Method, device and equipment for determining energy recovery strategy and storage medium
By acquiring information on the driving style and road conditions of electric vehicles, and using predictive models to predict SOC consumption, the problem of not being able to automatically determine energy recovery strategies in congested traffic conditions is solved, thus achieving intelligent energy management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DEEPAL AUTOMOBILE TECH CO LTD
- Filing Date
- 2023-07-17
- Publication Date
- 2026-05-29
AI Technical Summary
Current technologies cannot automatically determine the energy recovery strategy for electric vehicles in congested traffic conditions.
By acquiring the driver's driving style and traffic congestion information of the target vehicle, a pre-trained prediction model is used to predict the target SOC consumption value, and the target energy recovery strategy is determined based on the driving style and SOC consumption value.
It enables automatic determination of energy recovery strategies for electric vehicles under traffic congestion, improving the intelligence and efficiency of energy management.
Smart Images
Figure CN116811870B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical fields of intelligent transportation systems and energy management, specifically to a method, apparatus, device, and storage medium for determining an energy recovery strategy. Background Technology
[0002] Electric vehicles can reduce energy consumption and increase their driving range by using energy recovery methods.
[0003] In related technologies, energy recovery in traffic jams mainly relies on drivers manually setting energy recovery strategies in the vehicle. However, this energy recovery method cannot automatically determine the vehicle's energy recovery strategy in traffic jams. Summary of the Invention
[0004] This application provides a method, apparatus, device, and storage medium for determining an energy recovery strategy, to at least solve the technical problem in related technologies that cannot automatically determine a vehicle's energy recovery strategy under traffic congestion conditions. The technical solution of this application is as follows:
[0005] According to a first aspect of this application, a method for determining an energy recovery strategy is provided, comprising: acquiring the target driving style type of the driver of a target vehicle and the traffic congestion information of the target vehicle; the driving style type is used to indicate the driver's driving behavior and the driver's settings on the vehicle; predicting the target SOC consumption value of the target vehicle based on the driving style type of the target vehicle, the traffic congestion information of the target vehicle, and a pre-trained prediction model; the prediction model is used to predict the SOC consumption value of the vehicle after passing through road segments corresponding to different traffic congestion information with different driving style types; and determining the target energy recovery strategy of the target vehicle based on the driving style type of the target vehicle and the target SOC consumption value.
[0006] Based on the aforementioned technical means, this application can obtain the target driving style type of the driver of the target vehicle and the traffic congestion information of the target vehicle; based on the target vehicle's driving style type, traffic congestion information, and a pre-trained prediction model, it can predict the target SOC consumption value of the target vehicle. Furthermore, based on the target vehicle's driving style type and target SOC consumption value, it can determine the target energy recovery strategy of the target vehicle. Thus, by predicting the target SOC consumption value of the target vehicle and based on the driver's target driving style type, the target energy recovery strategy of the target vehicle can be automatically determined.
[0007] In one possible implementation, obtaining the driving style type of the target vehicle includes: obtaining the current driving style data of the target vehicle; the current driving style data is the driving style data at the current moment, and the driving style data includes the driver's driving behavior data and the vehicle's setting data; determining the distance between the current driving style data and multiple preset driving style data to obtain multiple distances; the multiple preset driving style data correspond one-to-one with multiple preset driving style types; and determining the preset driving style type corresponding to the minimum value among the multiple distances as the driving style type of the target vehicle.
[0008] Based on the aforementioned technical means, this application can obtain the current driving style data of the target vehicle; determine the distance between the current driving style data and multiple preset driving style data to obtain multiple distances; and determine the preset driving style type corresponding to the minimum value among the multiple distances as the driving style type of the target vehicle. Thus, by calculating the distance between the current driving style data and multiple preset driving style data, and finding that the preset driving style data corresponding to the minimum value among the multiple distances is most similar to the current driving style data, a method for determining the driving style type corresponding to the current driving style data is implemented.
[0009] In one possible implementation, the method further includes: acquiring historical driving style data of multiple sample vehicles; the historical driving style data being driving style data at historical moments; performing clustering processing on the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters; and determining the data of the center point of each driving style data cluster as preset driving style data to obtain multiple preset driving style data.
[0010] Based on the aforementioned technical means, this application can obtain historical driving style data from multiple sample vehicles; perform clustering processing on the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters; and determine the data of the center point of each driving style data cluster as preset driving style data, thereby obtaining multiple preset driving style data. In this way, multiple preset driving style data are determined.
