Method for predicting remaining driving range of pure electric vehicle

Abstract

The invention discloses a method for predicting the remaining driving range of a pure electric vehicle. The method comprises the steps that original data of the vehicle within a preset time span are collected and processed; training a joint prediction model according to the original data, and obtaining a predicted battery health state through the joint prediction model; training a mileage prediction model according to the combined prediction model and the original data; the charge state variation of the charging section is obtained, and the actual consumed energy of the battery is calculated according to the charge state variation and the predicted health state of the battery; obtaining the current environment temperature, obtaining predicted non-driving demand energy according to the original data and the current environment temperature, and calculating driving demand energy; and obtaining a prediction segment according to the driving demand energy, and inputting the working condition information, the average current and the residual electric quantity variation of the prediction segment into the mileage prediction model to obtain the residual driving mileage. According to the method for predicting the remaining driving range of the pure electric vehicle, the remaining driving range can be predicted in combination with the low-temperature environment and the battery recession condition, so that the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle range prediction, and in particular to a method for predicting the remaining range of a pure electric vehicle. Background Art

[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.

[0003] With the continuous advancement of battery technology, electric vehicles in low-temperature areas maintain a rapid growth trend. However, in the existing technology, the remaining driving range of electric vehicles is predicted to be too high due to the failure to consider the effects of low temperature and battery degradation, resulting in low accuracy in the prediction of the remaining driving range of electric vehicles in low-temperature environments. Summary of the invention

[0004] The purpose of the present invention is to at least solve the problem of low accuracy in predicting the remaining driving range of electric vehicles in low temperature environments. This purpose is achieved through the following technical solutions:

[0005] The present invention proposes a method for predicting the remaining driving range of a pure electric vehicle, comprising:

[0006] Collecting and processing raw data of the vehicle for a preset time span, wherein the raw data is used to characterize the vehicle status and the battery status, and the preset time span is greater than or equal to 1 year;

[0007] Extracting a plurality of pure driving kinematics segments from the raw data, wherein the vehicle has only driving motion in each of the pure driving kinematics segments;

[0008] Acquire historical prediction features that affect the vehicle's driving range according to each of the pure driving kinematic segments, and acquire the vehicle's current charging segment and current prediction features that affect the vehicle's driving range;

[0009] Screening out the pure driving kinematics segments whose similarity between the historical prediction feature and the current prediction feature is greater than or equal to the target similarity, and forming a template library;

[0010] Training a joint prediction model based on the original data, wherein the joint prediction model is used to predict the remaining battery power and the battery health state;

[0011] Inputting the characteristic parameters of the current charging segment into the joint prediction model to obtain a predicted battery health state;

[0012] A mileage prediction model is trained according to the combined prediction model and the original data, wherein the mileage prediction model is used to predict the remaining driving range of the battery;

[0013] Obtaining a charge state change in the charging section, and calculating actual battery energy consumption according to the charge state change and the predicted battery health state;

[0014] Acquiring the current ambient temperature, and obtaining the predicted non-driving demand energy according to the original data and the current ambient temperature;

[0015] Calculating driving demand energy according to the actual battery consumption energy and the predicted non-driving demand energy;

[0016] Acquire a prediction segment from the template library according to the driving demand energy, wherein the prediction segment is one or more of the pure driving kinematic segments in the template library, and the total energy consumption generated by the prediction segment is less than or equal to the driving demand energy;

[0017] Obtaining the operating condition information, average current and remaining power change of the predicted segment, wherein the operating condition information is used to characterize the type of operating condition of the vehicle and the time proportion of each operating condition of the vehicle;

[0018] The operating condition information, average current and remaining power change of the prediction segment are input into the mileage prediction model to obtain the remaining driving range of the vehicle.

[0019] The method for predicting the remaining driving range of a pure electric vehicle of the present invention can predict the remaining driving range of the vehicle in combination with the low temperature environment and the battery degradation condition, thereby improving the prediction accuracy of the remaining driving range of the electric vehicle.

[0020] In some embodiments, the step of collecting and processing raw data of the vehicle before a preset time span includes:

[0021] Acquiring a collection time according to a first preset time interval, collecting characteristic parameters corresponding to the collection time, and forming the original data;

[0022] Determine whether the original data has repeated anomalies;

[0023] According to the duplicate anomaly in the original data, retain one piece of data in the duplicate data, and delete other data in the duplicate data;

[0024] Comparing the time intervals of the adjacent collection times with the first preset time interval;

[0025] According to the time interval of the acquisition time being less than the first preset time interval, it is determined that there is redundant acquisition time, and according to the compared acquisition time and the acquisition time adjacent to the compared acquisition time, the redundant acquisition time and the characteristic parameters corresponding to the redundant acquisition time are acquired and deleted;

[0026] According to the fact that the time interval of the acquisition time is greater than the first preset time interval, it is determined that the acquisition time is missing, the missing acquisition time is obtained according to the compared acquisition time and the first preset time interval, and the characteristic parameters corresponding to the missing acquisition time are supplemented according to the characteristic parameters corresponding to the compared acquisition time.

[0027] In some embodiments, after acquiring the collection time according to the first preset time interval, collecting the characteristic parameters corresponding to the collection time, and forming the raw data, the step of collecting and processing the raw data of the vehicle before the preset time span further includes:

[0028] Screen and clean abnormal feature parameters based on the box plot method and basic logic;

[0029] Determine whether there is data missing for the characteristic parameter;

[0030] If there is missing data in the characteristic parameter, the missing data is supplemented by interpolation, or the missing data is deleted.

