicle.

[0124] In one embodiment, the classification module 540 is further configured to:

[0125] The driving characteristic parameters of multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment. The driving style categories include aggressive driving style and conservative driving style.

[0126] Specifically, in this step, the driving characteristic parameters in a single cluster include: mean absolute acceleration, standard deviation of absolute acceleration, mean absolute impact, and standard deviation of absolute impact.

[0127] Secondary clustering is performed on the driving characteristic parameters of multiple kinematic segments to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

[0128] Specifically, the driving characteristic parameters in the secondary clustering are 14 parameters listed in Table 1, including: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, and energy consumption per unit distance.

[0129] The above embodiments of this application first classify driving styles, and then classify driving conditions based on the classification of driving styles. This can fully classify the original data, and the final constructed driving conditions can include all categories of the original driving data, thus better reflecting the authenticity of the driving conditions.

[0130] In summary, this application has the following technical effects:

[0131] (1) The amount of data used in constructing typical driving conditions of electric vehicles is small. Only the GPS speed data of the vehicle is needed to construct typical driving conditions that are close to the actual driving energy consumption.

[0132] (2) Since driving style has a significant impact on the energy consumption of electric vehicles, this application first classifies driving styles during the construction of typical driving conditions. The final constructed driving conditions can include various driving categories of the original driving data, which can better reflect the authenticity of the driving conditions.

[0133] (3) Compared with the traditional PSOK-means clustering method, this application adopts an improved particle swarm optimization clustering method, which introduces a crossover operator in the process of particle swarm optimization, thereby accelerating the technical convergence of the particle swarm algorithm.

[0134] (4) In order to make the final typical driving conditions more realistically reflect the energy consumption of electric vehicles, a weighting factor was added when selecting short-distance driving conditions to increase the proportion of energy consumption per unit mileage, so that the selected kinematic segments can more realistically reflect the average energy consumption of the original data.

[0135] To verify the technical effect of the method and device for constructing the driving conditions of pure electric vehicles in this application embodiment, this application embodiment takes the data provided by the 2019 Huawei Cup Mathematical Modeling Competition as an example. The sampling frequency of the vehicle in the provided data is 1Hz. Using this as the original data, a typical driving condition of electric vehicles is constructed. The original data in this embodiment includes a total of 496,465 sampling data. After data preprocessing, abnormal data is removed, and the data is divided into 3,181 kinematic segments.

[0136] The mean absolute value of acceleration, standard deviation of absolute value of acceleration, mean absolute value of impact, and standard deviation of absolute value of impact were selected to perform a clustering of 3181 kinematic segments. The clustering results are shown in Table 2.

[0137] Table 2. Driving Style Clustering Results

[0138] Driving style categories Cluster Center Number of kinematic segments conservative [0.236,1.213,0.290,1.339] 2509 radical [0.409,2.698,0.420,2.382] 972

[0139] After clustering, 672 kinematic segments of aggressive driving style and 2509 kinematic segments of conservative driving style were obtained. The characteristic indicators of the kinematic segments of aggressive driving style were significantly higher than those of conservative driving style.

[0140] Secondary clustering was performed on both aggressive and conservative kinematic segments, resulting in three categories: low-speed, medium-speed, and high-speed.

[0141] The solution results for the working conditions are as follows:

[0142] (1) Principal component analysis and cumulative contribution rate, taking conservative driving style as an example:

[0143] Table 3. Principal component analysis results

[0144]

[0145]

[0146] Table 3 shows the results of principal component analysis. After PCA analysis, five principal components were obtained, with a cumulative variance contribution rate of 87.6%. These components can adequately represent most of the information in the original feature parameters.

