A method, system, and product for predicting energy consumption-related characteristics during navigation trips.

Abstract

This invention relates to a method, system, and product for predicting energy consumption-related features during navigation trips, belonging to the field of electric vehicle energy consumption-related feature prediction. The method includes: acquiring actual road driving data; extracting N actual road trips; acquiring navigation trip features of the N actual road trips and calculating energy consumption-related features; constructing training samples; constructing an estimation model for predicting energy consumption-related trip features using the navigation trip features; training the energy consumption-related trip feature estimation model using the training samples; and predicting the mathematical expectation and / or probability distribution of the energy consumption-related features based on the trained energy consumption-related trip feature estimation model. The mathematical expectation and / or probability distribution of the energy consumption-related features are used for energy consumption estimation of the navigation path. The above method in this invention can improve the convergence of the variables to be predicted, thereby significantly reducing the computational load of the prediction model and improving the accuracy of energy consumption prediction.

Description

Technical Field

[0001] This invention relates to the field of predicting energy consumption-related characteristics of electric vehicles, and in particular to a method, system, and product for predicting energy consumption-related characteristics during navigation trips. Background Technology

[0002] In recent years, the electric vehicle industry has developed rapidly, with the global number of electric vehicles exceeding 16 million by 2021. Consumers are eager to obtain accurate predictions of future travel energy consumption to alleviate range anxiety caused by insufficient battery capacity in electric vehicles. In addition to the energy consumption characteristics of electric vehicles themselves, the driving characteristics of future trips are also a major influencing factor on future travel energy consumption. However, existing methods for predicting future travel driving characteristics generally predict driving speed, which is a high-dimensional time series with complex influencing factors and strong randomness, making accurate prediction of this series extremely difficult. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, and product for predicting energy consumption-related features during navigation trips. By constructing more reasonable energy consumption-related driving features to replace the common predicted driving speed sequence, the complexity of predicting future trip driving features can be significantly reduced without affecting the essential problem of energy consumption prediction. This improves the convergence of the variables to be predicted, thereby significantly reducing the computational load of the prediction model and improving the accuracy of energy consumption prediction.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] In a first aspect, the present invention provides a method for predicting energy consumption-related features during a navigation journey, the prediction method comprising:

[0006] Acquire actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory.

[0007] Extract N actual road trips from the actual road driving data;

[0008] Calculate the navigation trip characteristics of the N actual road trips; the navigation trip characteristics include: trip length, number of intersections with traffic lights, and number of intersections without traffic lights;

[0009] Construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity;

[0010] Training samples are constructed based on the energy consumption-related features and the navigation trip features;

[0011] Construct an energy consumption-related travel characteristic estimation model;

[0012] The energy consumption-related travel feature estimation model is trained using the training samples.

[0013] The mathematical expectation and / or probability distribution of energy-related features are predicted based on the trained energy-related travel feature estimation model; the mathematical expectation and / or probability distribution of the energy-related features are used for energy consumption estimation of the navigation path.

[0014] Optionally, calculating the navigation trip features of the N actual road trips specifically includes the following steps:

[0015] Calculate speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity;

[0016] The relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity are calculated based on the speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity.

[0017] Optionally, the calculation of velocity intensity specifically adopts the following formula:

[0018]

[0019] in, Indicates velocity intensity. Indicates the first The first actual road journey The speed of each sampling point; Indicates the first Number of sampling points for each actual road trip;

[0020] The following formula is used to calculate low-speed braking intensity:

[0021]

[0022] in, Indicates low-speed braking intensity. Indicates braking energy. Indicates the first The actual road journey of the vehicle from the sampling point To the sampling point average speed, Indicates the sampling interval time. Indicates the speed threshold. Indicates the first Number of sampling points for each actual road trip; Indicates the first j The first actual road journey i The speed of each sampling point;

[0023] The following formula is used to calculate high-speed braking intensity:

[0024]

[0025] in, Indicates high-speed braking intensity;

[0026] The following formula is used to calculate slowdown intensity:

[0027]

[0028] in, Indicates the intensity of slow movement.

[0029] Optionally, the calculation of relative velocity intensity specifically adopts the following formula:

[0030]

[0031] in, Indicates relative velocity intensity. Indicates velocity intensity. Indicates the reference velocity intensity;

[0032] The following formula is used to calculate the relative low-speed braking intensity:

[0033]

[0034] in, Indicates the braking intensity at relatively low speeds. Indicates low-speed braking intensity. Indicates the baseline low-speed braking intensity;

[0035] The following formula is used to calculate the relative high-speed braking intensity:

[0036]

[0037] in, Indicates relative high-speed braking intensity. Indicates high-speed braking intensity. Indicates the reference high-speed braking intensity;

[0038] The following formula is used to calculate the relative slowdown intensity:

[0039]

[0040] in, Indicates relative slowdown intensity. Indicates the intensity of slowdown. This indicates the baseline slowdown intensity.