[0031] In some embodiments, the step of extracting a plurality of pure driving kinematic segments from the raw data comprises:

[0032] dividing the raw data into a plurality of unit segments according to a complete charging cycle and a complete discharging cycle;

[0033] dividing each of the unit segments into a plurality of kinematic segments according to a second preset time interval;

[0034] The kinematic segments are screened for pure driving kinematic segments.

[0035] In some embodiments, the step of obtaining historical prediction features that affect the vehicle's driving range according to each of the pure driving kinematic segments, and obtaining the vehicle's current charging segment and current prediction features that affect the vehicle's driving range, includes:

[0036] Read the date, time and vehicle position information of each pure driving kinematic segment;

[0037] According to the date, the time and the location information, the historical weather conditions, the historical temperature level and the historical congestion level of the city where the vehicle was historically located are obtained, and the historical working day status is determined according to the date, and the historical weather conditions, the historical temperature level, the historical congestion level and the historical working day status are used as the historical prediction features, wherein the historical working day status is used to characterize whether the vehicle is on a working day;

[0038] Acquire the current weather condition, the current temperature level, the current congestion level, and the current working day status of the city where the vehicle is currently located, and use the current weather condition, the current temperature level, the current congestion level, and the current working day status as the current prediction features, wherein the current working day status is used to characterize whether the vehicle is in a working day;

[0039] Get the vehicle's current charging section.

[0040] In some embodiments, the step of training the joint prediction model according to the original data includes:

[0041] Obtaining the average battery voltage, average battery current, instantaneous battery voltage, instantaneous battery current, battery temperature, maximum battery cell voltage, minimum battery cell voltage, remaining battery capacity and battery health status of each kinematic segment;

[0042] using the average battery voltage, the average battery current, the instantaneous battery voltage, the instantaneous battery current and the battery temperature as first training parameters for training a prediction model for predicting the remaining battery capacity, and performing a Pearson correlation analysis on each of the first training parameters and the remaining battery capacity to obtain a correlation coefficient of the first training parameter with respect to the remaining battery capacity;

[0043] using the average battery voltage, the average battery current, the battery temperature, the maximum battery cell voltage, and the minimum battery cell voltage as second training parameters for training a prediction model for predicting a battery health state, and performing a Pearson correlation analysis on each of the second training parameters and the battery health state to obtain a correlation coefficient of the second training parameter relative to the battery health state;

[0044] A first training parameter and a second training parameter having a correlation coefficient greater than or equal to a correlation limit are screened out, and the joint prediction model is trained according to the screened first training parameter and the second training parameter.

[0045] In some embodiments, the step of training the mileage prediction model according to the joint prediction model and the original data includes:

[0046] Inputting the first training parameter into the joint prediction model, and obtaining a change in the remaining battery power of each kinematic segment;

[0047] Obtaining the proportion of the speed lower than the preset value, the deceleration time proportion and the mileage of each kinematic segment, using the proportion of the speed lower than the preset value, the deceleration proportion, the change in the remaining battery power and the average current as third training parameters for training the mileage prediction model, and performing a Pearson correlation analysis on each of the third training parameters and the mileage to obtain a correlation coefficient of the third training parameter with respect to the mileage;

[0048] A third training parameter having a correlation coefficient greater than or equal to the correlation limit is selected, and the mileage prediction model is trained according to the selected third training parameter.

[0049] In some embodiments, the step of obtaining the current ambient temperature and obtaining the predicted non-driving demand energy according to the original data and the current ambient temperature includes:

[0050] Extracting a plurality of stationary segments from the raw data, wherein the vehicle is stationary in each stationary segment and generates non-driving energy consumption;

[0051] Acquiring the ambient temperature and the non-driving energy consumption in each of the stationary segments, and building a non-driving energy consumption prediction model according to the ambient temperature and the non-driving energy consumption;

[0052] The current ambient temperature is obtained, and the ambient temperature is input into the non-driving energy consumption prediction model to obtain the predicted non-driving required energy.

[0053] In some embodiments, the step of obtaining the ambient temperature and the non-driving energy consumption in each of the stationary segments, and building a non-driving energy consumption prediction model according to the ambient temperature and the non-driving energy consumption includes:

[0054] Acquire the ambient temperature of each of the static segments;

[0055] Obtaining the resting energy consumption in each stationary segment, where the resting energy consumption includes the energy consumption of heating the cabin by the air conditioner and the energy consumption of maintaining the operating temperature of the battery;

[0056] The resting energy consumption and the ambient temperature of the resting segment are regressed and fitted to obtain a non-driving energy consumption prediction model.

[0057] In some embodiments, the plurality of still segments are a plurality of consecutive still segments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference numerals are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A schematic diagram of a method for predicting the remaining driving range of a pure electric vehicle according to an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of mileage;

[0061] Figure 3 is a schematic diagram of the total battery voltage;

[0062] Figure 4 is a schematic diagram of battery cell voltage;

[0063] Figure 5 Schematic diagram of function regression fitting. DETAILED DESCRIPTION

[0064] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0065] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0066] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0067] For ease of description, spatial relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figure, such as "inside", "outside", "inner side", "outer side", "below", "below", "above", "above", etc. Such spatial relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the figure. For example, if the device in the figure is turned over, then the elements described as "below other elements or features" or "below other elements or features" will subsequently be oriented as "above other elements or features" or "above other elements or features". Therefore, the example term "below..." can include both upper and lower orientations. The device can be oriented otherwise (rotated 90 degrees or in other directions) and the spatial relative descriptors used in the text are interpreted accordingly.

