Energy consumption prediction method and device based on navigation path, medium and equipment

Abstract

The invention discloses an energy consumption prediction method and device based on a navigation path, a medium and equipment. The method comprises the following steps: building a target vehicle model and a simulation working condition library of a target vehicle; sequentially inputting each working condition curve in the simulation working condition library into the target vehicle model to obtain an energy consumption simulation result, and generating an energy consumption chart of the target vehicle; verifying an energy consumption chart of the target vehicle by adopting the actual navigation path and the corresponding actual energy consumption result; identifying target characteristic parameters in the predicted navigation path after the verification is passed; predicting a predicted energy consumption value of the target vehicle based on the target characteristic parameters and an energy consumption chart of the target vehicle; according to the method, a target vehicle model and a simulation working condition library are constructed, the target vehicle model and the simulation working condition library are utilized to generate an energy consumption chart, so that dependence on large-volume user data is reduced, the accuracy and applicability of the energy consumption chart are improved, a mode of looking up the energy consumption chart is utilized to replace simulation model estimation, and the estimation efficiency is improved. And the accuracy of energy consumption prediction is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle energy consumption prediction, and in particular to a method, device, medium and equipment for energy consumption prediction based on a navigation path. Background Art

[0002] With the continuous development of automobile technology and the enhancement of environmental awareness, more and more car companies have begun to explore innovative solutions and have launched their own energy consumption prediction systems for car navigation routes to address the problem of automobile energy consumption. The energy consumption prediction of car navigation routes mainly relies on advanced algorithms and technologies, aiming to help car owners plan driving routes more effectively, thereby reducing energy consumption and extending driving range. This prediction not only takes into account the driving distance, but also comprehensively considers multiple factors such as road conditions, slopes, and weather to ensure the accuracy of the prediction. Through rapid iterative updates and technological innovations, navigation service providers such as HD Maps have launched intelligent algorithms such as "range estimation", which can provide estimated consumption data for driving range based on travel routes and display sections with severe power consumption, helping users to grasp the charging time more accurately. Self-driving cars perceive the surrounding environment through on-board sensors, including information such as roads, traffic conditions, vehicle locations, and obstacles, and then autonomously analyze and judge, and select the optimal path to achieve the purpose of energy saving.

[0003] However, current technologies still face some common problems. For example, the technical route that relies on extracting and fitting historical big data requires a large amount of data, and the data set should also cover enough scenarios. At the same time, historical data sets may not be suitable for every new car development. The dynamic mechanism model can only be used for offline simulation and cannot meet the real-time requirements of actual driving. Therefore, it is necessary to develop a method to improve the accuracy and practicality of automobile energy consumption prediction. Summary of the invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a method, device, medium and equipment for energy consumption prediction based on a navigation path.

[0005] According to one aspect of the present application, a method for predicting energy consumption based on a navigation path is provided, comprising: building a target vehicle model of a target vehicle; constructing a simulation operating condition library of the target vehicle; wherein the simulation operating condition library includes a plurality of operating condition curves of characteristic parameters of the navigation path involved in the target vehicle; inputting each of the operating condition curves in the simulation operating condition library into the target vehicle model in turn to obtain an energy consumption simulation result of each of the operating condition curves; generating an energy consumption chart of the target vehicle according to the operating condition curves and the corresponding energy consumption simulation results; wherein the energy consumption chart represents the correspondence between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; verifying the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption results; if the verification is successful, obtaining the predicted navigation path of the target vehicle; identifying the target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation operating condition library; and predicting the predicted energy consumption value of the target vehicle based on the target characteristic parameters and the energy consumption chart of the target vehicle.

[0006] In one embodiment, the target vehicle model of the target vehicle includes: separately building an environment module, a driver module, a resistance module, a power system module and a control strategy module of the target vehicle model; connecting the environment module, the driver module, the resistance module, the power system module and the control strategy module based on an information interaction method to obtain the target vehicle model.

