Whole vehicle energy consumption model optimization method and device of vehicle

Abstract

The invention relates to the technical field of energy consumption management, in particular to a whole vehicle energy consumption model optimization method and device of a vehicle, and the method comprises the steps: obtaining actual working condition data uploaded by at least one vehicle; processing the actual working condition data and the state data of the at least one vehicle to obtain processing data meeting preset useful conditions; and transmitting the processing data to each interface of a pre-constructed whole vehicle energy consumption model in batches so as to generate replacement parameters of the whole vehicle energy consumption model based on the processing data, and updating corresponding model parameters in the whole vehicle energy consumption model. Therefore, the problem that the whole vehicle energy consumption model of the vehicle cannot fully consider various change factors influencing the whole vehicle energy consumption model in an actual use scene in the related technology, so that the whole vehicle energy consumption model cannot fully consider various change factors influencing the whole vehicle energy consumption model along with the increase of the driving time of the vehicle and the change of the performance of the vehicle is solved. And the deviation between the energy consumption result calculated by the constructed whole vehicle energy consumption model and the actual energy consumption condition is gradually increased, so that the model is difficult to play an expected role, and the like.

Description

Technical Field

[0001] The present application relates to the field of energy consumption management technology, and in particular to a method and device for optimizing a vehicle energy consumption model. Background Art

[0002] As an important part of the entire life cycle of hybrid vehicles, the vehicle energy consumption model plays an important role in improving the accuracy of hybrid vehicle range estimation, optimizing energy management strategies, assisting driving decisions, evaluating and optimizing vehicle performance, and optimizing charging strategies.

[0003] In related technologies, most vehicle energy consumption models are based on vehicle physical models, with factors such as batteries, motors, and transmission systems as initial model parameters or using limited standard test data to establish energy consumption calculation models. Data is then collected in real time through the vehicle sensor network, and software algorithms are used to calculate the energy consumption of the entire vehicle in real time to evaluate the vehicle's energy consumption status.

[0004] However, in related technologies, the vehicle's energy consumption model is mostly finalized after relying on the initial model parameters or limited standard test data, and fails to fully consider the various changing factors that affect the vehicle's energy consumption model in actual usage scenarios. As a result, as the vehicle's driving time increases and its own performance changes, the deviation between the energy consumption results calculated by the constructed vehicle energy consumption model and the actual energy consumption situation gradually increases, making it difficult for the model to play its expected role, which urgently needs to be solved. Summary of the Invention

[0005] The present application provides a method and device for optimizing the whole vehicle energy consumption model of a vehicle to solve the problem in related technologies that the whole vehicle energy consumption model of a vehicle is finalized after being established based on the initial model parameters or limited standard test data, and fails to fully consider the various changing factors that affect the whole vehicle energy consumption model in actual usage scenarios. As a result, as the vehicle's driving time increases and its own performance changes, the deviation between the energy consumption results calculated by the constructed whole vehicle energy consumption model and the actual energy consumption situation gradually increases, making it difficult for the model to play its expected role.

[0006] The first aspect of the present application is an embodiment that provides a method for optimizing a vehicle energy consumption model, which is applied to a server and includes the following steps: obtaining actual operating condition data uploaded by at least one vehicle; processing the actual operating condition data and the status data of the at least one vehicle to obtain processed data that meets preset useful conditions; transmitting the processed data in batches to various interfaces of a pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data, and updating the corresponding model parameters in the vehicle energy consumption model.

[0007] Through the above technical means, the embodiment of the present application can collect actual working condition data and vehicle status data, fully considering the various changing factors that affect the vehicle energy consumption model in actual usage scenarios; then utilizing the high computing power of the cloud, process the returned vehicle data and generate replacement parameters of the vehicle energy consumption model to replace the corresponding original model parameters in the vehicle energy consumption model, thereby realizing the personalized vehicle energy consumption model optimization of the vehicle, so that the vehicle energy consumption model of each vehicle is the most suitable for its own vehicle, improving the authenticity and effectiveness of the vehicle energy consumption model, and maximizing the impact of the vehicle energy consumption model of each vehicle.

[0008] Optionally, in one embodiment of the present application, obtaining actual operating condition data uploaded by at least one vehicle includes: collecting driving data of the at least one vehicle each time it travels, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature, as the actual operating condition data.

[0009] Through the above technical means, the present application can collect a variety of driving data of at least one vehicle during each driving process, and can comprehensively consider the impact of various factors on energy consumption, thereby improving the optimization performance of the vehicle energy consumption model.