[0068] With the continuous advancement of battery technology, the number of electric vehicles in low-temperature areas has maintained a rapid growth trend. However, in the existing technology, the remaining driving range of electric vehicles in low-temperature environments is predicted to be too high because the effects of low temperature and battery degradation are not considered. Among them, the low-temperature environment refers to an environment of 0 degrees Celsius or below.

[0069] The present invention aims to at least solve the problem of low accuracy in predicting the remaining cruising range of an electric vehicle in a low temperature environment. To this end, an embodiment of the present invention proposes a method for predicting the remaining cruising range of a pure electric vehicle, which can predict the remaining cruising range of the vehicle in combination with the low temperature environment and the battery degradation condition, thereby improving the prediction accuracy of the remaining cruising range of the electric vehicle.

[0070] The remaining driving range prediction method for a pure electric vehicle according to an embodiment of the present invention is described below with reference to the accompanying drawings.

[0071] like Figure 1 As shown, the method for predicting the remaining driving range of a pure electric vehicle according to an embodiment of the present invention includes:

[0072] S100, collecting and processing raw data of the vehicle within a preset time span, wherein the raw data is used to characterize the vehicle status and the battery status, and the preset time span is greater than or equal to 1 year;

[0073] S200, extracting a plurality of pure driving kinematics segments from the original data, wherein the vehicle has only driving motion in each pure driving kinematics segment;

[0074] S300, obtaining historical prediction features that affect the vehicle's driving range according to each pure driving kinematics segment, and obtaining the vehicle's current charging segment and current prediction features that affect the vehicle's driving range;

[0075] S400, screening out pure driving kinematics segments whose similarity between historical prediction features and current prediction features is greater than or equal to the target similarity, and forming a template library;

[0076] S500, training a joint prediction model according to the original data, wherein the joint prediction model is used to predict the remaining battery power and the battery health status;

[0077] S600, inputting characteristic parameters of the current charging segment into a joint prediction model to obtain a predicted battery health state;

[0078] S700: training a mileage prediction model according to the combined prediction model and the original data, where the mileage prediction model is used to predict the remaining driving range of the battery;

[0079] S800, obtaining a charge state change in the charging stage, and calculating actual battery energy consumption according to the charge state change and the predicted battery health status;

[0080] S900, obtaining the current ambient temperature, and obtaining predicted non-driving demand energy according to the original data and the current ambient temperature;

[0081] S1000, calculating driving demand energy according to actual battery energy consumption and predicted non-driving demand energy;

[0082] S1100, obtaining a prediction segment from a template library according to the driving demand energy, wherein the prediction segment is one or more pure driving kinematic segments in the template library, and the total energy consumption generated by the prediction segment is less than or equal to the driving demand energy;

[0083] S1200, obtaining operating condition information, average current, and remaining power change of the prediction segment, wherein the operating condition information is used to characterize the type of operating condition of the vehicle and the time proportion of each operating condition of the vehicle;

[0084] S1300: Input the operating condition information, average current and remaining power change of the prediction segment into a mileage prediction model to obtain the remaining driving range of the vehicle.

[0085] S100. Collect and process raw data of a vehicle within a preset time span, wherein the raw data is used to characterize a vehicle state and a battery state, and the preset time span is greater than or equal to 1 year.

[0086] The time span of the original data is greater than or equal to 1 year, that is, the original data has a large time span. By analyzing data with a large time span, the driving mode and energy consumption characteristics of the vehicle under different conditions can be captured, thereby improving the accuracy of the range prediction. Data with a large time span includes a variety of different driving conditions, such as different road conditions, vehicle speeds, driving behaviors, etc. These factors all have an impact on the vehicle's energy consumption and remaining range. Combining these factors can provide a more comprehensive prediction. Data with a large time span can help the model learn the characteristics of different driving styles and road types, enhance the model's adaptability to driver behavior characteristics, and improve the accuracy of the prediction.

[0087] As an example, raw data is collected according to Table 1.

[0088]

[0089] Table 1

[0090] It should be noted that Table 1 exemplarily lists some characteristic parameters of the original data. The original data may also include other characteristic parameters. Those skilled in the art may also select the type of characteristic parameters to be collected according to actual conditions.

[0091] In some embodiments, S100, the step of collecting and processing raw data of the vehicle before a preset time span, includes:

[0092] Acquire a collection time according to a first preset time interval, collect characteristic parameters corresponding to the collection time, and form raw data;

[0093] Determine whether there are duplicate anomalies in the original data;

[0094] According to the duplicate anomaly in the original data, one piece of data in the duplicate data is retained, and the other data in the duplicate data is deleted;

[0095] Comparing the time interval between adjacent collection times with the first preset time interval;

[0096] According to the time interval of the acquisition time being less than the first preset time interval, it is determined that there is redundant acquisition time, and according to the compared acquisition time and the acquisition time adjacent to the compared acquisition time, the redundant acquisition time and the characteristic parameters corresponding to the redundant acquisition time are acquired and deleted;

[0097] Based on the fact that the time interval of the acquisition time is greater than the first preset time interval, it is determined that there is a missing acquisition time, the missing acquisition time is obtained based on the compared acquisition time and the first preset time interval, and the characteristic parameters corresponding to the missing acquisition time are supplemented based on the characteristic parameters corresponding to the compared acquisition time.