[0007] In one embodiment, constructing a simulation operating condition library of the target vehicle includes: obtaining multiple actual driving data; wherein the actual driving data includes driving time, driving speed, and driving distance; obtaining characteristic parameters that affect the energy consumption of the target vehicle, and selecting non-related characteristic parameters as simulation characteristic parameters; wherein the number of the simulation characteristic parameters is less than a preset number; combining the simulation characteristic parameters and combining them with the actual driving data to generate a combined operating condition curve; dividing the actual driving data into multiple data segments, and randomly combining multiple data segments to obtain a reorganized operating condition curve; constructing the simulation operating condition library based on the combined operating condition curve and the reorganized operating condition curve.

[0008] In one embodiment, generating the energy consumption chart of the target vehicle based on the operating curve and the corresponding energy consumption simulation result includes: matching the characteristic parameter combination of the operating curve and the corresponding energy consumption simulation result to obtain an initial data pair; constructing an initial chart based on the initial data pair; wherein the initial chart is a multidimensional matrix; optimizing the data in the initial chart to obtain an optimized chart; compressing the optimized chart and establishing an index to obtain the energy consumption chart of the target vehicle.

[0009] In one embodiment, the use of the actual navigation path and the corresponding actual energy consumption results to verify the energy consumption chart of the target vehicle includes: using the energy consumption image of the target vehicle to predict the actual navigation path to obtain a corresponding prediction result; calculating the error value between the prediction result and the actual energy consumption result; if the error value is less than a preset difference, determining that the verification is passed.

[0010] In one embodiment, the energy consumption prediction method based on the navigation path further includes: if the verification fails, analyzing the deviation area in the energy consumption chart corresponding to the error value; and adjusting the construction parameters of the energy consumption chart based on the deviation area until the verification passes.

[0011] In one embodiment, the identifying of the target characteristic parameters in the predicted navigation path includes: acquiring corresponding road information and environmental information according to the predicted navigation path; generating a working condition curve of the predicted navigation path according to the road information and the environmental information; and obtaining the target characteristic parameters according to the working condition curve of the predicted navigation path.

[0012] According to another aspect of the present application, there is provided an energy consumption prediction device based on a navigation path, comprising: a vehicle model building module for building a target vehicle model of a target vehicle; a simulation condition building module for building a simulation condition library of the target vehicle; wherein the simulation condition library includes a plurality of combined conditions of characteristic parameters of the navigation path involved in the target vehicle; a simulation energy consumption calculation module for inputting each of the combined conditions in the simulation condition library into the target vehicle model in turn to obtain an energy consumption simulation result of each of the combined conditions; an energy consumption chart generation module for generating an energy consumption chart of the target vehicle according to the combined conditions and the corresponding energy consumption simulation results; wherein, The energy consumption chart represents the correspondence between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; an energy consumption chart verification module is used to verify the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption results; a predicted path acquisition module is used to obtain the predicted navigation path of the target vehicle if the verification is passed; a target parameter identification module is used to identify the target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; a target energy consumption prediction module is used to predict the predicted energy consumption value of the target vehicle based on the target characteristic parameters and the energy consumption chart of the target vehicle.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above-mentioned methods for energy consumption prediction based on a navigation path.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is used to execute any of the above-mentioned energy consumption prediction methods based on navigation paths.

[0015] The present application provides a method, device, medium and equipment for predicting energy consumption based on navigation path, which comprises building a target vehicle model of the target vehicle; constructing a simulation operating condition library of the target vehicle; wherein the simulation operating condition library comprises a plurality of operating condition curves of characteristic parameters of the navigation path involved in the target vehicle; inputting each operating condition curve in the simulation operating condition library into the target vehicle model in turn to obtain the energy consumption simulation result of each operating condition curve; generating an energy consumption chart of the target vehicle according to the operating condition curve and the corresponding energy consumption simulation result; wherein the energy consumption chart represents the corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; and using the actual navigation path and the corresponding actual energy consumption result to verify the target vehicle. The energy consumption chart of the target vehicle is obtained; if the verification is passed, the predicted navigation path of the target vehicle is obtained; the target characteristic parameters in the predicted navigation path are identified; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; based on the target characteristic parameters and the energy consumption chart of the target vehicle, the predicted energy consumption value of the target vehicle is predicted; that is, a target vehicle model and a simulation condition library are constructed, and an energy consumption chart is generated using the target vehicle model and the simulation condition library to reduce dependence on large amounts of user data, thereby improving the accuracy and applicability of the energy consumption chart, and using the method of consulting the energy consumption chart instead of the simulation model estimation to further improve the accuracy of the energy consumption prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 It is a flowchart of an energy consumption prediction method based on a navigation path provided by an exemplary embodiment of the present application.