[0041] Among them, the reference speed strength, reference high-speed braking strength, reference low-speed braking strength and reference slowing strength are calculated through the reference working condition. The reference working condition can be calculated by selecting any four working conditions in which the strength is not zero, such as the China Comprehensive Standard Working Condition CLTC.

[0042] Optionally, the energy consumption-related travel characteristic estimation model is a deterministic performance consumption characteristic estimation model or an energy consumption characteristic probability estimation model.

[0043] Secondly, based on the above-described method of this invention, this invention further provides a system for predicting energy consumption-related characteristics during navigation trips, the prediction system comprising:

[0044] The actual road driving data acquisition module is used to acquire actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory;

[0045] The actual road trip extraction module is used to extract N actual road trips from the actual road driving data;

[0046] The navigation trip feature calculation module is used to calculate the navigation trip features of the N actual road trips; the navigation trip features include: trip length, number of intersections with traffic lights, and number of intersections without traffic lights;

[0047] An energy consumption-related feature construction module is used to construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity;

[0048] A training sample construction module is used to construct training samples based on the energy consumption-related features and the navigation trip features;

[0049] The module for building energy consumption-related travel feature estimation models is used to construct such models.

[0050] The training module is used to train the energy consumption-related travel feature estimation model using the training samples;

[0051] The prediction module is used to predict the mathematical expectation and / or probability distribution of energy-related features based on the trained energy-related travel feature estimation model; the mathematical expectation and / or probability distribution of the energy-related features are used for energy consumption estimation of the navigation path.

[0052] Optionally, the navigation trip feature calculation module specifically includes the following units:

[0053] Speed ​​intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity calculation units are used to calculate speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity.

[0054] The relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity calculation unit is used to calculate the relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity based on the speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity.

[0055] Optionally, the energy consumption-related travel characteristic estimation model can be a deterministic performance consumption characteristic estimation model or an energy consumption characteristic probability estimation model.

[0056] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described method for predicting energy consumption-related characteristics during navigation.

[0057] Fourthly, the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, which, when executed by a processor, implements the above-described method for predicting energy consumption-related features during navigation.

[0058] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0059] This invention utilizes relative low-speed braking intensity and relative high-speed braking intensity. Based on extensive data, for urban roads, the correlation between low-speed braking intensity and high-speed braking intensity and energy consumption differs significantly. Therefore, the high / low-speed braking intensity model constructed in this invention has a fundamental difference in model performance and is more suitable for energy consumption prediction of navigation paths. This invention predicts four features: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity. These four features have low dimensionality, strong determinism, and good convergence, reducing the difficulty of prediction. Based on the longitudinal dynamics of electric vehicles, this invention constructs four speed statistics for the energy consumption calculation method of this invention. These four speed statistics have a higher linear correlation with travel energy consumption and are more suitable for calculating travel energy consumption. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart of the method for predicting energy consumption-related features during navigation trips according to the present invention;

[0062] Figure 2This is a simplified structural diagram of the energy consumption-related travel characteristic estimation model of the present invention;

[0063] Figure 3 For the present invention and Relationship diagram;

[0064] Figure 4 For the present invention and Relationship diagram;

[0065] Figure 5 For the present invention and Relationship diagram;

[0066] Figure 6 For the present invention and Relationship diagram;

[0067] Figure 7 For the present invention Distribution diagram;

[0068] Figure 8 For the present invention Distribution diagram;

[0069] Figure 9 For the present invention Distribution diagram;

[0070] Figure 10 For the present invention A schematic diagram of the distribution. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] The purpose of this invention is to provide a method, system, and product for predicting energy consumption-related features during navigation trips. By constructing more reasonable energy consumption-related driving features to replace the common predicted driving speed sequence, the complexity of predicting future trip driving features can be significantly reduced without affecting the essential problem of energy consumption prediction. This improves the convergence of the variables to be predicted, thereby significantly reducing the computational load of the prediction model and improving the prediction accuracy.

[0073] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] Figure 1 This is a flowchart of the method for predicting energy consumption-related features during navigation trips according to the present invention, as shown below. Figure 1 As shown, the method in this invention includes:

[0075] Step 1: Obtain actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory.