[0010] Optionally, in one embodiment of the present application, the processing of the actual working condition data and the status data of the at least one vehicle to obtain processed data that meets preset useful conditions includes: converting the data format of the actual working condition data and the status data of the at least one vehicle to obtain standard data that meets the target format; detecting whether there are residual values ​​in the standard data; when detecting that there are data residual values ​​in the standard data, calculating the mean of the data items corresponding to the data residual values; and based on the mean, supplementing the data residual values ​​of the data items to obtain the processed data.

[0011] Through the above technical means, the embodiment of the present application can perform data conversion and missing value supplement operations on the vehicle's actual operating data and status data, providing sufficient and effective data support for the optimization process of the vehicle energy consumption model.

[0012] Optionally, in one embodiment of the present application, it also includes: obtaining identification information of the current driver of the at least one vehicle; using the identification information as an index, querying the driving habits of the current driver from a preset database to determine the replacement parameters based on the driving habits.

[0013] Through the above technical means, the embodiments of the present application can take the driver's driving habits into consideration when optimizing the vehicle energy consumption model, thereby understanding the impact of changing the vehicle's power demand pattern and energy management strategy according to the driver's habits on the vehicle's energy consumption, thereby improving the scenario adaptability of the vehicle's energy consumption model prediction.

[0014] Optionally, in one embodiment of the present application, the generation of replacement parameters of the whole vehicle energy consumption model based on the processed data and the updating of corresponding model parameters in the whole vehicle energy consumption model include: generating at least one of the battery replacement parameters and the engine replacement parameters in the whole vehicle energy consumption model based on the processed data; and updating the corresponding model parameters in the whole vehicle energy consumption model according to the at least one.

[0015] Through the above technical means, the embodiment of the present application can generate at least one of the battery replacement parameters and engine replacement parameters in the vehicle energy consumption model based on the processed data. Thus, the importance of the energy conversion efficiency, operating condition adaptability and dynamic characteristics of the power battery and engine in the vehicle to the energy consumption differences of the whole vehicle can be utilized to ensure the effectiveness of each optimization and update of the vehicle energy consumption model.

[0016] The second aspect of the present application provides a vehicle energy consumption model optimization device for a vehicle, which is applied to a server and includes: a first acquisition module for acquiring actual operating condition data uploaded by at least one vehicle; a processing module for processing the actual operating condition data and the status data of the at least one vehicle to obtain processed data that meets preset useful conditions; an update module for transmitting the processed data in batches to various interfaces of a pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data and update the corresponding model parameters in the vehicle energy consumption model.

[0017] Through the above technical means, the embodiment of the present application can collect actual working condition data and vehicle status data, fully considering the various changing factors that affect the vehicle energy consumption model in actual usage scenarios; then utilizing the high computing power of the cloud, process the returned vehicle data and generate replacement parameters of the vehicle energy consumption model to replace the corresponding original model parameters in the vehicle energy consumption model, thereby realizing the personalized vehicle energy consumption model optimization of the vehicle, so that the vehicle energy consumption model of each vehicle is the most suitable for its own vehicle, improving the authenticity and effectiveness of the vehicle energy consumption model, and maximizing the effect of the vehicle energy model of each vehicle.

[0018] Optionally, in one embodiment of the present application, the first acquisition module includes: an acquisition unit for collecting driving data of each driving of the at least one vehicle, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature, as the actual operating condition data.

[0019] Through the above technical means, the present application can collect a variety of driving data of at least one vehicle during each driving process, and can comprehensively consider the impact of various factors on energy consumption, thereby improving the optimization performance of the vehicle energy consumption model.

[0020] Optionally, in one embodiment of the present application, the processing module includes: a conversion unit for converting the data format of the actual working condition data and the status data of the at least one vehicle to obtain standard data that meets the target format; a detection unit for detecting whether there are residual values ​​in the standard data; a calculation unit for calculating the mean of the data items corresponding to the data residual values ​​when detecting the presence of data residual values ​​in the standard data; and a supplement unit for supplementing the data residual values ​​of the data items based on the mean to obtain the processed data.

[0021] Through the above technical means, the embodiment of the present application can perform data conversion and missing value supplement operations on the vehicle's actual operating data and status data, providing sufficient and effective data support for the optimization process of the vehicle energy consumption model.

[0022] Optionally, in one embodiment of the present application, it also includes: a second acquisition module for obtaining the identification information of the current driver of the at least one vehicle; a query module for querying the driving habits of the current driver from a preset database using the identification information as an index to determine the replacement parameters based on the driving habits.

[0023] Through the above technical means, the embodiments of the present application can take the driver's driving habits into consideration when optimizing the vehicle energy consumption model, thereby understanding the impact of changing the vehicle's power demand pattern and energy management strategy according to the driver's habits on the vehicle's energy consumption, thereby improving the scenario adaptability of the vehicle's energy consumption model prediction.