[0018] Figure 2 It is a structural diagram of an energy consumption prediction device based on a navigation path provided by an exemplary embodiment of the present application.

[0019] Figure 3 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0021] Figure 1 FIG. 1 is a flow chart of an energy consumption prediction method based on a navigation path provided by an exemplary embodiment of the present application. Figure 1 As shown, the energy consumption prediction method based on the navigation path includes the following steps: Step 110: Build a target vehicle model of the target vehicle.

[0022] The target vehicle model refers to any simulation model of the target vehicle that needs to predict the energy consumption of the navigation path and can be simulated by model building, such as a Simulink model, where the types of target vehicles include but are not limited to: fuel vehicles, pure electric vehicles, hybrid vehicles, fuel cell vehicles, etc. This application clarifies the output and parameter collection of the target vehicle model, determines the output energy consumption index of the target vehicle model, collects and configures the modeling parameters required in the target vehicle modeling process, such as vehicle curb weight, drag coefficient, component characteristic curves, etc., and builds the target vehicle model of the target vehicle based on the output and modeling parameters of the target vehicle model.

[0023] Step 120: Build a simulation condition library of the target vehicle.

[0024] The simulation working condition library includes multiple working condition curves of characteristic parameters of the navigation path involved in the target vehicle, that is, a two-dimensional array of distance or time corresponding to vehicle speed. The present application constructs a simulation working condition library of the target vehicle to obtain the working condition curves involved in the target vehicle, so as to reduce the interference of other historical data and thus improve the accuracy of the target vehicle model.

[0025] Step 130: Input each operating condition curve in the simulation operating condition library into the target vehicle model in sequence to obtain the energy consumption simulation result of each operating condition curve.

[0026] This application loads the constructed target vehicle model into the modeling environment (simulink), and for each operating condition curve, loads it into the simulation environment in turn as the input of the target vehicle model, simulates the operating status of the vehicle under the corresponding conditions in the simulation environment, and records the energy consumption simulation results of the target vehicle model corresponding to the operating condition curve corresponding to each characteristic parameter combination.

[0027] Step 140: Generate an energy consumption chart of the target vehicle according to the operating condition curve and the corresponding energy consumption simulation result.

[0028] The energy consumption chart represents the correspondence between the characteristic parameters of the navigation path of the target vehicle and the energy consumption. The present application establishes a correspondence between the operating curve and the corresponding energy consumption simulation result to obtain the energy consumption chart of the target vehicle, and the energy consumption result of the corresponding operating curve can be obtained by querying the energy consumption chart.

[0029] Step 150: Verify the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption result.

[0030] After generating the energy consumption chart of the target vehicle, the present application verifies the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption results to ensure the accuracy of the energy consumption chart of the target vehicle.

[0031] Step 160: If the verification is successful, the predicted navigation path of the target vehicle is obtained.

[0032] If the verification is successful, it means that the accuracy of the energy consumption chart of the target vehicle meets the requirements. At this time, the energy consumption chart can be used to predict the energy consumption of the navigation path of the target vehicle and vehicles of the same type. Specifically, by obtaining the predicted navigation path of the target vehicle, the energy consumption result of the predicted navigation path can be predicted.

[0033] Step 170: Identify target feature parameters in the predicted navigation path.

[0034] The target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation working condition library. After the predicted navigation path of the target vehicle is obtained, the target characteristic parameters of the predicted navigation path are identified to obtain the characteristics of the predicted navigation path, so that the energy consumption value of the target vehicle can be predicted based on the target characteristic parameters.

[0035] Step 180: Based on the target characteristic parameters and the energy consumption chart of the target vehicle, a predicted energy consumption value of the target vehicle is predicted.