[0076] Specifically, actual road driving data is acquired, and the data samples include, but are not limited to, time, vehicle speed, and GPS information of the driving trajectory. It is recommended that the sampling frequency for time and vehicle speed be no less than 1Hz, and that the actual driving data samples cover as many different road types as possible. The acquired data should ideally be from the same driver and the same vehicle model, but the method in this invention is also applicable to datasets with mixed driver and vehicle models.

[0077] Step 2: Extract from the actual road driving data N The actual road journey.

[0078] Specifically, extract from the actual road driving data obtained in step 1 The number of speed sampling points within each actual road trip (i.e., a trip where both the starting and ending speeds are 0, and the sample data is continuous from the start time to the end time) is: .

[0079] Step 3: Calculate the above N The navigation trip features of an actual road trip include: trip length, number of intersections with traffic lights, and number of intersections without traffic lights.

[0080] Specifically, navigation trip characteristics are calculated based on sample data and navigation map information. This includes, but is not limited to: travel distance (total mileage between the starting point and the ending point), number of intersections with traffic lights (number of intersections with traffic lights along the route between the starting point and the ending point), and number of intersections without traffic lights (number of intersections without traffic lights along the route between the starting point and the ending point that may affect vehicle speed).

[0081] Step 4: Construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity.

[0082] Constructing energy consumption-related features It includes, but is not limited to, four characteristics: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity. The specific calculation method is as follows:

[0083] First, the speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-down intensity are calculated using equations (1)-(4).

[0084] Speed ​​intensity:

[0085] (1)

[0086] Braking strength:

[0087] First, define braking energy:

[0088] (2)

[0089] (3)

[0090] in For the first The first actual road journey The speed of each sampling point The sampling interval time. For the quality of the car, The vehicle departed from the sampling point. To the sampling point The average velocity can be expressed as:

[0091] (4)

[0092] It is the target coefficient related to the calculation of load force.

[0093] Calculating braking energy If the vehicle's coasting braking characteristics are known, then formula (2) should be applied; otherwise, formula (3) should be applied.

[0094] The braking intensity can be determined based on a certain threshold speed. Divide it into two parts:

[0095] Low-speed braking intensity:

[0096] (5)

[0097] High-speed braking intensity:

[0098] (6)

[0099] Slow traffic intensity:

[0100] (7)

[0101] Calculate the relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity by selecting a baseline operating condition (such as CLTC-P (China Driving Test Cycle)).

[0102] (8)

[0103] (9)

[0104] (10)

[0105] (11)

[0106] Among them, the baseline speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity are... Calculate according to equations (1)-(7).

[0107] Step 5: Construct training samples based on the energy consumption-related features and the navigation trip features.

[0108] Navigation trip features for the p-th trip Energy consumption related characteristics To form a trip sample, The model learning dataset, or training samples, consists of several travel samples.

[0109] Step 6: Construct an energy consumption-related travel feature estimation model.

[0110] Two methods can be used to construct an energy consumption-related travel characteristic estimation model:

[0111] Method 1: Construct a deterministic performance consumption feature estimation model. Apply deterministic data-driven methods (such as neural networks, random forests, ensemble learning, etc.), using the model learning dataset constructed in step 2 as the data foundation, focusing on navigation trip features in the dataset. Energy consumption-related features as input As the output training data model, it realizes the mathematical expectation of estimating energy consumption-related features based on navigation trip features;

[0112] Method 2: Construct a probabilistic estimation model for energy consumption characteristics. Apply probabilistic data-driven methods (such as Bayesian methods, quantile models, etc.), using the model learning dataset constructed in step 2 as the data foundation, and focusing on navigation trip features in the dataset. Energy consumption-related features as input As the output training data model, it enables the estimation of the probability distribution of energy consumption-related features based on navigation trip characteristics.

[0113] The model structures of both methods above are usable. Figure 2 express.

[0114] Step 7: Train the energy consumption-related travel feature estimation model using the training samples.

[0115] Step 8: Predict the mathematical expectation and / or probability distribution of energy consumption-related features based on the trained energy consumption-related trip feature estimation model; the mathematical expectation and / or probability distribution of the energy consumption-related features are used for energy consumption estimation of the navigation path.

[0116] For a given navigation trip, use the map API to obtain the navigation trip features along the navigation path. Substitute the values ​​into step 6 to construct an energy consumption feature estimation model, calculate the expected value and / or probability distribution of the corresponding energy consumption-related features, and use the results to estimate the energy consumption of the navigation path.