[0098] The acquisition time is acquired according to the first preset time interval, the characteristic parameters corresponding to the acquisition time are acquired, and raw data are formed.

[0099] When data is collected at fixed time intervals, the model can better capture patterns and trends in the time series, which helps the model learn the intrinsic structure of the time series to improve the accuracy of predictions.

[0100] Training the model on data at fixed time intervals can reduce the complexity of dealing with irregular sampling intervals. The model can more directly utilize the continuity and periodicity characteristics of time series data, thereby simplifying the model training and prediction process.

[0101] By collecting data at regular intervals, the model can generalize better to future data, thereby improving the model's predictive power for future data.

[0102] As some examples, the first preset time interval is 10 seconds. By setting the first preset time interval to 10 seconds, it is possible to avoid the time for collecting data being too long or too short.

[0103] Determine whether there are duplicate anomalies in the original data.

[0104] Determine whether the duplicate content in the original data needs to be processed based on whether there are duplicate anomalies in the original data.

[0105] According to the duplicate anomaly in the original data, one piece of data in the duplicate data is retained, and the other data in the duplicate data is deleted.

[0106] Keep one piece of data in the duplicate data, which can be any piece of data in the duplicate data.

[0107] By retaining one piece of duplicate data and deleting other pieces of duplicate data, the duplicate data can be removed, thereby preventing the duplicate data from affecting subsequent steps.

[0108] As some examples, the replicate data are shown in Table 2.

[0109]

[0110] Table 2

[0111] The time interval between adjacent collection times is compared with the first preset time interval.

[0112] By comparing the time intervals between adjacent acquisition times with the first preset time interval, it is possible to determine whether there is a problem with the time interval for acquiring data, so as to facilitate processing of the original data in subsequent steps.

[0113] According to the time interval being less than the first preset time interval, it is determined that there is redundant collection time, and according to the compared collection time and the collection time adjacent to the compared collection time, the redundant collection time and the characteristic parameters corresponding to the redundant collection time are acquired and deleted.

[0114] When the time interval is less than the first preset time interval, it indicates that there is excess collection time. The excess collection time can be determined by the collection time adjacent to the comparison time, and the excess collection time and the characteristic parameters corresponding to the excess collection time are deleted to avoid the influence of erroneous collection data on subsequent steps.

[0115] As shown in Table 3, taking the first preset time of 10s as an example, the interval between the acquisition time 20240101101354 and the acquisition time 20240101101401 is 7s, and the interval between the acquisition time 20240101101354 and the acquisition time 20240101101404 is 10s. It is thus determined that the acquisition time 20240101101401 is redundant acquisition time, and the redundant acquisition time and the characteristic parameters corresponding to the redundant acquisition time are deleted to avoid the influence of erroneous acquisition data on subsequent steps.

[0116]

[0117] Table 3

[0118] According to the time interval being greater than the first preset time interval, it is determined that there is missing collection time, the missing collection time is obtained according to the compared collection time and the first preset time interval, and the characteristic parameters corresponding to the missing collection time are supplemented according to the characteristic parameters corresponding to the compared collection time.

[0119] If the time interval is greater than the first preset time interval, it indicates that there is missing collection time. The missing collection time is obtained based on the compared collection time and the first preset time interval, and the characteristic parameters corresponding to the missing collection time are supplemented based on the characteristic parameters corresponding to the compared collection time to avoid the lost collection data from affecting subsequent steps.

[0120] Specifically, the missing acquisition time is obtained by interpolation of the characteristic parameters of the compared acquisition time.

[0121] As shown in Table 4, taking the first preset time of 10s as an example, the interval between the acquisition time 20230101010249 and the acquisition time 20230101010309 is 20s, thereby judging that there is data loss between the acquisition time 20230101010249 and the acquisition time 20230101010309, and the lost acquisition time is 20230101010259. According to the feature parameters corresponding to the acquisition time 20230101010249 and the feature parameters corresponding to the acquisition time 20230101010309, the feature parameters corresponding to the acquisition time 20230101010259 are obtained by interpolation.

[0122]

[0123] Table 4

[0124] Through this embodiment, the abnormal collection time and the characteristic parameters corresponding to the abnormal collection time can be processed, thereby preventing the abnormal parameters from affecting subsequent steps.

[0125] In some embodiments, after acquiring the collection time according to the first preset time interval, collecting the characteristic parameters corresponding to the collection time, and forming the raw data, S100, the step of collecting and processing the raw data of the vehicle before the preset time span further includes:

[0126] Screen and clean abnormal feature parameters based on the box plot method and basic logic;

[0127] Determine whether there is data missing for feature parameters;

[0128] If there is missing data in the feature parameters, the missing data is supplemented by interpolation, or the missing data is deleted.

[0129] Combination Figure 2 , Figure 3 and Figure 4 As shown, the abnormal jumps of characteristic parameters that are obviously inconsistent with basic common sense can be found through the box plot, so as to process the abnormal characteristic parameters.

[0130] This embodiment can process abnormal characteristic parameters and missing characteristic parameters to prevent the abnormal and missing characteristic parameters from affecting subsequent operations.

[0131] Specifically, when there are fewer abnormal feature parameters, the abnormal feature parameters are deleted and the deleted feature parameters are supplemented by interpolation. When there are more abnormal features, the data is deleted. This can reduce the complexity of data processing and improve the efficiency of data processing.