[0147] (2) The conservative kinematic segments were classified into velocity segments, and the classification results are shown in Table 4:

[0148] Table 4. Speed ​​Clustering Results for Conservative Driving Styles

[0149] Driving speed category Average speed (km / h) Number of short trips low speed 16.0 736 medium speed 46.3 1110 high speed 82.1 663

[0150] Considering the simplicity and speed of the K-Means algorithm, and its effectiveness when the classification features of the points to be classified are obvious, but its poor clustering performance when there are many clusters and the distance between cluster centers is close, making it difficult to reach the global optimum, this paper adopts a stochastic optimization technique based on crossover operators and population to first determine better initial cluster centers, and then uses the K-means algorithm for cluster analysis based on these initial cluster centers. This method can improve the fuzziness of the classification boundaries. Figure 3 The results of classifying driving styles based on kinematic segments are shown in the diagram. Figure 4 The diagram shows the results of classifying driving speeds based on kinematic segments, from... Figure 3 and Figure 4 It can be seen that the method in this application achieves good classification results.

[0151] The classification accuracy evaluation results are shown in Table 5, which presents the classification effectiveness evaluation indicators for K-means and the proposed classification method. Compared with K-Means clustering, the smaller SP and DVI values ​​of the proposed method indicate closer intra-class distances; while larger SP and DVI values ​​indicate greater inter-class distances. The results show that the proposed classification method can effectively improve intra-class similarity and reduce inter-class similarity.

[0152] Table 5. Results of Classification Effectiveness Evaluation

[0153]

[0154] After analyzing the driving conditions, the driving conditions are spliced ​​together according to the relative error from smallest to largest. When calculating the error, to better reflect the importance of energy consumption per unit mile in selecting driving conditions, this application sets the weight μ of energy consumption per unit mile to 3. To better reflect the realism of typical driving conditions, this application sets the total length of typical driving conditions to 3000s; thus, conservative driving style and aggressive driving style account for 2400s and 600s respectively. In the conservative driving style, low-speed conditions account for 1130s, medium-speed conditions for 650s, and high-speed conditions for 600s; in the aggressive driving style, low-speed conditions account for 150s, medium-speed conditions for 270s, and high-speed conditions for 180s. A total of 7 kinematic segments are selected from the aggressive driving style and 32 kinematic segments are selected from the conservative driving style, which are then spliced ​​together to obtain the typical driving conditions for electric vehicles. Figure 5 A schematic diagram of typical driving conditions for a pure electric vehicle is shown, such as... Figure 5 As shown.

[0155] The typical driving conditions obtained in this application, the traditional kinematic segment method, and the original data are compared as shown in Table 6:

[0156] Table 6 Comparison of Operating Condition Characteristics

[0157] Feature parameters Raw data Representative working conditions Traditional methods average speed 22.92 21.94 19.54 Average driving speed 34.58 35.20 32.63 average acceleration 0.385 0.406 0.365 Average deceleration -0.438 -0.453 -0.503 speed standard deviation 20.7 23.6 19.7 Acceleration standard deviation 1.51 1.62 1.41 Deceleration standard deviation 2.11 2.18 2.26 Idle time percentage 0.27 0.25 0.30 Percentage of constant speed time 0.45 0.42 0.39 Acceleration time percentage 0.16 0.19 0.21 Deceleration time percentage 0.12 0.14 0.10 Maximum speed 118.3 116.5 108.5 Energy consumption per unit distance 80.06 84.36 70.53

[0158] Analysis of the driving condition construction results shows that the representative driving condition curves constructed in this application have a stronger fit with the original data compared with the driving condition data characteristics constructed by the traditional kinematic segment method. In particular, the energy consumption per unit mile of electric vehicles is closer to the original data, proving that the driving condition construction method proposed in this application has higher accuracy. The typical driving conditions of electric vehicles constructed using the method of this application can better guide the calibration of the driving range of electric vehicles. Moreover, the constructed driving conditions can reflect the vehicle driving characteristics under the statistics of big data systems and have better consistency with actual driving conditions, proving the effectiveness and reliability of the method.