[0011] In one possible implementation, the method further includes: acquiring multiple sample data and a sample SOC consumption value corresponding to each sample data; each sample data includes the vehicle's driving style type and road congestion information; and constructing a prediction model based on the multiple sample data and the sample SOC consumption value corresponding to each sample data.
[0012] Based on the aforementioned technical means, this application can acquire multiple sample data and the corresponding sample SOC consumption value for each sample data; and construct a prediction model based on the multiple sample data and the corresponding sample SOC consumption value for each sample data. This achieves a method for constructing a prediction model.
[0013] In one possible implementation, each of the above sample data also includes vehicle external temperature, vehicle external humidity, and high-voltage accessory SOC activation status.
[0014] Based on the aforementioned technical means, each sample data in this application also includes vehicle external temperature, vehicle external humidity, and the SOC (State of Charge) status of high-voltage accessories. Thus, by adding each sample data point, the accuracy of the constructed prediction model can be improved.
[0015] In one possible implementation, determining the target energy recovery strategy of the target vehicle based on the target vehicle's driving style type and target SOC consumption value includes: determining the target recovery strategy of the target vehicle corresponding to the target vehicle's driving style type and target SOC consumption value from a preset correspondence; the preset correspondence includes different energy recovery strategies corresponding to different driving style types and different SOC consumption values.
[0016] Based on the aforementioned technical means, this application can determine the target vehicle's driving style type and target SOC consumption value, and the corresponding target recovery strategy, from a preset correspondence based on this correspondence. In this way, the target recovery strategy for the target vehicle is determined through the preset correspondence.
[0017] According to a second aspect provided in this application, an energy recovery strategy determination apparatus is provided, comprising an acquisition unit, a prediction unit, and a determination unit; the acquisition unit is used to acquire the target driving style type of the driver of the target vehicle and the traffic congestion information of the target vehicle; the driving style type is used to indicate the driver's driving behavior and the driver's settings on the vehicle; the prediction unit is used to predict the target SOC consumption value of the target vehicle based on the target vehicle's driving style type, the target vehicle's traffic congestion information, and a pre-trained prediction model; the prediction model is used to predict the SOC consumption value of the vehicle after passing through road segments corresponding to different traffic congestion information with different driving style types; the determination unit is used to determine the target energy recovery strategy of the target vehicle based on the target vehicle's driving style type and the target SOC consumption value.
[0018] In one possible implementation, the acquisition unit is specifically used to: acquire the current driving style data of the target vehicle; the current driving style data is the driving style data at the current moment, and the driving style data includes the driver's driving behavior data and the vehicle's setting data; determine the distance between the current driving style data and multiple preset driving style data to obtain multiple distances; the multiple preset driving style data correspond one-to-one with multiple preset driving style types; and determine the preset driving style type corresponding to the minimum value among the multiple distances as the driving style type of the target vehicle.
[0019] In one possible implementation, the above-mentioned device further includes: a processing unit; and an acquisition unit, which is further configured to acquire historical driving style data of multiple sample vehicles; the historical driving style data is driving style data at historical moments.
[0020] The processing unit is used to cluster the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters; the determination unit is also used to determine the data of the center point of each driving style data cluster as the preset driving style data to obtain multiple preset driving style data.
[0021] In one possible implementation, the above-mentioned device further includes: a construction unit; an acquisition unit, further configured to acquire multiple sample data and a sample SOC consumption value corresponding to each sample data; each sample data includes the vehicle's driving style type and road congestion information; and a construction unit, configured to construct a prediction model based on the multiple sample data and the sample SOC consumption value corresponding to each sample data.
[0022] In one possible implementation, each of the above sample data also includes vehicle external temperature, vehicle external humidity, and high-voltage accessory SOC activation status.
[0023] In one possible implementation, the determining unit is specifically used to: determine the target energy recovery strategy of the target vehicle corresponding to the target vehicle's driving style type and target SOC consumption value from a preset correspondence based on the target vehicle's driving style type and target SOC consumption value; the preset correspondence includes different energy recovery strategies corresponding to different driving style types and different SOC consumption values.
[0024] According to a third aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.
[0025] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.
[0026] According to the fifth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.