[0024] Optionally, in one embodiment of the present application, the update module includes: a generation unit for generating at least one of the battery replacement parameters and the engine replacement parameters in the whole vehicle energy consumption model based on the processed data; and an update unit for updating the corresponding model parameters in the whole vehicle energy consumption model according to the at least one.

[0025] Through the above technical means, the embodiment of the present application can generate at least one of the battery replacement parameters and engine replacement parameters in the vehicle energy consumption model based on the processed data. Thus, the importance of the energy conversion efficiency, operating condition adaptability and dynamic characteristics of the power battery and engine in the vehicle to the energy consumption differences of the whole vehicle can be utilized to ensure the effectiveness of each optimization and update of the vehicle energy consumption model.

[0026] The third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle energy consumption model optimization method as described in the above embodiment.

[0027] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned vehicle energy consumption model optimization method.

[0028] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned vehicle energy consumption model optimization method.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a vehicle energy consumption model optimization method according to an embodiment of the present application; Figure 2 This is a flowchart of a method for constructing a personalized energy consumption model based on data reflow according to an embodiment of the present application; Figure 3 This is a functional logic framework diagram of a method for building a personalized energy consumption model based on data reflow according to an embodiment of the present application; Figure 4 A schematic diagram of the structure of a vehicle energy consumption model optimization device provided according to an embodiment of the present application; Figure 5 A schematic structural diagram of a vehicle provided according to an embodiment of the present application.

[0031] Reference numerals: 10-Vehicle energy consumption model optimization device: 100-first acquisition module, 200-processing module and 300-update module; 501-memory, 502-processor and 503-communication interface. DETAILED DESCRIPTION

[0032] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0033] The following describes the vehicle energy consumption model optimization method and device of the embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology, the vehicle energy consumption model of the vehicle is finalized after being established based on the initial parameters of the model or limited standard test data, and fails to fully consider the various changing factors that affect the vehicle energy consumption model in the actual use scenario, which will lead to the increase of vehicle driving time and changes in its own performance. The deviation between the energy consumption results calculated by the constructed vehicle energy consumption model and the actual energy consumption situation gradually increases, resulting in the problem that the model is difficult to play its expected role. The present application provides a vehicle energy consumption model optimization method, in which the actual working condition data collected during driving uploaded by the vehicle can be obtained based on the cloud server, and the vehicle status data can be combined and input into the vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model to update the corresponding original model parameters in the vehicle energy consumption model. Thus, it is achieved that on the basis of deploying the whole vehicle energy consumption model to the cloud, the actual working condition data and vehicle status data are collected by the cloud, and the various changing factors that affect the whole vehicle energy consumption model in the actual use scenario are fully considered; then the high computing power of the cloud is used to process the returned vehicle data and generate replacement parameters of the whole vehicle energy consumption model to replace the corresponding original model parameters in the whole vehicle energy consumption model, so as to realize the personalized whole vehicle energy consumption model optimization of the vehicle, so that the whole vehicle energy consumption model of each vehicle is the most suitable for its own vehicle, improve the authenticity and effectiveness of the whole vehicle energy consumption model, maximize the effect of the whole vehicle energy model of each vehicle, and provide strong support for the regular parameter update of the whole vehicle energy consumption model. Thus, it solves the problem in the related art that the whole vehicle energy consumption model of the vehicle relies on the initial parameters of the model or limited standard test data to be established and finalized, and fails to fully consider the various changing factors that affect the whole vehicle energy consumption model in the actual use scenario, which will cause the deviation between the energy consumption result calculated by the constructed whole vehicle energy consumption model and the actual energy consumption situation to gradually increase as the vehicle driving time increases and its own performance changes, making it difficult for the model to play its expected role.

[0034] Specifically, Figure 1 A flowchart of a method for optimizing a vehicle energy consumption model provided in an embodiment of the present application.

[0035] like Figure 1As shown, the vehicle energy consumption model optimization method of the vehicle is applied to the server, including the following steps: In step S101, actual operating condition data uploaded by at least one vehicle is obtained.

[0036] In some embodiments, the vehicle's energy consumption model is mostly applied in practice after the initial model is trained with initial model parameters or limited standard test data. Then, during the actual application process, the vehicle's real-time driving data is collected as the input of the model to calculate the vehicle's energy consumption results.

[0037] However, as the driving time of the vehicle increases and the various parts of the vehicle itself undergo certain changes, the vehicle energy consumption model that has been established will gradually no longer be suitable for the vehicle, which may cause the energy consumption results calculated by the vehicle energy consumption model that has been established to be different from the actual energy consumption of the vehicle, and the vehicle energy consumption model will not be able to exert the effect it should have.