[0159] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for constructing typical driving conditions for a pure electric vehicle, characterized in that, include: Acquire driving data of a vehicle over a period of time, the driving data including the vehicle's speed data at each moment; The driving data is divided into multiple kinematic segments; Determine the driving characteristic parameters for each of the kinematic segments; the driving characteristic parameters include energy consumption per unit distance. The multiple kinematic segments are classified based on the driving characteristic parameters to determine the driving category of each kinematic segment; the driving category of each kinematic segment includes a driving style category and a driving speed category; The weight of the energy consumption ratio per unit mileage is determined, and based on the weight of the energy consumption ratio per unit mileage and the driving characteristic parameters, multiple kinematic segments are selected from the kinematic segments belonging to the same driving category to form a typical working condition. Multiple kinematic segments corresponding to the typical operating conditions of each driving category are spliced ​​together to form a typical driving condition of a pure electric vehicle. Specifically, based on the weight of the energy consumption per unit mile ratio and the driving characteristic parameters, multiple kinematic segments are selected from the kinematic segments belonging to the same driving category to form a typical working condition, including: Determine the percentage of the sum of the times of kinematic segments in each driving category in the total time of all kinematic segments; Based on the aforementioned proportions and the total time of typical operating conditions, determine the typical operating condition sub-time corresponding to each driving category; For each driving category, the cumulative parameter value of each kinematic segment is calculated based on the weight of the energy consumption per unit distance of each kinematic segment and the driving characteristic parameters. Calculate the error between the cumulative parameter value of each kinematic segment and the mean of the cumulative parameter values ​​of all kinematic segments in the driving category; Based on the error and the typical operating condition sub-time, select multiple kinematic segments from the kinematic segments in the driving category to form the typical operating condition; The calculation of the cumulative parameter value for each kinematic segment includes: The driving characteristic parameters are converted into dimensionless constants using the following formula: in, For the kth driving category, the th The first kinematic segment One driving characteristic parameter, For the k-th driving category, the k-th ... The maximum value of each driving characteristic parameter. For the k-th driving category, the k-th ... The minimum value of each driving characteristic parameter. For the kth driving category, the th The first kinematic segment Dimensionless constants of each driving characteristic parameter For the kth driving category, the th Energy consumption per unit distance of a kinematic segment This represents the maximum energy consumption per unit distance for all kinematic segments in the k-th driving category. This represents the minimum energy consumption per unit distance for all kinematic segments in the k-th driving category. For the kth driving category, the th The dimensionless constant of the energy consumption per unit distance of a kinematic segment; Let be the number of kinematic segments in the k-th driving category. The number of driving characteristic parameters for each kinematic segment. The driving characteristic parameters do not include the energy consumption ratio per unit distance. The number of driving categories; Calculate the cumulative parameter values ​​for each kinematic segment. The following formula is used: in, For the kth driving category, the th The cumulative parameter values ​​of each kinematic segment. The weight representing the energy consumption per unit distance. >

1.

2. The method as described in claim 1, characterized in that, in, Based on the driving characteristic parameters, the multiple kinematic segments are classified to determine the driving category of each kinematic segment, including: The driving characteristic parameters of the multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment, and the driving style category includes aggressive driving style and conservative driving style; The driving characteristic parameters of the multiple kinematic segments are clustered in a secondary manner to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

3. The method as described in claim 2, characterized in that, The driving characteristic parameters in the first cluster include: mean absolute value of acceleration, standard deviation of absolute value of acceleration, mean absolute value of impact, and standard deviation of absolute value of impact.

4. The method as described in claim 2, characterized in that, The driving characteristic parameters in the secondary clustering include: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, and energy consumption per unit distance.

5. The method as described in claim 2, characterized in that, in, Clustering is performed on the driving characteristic parameters of the multiple kinematic segments to determine the driving style category of each kinematic segment, including: Determine the number of driving style categories; Multiple initial cluster centers are determined using a population-based stochastic optimization algorithm, the number of which is the same as the number of driving style categories; the population-based stochastic optimization algorithm employs a crossover operator. Based on the multiple initial cluster centers, the k-means clustering method is used to cluster the driving characteristic parameters of the multiple kinematic segments to determine the driving style category of each kinematic segment.

6. The method as described in claim 2, characterized in that, in, Secondary clustering is performed on the driving characteristic parameters of the multiple kinematic segments to determine the driving speed category of each kinematic segment, including: Determine the number of driving speed categories; Multiple initial cluster centers are determined using a population-based stochastic optimization algorithm, the number of which is the same as the number of driving speed categories; the population-based stochastic optimization algorithm employs a crossover operator. Based on the multiple initial cluster centers, the k-means clustering method is used to cluster the driving characteristic parameters of the multiple kinematic segments to determine the driving speed category of each kinematic segment.