[0117] Example

[0118] The invention will be further explained and illustrated below with a specific example:

[0119] Step 1: Obtain actual road driving data

[0120] Obtain 14 days of actual road driving data of a certain vehicle model and its driver, including time, speed, and GPS information of driving trajectory. The sampling frequency of all relevant data is 10Hz. The actual driving data includes urban main roads, expressways, congested road sections, highways, etc.

[0121] Step 2: Select navigation trip features

[0122] Extract 100 actual road trips from step 1. Calculate the navigation trip characteristics based on the sample data and information from the Gaode Map API: trip length, number of intersections, denoted as... F 1, F 2.

[0123] Step 3: Construct energy-related travel features

[0124] Energy consumption-related characteristics were selected, including: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity, denoted as [missing information]. The calculation is performed using equations (1)-(7). The reference working condition is CLTC-P. The speed threshold is selected when calculating the braking intensity. .in, respectively with Relationship such as Figures 3-6 As shown.

[0125] Step 4: Construct an energy consumption characteristic estimation model

[0126] A probabilistic estimation model for energy consumption characteristics is selected and constructed. The random forest quantile method is applied to train the model. Estimate energy consumption-related characteristics respectively The probability distribution.

[0127] Step 5: Prediction of driving characteristics related to energy consumption during navigation trips

[0128] Given a new navigation route, use the map API to obtain the navigation route features. =5km, =3, substitute into step 3 to construct the energy consumption feature estimation model, calculate the probability distribution of the corresponding energy consumption related features, and the prediction results are as follows. Figure 7-10 As shown, the prediction results indicate that for a navigation trip with a future distance of 5 km and 3 traffic light intersections, the distribution of four driving characteristics related to energy consumption can be described as follows:

[0129] The 95% confidence interval for relative velocity intensity is approximately (0.39, 0.75).

[0130] The 95% confidence interval for relative low-speed braking intensity is approximately (0.07, 0.33).

[0131] The 95% confidence interval for relative high-speed braking intensity is approximately (0.55, 2.03).

[0132] The 95% confidence interval for relative slowdown intensity is approximately (0.05, 0.69).

[0133] The method described in this invention can obtain the energy consumption probability distribution of a navigation trip with 3 traffic light intersections and a future distance of 5 km based on the above distribution using a linear estimation algorithm.

[0134] Based on the method described above, this invention further provides a system for predicting energy consumption-related characteristics during navigation trips, the prediction system comprising:

[0135] The actual road driving data acquisition module is used to acquire actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory.

[0136] The actual road trip extraction module is used to extract N actual road trips from the actual road driving data.

[0137] The navigation trip feature calculation module is used to calculate the navigation trip features of the N actual road trips; the navigation trip features include: trip length, number of intersections with traffic lights, and number of intersections without traffic lights.

[0138] An energy consumption-related feature construction module is used to construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity.

[0139] The training sample construction module is used to construct training samples based on the energy consumption-related features and the navigation trip features.

[0140] The module for constructing energy consumption-related travel feature estimation models is used to build such models.

[0141] The training module is used to train the energy consumption-related travel feature estimation model using the training samples.

[0142] The prediction module is used to predict the mathematical expectation and / or probability distribution of energy-related features based on the trained energy-related travel feature estimation model; the mathematical expectation and / or probability distribution of the energy-related features are used for energy consumption estimation of the navigation path.

[0143] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described method for predicting energy consumption-related characteristics during navigation.

[0144] In practical applications, the aforementioned electronic devices can be servers.

[0145] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0146] The processor, communication interface, and memory communicate with each other via a communication bus.

[0147] A communication interface is used to communicate with other devices.

[0148] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0149] Specifically, the program may include program code, which includes computer operation instructions.

[0150] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0151] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0152] Based on the description of the above embodiments, this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods described in any embodiment.

[0153] The energy consumption-related feature prediction system for navigation trips provided in this application exists in various forms, including but not limited to:

[0154] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and are primarily designed to provide voice and data communication. These terminals include smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones.

[0155] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, have computing and processing functions, and generally also have mobile internet access capabilities. These terminals include: PDAs, MIDs, and UMPCs, such as iPads.

[0156] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0157] (4) Other electronic devices with data interaction functions.

[0158] Specific embodiments of the subject matter have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0159] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0160] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0165] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0166] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, and CD-ROM.