[0036] Specifically, the obtained characteristic parameter combination is mapped to the corresponding dimension of the multidimensional energy consumption chart, the energy consumption chart index corresponding to the characteristic parameter combination is obtained, and the energy consumption value of the corresponding position is found according to the mapped coordinates. If a boundary condition or sparse area that is not directly simulated is encountered during the query process (that is, the corresponding energy consumption value of the characteristic parameter combination is not clearly given in the energy consumption chart), an interpolation method (such as linear interpolation, spline interpolation, etc.) can be used to estimate the energy consumption value of the point, that is, the energy consumption value of the characteristic parameter combination is estimated according to the energy consumption value corresponding to the adjacent parameter combination of the characteristic parameter combination, so as to ensure the continuity and accuracy of the prediction.

[0037] The present application provides an energy consumption prediction method based on a navigation path, which comprises building a target vehicle model of a target vehicle; constructing a simulation operating condition library of the target vehicle; wherein the simulation operating condition library comprises a plurality of operating condition curves of characteristic parameters of the navigation path involved in the target vehicle; inputting each operating condition curve in the simulation operating condition library into the target vehicle model in turn to obtain an energy consumption simulation result of each operating condition curve; generating an energy consumption chart of the target vehicle according to the operating condition curve and the corresponding energy consumption simulation result; wherein the energy consumption chart represents the corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; and verifying the energy consumption of the target vehicle using the actual navigation path and the corresponding actual energy consumption result. energy consumption chart; if the verification is passed, the predicted navigation path of the target vehicle is obtained; the target characteristic parameters in the predicted navigation path are identified; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; based on the target characteristic parameters and the energy consumption chart of the target vehicle, the predicted energy consumption value of the target vehicle is predicted; that is, a target vehicle model and a simulation condition library are constructed, and an energy consumption chart is generated using the target vehicle model and the simulation condition library to reduce dependence on large amounts of user data, thereby improving the accuracy and applicability of the energy consumption chart, and using the method of consulting the energy consumption chart instead of the simulation model estimation to further improve the accuracy of the energy consumption prediction.

[0038] In one embodiment, the specific implementation method of the above step 110 can be: separately build the environment module, driver module, resistance module, power system module and control strategy module of the target vehicle model; connect the environment module, driver module, resistance module, power system module and control strategy module based on information interaction to obtain the target vehicle model.

[0039] Specifically, the present application models the target vehicle model in modules according to the built-in or customized modules of the modeling environment, which may include: environment module, driver module, resistance module, power system module, control strategy module, etc.; the specific modules built by the module modeling are connected with signal or other information interaction lines according to the actual vehicle configuration to complete the construction of the target vehicle model. And after the target vehicle model is built, the model parameters are calibrated using known data or experimental results such as energy consumption test results under standard working conditions, and the output of the target vehicle model is observed to see whether it meets expectations, so as to ensure the accuracy of the target vehicle model.

[0040] In one embodiment, the specific implementation method of the above step 120 may be: obtaining multiple actual driving data; wherein the actual driving data includes driving time, driving speed, and driving distance; obtaining characteristic parameters that affect the energy consumption of the target vehicle, and selecting non-related characteristic parameters as simulation characteristic parameters; wherein the number of simulation characteristic parameters is less than a preset number; combining the simulation characteristic parameters and combining them with the actual driving data to generate a combined operating condition curve; dividing the actual driving data into multiple data segments, and randomly combining the multiple data segments to obtain a reorganized operating condition curve; and constructing a simulation operating condition library based on the combined operating condition curve and the reorganized operating condition curve.

[0041] Specifically, collect no less than 100,000 pieces of actual driving data, summarize all characteristic parameters that affect the energy consumption of the navigation path, analyze the correlation between the characteristic parameters through the correlation coefficient method, and retain only independent non-correlated characteristic parameters based on engineering experience, and try to ensure that the number of characteristic parameters does not exceed a preset number (for example, 6); wherein, characteristic parameters refer to specific indicators that affect the energy consumption of the navigation path of the target vehicle, and may include: average speed of the navigation path, average acceleration of the navigation path, average acceleration of the navigation path, driving time of the navigation path, weather, ambient temperature, slope, wind speed and direction, altitude difference, cockpit set temperature, air conditioning switch, battery temperature, ambient temperature, etc. The data format, value range, step size, and unit of non-related characteristic parameters are set. For example, the average speed of the navigation path has a data format of numerical type, a value range of [0,140], a step size of 10, and a unit of km / h. Then, the values ​​of all characteristic parameters are combined, and a working condition curve is generated for each characteristic parameter combination according to the working condition generation algorithm and the actual driving data to obtain a combined working condition curve. The specific working condition generation algorithm can be the short-stroke method, the Monte Carlo-Markov method, the equivalent energy method, etc. In addition, the actual driving data is divided into an actual driving segment library, and the segments in the actual driving segment library are randomly combined to generate a new working condition curve to obtain a reorganized working condition curve. The combined working condition curve and the reorganized working condition curve are combined to form a simulation working condition library of the target vehicle model.