[0038] Based on this, the embodiments of the present application can obtain actual operating condition data uploaded by at least one vehicle to perform personalized updates to the vehicle energy consumption model that has already been established for each vehicle. Based on computing power considerations, the embodiments of the present application can deploy the vehicle energy consumption model to the cloud, and use the cloud server to obtain the actual operating condition data uploaded by at least one vehicle, store it, and process it, so as to optimize the vehicle energy consumption model that has already been established based on this actual operating condition data.

[0039] Furthermore, the cloud server can not only obtain the actual working condition data uploaded by one vehicle, but also obtain the actual working condition data uploaded by multiple vehicles. When the vehicles are in the same situation and some of them have not uploaded the actual working condition data, certain inferences can be made based on the actual working condition data of the same or similar vehicles. However, the actual working condition data uploaded by the vehicle itself is the best data.

[0040] The embodiment of the present application can obtain four-dimensional operating condition data that can accurately reflect the actual conditions of at least one vehicle, providing an accurate data basis for the calculation and optimization of the vehicle energy consumption model, thereby combining the geographical location to reflect the impact of actual road conditions on energy consumption and the energy consumption under different driving conditions, which helps to make the optimized vehicle energy consumption model calculation closer to the real scene.

[0041] Optionally, in one embodiment of the present application, obtaining actual operating condition data uploaded by at least one vehicle includes: collecting driving data of at least one vehicle each time it travels, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature, as actual operating condition data.

[0042] Based on the relevant descriptions of other embodiments, it can be understood that the present application can obtain actual operating condition data uploaded to the cloud server by at least one vehicle as a data basis.

[0043] In actual implementation, the actual operating condition data here can be understood as the four-dimensional operating condition data of each vehicle driving process. Among them, the four-dimensional operating condition data is a comprehensive data used to describe the vehicle's driving status. It contains information in four dimensions, usually time, speed, acceleration, and geographic location.

[0044] Based on this, the embodiment of the present application can prepare data for optimizing the energy consumption model of the entire vehicle by collecting driving data that can accurately reflect the vehicle's driving at different speeds and accelerations at different times and locations as actual working condition data.

[0045] The driving data collected in this application includes, but is not limited to, at least one of the following: latitude and longitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery charge, motor speed, and motor temperature at different vehicle locations. The collection method may include, but is not limited to, collecting the vehicle's driving data at each time using a GPS device installed in the vehicle and data embedding equipment.

[0046] This application can collect multiple driving data of at least one vehicle during each driving process, comprehensively consider the impact of various factors on energy consumption, and thus improve the overall performance of the updated vehicle energy consumption model.

[0047] Step S102 : Processing the actual working condition data and the status data of at least one vehicle to obtain processed data that meets a preset useful condition.

[0048] It can be understood that the preset useful conditions here refer to certain conditions that the actual operating condition data and status data need to meet when optimizing the vehicle energy consumption model using the actual operating condition data and the status data of at least one vehicle, for example, the data must be complete.

[0049] In certain embodiments, after obtaining the actual operating condition data of the vehicle, in order to make the optimized vehicle energy consumption model more consistent with the vehicle's current state, the present application can also obtain the state data of at least one vehicle, thereby combining the two to achieve the optimization of the vehicle energy consumption model.

[0050] Among them, the vehicle status data here can be understood as the various states of the vehicle currently in, covering the basic attributes of the vehicle (such as vehicle model, component model, etc.), component status (such as whether the door is closed, whether the tire pressure is normal, etc.), system status (such as whether the charging system is normal, fault code information, etc.), etc., which includes both static and dynamic information, which is different from the actual operating data of the vehicle.

[0051] Among them, the actual working condition data and status data of the vehicle during each driving process can be called reflux data, which can be, but is not limited to, collected by the vehicle through GPS equipment and data embedding equipment installed on the car, and then transmitted to the data reflux module on the vehicle side through the CAN line. After the data reflux module receives the data from the vehicle side, it sends each vehicle side driving data and vehicle status data to the data cache module of the cloud server through wireless Ethernet to facilitate cloud call.

[0052] Subsequently, the embodiment of the present application can process the actual working condition data and status data on the cloud server to obtain processed data that meets certain useful conditions, so as to use the processed data that meets certain useful conditions to optimize the vehicle's overall energy consumption model.

[0053] The embodiment of the present application can process the actual operating condition data and status data of the vehicle to optimize the energy consumption model of the entire vehicle. The actual operating condition data and status data can comprehensively reflect the energy consumption of the vehicle during operation, so that the optimized energy consumption model of the entire vehicle can more accurately simulate the actual driving conditions. The impact of vehicle components and systems on energy consumption is considered through status data, thereby improving the accuracy of its prediction of the energy consumption of the entire vehicle.