[0027] It should be noted that the technical effects of any of the implementation methods in the second to fifth aspects can be found in the technical effects of the corresponding implementation methods in the first aspect, and will not be repeated here.
[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application.
[0029] Therefore, the above-mentioned technical features of this application have the following beneficial effects:
[0030] (1) The target vehicle's SOC consumption value can be predicted by acquiring the target driving style type of the driver and the traffic congestion information of the target vehicle, and by using the target vehicle's driving style type, traffic congestion information, and a pre-trained prediction model. Furthermore, the target energy recovery strategy of the target vehicle is determined based on the target driving style type and the target SOC consumption value. Thus, by predicting the target SOC consumption value of the target vehicle and based on the driver's target driving style type, the target energy recovery strategy of the target vehicle can be automatically determined under traffic congestion conditions.
[0031] (2) This can be achieved by acquiring the current driving style data of the target vehicle; determining the distance between the current driving style data and multiple preset driving style data to obtain multiple distances; and determining the preset driving style type corresponding to the minimum value among the multiple distances as the driving style type of the target vehicle. In this way, by calculating the distance between the current driving style data and multiple preset driving style data, the preset driving style data corresponding to the minimum value among the multiple distances is most similar to the current driving style data, thus realizing a method for determining the driving style type corresponding to the current driving style data.
[0032] (3) This can be achieved by acquiring historical driving style data from multiple sample vehicles; clustering the historical driving style data from multiple sample vehicles to obtain multiple driving style data clusters; and determining the data at the center point of each driving style data cluster as the preset driving style data, thus obtaining multiple preset driving style data. In this way, multiple preset driving style data can be determined.
[0033] (4) A prediction model can be constructed by acquiring multiple sample data and the corresponding sample SOC consumption value for each sample data. In this way, a method for constructing a prediction model is realized.
[0034] (5) By increasing the amount of data for each sample, the accuracy of the constructed prediction model can be improved.
[0035] (6) The target vehicle's driving style type and target SOC consumption value can be determined from a preset correspondence, along with the corresponding target recovery strategy. In this way, the target recovery strategy of the target vehicle is determined through the preset correspondence. Attached Figure Description
[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.
[0037] Figure 1 This is a flowchart illustrating a method for determining an energy recovery strategy according to an exemplary embodiment;
[0038] Figure 2 This is a flowchart illustrating a method for determining another energy recovery strategy according to an exemplary embodiment;
[0039] Figure 3 This is a flowchart illustrating a method for determining another energy recovery strategy according to an exemplary embodiment;
[0040] Figure 4 This is a schematic diagram illustrating yet another method for determining an energy recovery strategy according to an exemplary embodiment;
[0041] Figure 5 This is a block diagram illustrating an energy recovery strategy determination device according to an exemplary embodiment;
[0042] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation
[0043] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.
[0044] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0045] For ease of understanding, the method for determining the energy recovery strategy provided in this application will be described in detail below with reference to the accompanying drawings.
[0046] Figure 1 This is a flowchart illustrating a method for determining an energy recovery strategy according to an exemplary embodiment, such as... Figure 1 As shown, the method for determining this energy recovery strategy includes the following steps:
[0047] S101. The electronic device acquires the target driving style type of the driver of the target vehicle, as well as the traffic congestion information of the target vehicle.
[0048] Among them, driving style type is used to indicate the driver's driving behavior and the driver's settings for the vehicle.
[0049] As one possible implementation, the electronic device acquires the current driving style data of the target vehicle and calculates the distance between the current driving style data and multiple preset driving style data, obtaining multiple distances. Further, the electronic device determines the preset driving style type corresponding to the minimum value among the multiple distances as the driving style type of the target vehicle.
[0050] Electronic devices obtain traffic congestion information about the target vehicle from the target vehicle's navigation software.
[0051] It should be noted that the current driving style data refers to the driving style data at the current moment, which includes the driver's driving behavior data and the vehicle's settings data.
[0052] Multiple preset driving style data correspond one-to-one with multiple preset driving style types.
[0053] For example, driver behavior data includes the number of times the driver accelerates rapidly at 100 km / h, the number of times the accelerator pedal is fully depressed at 100 km / h, the number of times the driver brakes suddenly at 100 km / h, the number of times the driver decelerates suddenly at 100 km / h, the mileage driven at night at 100 km / h, the mileage driven while fatigued at 100 km / h, the number of times the driver accelerates rapidly while fatigued at 100 km / h, whether the driver smokes in the car, and whether the driver makes a phone call while driving.