[0132] Specifically, when the missing characteristic parameters are relatively few, the missing characteristic parameters are deleted, and the deleted characteristic parameters are supplemented by interpolation. When the missing characteristic parameters are relatively many, the data are deleted. This can reduce the complexity of data processing, thereby improving the efficiency of data processing.

[0133] S200: Extract multiple pure driving kinematics segments from the original data, wherein the vehicle only has driving motion in each pure driving kinematics segment.

[0134] The pure driving kinematics segment can reflect the driving information of the vehicle. The pure driving kinematics segment is extracted so as to predict the mileage of the vehicle based on the driving information of the pure driving kinematics segment in the subsequent steps.

[0135] In some embodiments, S200, the step of extracting a plurality of pure driving kinematic segments from the original data, comprises:

[0136] Dividing the raw data into multiple unit segments according to complete charging cycles and complete discharging cycles;

[0137] dividing each unit segment into a plurality of kinematic segments according to a second preset time interval;

[0138] Filter the kinematic segments for pure driving kinematic segments.

[0139] By dividing the data according to the complete charging cycle and the complete discharging cycle, the kinematic segment can be a motion segment of a charging cycle or a motion segment of a discharging cycle, thereby avoiding the kinematic segment including both a charging cycle and a discharging cycle.

[0140] The unit segments are further divided into kinematic segments, and the pure driving kinematic segments are screened out, thereby obtaining the pure driving kinematic segments.

[0141] S300. Obtain historical prediction features that affect the vehicle's driving range according to each pure driving kinematics segment, and obtain the vehicle's current charging segment and current prediction features that affect the vehicle's driving range.

[0142] It should be noted that the current charging section may be the charging section where an uncharged vehicle was charged last time, or may be the charging section where a charged vehicle is currently charging.

[0143] The historical prediction features that affect the vehicle's driving range can be obtained through pure driving kinematics segments. The similarity between the current prediction features and the historical prediction features that affect the vehicle's driving range can be judged based on the current charging segment, and the relevance of each pure driving kinematics segment to the current one can be judged.

[0144] In some embodiments, S300, the step of obtaining historical prediction features that affect the vehicle's driving range according to each pure driving kinematics segment, and obtaining the vehicle's current charging segment and current prediction features that affect the vehicle's driving range, includes:

[0145] Read the date, time and vehicle position information of each pure driving kinematic segment;

[0146] According to the date, time and location information, the historical weather conditions, historical temperature levels and historical congestion levels of the city where the vehicle has been located in the past are obtained, and the historical working day status is determined according to the date, and the historical weather conditions, historical temperature levels, historical congestion levels and historical working day status are used as historical prediction features, among which the historical working day status is used to characterize whether the vehicle is on a working day;

[0147] The current weather condition, current temperature level, current congestion level and current working day status of the city where the vehicle is currently located are obtained, and the current weather condition, current temperature level, current congestion level and current working day status are used as current prediction features, wherein the current working day status is used to characterize whether the vehicle is in a working day;

[0148] Get the vehicle's current charging section.

[0149] The historical weather status and the current weather status both include sunny, rainy, heavy rain, light snow, and heavy snow. The historical temperature level and the current temperature level both include relatively low temperature, low temperature, and very low temperature. The historical congestion level and the current congestion level both include peak and flat peaks. The historical working day status and the current working day status both include working days and non-working days.

[0150] Based on the historical weather conditions, historical temperature levels, historical congestion levels, historical weekday conditions, and the similarity between the current weather conditions, current temperature levels, historical congestion levels, and current weekday conditions, the similarity between the pure kinematic segment and the current vehicle can be determined, thereby facilitating prediction.

[0151] S400, screening out pure driving kinematics segments whose similarity between historical prediction features and current prediction features is greater than or equal to the target similarity, and forming a template library.

[0152] Preliminary screening is performed using pure driving fragments and a template library is formed to reduce the difficulty of screening in subsequent steps.

[0153] As an example, the target similarity is 95%.

[0154] Specifically, the operating condition prediction architecture includes a feature matching layer, and steps S300 and S400 are executed to construct a template library.

[0155] S500: training a joint prediction model according to original data, wherein the joint prediction model is used to predict the remaining battery power and the battery health status.

[0156] The joint model is trained to predict the battery status.

[0157] Specifically, the joint prediction model is trained according to the WOA-XGBoost algorithm.

[0158] In some embodiments, S500, the step of training the joint prediction model according to the original data, includes:

[0159] Obtain the average battery voltage, average battery current, instantaneous battery voltage, instantaneous battery current, battery temperature, maximum battery cell voltage, minimum battery cell voltage, remaining battery capacity and battery health status for each kinematic segment;

[0160] using the average battery voltage, the average battery current, the instantaneous battery voltage, the instantaneous battery current and the battery temperature as first training parameters for training a prediction model for predicting the remaining battery capacity, and performing a Pearson correlation analysis on each first training parameter and the remaining battery capacity to obtain a correlation coefficient of the first training parameter relative to the remaining battery capacity;

[0161] using the average battery voltage, the average battery current, the battery temperature, the maximum battery cell voltage, and the minimum battery cell voltage as second training parameters for training a prediction model for predicting a battery health state, and performing a Pearson correlation analysis on each second training parameter and the battery health state to obtain a correlation coefficient of the second training parameter relative to the battery health state;

[0162] A first training parameter and a second training parameter whose correlation coefficient is greater than or equal to a correlation limit are screened out, and a joint prediction model is trained according to the screened first training parameter and the second training parameter.