[0054] Optionally, in one embodiment of the present application, actual working condition data and status data of at least one vehicle are processed to obtain processed data that meets preset useful conditions, including: converting the data format of the actual working condition data and the status data of at least one vehicle to obtain standard data that meets the target format; detecting whether there are residual values ​​in the standard data; when detecting that there are data residual values ​​in the standard data, calculating the mean of the data items corresponding to the data residual values; based on the mean, supplementing the data residual values ​​of the data items to obtain processed data.

[0055] In other embodiments, when actual working condition data and status data are processed to obtain processed data that meets certain useful conditions, the present application mainly includes but is not limited to two parts: format conversion and data supplementation.

[0056] Specifically, in order to ensure that the data can meet the data requirements for optimizing the vehicle's energy consumption model, the embodiment of the present application can first convert the format of the actual working condition data and status data, and convert the actual working condition data and status data into standard data that meets the data requirements of the vehicle's energy consumption model.

[0057] Furthermore, in order to take into account the possibility of some incomplete data in the data, the embodiment of the present application can detect whether there are residual values ​​in the standard data, and calculate the mean of the data items corresponding to the residual values ​​when the residual values ​​are detected in the standard data, thereby using the mean of the data items to supplement the residual values ​​of the data items.

[0058] The data items here can be understood as the data categories included in the actual operating condition data and the data categories included in the status data, such as vehicle speed, acceleration, battery voltage, battery current, vehicle model, engine model, tire pressure, etc. For example, if the driving speed data for a certain driving process is missing, the missing driving speed data can be supplemented by the average driving speed of other driving processes in the recent period under the same or highly similar conditions.

[0059] Additionally, the embodiments of the present application may also directly perform data cleaning on the actual working condition data and status data after converting the formats of the actual working condition data and status data, thereby removing noise, errors, and duplicate data in the data, and processing missing values ​​and outliers to improve data quality and make the data more suitable for subsequent analysis, modeling, and other processing processes.

[0060] The embodiments of the present application can perform operations such as data conversion and missing value supplementation on the vehicle's actual operating condition data and status data, providing effective data support for the optimization process of the vehicle energy consumption model.

[0061] Step S103: The processed data batches are transmitted to the various interfaces of the pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data and update the corresponding model parameters in the vehicle energy consumption model.

[0062] As a possible implementation method, after obtaining processed data that meets certain useful conditions, the embodiment of the present application can transmit the processed data in batches to the various interfaces of the pre-built vehicle energy consumption model, thereby generating replacement parameters for the vehicle energy consumption model, and then using the replacement parameters to replace (update) the original model parameters in the vehicle energy consumption model, thereby achieving the effect of optimizing the vehicle energy consumption model.

[0063] The interfaces of the pre-built vehicle energy consumption model can be understood as interfaces between the pre-built data processing module in the cloud server and the vehicle energy consumption model. Thus, in the embodiment of the present application, the actual operating condition data and status data of the vehicle can be processed by the data processing module of the cloud server and then transmitted to the vehicle energy consumption model.

[0064] The replacement parameters herein can be understood as model parameters of a vehicle energy consumption model that are determined based on the vehicle's actual operating condition and status data to better suit the vehicle's current conditions. The number of replacement parameters may be limited to a subset of model parameter categories in the vehicle energy consumption model, or all of them may be replaced. The specific replacement parameters are determined by actual circumstances and are provided for illustrative purposes only and are not intended to be limiting.

[0065] For example, when a vehicle uses an existing vehicle energy consumption model to calculate the vehicle energy consumption, it is found that the calculated energy output and recovery part of the vehicle's power battery are seriously inconsistent with the actual energy output and recovery part of the vehicle's power battery. At this time, the replacement parameters of the power battery part in the existing vehicle model can be recalculated based on the processed data, and then the model parameters of the power battery part in the original vehicle energy consumption model can be replaced and updated.

[0066] It should be noted that before updating the model parameters in the vehicle energy consumption model, an initial vehicle energy consumption analysis should be built based on the initial parameters of each type of vehicle and the WLTC standard operating conditions and deployed to the cloud to facilitate storage and calculation during the optimization process.

[0067] In addition, specific parameters in the battery module and engine module in the vehicle energy consumption model, such as battery SOC, engine mean effective pressure and other parameters, will not change much in the short term. At this time, the embodiment of the present application can cache the vehicle-side data in the recent period through the data cache module of the cloud server to avoid waste of resources and computing power.

[0068] The embodiment of the present application can generate replacement parameters based on different driving condition data and status data of different vehicles to update the corresponding model parameters in the vehicle energy consumption model, thereby realizing personalized vehicle energy consumption model optimization, so that the vehicle energy consumption model of each vehicle is most suitable for its own vehicle, improving the authenticity and effectiveness of the vehicle energy consumption model, and based on the characteristics of data collection, the present application can realize regular updating of the model parameters of the vehicle energy consumption model, greatly improving the intelligence level of the present application.