[0054] The vehicle's settings include the vehicle's energy recovery mode settings and steering wheel power assist mode.
[0055] The traffic congestion information for the target vehicle includes the congestion conditions, congestion duration, and estimated travel time.
[0056] In practical applications, electronic devices perform feature filtering, abnormal data removal, and missing value filling on the current driving style data and traffic congestion information of the target vehicle.
[0057] Since the number of times the vehicle accelerates rapidly at 100 km / h, the number of times the accelerator pedal is fully depressed at 100 km / h, the number of times the vehicle brakes suddenly at 100 km / h, the number of times the vehicle decelerates suddenly at 100 km / h, the number of times the vehicle travels at high speeds, the number of kilometers driven at night, the number of kilometers driven while fatigued, and the number of times the vehicle accelerates rapidly while fatigued are all numerical variables, the electronic device normalizes these numerical variables based on a data normalization algorithm to eliminate the influence of magnitude.
[0058] Since whether the driver smokes in the car, whether the driver makes a phone call while driving, the vehicle's energy recovery mode setting, and the steering wheel power assist mode are categorical variables, the electronic device uses a dummy variable generation algorithm to generate dummy variables for these categorical variables.
[0059] The following is a detailed introduction to the data normalization algorithm:
[0060] The electronic device normalizes the above numerical variables to satisfy the following formula:
[0061]
[0062] Among them, X t For numerical variables before normalization, min(X) is the minimum value among the numerical variables, max(X) is the maximum value among the numerical variables, and Y is the maximum value among the numerical variables. t These are the numerical variables after normalization.
[0063] The following details the dummy variable generation algorithm:
[0064] The electronic device performs dummy variable generation on the above-mentioned categorical variables in accordance with the following formula:
[0065]
[0066] If the categorical variable X before the virtual variable generation process has t categories, and the value of each category is X(t), then the electronic device can generate t-1 virtual variables, and Y(t-1) is the virtual variable generated after the transformation.
[0067] S102. The electronic device predicts the target SOC consumption value of the target vehicle based on the target vehicle's driving style type, road congestion information, and a pre-trained prediction model.
[0068] The prediction model is used to predict the SOC value consumed by vehicles after passing through road segments with different traffic congestion information with different driving styles.
[0069] As one possible implementation, electronic devices input the target vehicle's driving style type and traffic congestion information into a pre-trained prediction model. The pre-trained prediction model is then processed to output the target vehicle's SOC consumption value.
[0070] S103. The electronic device determines the target energy recovery strategy for the target vehicle based on the target vehicle's driving style and target SOC consumption value.
[0071] As one possible implementation, the electronic device obtains the current SOC (State of Charge) remaining value of the target vehicle.
[0072] The electronic device calculates the difference between the current SOC remaining value and the target SOC consumption value, and determines this difference as the target SOC predicted remaining value.
[0073] The electronic device queries the target vehicle's driving style type and the target SOC prediction remaining value from the preset correspondence, and the corresponding target energy recovery strategy.
[0074] It should be noted that the preset correspondence includes different energy recovery strategies corresponding to different driving style types and different predicted residual SOC values.
[0075] For example, driving style types include Sport, Leisure, and Standard, and energy recovery strategies include High, Medium, and Low. The preset correspondence is shown in Table 1 below:
[0076] Table 1 Preset Correspondence
[0077] Driving style SOC Predicted Residual Value Energy recovery strategy sports 20～40 high Leisure 20～40 high standard 20～40 high sports 40～60 high Leisure 40～60 middle standard 40～60 middle sports 60 and above middle Leisure 60 and above Low standard 60 and above Low
[0078] If the target vehicle's driving style is Sport and the target SOC prediction residual value is greater than 60, then the electronic equipment determines the target vehicle's target energy recovery strategy as Medium.
[0079] Understandably, the technical solution provided in this application obtains the target driving style type of the driver of the target vehicle and the traffic congestion information of the target vehicle; based on the target vehicle's driving style type, traffic congestion information, and a pre-trained prediction model, it predicts the target SOC consumption value of the target vehicle. Furthermore, based on the target vehicle's driving style type and target SOC consumption value, it determines the target energy recovery strategy of the target vehicle. Thus, by predicting the target SOC consumption value of the target vehicle and based on the driver's target driving style type, it automatically determines the target energy recovery strategy of the target vehicle under traffic congestion conditions.