[0167] Digital multifunction optical disc (DVD) or other optical storage, magnetic cassette tape, magnetic tape, disk storage or other magnetic storage devices

[0168] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0169] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0170] This application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This application can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0171] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0172] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting energy consumption-related features during navigation trips, characterized in that, The prediction method includes: Acquire actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory. Extract from the actual road driving data N A real road journey; the real road journey is a journey in which the starting speed and ending speed are both 0, and the sample data is continuous from the starting time to the ending time period; Calculate the N The navigation journey characteristics of an actual road trip include: journey length, number of intersections with traffic lights, and number of intersections without traffic lights. Construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity; Training samples are constructed based on the energy consumption-related features and the navigation trip features; Construct an energy consumption-related travel characteristic estimation model; the energy consumption-related travel characteristic estimation model is either a performance consumption characteristic estimation model or an energy consumption characteristic probability estimation model. The energy consumption-related travel feature estimation model is trained using the training samples. The mathematical expectation and / or probability distribution of energy-related features are predicted based on the trained energy-related travel feature estimation model; the mathematical expectation and / or probability distribution of the energy-related features are used for energy consumption estimation of the navigation path.

2. The method for predicting energy consumption-related characteristics during navigation trips according to claim 1, characterized in that, The specific steps involved in constructing energy consumption-related features are as follows: Calculate speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity; The relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity are calculated based on the speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity.

3. The method for predicting energy consumption-related characteristics during navigation trips according to claim 2, characterized in that, The calculation of velocity intensity specifically uses the following formula: in, Indicates velocity intensity. Indicates the first The first actual road journey The speed of each sampling point Indicates the first Number of sampling points for each actual road trip; The following formula is used to calculate low-speed braking intensity: in, Indicates low-speed braking intensity. Indicates braking energy. Indicates the first The actual road journey of the vehicle from the sampling point To the sampling point average speed, Indicates the sampling interval time. Indicates the speed threshold. Indicates the first Number of sampling points for each actual road trip; Indicates the first j The first actual road journey i The speed of each sampling point; The following formula is used to calculate high-speed braking intensity: in, Indicates high-speed braking intensity; The following formula is used to calculate slowdown intensity: in, Indicates the intensity of slow movement.

4. The method for predicting energy consumption-related characteristics during navigation trips according to claim 1, characterized in that, The calculation of relative velocity intensity specifically uses the following formula: in, Indicates relative velocity intensity. Indicates velocity intensity. Indicates the reference velocity intensity; The following formula is used to calculate the relative low-speed braking intensity: in, Indicates the braking intensity at relatively low speeds. Indicates low-speed braking intensity. Indicates the baseline low-speed braking intensity; The following formula is used to calculate the relative high-speed braking intensity: in, Indicates relative high-speed braking intensity. Indicates high-speed braking intensity. Indicates the reference high-speed braking intensity; The following formula is used to calculate the relative slowdown intensity: in, Indicates relative slowdown intensity. Indicates the intensity of slowdown. This indicates the baseline slowdown intensity.

5. A system for predicting energy consumption-related characteristics during navigation trips, characterized in that, The prediction system includes: The actual road driving data acquisition module is used to acquire actual road driving data; the actual road driving data includes: time, vehicle speed, and GPS information of the driving trajectory; The actual road trip extraction module is used to extract N actual road trips from the actual road driving data; the actual road trip is a trip where the starting speed and ending speed are both 0, and the sample data is continuous within the time period from the start time to the end time. The navigation trip feature calculation module is used to calculate the navigation trip features of the N actual road trips; the navigation trip features include: trip length, number of intersections with traffic lights, and number of intersections without traffic lights; An energy consumption-related feature construction module is used to construct energy consumption-related features; the energy consumption-related features include: relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity; A training sample construction module is used to construct training samples based on the energy consumption-related features and the navigation trip features; The module for constructing energy consumption-related travel characteristic estimation models is used to build such models. These models can be either deterministic energy consumption characteristic estimation models or probabilistic energy consumption characteristic estimation models. The training module is used to train the energy consumption-related travel feature estimation model using the training samples; The prediction module is used to predict the mathematical expectation and / or probability distribution of energy-related features based on the trained energy-related travel feature estimation model; the mathematical expectation and / or probability distribution of the energy-related features are used for energy consumption estimation of the navigation path.

6. The energy consumption-related feature prediction system during navigation trips according to claim 5, characterized in that, The navigation trip feature calculation module specifically includes the following units: Speed ​​intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity calculation units are used to calculate speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity. The relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity calculation unit is used to calculate the relative speed intensity, relative low-speed braking intensity, relative high-speed braking intensity, and relative slow-moving intensity based on the speed intensity, low-speed braking intensity, high-speed braking intensity, and slow-moving intensity.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method for predicting energy consumption-related features during a navigation trip as described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for predicting energy consumption-related features during a navigation journey as described in any one of claims 1-4.

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