[0069] Optionally, in one embodiment of the present application, replacement parameters of the whole vehicle energy consumption model are generated based on the processed data, and the corresponding model parameters in the whole vehicle energy consumption model are updated, including: generating at least one of the battery replacement parameters and the engine replacement parameters in the whole vehicle energy consumption model based on the processed data; and updating the corresponding model parameters in the whole vehicle energy consumption model based on at least one of them.

[0070] In certain embodiments, given that the power battery and engine are the direct sources and conversion cores of vehicle energy, their performance and efficiency directly determine the scale and characteristics of the energy consumption of the entire vehicle. Therefore, when this application generates replacement parameters of the entire vehicle energy consumption model based on processed data and updates the corresponding model parameters in the entire vehicle energy consumption model, the replacement parameters mainly include but are not limited to two parts: battery replacement parameters and engine replacement parameters.

[0071] Among them, the battery replacement parameters here mainly refer to the model parameters related to the vehicle's power battery in the vehicle energy consumption model, such as the basic physical parameters of the power battery (such as battery capacity, battery voltage, internal resistance), dynamic characteristic parameters (such as charge and discharge efficiency, temperature characteristics, cycle life parameters), control strategy related parameters (such as SOC (State of Charge) threshold, power limit), etc.

[0072] The engine replacement parameters here mainly refer to the model parameters related to the vehicle's power battery in the vehicle energy consumption model, such as the engine's power performance parameters (such as maximum power, maximum torque, speed-torque characteristic curve and thermal efficiency), fuel consumption parameters (such as idle fuel consumption, fuel density and calorific value), operating status parameters (such as air-fuel ratio, exhaust gas recirculation (EGR) rate, cooling system efficiency), etc.

[0073] The embodiment of the present application can generate at least one of the battery replacement parameters and engine replacement parameters in the vehicle energy consumption model based on the processed data. Thus, the importance of the energy conversion efficiency, operating condition adaptability and dynamic characteristics of the power battery and engine in the vehicle to the energy consumption differences of the whole vehicle can be utilized to ensure the effectiveness of each optimization and update of the vehicle energy consumption model.

[0074] Optionally, in one embodiment of the present application, it also includes: obtaining identification information of the current driver of at least one vehicle; using the identification information as an index, querying the driving habits of the current driver from a preset database to determine replacement parameters based on the driving habits.

[0075] It is understandable that different drivers have different driving habits. Some drivers have a steady driving style, while some have an aggressive driving style. In comparison, the aggressive driving style may cause more energy consumption due to frequent starting and braking during driving, and correspondingly, the calculation may be more complicated.

[0076] In other embodiments, when optimizing and updating the vehicle's energy consumption model, the present application can obtain the driver's identification information, so as to query the current driver's driving habits from a preset database using the identification information as an index, thereby determining replacement parameters based on the driver's driving habits.

[0077] The preset database herein can be understood as a database that records information related to all drivers of a vehicle, for example, the driver's name, age, driving habits, etc. The identification information herein can be understood as an index item that can be used when searching in the database, for example, the driver's name, etc.

[0078] Different drivers have different driving habits, and the energy consumption caused by driving the same vehicle on the same route in the same time period is also different. In order to improve the accuracy of the calculation of the vehicle energy consumption model, the embodiment of the present application can also use the identification information of the current driver of the vehicle as an index to query the current driver's driving habits from a certain database, so as to determine the replacement parameters of the vehicle energy consumption model based on the driver's driving habits, such as driving style characteristic parameters (such as speed fluctuation rate, cruising speed preference), energy management related parameters (such as brake energy recovery usage tendency, accessory activation frequency and intensity), dynamic response parameters (such as following distance and start-stop frequency, slope power output strategy), etc.

[0079] The embodiments of the present application can take the driver's driving habits into consideration when optimizing the vehicle energy consumption model, thereby understanding the impact of changing the vehicle's power demand pattern and energy management strategy according to the driver's habits on the vehicle's energy consumption, thereby improving the scenario adaptability of the vehicle energy consumption model prediction.

[0080] The present application is explained in detail below using a specific embodiment.