[0163] The joint model can be trained by using the first training parameter and the second training parameter in the kinematic segment, and the accuracy of the joint model training can be improved by screening out the first training parameter and the second training parameter whose correlation coefficient is greater than or equal to the correlation limit.

[0164] As an example, the correlation limit is 0.8.

[0165] S600: Input characteristic parameters of the current charging segment into a joint prediction model to obtain a predicted battery health state.

[0166] By predicting the battery health status, the vehicle's current battery degradation level can be reflected, making the predicted driving range more accurate.

[0167] S700: Train a mileage prediction model based on the combined prediction model and the original data, where the mileage prediction model is used to predict the remaining battery driving range.

[0168] The mileage prediction model can be used to predict vehicle mileage.

[0169] Specifically, the mileage prediction model is trained using the lightGBM algorithm.

[0170] In some embodiments, S700, the step of training the mileage prediction model according to the joint prediction model and the original data, includes:

[0171] Inputting the first training parameter into the joint prediction model, and obtaining the change in the remaining battery power of each kinematic segment;

[0172] Obtaining the proportion of the speed lower than the preset value, the deceleration time proportion and the mileage of each kinematic segment, using the proportion of the speed lower than the preset value, the deceleration proportion, the change in the remaining battery power and the average current as third training parameters for training the mileage prediction model, and performing a Pearson correlation analysis on each third training parameter and the mileage to obtain a correlation coefficient of the third training parameter relative to the mileage;

[0173] A third training parameter whose correlation coefficient is greater than or equal to the correlation limit is selected, and the mileage prediction model is trained according to the selected third training parameter.

[0174] The accuracy of joint model training can be improved by screening out the first training parameter and the second training parameter whose correlation coefficient is greater than or equal to the correlation limit.

[0175] As an example, the preset value is 20 kilometers per hour. It is understandable that, without considering the direction of movement, the value of the speed is a positive number, so the proportion of speeds lower than the preset value is the proportion of speeds between 0 and 20 kilometers per hour.

[0176] Specifically, the proportion P of the speed lower than the preset value is calculated by the following formula: low_speed

[0177]

[0178] Among them, n low_speed is the sampling point in the kinematic segment where the speed is lower than the preset value, and n0 is all the sampling points in the kinematic segment.

[0179] The average current I is calculated by the following formula average

[0180]

[0181] Where i is the instantaneous current and n0 is all the sampling points in the kinematic segment.

[0182] The deceleration time ratio P is calculated by the following formula reduce_speed

[0183]

[0184] Among them, n reduce_speed is the number of sampling points in the kinematic segment where the speed is in a deceleration state, and n0 is all the sampling points in the kinematic segment.

[0185] S800 , obtaining a charge state change ΔSOC during the charging stage, and calculating actual battery energy consumption Econ according to the charge state change ΔSOC and the predicted battery health state SOH.

[0186] As an example, the actual energy consumption Econ of the battery is calculated by the following formula:

[0187] Econ=C·SOH·△SOC

[0188] Where C is the nominal capacity of the battery.

[0189] S900: Obtain the current ambient temperature, and obtain predicted non-driving demand energy according to the original data and the current ambient temperature.

[0190] By predicting non-driving energy demand, the impact of low temperature environment on battery rest consumption can be taken into account, thereby making the predicted driving energy demand in low temperature environment more accurate.

[0191] In some embodiments, S900, obtaining the current ambient temperature, and obtaining the predicted non-driving demand energy according to the original data and the current ambient temperature, includes:

[0192] Extracting a plurality of stationary segments from the original data, wherein the vehicle is stationary in each stationary segment and generates non-driving energy consumption;

[0193] Obtain the ambient temperature and non-driving energy consumption in each stationary segment, and build a non-driving energy consumption prediction model based on the ambient temperature and non-driving energy consumption;

[0194] The current ambient temperature is obtained and input into a non-driving energy consumption prediction model to obtain a predicted non-driving required energy.

[0195] A prediction model is established based on the relationship between the ambient temperature and non-driving energy consumption in the stationary segment, and the current non-driving energy is predicted by the current ambient temperature, thereby realizing the prediction of the current non-driving energy.

[0196] In some embodiments, the step of obtaining the ambient temperature and non-driving energy consumption in each stationary segment, and building a non-driving energy consumption prediction model according to the ambient temperature and non-driving energy consumption includes:

[0197] Get the ambient temperature of each stationary segment;

[0198] Obtain the resting energy consumption in each stationary segment, which includes the energy consumption of the air conditioner to heat the cabin and the energy consumption of the battery to maintain the operating temperature;

[0199] The resting energy consumption and the ambient temperature of the stationary segment are regressed and fitted to obtain the non-driving energy consumption prediction model.

[0200] The resting energy consumption is mainly generated by the air conditioning heating the cabin and heating the battery to maintain the operating temperature of the battery. Therefore, building a model based on the energy consumption of the air conditioning heating the cabin and the energy consumption of maintaining the battery at operating temperature can reduce the workload while ensuring the accuracy of the prediction model, thereby improving the efficiency of building the energy consumption prediction model.

[0201] like Figure 5 As shown, as an example, the resting energy consumption and the ambient temperature of the static segment are fitted by means of functional regression.