[0042] In one embodiment, the specific implementation method of the above-mentioned step 140 may be: matching the characteristic parameter combination of the operating condition curve and the corresponding energy consumption simulation result to obtain an initial data pair; constructing an initial chart based on the initial data pair; wherein the initial chart is a multidimensional matrix; optimizing the data in the initial chart to obtain an optimized chart; compressing the optimized chart and establishing an index to obtain an energy consumption chart of the target vehicle.

[0043] Specifically, the energy consumption simulation results corresponding to each characteristic parameter combination are sorted out. For example, the characteristic parameters (such as average speed, acceleration, weather conditions, etc.) are paired with the corresponding energy consumption values ​​to obtain initial data pairs to form a preliminary data set. According to the preliminary data set and in combination with the number and value range of the set characteristic parameters, an initial chart is created, wherein the initial chart is a matrix structure, the dimension of the matrix corresponds to the number of characteristic parameters, and the length of each dimension corresponds to the value range and step size of the corresponding characteristic parameter. For example, considering 5 independent characteristic parameters, each characteristic parameter has n1, n2, n3, n4, and n5 values, then the initial chart is n1 × n2 × n3 × n4 × n5, each element of the matrix is ​​initially empty or set to a specific identifier, ready to store the energy consumption value under the corresponding characteristic parameter combination; traverse all recorded simulation results (including operating curves and corresponding energy consumption values), fill them into the corresponding positions in the multidimensional matrix, locate the exact position in the matrix according to the characteristic parameter combination, and then fill in the corresponding energy consumption value. For example, the characteristic parameters corresponding to a simulation are (speed v1, acceleration a2, temperature t3), then find the cell where the corresponding dimensions of v1, a2, and t3 intersect in the matrix, and fill in the energy consumption value of the simulation.

[0044] After all simulation results are correctly filled in, the multi-dimensional energy consumption chart is checked and optimized, including processing missing values ​​caused by certain extreme conditions or non-simulation, using interpolation or other methods to estimate and fill in, and at the same time checking the rationality of the data, eliminating or correcting obviously erroneous simulation results, to ensure the accuracy and completeness of the energy consumption chart.

[0045] In order to improve query efficiency, especially when the multidimensional matrix is ​​very large, it is possible to consider compressing the energy consumption chart, such as using sparse matrix storage to reduce storage space requirements, or establishing an efficient index structure to quickly find the energy consumption value under a specific feature parameter combination.

[0046] In one embodiment, the specific implementation method of the above step 150 can be: use the energy consumption image of the target vehicle to predict the actual navigation path and obtain the corresponding prediction result; calculate the error value between the prediction result and the actual energy consumption result; if the error value is less than the preset difference, it is determined that the verification is passed.

[0047] Specifically, the constructed multi-dimensional energy consumption chart is used to predict the energy consumption of each instance in the actual driving data set to obtain the prediction result, and the prediction result is compared with the actual energy consumption result in the actual driving data set, and the error value (such as mean square error MSE, mean absolute error MAE or relative error, etc.) is calculated to evaluate the prediction accuracy. If the error value is less than the preset difference, it is determined that the verification is passed.

[0048] In one embodiment, the above-mentioned energy consumption prediction method based on navigation path may further include: if verification fails, analyzing the deviation area in the energy consumption chart corresponding to the error value; adjusting the construction parameters of the energy consumption chart based on the deviation area until verification passes.