[0080] In some embodiments, in order to obtain multiple preset driving style data, such as Figure 2 As shown in the embodiments of this application, the method for determining the energy recovery strategy further includes the following steps:
[0081] S201. Electronic devices acquire historical driving style data from multiple sample vehicles.
[0082] Among them, the historical driving style data refers to the driving style data at historical moments.
[0083] As one possible approach, electronic devices can acquire historical driving style data of multiple sample vehicles from vehicle data uploaded by multiple sample vehicles.
[0084] S202. Electronic devices perform clustering processing on the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters.
[0085] As one possible implementation, the electronic device processes the historical driving style data of multiple sample vehicles using a k-means clustering algorithm based on a pre-set k value to obtain multiple driving style data clusters.
[0086] It should be noted that the number of multiple driving style data clusters is equal to the value of k.
[0087] The following is a detailed introduction to the k-means clustering algorithm:
[0088] For a historical driving style dataset of multiple sample vehicles, X = x1, x2, x3, ..., x n x1 represents the first historical driving style data.
[0089] The electronic device randomly selects k data points as cluster centers Cj from the historical driving style data of multiple sample vehicles based on a pre-set k value.
[0090] Electronic devices calculate each historical driving style data x i With each cluster center C j distance dij Each historical driving style data point is assigned to the nearest cluster center C. j In cluster j, the following formula three is satisfied:
[0091] d ij =||x i -C j || 2
[0092] k i =argmind ij Formula 3
[0093] For each cluster j, the electronic device updates the mean of all historical driving style data in the cluster to the cluster center Cj, satisfying the following formula:
[0094]
[0095] Where Sj represents the set of data points contained in the j-th cluster.
[0096] The electronic device iteratively updates the cluster centroids, minimizing the sum of squared distances between the historical driving style data within each cluster and its cluster centroid, until the cluster centroids no longer change or the preset maximum number of iterations is reached, satisfying the following formula:
[0097]
[0098] The electronic device outputs k cluster centers Cj and the cluster set Sj corresponding to each cluster center.
[0099] S203. The electronic device determines the data at the center point of each driving style data cluster as the preset driving style data, thereby obtaining multiple preset driving style data.
[0100] It should be noted that the center point of each driving style data cluster is the center point of each driving style data cluster after the k-means clustering algorithm has been updated.
[0101] As is understood, the technical solution provided in this application obtains historical driving style data from multiple sample vehicles; performs clustering processing on the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters; and determines the data at the center point of each driving style data cluster as preset driving style data, thereby obtaining multiple preset driving style data. In this way, multiple preset driving style data are determined.
[0102] In some embodiments, in order to construct a predictive model, such as Figure 3 As shown in the embodiments of this application, the method for determining the energy recovery strategy further includes the following steps:
[0103] S301. The electronic device acquires multiple sample data and the corresponding sample SOC consumption value for each sample data.
[0104] Each sample data point includes the vehicle's driving style type and traffic congestion information.
[0105] S302. Electronic devices construct a prediction model based on multiple sample data and the corresponding sample SOC consumption value for each sample data.
[0106] As one possible implementation, the electronic device uses multiple sample data as independent variables, the sample SOC consumption value corresponding to each sample data as the dependent variable, and trains a prediction model based on the linear regression model method.
[0107] In practical applications, each sample data also includes vehicle external temperature, vehicle external humidity, and the SOC (State of Charge) status of high-voltage accessories.
[0108] Electronic devices are used to train and test the prediction model, with a training-to-test ratio of 7:3.
[0109] Electronic devices evaluate the performance and accuracy of predictive models using the area under the curve (AUC) and accuracy.
[0110] Receiver operating characteristic (ROC) curves are a useful visualization tool for comparing two classification models. The ROC curve shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of a given model, where TPR = TP / (TP+FN) = sensitivity, and FPR = FP / (TN+FP) = 1-specificity. Therefore, the ROC curve can also be understood as the trajectory of sensitivity and 1-specificity at different thresholds.
[0111] like Figure 4 As shown, the area under the ROC curve is a measure of model accuracy (AUC). Generally, the higher the AUC value, the better the model's performance. An AUC value between 0.7 and 0.9 indicates that the predictive model has a certain degree of accuracy.