[0202] In some embodiments, the plurality of static segments are a plurality of continuous static segments, so as to further increase the prediction accuracy of the energy consumption prediction model.

[0203] S1000: Calculate driving demand energy based on actual battery energy consumption and predicted non-driving demand energy.

[0204] The driving demand energy is the actual battery consumption energy minus the predicted non-driving demand energy.

[0205] S1100. Obtain a prediction segment from a template library according to driving demand energy, wherein the prediction segment is one or more pure driving kinematic segments in the template library, and a total energy consumption generated by the prediction segment is less than or equal to the driving demand energy.

[0206] The pure driving kinematics segments in the template library are further screened by the driving demand energy, so as to obtain the pure driving kinematics segments similar to the current condition of the vehicle, so as to be able to predict the vehicle more accurately.

[0207] Furthermore, when there are multiple prediction segments, one of the multiple prediction segments is selected for prediction.

[0208] Specifically, the operating condition architecture includes an operating condition prediction layer, which is used to execute step S1100.

[0209] S1200: Obtain the operating condition information, average current, and remaining power change of the predicted segment, wherein the operating condition information is used to characterize the type of the vehicle's operating condition and the time proportion of each operating condition of the vehicle. For example, the operating condition information is used to characterize whether the vehicle has a high-speed operating condition, a suburban operating condition, and an urban operating condition, and is used to characterize the time proportion of the high-speed operating condition, the suburban operating condition, and the urban operating condition.

[0210] The operating condition information of the predicted segment is used as the operating condition information when the vehicle is moving, and combined with the average current and the change in the remaining power, so as to facilitate the prediction of the vehicle's mileage in subsequent steps.

[0211] Specifically, the operating condition information of the predicted segment is obtained through K-means clustering.

[0212] S1300: Input the operating condition information, average current and remaining power change of the prediction segment into a mileage prediction model to obtain the remaining driving range of the vehicle.

[0213] Specifically, the mileage prediction model includes an energy consumption layer for executing steps S800 , S900 , and S1000 , and a mileage prediction layer for executing steps S700 and S1300 .

[0214] It should be noted that the location information and data involved in the present invention (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0215] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0216] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute instructions), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples of computer-readable storage media (a non-exhaustive list) include the following: an electrical connection with one or more wirings (electronic devices), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory, or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM, Compact Disc Read-Only Memory). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0217] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA, Programmable Gate Array), a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0218] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for predicting the remaining driving range of a pure electric vehicle, characterized in that: include: Collecting and processing raw data of the vehicle for a preset time span, wherein the raw data is used to characterize the vehicle status and the battery status, and the preset time span is greater than or equal to 1 year; Extracting a plurality of pure driving kinematics segments from the raw data, wherein the vehicle has only driving motion in each of the pure driving kinematics segments; Acquire historical prediction features that affect the vehicle's driving range according to each of the pure driving kinematic segments, and acquire the vehicle's current charging segment and current prediction features that affect the vehicle's driving range; Screening out the pure driving kinematics segments whose similarity between the historical prediction feature and the current prediction feature is greater than or equal to the target similarity, and forming a template library; Training a joint prediction model based on the original data, wherein the joint prediction model is used to predict the remaining battery power and the battery health state; Inputting the characteristic parameters of the current charging segment into the joint prediction model to obtain a predicted battery health state; A mileage prediction model is trained according to the combined prediction model and the original data, wherein the mileage prediction model is used to predict the remaining driving range of the battery; Obtaining a charge state change in the charging section, and calculating actual battery energy consumption according to the charge state change and the predicted battery health state; Acquiring the current ambient temperature, and obtaining the predicted non-driving demand energy according to the original data and the current ambient temperature; Calculating driving demand energy according to the actual battery consumption energy and the predicted non-driving demand energy; Acquire a prediction segment from the template library according to the driving demand energy, wherein the prediction segment is one or more of the pure driving kinematic segments in the template library, and the total energy consumption generated by the prediction segment is less than or equal to the driving demand energy; Obtaining the operating condition information, average current and remaining power change of the predicted segment, wherein the operating condition information is used to characterize the type of operating condition of the vehicle and the time proportion of each operating condition of the vehicle; The operating condition information, average current and remaining power change of the prediction segment are input into the mileage prediction model to obtain the remaining driving range of the vehicle.

2. The method for predicting the remaining driving range of a pure electric vehicle according to claim 1, characterized in that: The step of collecting and processing the original data of the vehicle before the preset time span includes: Acquiring a collection time according to a first preset time interval, collecting characteristic parameters corresponding to the collection time, and forming the original data; Determine whether the original data has repeated anomalies; According to the duplicate anomaly existing in the original data, retain one piece of data in the duplicate data, and delete other data in the duplicate data; Comparing the time intervals of the adjacent collection times with the first preset time interval; According to the time interval of the acquisition time being less than the first preset time interval, it is determined that there is redundant acquisition time, and according to the compared acquisition time and the acquisition time adjacent to the compared acquisition time, the redundant acquisition time and the characteristic parameters corresponding to the redundant acquisition time are acquired and deleted; According to the fact that the time interval of the acquisition time is greater than the first preset time interval, it is determined that the acquisition time is missing, the missing acquisition time is obtained according to the compared acquisition time and the first preset time interval, and the characteristic parameters corresponding to the missing acquisition time are supplemented according to the characteristic parameters corresponding to the compared acquisition time.