[0049] If the error value is greater than or equal to the preset difference, it is determined that the verification has failed. At this time, the source of the prediction error is analyzed, the deviation area in the energy consumption chart is identified, and the construction parameters of the energy consumption chart are fine-tuned according to the results of the deviation analysis, such as adjusting the granularity of the characteristic parameters, optimizing the interpolation method, or recalibrating the model parameters; and verify again after fine-tuning, and repeat the fine-tuning if it fails until the prediction error reaches an acceptable level. In addition, the present application can also test the performance of the multidimensional energy consumption chart under boundary conditions and extreme conditions to ensure that the prediction is still reliable under extreme conditions, and use the orthogonal test method to conduct small-scale tests on actual vehicles to verify the consistency between the energy consumption predicted by the multidimensional energy consumption chart and the actual energy consumption. According to the field test results, the multidimensional energy consumption chart is further adjusted and improved to form a closed-loop optimization process.

[0050] In one embodiment, the specific implementation method of the above step 170 may be: according to the predicted navigation path, obtain corresponding road information and environmental information; according to the road information and environmental information, generate a working condition curve of the predicted navigation path; according to the working condition curve of the predicted navigation path, obtain the target characteristic parameters.

[0051] Specifically, according to the predicted navigation path for energy consumption prediction as needed, the road information and environmental information corresponding to the predicted navigation path are obtained, wherein the road information at least includes the length of the road section, the estimated travel time, the congestion status, and the location of the traffic lights, and the environmental information at least includes the weather, temperature, and wind speed and direction; based on the obtained road information and environmental information of the predicted navigation path, a working condition curve of the predicted navigation path is generated using a road working condition generation algorithm; based on the working condition curve of the predicted navigation path, characteristic parameters involved in the multidimensional energy consumption chart are calculated to obtain target characteristic parameters; the calculated target characteristic parameters, the vehicle's own attribute parameters such as the air-conditioning setting temperature, and environmental parameters such as temperature are combined to obtain a final input characteristic parameter combination used for multidimensional energy consumption chart lookup.

[0052] Figure 2 FIG. 1 is a schematic diagram of a navigation path-based energy consumption prediction device provided by an exemplary embodiment of the present application. Figure 2As shown, the energy consumption prediction device 20 based on the navigation path includes: a vehicle model building module 21, which is used to build a target vehicle model of a target vehicle; a simulation condition building module 22, which is used to build a simulation condition library of the target vehicle; wherein the simulation condition library includes multiple combined conditions of characteristic parameters of the navigation path involved in the target vehicle; a simulation energy consumption calculation module 23, which is used to input each combined condition in the simulation condition library into the target vehicle model in turn to obtain an energy consumption simulation result of each combined condition; an energy consumption chart generation module 24, which is used to generate an energy consumption chart of the target vehicle according to the combined condition and the corresponding energy consumption simulation result; wherein the energy consumption chart table The corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption is shown; the energy consumption chart verification module 25 is used to verify the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption results; the predicted path acquisition module 26 is used to obtain the predicted navigation path of the target vehicle if the verification is passed; the target parameter identification module 27 is used to identify the target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; the target energy consumption prediction module 28 is used to predict the predicted energy consumption value of the target vehicle based on the target characteristic parameters and the energy consumption chart of the target vehicle.

[0053] The present application provides an energy consumption prediction device based on a navigation path, which builds a target vehicle model of a target vehicle through a vehicle model building module 21; a simulation working condition building module 22 builds a simulation working condition library of the target vehicle; wherein the simulation working condition library includes multiple working condition curves of characteristic parameters of the navigation path involved in the target vehicle; a simulation energy consumption calculation module 23 inputs each working condition curve in the simulation working condition library into the target vehicle model in turn to obtain an energy consumption simulation result of each working condition curve; an energy consumption chart generation module 24 generates an energy consumption chart of the target vehicle according to the working condition curve and the corresponding energy consumption simulation result; wherein the energy consumption chart represents the corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; an energy consumption chart verification module 25 uses the actual navigation path and the corresponding actual energy consumption to verify the relationship between the energy consumption and the energy consumption. The result verifies the energy consumption chart of the target vehicle; if the verification is passed, the predicted path acquisition module 26 obtains the predicted navigation path of the target vehicle; the target parameter identification module 27 identifies the target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; the target energy consumption prediction module 28 predicts the predicted energy consumption value of the target vehicle based on the target characteristic parameters and the energy consumption chart of the target vehicle; that is, constructs a target vehicle model and a simulation condition library, and uses the target vehicle model and the simulation condition library to generate an energy consumption chart to reduce dependence on large amounts of user data, thereby improving the accuracy and applicability of the energy consumption chart, and uses the method of consulting the energy consumption chart instead of the simulation model estimation to further improve the accuracy of the energy consumption prediction.