[0112] As is understood, the technical solution provided in this application acquires multiple sample data and the corresponding sample SOC consumption value for each sample data; and constructs a prediction model based on the multiple sample data and the corresponding sample SOC consumption value for each sample data. This provides a method for constructing a prediction model.
[0113] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the energy recovery strategy determination device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0114] This application embodiment can, based on the above method, exemplarily divide the energy recovery strategy determination device or electronic device into functional modules. For example, the energy recovery strategy determination device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.
[0115] Figure 5 This is a block diagram illustrating an energy recovery strategy determination device according to an exemplary embodiment. (Refer to...) Figure 5 The energy recovery strategy determination device 400 includes: an acquisition unit 401, a prediction unit 402, and a determination unit 403.
[0116] The acquisition unit 401 is used to acquire the target driving style type of the driver of the target vehicle and the traffic congestion information of the target vehicle; the driving style type is used to indicate the driver's driving behavior and the driver's settings for the vehicle.
[0117] The prediction unit 402 is used to predict the target SOC consumption value of the target vehicle based on the driving style type of the target vehicle, the road congestion information of the target vehicle, and the pre-trained prediction model; the prediction model is used to predict the SOC consumption value of the vehicle after passing through road segments corresponding to different road congestion information with different driving style types.
[0118] The determining unit 403 is used to determine the target energy recovery strategy of the target vehicle based on the driving style type of the target vehicle and the target SOC consumption value.
[0119] Optional, such as Figure 5 As shown, the acquisition unit 401 provided in this application embodiment is specifically used to: acquire the current driving style data of the target vehicle; the current driving style data is the driving style data at the current moment, and the driving style data includes the driver's driving behavior data and the vehicle's setting data.
[0120] Determine the distance between the current driving style data and multiple preset driving style data to obtain multiple distances; the multiple preset driving style data correspond one-to-one with multiple preset driving style types.
[0121] The preset driving style type corresponding to the minimum value among multiple distances is determined as the driving style type of the target vehicle.
[0122] Optional, such as Figure 5 As shown, the energy recovery strategy determination device 400 provided in this application embodiment further includes a processing unit 404.
[0123] The acquisition unit 401 is also used to acquire historical driving style data of multiple sample vehicles; the historical driving style data is the driving style data at historical moments.
[0124] The processing unit 404 is used to perform clustering processing on the historical driving style data of multiple sample vehicles to obtain multiple driving style data clusters.
[0125] The determining unit 403 is also used to determine the data of the center point of each driving style data cluster as preset driving style data, thereby obtaining multiple preset driving style data.
[0126] Optional, such as Figure 5 As shown, the energy recovery strategy determination device 400 provided in this application embodiment further includes: a construction unit 405.
[0127] The acquisition unit 401 is also used to acquire multiple sample data and the sample SOC consumption value corresponding to each sample data; each sample data includes the vehicle's driving style type and road congestion information.
[0128] Building unit 405 is used to build a prediction model based on multiple sample data and the sample SOC consumption value corresponding to each sample data.
[0129] Optionally, each sample data in the energy recovery strategy determination device 400 provided in this application embodiment may also include vehicle external temperature, vehicle external humidity, and high-voltage accessory SOC activation status.
[0130] Optional, such as Figure 5 As shown, the determining unit 403 provided in this application embodiment is specifically used to: determine the target energy recovery strategy of the target vehicle based on the target vehicle's driving style type and target SOC consumption value from a preset correspondence; the preset correspondence includes different energy recovery strategies corresponding to different driving style types and different SOC consumption values.
[0131] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0132] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device 500 includes, but is not limited to, a processor 501 and a memory 502.
[0133] The memory 502 described above is used to store the executable instructions of the processor 501. It is understood that the processor 501 is configured to execute instructions to implement the method for determining the energy recovery strategy in the above embodiments.
[0134] It should be noted that those skilled in the art will understand that Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 6 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.
[0135] Processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 502, and by calling data stored in memory 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 501 may include one or more processing units. Optionally, processor 501 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 501.
[0136] The memory 502 can be used to store software programs and various data. The memory 502 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0137] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions, which can be executed by a processor 501 of an electronic device 500 to implement the method for determining the energy recovery strategy in the above embodiments.