3. The method for predicting the remaining driving range of a pure electric vehicle according to claim 2, characterized in that: After acquiring the collection time according to the first preset time interval, collecting the characteristic parameters corresponding to the collection time, and forming the raw data, the step of collecting and processing the raw data of the vehicle before the preset time span also includes: Screen and clean abnormal feature parameters based on the box plot method and basic logic; Determine whether there is data missing for the characteristic parameter; If there is missing data in the characteristic parameter, the missing data is supplemented by interpolation, or the missing data is deleted.

4. The method for predicting the remaining driving range of a pure electric vehicle according to claim 3, characterized in that: The step of extracting a plurality of pure driving kinematics segments from the raw data comprises: dividing the raw data into a plurality of unit segments according to a complete charging cycle and a complete discharging cycle; dividing each of the unit segments into a plurality of kinematic segments according to a second preset time interval; The kinematic segments are screened for pure driving kinematic segments.

5. The method for predicting the remaining driving range of a pure electric vehicle according to claim 1, characterized in that: The step of obtaining the historical prediction features that affect the vehicle's driving range according to each of the pure driving kinematics segments, and obtaining the vehicle's current charging segment and the current prediction features that affect the vehicle's driving range, comprises: Read the date, time and vehicle position information of each pure driving kinematic segment; According to the date, the time and the location information, the historical weather conditions, the historical temperature level and the historical congestion level of the city where the vehicle was historically located are obtained, and the historical working day status is determined according to the date, and the historical weather conditions, the historical temperature level, the historical congestion level and the historical working day status are used as the historical prediction features, wherein the historical working day status is used to characterize whether the vehicle is on a working day; Acquire the current weather condition, the current temperature level, the current congestion level, and the current working day status of the city where the vehicle is currently located, and use the current weather condition, the current temperature level, the current congestion level, and the current working day status as the current prediction features, wherein the current working day status is used to characterize whether the vehicle is in a working day; Get the vehicle's current charging section.

6. The method for predicting the remaining driving range of a pure electric vehicle according to claim 4, characterized in that: The step of training the joint prediction model according to the original data comprises: Obtaining the average battery voltage, average battery current, instantaneous battery voltage, instantaneous battery current, battery temperature, maximum battery cell voltage, minimum battery cell voltage, remaining battery capacity and battery health status of each kinematic segment; using the average battery voltage, the average battery current, the instantaneous battery voltage, the instantaneous battery current and the battery temperature as first training parameters for training a prediction model for predicting the remaining battery capacity, and performing a Pearson correlation analysis on each of the first training parameters and the remaining battery capacity to obtain a correlation coefficient of the first training parameter with respect to the remaining battery capacity; using the average battery voltage, the average battery current, the battery temperature, the maximum battery cell voltage, and the minimum battery cell voltage as second training parameters for training a prediction model for predicting a battery health state, and performing a Pearson correlation analysis on each of the second training parameters and the battery health state to obtain a correlation coefficient of the second training parameter relative to the battery health state; A first training parameter and a second training parameter having a correlation coefficient greater than or equal to a correlation limit are screened out, and the joint prediction model is trained according to the screened first training parameter and the second training parameter.

7. The method for predicting the remaining driving range of a pure electric vehicle according to claim 6, characterized in that: The step of training the mileage prediction model according to the joint prediction model and the original data includes: Inputting the first training parameter into the joint prediction model, and obtaining a change in the remaining battery power of each kinematic segment; Obtaining the proportion of the speed lower than the preset value, the deceleration time proportion and the mileage of each kinematic segment, using the proportion of the speed lower than the preset value, the deceleration proportion, the change in the remaining battery power and the average current as third training parameters for training the mileage prediction model, and performing a Pearson correlation analysis on each of the third training parameters and the mileage to obtain a correlation coefficient of the third training parameter with respect to the mileage; A third training parameter having a correlation coefficient greater than or equal to the correlation limit is selected, and the mileage prediction model is trained according to the selected third training parameter.

8. The method for predicting the remaining driving range of a pure electric vehicle according to claim 1, characterized in that: The step of obtaining the current ambient temperature and obtaining the predicted non-driving demand energy according to the original data and the current ambient temperature includes: Extracting a plurality of stationary segments from the raw data, wherein the vehicle is stationary in each stationary segment and generates non-driving energy consumption; Acquiring the ambient temperature and the non-driving energy consumption in each of the stationary segments, and building a non-driving energy consumption prediction model according to the ambient temperature and the non-driving energy consumption; The current ambient temperature is obtained, and the ambient temperature is input into the non-driving energy consumption prediction model to obtain the predicted non-driving required energy.

9. The method for predicting the remaining driving range of a pure electric vehicle according to claim 8, characterized in that: The step of obtaining the ambient temperature and the non-driving energy consumption in each stationary segment, and building a non-driving energy consumption prediction model according to the ambient temperature and the non-driving energy consumption, comprises: Acquire the ambient temperature of each of the static segments; Obtaining the resting energy consumption in each stationary segment, where the resting energy consumption includes the energy consumption of heating the cabin by the air conditioner and the energy consumption of maintaining the operating temperature of the battery; The resting energy consumption and the ambient temperature of the resting segment are regressed and fitted to obtain a non-driving energy consumption prediction model.

10. The method for predicting the remaining driving range of a pure electric vehicle according to claim 8, characterized in that: The multiple still segments are multiple continuous still segments.

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