[0054] In one embodiment, the above-mentioned vehicle model building module 21 can be further configured as: respectively building the environment module, driver module, resistance module, power system module and control strategy module of the target vehicle model; connecting the environment module, driver module, resistance module, power system module and control strategy module based on information interaction to obtain the target vehicle model.

[0055] In one embodiment, the above-mentioned simulation operating condition construction module 22 can be further configured as: obtaining multiple actual driving data; wherein the actual driving data includes driving time, driving speed, and driving distance; obtaining characteristic parameters that affect the energy consumption of the target vehicle, and selecting non-related characteristic parameters as simulation characteristic parameters; wherein the number of simulation characteristic parameters is less than a preset number; combining the simulation characteristic parameters and combining them with the actual driving data to generate a combined operating condition curve; dividing the actual driving data into multiple data segments, and randomly combining multiple data segments to obtain a reorganized operating condition curve; constructing a simulation operating condition library based on the combined operating condition curve and the reorganized operating condition curve.

[0056] In one embodiment, the energy consumption chart generation module 24 can be further configured as follows: matching the characteristic parameter combination of the operating condition curve and the corresponding energy consumption simulation result to obtain an initial data pair; constructing an initial chart based on the initial data pair; wherein the initial chart is a multidimensional matrix; optimizing the data in the initial chart to obtain an optimized chart; compressing the optimized chart and indexing it to obtain an energy consumption chart for the target vehicle.

[0057] In one embodiment, the energy consumption chart verification module 25 can be further configured to: use the energy consumption image of the target vehicle to predict the actual navigation path and obtain the corresponding prediction result; calculate the error value between the prediction result and the actual energy consumption result; if the error value is less than the preset difference, it is determined that the verification is passed.

[0058] In one embodiment, the above-mentioned navigation path-based energy consumption prediction device 20 can be further configured as follows: if the verification fails, analyzing the deviation area in the energy consumption chart corresponding to the error value; adjusting the construction parameters of the energy consumption chart based on the deviation area until the verification passes.

[0059] In one embodiment, the target parameter identification module 27 may be further configured to: obtain corresponding road information and environmental information based on the predicted navigation path; generate a working condition curve of the predicted navigation path based on the road information and environmental information; and obtain target characteristic parameters based on the working condition curve of the predicted navigation path.

[0060] Below, reference Figure 3The electronic device according to the embodiment of the present application is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signal from them.

[0061] Figure 3 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0062] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0063] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0064] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the methods of the various embodiments of the present application described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0065] In one example, the electronic device 10 may further include: an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0066] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, which is used to receive the collected input signals from the first device and the second device.

[0067] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0068] The output device 14 can output various information to the outside, including the determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0069] Of course, to simplify, Figure 3Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 10 may also include any other appropriate components.

[0070] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0071] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0072] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0073] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0074] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0075] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0076] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0077] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0078] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for predicting energy consumption based on a navigation path, characterized in that: include: Build a target vehicle model of the target vehicle; Constructing a simulation operating condition library of the target vehicle; wherein the simulation operating condition library includes a plurality of operating condition curves of characteristic parameters of the navigation path involved in the target vehicle; Input each of the operating condition curves in the simulation operating condition library into the target vehicle model in sequence to obtain an energy consumption simulation result of each of the operating condition curves; Generate an energy consumption chart of the target vehicle according to the operating condition curve and the corresponding energy consumption simulation result; wherein the energy consumption chart represents the corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; Verifying the energy consumption chart of the target vehicle using the actual navigation path and the corresponding actual energy consumption results; If the verification is successful, the predicted navigation path of the target vehicle is obtained; Identifying target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; Based on the target characteristic parameters and the energy consumption chart of the target vehicle, a predicted energy consumption value of the target vehicle is predicted.