[0081] Figure 2 This is a flowchart of a method for constructing a personalized energy consumption model based on data reflow according to an embodiment of the present application. Figure 3 This is a functional logic framework diagram of a method for constructing a personalized energy consumption model based on data reflow according to an embodiment of the present application. Figure 2 and Figure 3 As shown: Step S201: Build a vehicle energy consumption model based on initial parameters (such as four-dimensional operating condition data, driver's driving habits, vehicle SOC, engine parameters, battery module parameters, etc.) and deploy it to the cloud; Step S202: The vehicle side collects real-time operating data of the vehicle (such as four-dimensional operating data, driver's driving habits, engine parameters, battery module parameters) and vehicle status data through a data acquisition module; Step S203, sending these data to the data return module on the vehicle side; Step S204, returning the collected data from the data return module to the data cache module of the cloud server based on the Ethernet medium; Step S205: the data cache module regularly processes the data and transmits it to the vehicle energy consumption model; In step S206 , the vehicle energy consumption model generates replacement parameters based on the processed return data, and updates the model parameters in the vehicle energy consumption model with the replacement parameters, thereby optimizing (updating) the vehicle energy consumption model.

[0082] According to the vehicle energy consumption model optimization method proposed in the embodiment of the present application, the actual working condition data collected during driving uploaded by the vehicle can be obtained based on the cloud server, and combined with the vehicle status data, it is input into the vehicle energy consumption model to generate replacement parameters for the vehicle energy consumption model to update the corresponding original model parameters in the vehicle energy consumption model. In this way, on the basis of deploying the vehicle energy consumption model to the cloud, the cloud is used to collect actual working condition data and vehicle status data, and the various changing factors affecting the vehicle energy consumption model in the actual use scenario are fully considered; and then the high computing power of the cloud is used to process the returned vehicle data and generate replacement parameters for the vehicle energy consumption model to replace the corresponding original model parameters in the vehicle energy consumption model, thereby realizing the personalized vehicle energy consumption model optimization of the vehicle, so that the vehicle energy consumption model of each vehicle is the most suitable for its own vehicle, improving the authenticity and effectiveness of the vehicle energy consumption model, maximizing the effect of the vehicle energy model of each vehicle, and providing strong support for the regular parameter update of the vehicle energy consumption model. This solves the problem in related technologies that the vehicle's energy consumption model is finalized after relying on initial model parameters or limited standard test data, and fails to fully consider the various changing factors that affect the vehicle's energy consumption model in actual usage scenarios. As a result, as the vehicle's driving time increases and its own performance changes, the deviation between the energy consumption results calculated by the constructed vehicle energy consumption model and the actual energy consumption situation gradually increases, making it difficult for the model to play its expected role.

[0083] Next, a vehicle energy consumption model optimization device for a vehicle proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0084] Figure 4 It is a structural diagram of the vehicle energy consumption model optimization device of the embodiment of the present application.

[0085] like Figure 4 As shown, the vehicle energy consumption model optimization device 10 includes: a first acquisition module 100, a processing module 200 and an update module 300.

[0086] The first acquisition module 100 is used to acquire actual operating condition data uploaded by at least one vehicle.

[0087] The processing module 200 is used to process the actual working condition data and the status data of at least one vehicle to obtain processed data that meets preset useful conditions.

[0088] The updating module 300 is used to transmit the processed data batches to the various interfaces of the pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data and update the corresponding model parameters in the vehicle energy consumption model.

[0089] Optionally, in one embodiment of the present application, the first acquisition module 100 includes: an acquisition unit for collecting driving data of at least one vehicle each time it travels, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature as actual operating condition data.

[0090] Optionally, in one embodiment of the present application, the processing module 200 includes: a conversion unit, a detection unit, a calculation unit, and a supplementation unit.

[0091] The conversion unit is used to convert the data formats of the actual working condition data and the status data of at least one vehicle to obtain standard data that meets the target format.

[0092] The detection unit is used to detect whether there are residual values ​​in the standard data.

[0093] The calculation unit is used to calculate the mean of the data items corresponding to the data residual values ​​when detecting that the data residual values ​​exist in the standard data.

[0094] The supplementing unit is used to supplement the data residual value of the data item based on the mean value to obtain processed data.

[0095] Optionally, in one embodiment of the present application, it further includes: a second acquisition module and a query module.

[0096] The second acquisition module is used to obtain identification information of the current driver of at least one vehicle.

[0097] The query module is used to query the current driver's driving habits from a preset database using the identification information as an index to determine the replacement parameters according to the driving habits.

[0098] Optionally, in one embodiment of the present application, the update module 300 includes: a generation unit and an update unit.

[0099] The generating unit is used to generate at least one of the battery replacement parameters and the engine replacement parameters in the vehicle energy consumption model based on the processed data.

[0100] An updating unit is used to update corresponding model parameters in the vehicle energy consumption model according to at least one of them.

[0101] It should be noted that the above explanation of the embodiment of the vehicle energy consumption model optimization method is also applicable to the vehicle energy consumption model optimization device of this embodiment, and will not be repeated here.