7. The method as described in claim 1, characterized in that, in, The driving characteristic parameters include: kinematic segment time, average speed including idling, average speed excluding idling, running distance, maximum speed, maximum acceleration, maximum deceleration, average acceleration, average deceleration, idling time ratio, acceleration time ratio, deceleration time ratio, constant speed time ratio, energy consumption per unit distance, mean absolute value of acceleration, standard deviation of absolute value of acceleration, mean absolute value of impact, and standard deviation of absolute value of impact.

8. A device for constructing typical driving conditions of a pure electric vehicle, characterized in that, include: A driving data acquisition module is used to acquire driving data of a vehicle over a period of time, including the vehicle's speed data at each moment. The kinematic segmentation module is used to divide the driving data into multiple kinematic segments; A driving characteristic parameter determination module is used to determine the driving characteristic parameters of each kinematic segment; the driving characteristic parameters include energy consumption per unit distance. A classification module is used to classify the plurality of kinematic segments based on the driving feature parameters and determine the driving category of each kinematic segment; the driving category of each kinematic segment includes a driving style category and a driving speed category; The typical operating condition segment selection module is used to determine the weight of the energy consumption ratio per unit mileage, and based on the weight of the energy consumption ratio per unit mileage and the driving characteristic parameters, selects multiple kinematic segments from the kinematic segments belonging to the same driving category to form a typical operating condition. The typical operating condition splicing module is used to splice together multiple kinematic segments corresponding to each driving category to form a typical driving condition for a pure electric vehicle. Specifically, based on the weight of the energy consumption per unit mile ratio and the driving characteristic parameters, multiple kinematic segments are selected from the kinematic segments belonging to the same driving category to form a typical working condition, including: Determine the percentage of the sum of the times of kinematic segments in each driving category in the total time of all kinematic segments; Based on the aforementioned proportions and the total time of typical operating conditions, determine the typical operating condition sub-time corresponding to each driving category; For each driving category, the cumulative parameter value of each kinematic segment is calculated based on the weight of the energy consumption per unit distance of each kinematic segment and the driving characteristic parameters. Calculate the error between the cumulative parameter value of each kinematic segment and the mean of the cumulative parameter values ​​of all kinematic segments in the driving category; Based on the error and the typical operating condition sub-time, select multiple kinematic segments from the kinematic segments in the driving category to form the typical operating condition; The calculation of the cumulative parameter value for each kinematic segment includes: The driving characteristic parameters are converted into dimensionless constants using the following formula: in, For the kth driving category, the th The first kinematic segment One driving characteristic parameter, For the k-th driving category, the k-th ... The maximum value of each driving characteristic parameter. For the k-th driving category, the k-th ... The minimum value of each driving characteristic parameter. For the kth driving category, the th The first kinematic segment Dimensionless constants of each driving characteristic parameter For the kth driving category, the th Energy consumption per unit distance of a kinematic segment This represents the maximum energy consumption per unit distance for all kinematic segments in the k-th driving category. This represents the minimum energy consumption per unit distance for all kinematic segments in the k-th driving category. For the kth driving category, the th The dimensionless constant of the energy consumption per unit distance of a kinematic segment; Let be the number of kinematic segments in the k-th driving category. The number of driving characteristic parameters for each kinematic segment. The driving characteristic parameters do not include the energy consumption ratio per unit distance. The number of driving categories; Calculate the cumulative parameter values ​​for each kinematic segment. The following formula is used: in, For the kth driving category, the th The cumulative parameter values ​​of each kinematic segment. The weight representing the energy consumption per unit distance. >

1.

9. The apparatus as claimed in claim 8, characterized in that, The classification module is also used for: The driving characteristic parameters of the multiple kinematic segments are clustered once to determine the driving style category of each kinematic segment, and the driving style category includes aggressive driving style and conservative driving style; The driving characteristic parameters of the multiple kinematic segments are clustered in a secondary manner to determine the driving speed category of each kinematic segment, which includes low speed, medium speed and high speed.

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