2. The energy consumption prediction method based on navigation path according to claim 1 is characterized in that: The target vehicle model of the target vehicle is constructed as follows: Respectively building the environment module, driver module, resistance module, power system module and control strategy module of the target vehicle model; The environment module, the driver module, the resistance module, the power system module and the control strategy module are connected based on an information interaction method to obtain the target vehicle model.

3. The energy consumption prediction method based on navigation path according to claim 1 is characterized in that: The construction of the simulation operating condition library of the target vehicle comprises: Acquire multiple pieces of actual driving data; wherein the actual driving data includes driving time, driving speed, and driving distance; Acquire characteristic parameters that affect the energy consumption of the target vehicle, and select non-related characteristic parameters as simulation characteristic parameters; wherein the number of the simulation characteristic parameters is less than a preset number; Combining the simulation characteristic parameters and combining them with the actual driving data to generate a combined operating condition curve; Dividing the actual driving data into a plurality of data segments, and randomly combining the plurality of data segments to obtain a reorganized operating condition curve; The simulation operating condition library is constructed according to the combined operating condition curve and the reorganized operating condition curve.

4. The energy consumption prediction method based on navigation path according to claim 1 is characterized in that: The step of generating an energy consumption chart of the target vehicle according to the operating condition curve and the corresponding energy consumption simulation result comprises: Matching the characteristic parameter combination of the operating condition curve and the corresponding energy consumption simulation result to obtain an initial data pair; According to the initial data pair, construct an initial chart; wherein the initial chart is a multidimensional matrix; Optimizing the data in the initial chart to obtain an optimized chart; The optimization chart is compressed and indexed to obtain an energy consumption chart of the target vehicle.

5. The energy consumption prediction method based on navigation path according to claim 1 is characterized in that: The energy consumption chart of the target vehicle verified by using the actual navigation path and the corresponding actual energy consumption result includes: Predicting the actual navigation path using the energy consumption image of the target vehicle to obtain a corresponding prediction result; Calculating an error value between the predicted result and the actual energy consumption result; If the error value is smaller than the preset difference value, it is determined that the verification is passed.

6. The energy consumption prediction method based on navigation path according to claim 5 is characterized in that: The energy consumption prediction method based on the navigation path also includes: If the verification fails, analyzing the deviation area in the energy consumption chart corresponding to the error value; The construction parameters of the energy consumption chart are adjusted based on the deviation area until the verification is passed.

7. The energy consumption prediction method based on navigation path according to claim 1 is characterized in that: The identifying target feature parameters in the predicted navigation path comprises: According to the predicted navigation path, obtaining corresponding road information and environmental information; generating a working condition curve of the predicted navigation path according to the road information and the environmental information; The target characteristic parameters are obtained according to the operating condition curve of the predicted navigation path.

8. An energy consumption prediction device based on navigation path, characterized in that: include: A vehicle model building module, used to build a target vehicle model of a target vehicle; A simulation condition construction module, used to construct a simulation condition library of the target vehicle; wherein the simulation condition library includes a plurality of combined conditions of characteristic parameters of the navigation path involved in the target vehicle; A simulation energy consumption calculation module, used for sequentially inputting each of the combined operating conditions in the simulation operating condition library into the target vehicle model to obtain an energy consumption simulation result for each of the combined operating conditions; An energy consumption chart generating module, used to generate an energy consumption chart of the target vehicle according to the combined operating conditions and the corresponding energy consumption simulation results; wherein the energy consumption chart represents the corresponding relationship between the characteristic parameters of the navigation path of the target vehicle and the energy consumption; An energy consumption chart verification module, used to verify the energy consumption chart of the target vehicle using an actual navigation path and a corresponding actual energy consumption result; A predicted path acquisition module, used to acquire the predicted navigation path of the target vehicle if the verification is passed; A target parameter identification module, used to identify target characteristic parameters in the predicted navigation path; wherein the target characteristic parameters are part or all of the characteristic parameters of the navigation path in the simulation condition library; The target energy consumption prediction module is used to predict the predicted energy consumption value of the target vehicle based on the target characteristic parameters and the energy consumption chart of the target vehicle.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the energy consumption prediction method based on the navigation path as described in any one of claims 1-7 above.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the energy consumption prediction method based on the navigation path as described in any one of claims 1-7 above.

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