[0102] According to the vehicle energy consumption model optimization device proposed in the embodiment of the present application, the actual working condition data collected during driving uploaded by the vehicle can be obtained based on the cloud server, and combined with the vehicle status data, it is input into the vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model to update the corresponding original model parameters in the vehicle energy consumption model. Thus, on the basis of deploying the vehicle energy consumption model to the cloud, the actual working condition data and vehicle status data are collected by the cloud, and the various changing factors affecting the vehicle energy consumption model in the actual use scenario are fully considered; and then the high computing power characteristics of the cloud are used to process the returned vehicle data and generate replacement parameters of the vehicle energy consumption model to replace the corresponding original model parameters in the vehicle energy consumption model, thereby realizing the personalized vehicle energy consumption model optimization of the vehicle, so that the vehicle energy consumption model of each vehicle is the most suitable for its own vehicle, improving the authenticity and effectiveness of the vehicle energy consumption model, maximizing the force of the vehicle energy model of each vehicle, and providing strong support for the regular parameter update of the vehicle energy consumption model, greatly improving the intelligence level of the present application. This solves the problem in related technologies that the vehicle's energy consumption model is finalized after relying on initial model parameters or limited standard test data, and fails to fully consider the various changing factors that affect the vehicle's energy consumption model in actual usage scenarios. As a result, as the vehicle's driving time increases and its own performance changes, the deviation between the energy consumption results calculated by the constructed vehicle energy consumption model and the actual energy consumption situation gradually increases, making it difficult for the model to play its expected role.

[0103] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include: Memory 501 , processor 502 , and computer programs stored in the memory 501 and executable on the processor 502 .

[0104] When the processor 502 executes the program, the vehicle energy consumption model optimization method provided in the above embodiment is implemented.

[0105] Furthermore, the vehicle further comprises: The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0106] The memory 501 is used to store computer programs that can be run on the processor 502 .

[0107] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0108] If the memory 501, processor 502, and communication interface 503 are implemented independently, the communication interface 503, memory 501, and processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0109] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0110] The processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0111] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned vehicle energy consumption model optimization method is implemented.

[0112] An embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the vehicle energy consumption model optimization method provided in the embodiment of the present application is implemented.

[0113] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0115] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0116] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0117] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0118] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0119] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0120] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A vehicle energy consumption model optimization method, characterized in that: Applied to a server, wherein the method comprises the following steps: Obtaining actual operating condition data uploaded by at least one vehicle; Processing the actual operating condition data and the status data of the at least one vehicle to obtain processed data that meets a preset useful condition; The processed data are batch-transmitted to the various interfaces of the pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data and update the corresponding model parameters in the vehicle energy consumption model.

2. The method according to claim 1, characterized in that The obtaining of actual operating condition data uploaded by at least one vehicle includes: Collect driving data of each driving of the at least one vehicle, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature, as the actual operating condition data.

3. The method according to claim 1, characterized in that The processing of the actual operating condition data and the status data of the at least one vehicle to obtain processed data that meets a preset useful condition includes: Converting the data formats of the actual operating condition data and the status data of the at least one vehicle to obtain standard data meeting a target format; Detecting whether there is a residual value in the standard data; When detecting that data residual values ​​exist in the standard data, calculating the mean of the data items corresponding to the data residual values; Based on the mean, the data residual value of the data item is supplemented to obtain the processed data.

4. The method according to claim 1, wherein Also includes: obtaining identification information of a current driver of the at least one vehicle; The identification information is used as an index to query the driving habits of the current driver from a preset database to determine the replacement parameter according to the driving habits.

5. The method according to claim 1, wherein Generating replacement parameters of the vehicle energy consumption model based on the processed data and updating corresponding model parameters in the vehicle energy consumption model include: generating at least one of a battery replacement parameter and an engine replacement parameter in the vehicle energy consumption model based on the processed data; Update corresponding model parameters in the vehicle energy consumption model according to at least one of the above.

6. A vehicle energy consumption model optimization device, characterized in that: Applied to a server, wherein the device comprises: An acquisition module, configured to acquire actual operating condition data uploaded by at least one vehicle; a processing module, configured to process the actual operating condition data and the status data of the at least one vehicle to obtain processed data that meets a preset useful condition; An update module is used to transmit the processed data in batches to the various interfaces of the pre-built vehicle energy consumption model to generate replacement parameters of the vehicle energy consumption model based on the processed data and update the corresponding model parameters in the vehicle energy consumption model.

7. The device according to claim 6, characterized in that The acquisition module includes: An acquisition unit is used to collect driving data of each driving of the at least one vehicle, wherein the driving data includes at least one of longitude and latitude, vehicle speed, acceleration, battery voltage, battery current, remaining battery power, motor speed, and motor temperature, as the actual operating condition data.

8. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle energy consumption model optimization method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle energy consumption model optimization method as described in any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed, it is used to implement the vehicle energy consumption model optimization method as described in any one of claims 1 to 5.

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