[0138] In actual implementation, Figure 5 The functions of the acquisition unit 401, prediction unit 402, determination unit 403, processing unit 404, and construction unit 405 can all be provided by... Figure 6 The processor 501 calls the computer program stored in the memory 502 to implement the process. The specific execution process can be found in the description of the energy recovery strategy determination method in the previous embodiment, and will not be repeated here.
[0139] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.
[0140] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 501 of an electronic device to complete the method for determining the energy recovery strategy in the above embodiments.
[0141] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above-described method for determining the energy recovery strategy, and can achieve the same technical effect as the above-described method for determining the energy recovery strategy. To avoid repetition, they will not be described again here.
[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0143] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0144] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0145] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.
[0147] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope 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 determining an energy recovery strategy, characterized in that, The method includes: Obtain the target driving style type of the driver of the target vehicle, as well as the traffic congestion information of the target vehicle; the driving style type is used to indicate the driver's driving behavior and the driver's settings for the vehicle. Based on the driving style type of the target vehicle, the road congestion information of the target vehicle, and the pre-trained prediction model, the target state of charge (SOC) consumption value of the target vehicle is predicted; the prediction model is used to predict the SOC consumption value of the vehicle after passing through road segments corresponding to different road congestion information with different driving style types. The target energy recovery strategy for the target vehicle is determined based on the driving style type of the target vehicle and the target SOC consumption value.
2. The method according to claim 1, characterized in that, The acquisition of the target vehicle's driving style type includes: Obtain the current driving style data of the target vehicle; the current driving style data is the driving style data at the current moment, and the driving style data includes the driver's driving behavior data and the vehicle's setting data; The distance between the current driving style data and multiple preset driving style data is determined to obtain multiple distances; the multiple preset driving style data correspond one-to-one with multiple preset driving style types; The preset driving style type corresponding to the minimum value among the multiple distances is determined as the driving style type of the target vehicle.
3. The method according to claim 2, characterized in that, The method further includes: Acquire historical driving style data from multiple sample vehicles; the historical driving style data refers to driving style data at historical moments. Clustering is performed on the historical driving style data of the multiple sample vehicles to obtain multiple driving style data clusters; The data at the center point of each driving style data cluster is determined as the preset driving style data, thus obtaining the multiple preset driving style data.
4. The method according to claim 1, characterized in that, The method further includes: Acquire multiple sample data and the corresponding SOC consumption value for each sample data; each sample data includes the vehicle's driving style type and road congestion information; The prediction model is constructed based on the multiple sample data and the sample SOC consumption value corresponding to each sample data.
5. The method according to claim 4, characterized in that, Each sample data also includes vehicle external temperature, vehicle external humidity, and the SOC (State of Charge) status of high-voltage accessories.
6. The method according to claim 1, characterized in that, The step of determining the target energy recovery strategy for the target vehicle based on its driving style type and target SOC consumption value includes: Based on the target vehicle's driving style type and target SOC consumption value, the target energy recovery strategy corresponding to the target vehicle's driving style type and target SOC consumption value is determined from a preset correspondence; the preset correspondence includes different energy recovery strategies corresponding to different driving style types and different SOC consumption values.
7. A device for determining an energy recovery strategy, characterized in that, The device includes: an acquisition unit, a prediction unit, and a determination unit; The acquisition unit is used to acquire the target driving style type of the driver of the target vehicle, as well as the traffic congestion information of the target vehicle; the driving style type is used to indicate the driver's driving behavior and the driver's settings for the vehicle. The prediction unit is used to predict the target SOC consumption value of the target vehicle based on the driving style type of the target vehicle, the road congestion information of the target vehicle, and the pre-trained prediction model; the prediction model is used to predict the SOC consumption value of the vehicle after passing through road segments corresponding to different road congestion information with different driving style types. The determining unit is used to determine the target energy recovery strategy of the target vehicle based on the driving style type of the target vehicle and the target SOC consumption value.
8. The apparatus according to claim 7, characterized in that, The acquisition unit is specifically used for: Obtain the current driving style data of the target vehicle; the current driving style data is the driving style data at the current moment, and the driving style data includes the driver's driving behavior data and the vehicle's setting data; The distance between the current driving style data and multiple preset driving style data is determined to obtain multiple distances; the multiple preset driving style data correspond one-to-one with multiple preset driving style types; The preset driving style type corresponding to the minimum value among the multiple distances is determined as the driving style type of the target vehicle.
9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 6.