WO2024082201A1

WO2024082201A1 - Cruising range estimation method and apparatus

Info

Publication number: WO2024082201A1
Application number: PCT/CN2022/126317
Authority: WO
Inventors: 林海波
Original assignee: 宁德时代新能源科技股份有限公司; 宁德时代(上海)智能科技有限公司
Priority date: 2022-10-20
Filing date: 2022-10-20
Publication date: 2024-04-25

Abstract

A cruising range estimation method and apparatus. The method comprises: acquiring a first running path, remaining energy and real-time driving behavior data of a vehicle; then performing driving style identification on the real-time driving behavior data to obtain a real-time driving style, and predicting a vehicle operation working condition of the vehicle according to the first running path and the real-time driving style; controlling a vehicle mirroring model to run under the vehicle operation working condition, so as to determine predicted energy consumption, wherein the vehicle mirroring model is constructed according to a digital twin algorithm; determining target energy consumption according to the predicted energy consumption and historical average energy consumption; and estimating a cruising range of the vehicle according to the remaining energy and the target energy consumption. The predicted energy consumption obtained by means of digital twin technology and the historical average energy consumption obtained by means of historical data of the vehicle are taken into account to determine the target energy consumption, and the cruising range can be estimated more accurately according to the target energy consumption.

Description

A method and device for estimating cruising range

Technical Field

The present application relates to the field of vehicle technology, and more specifically, to a method and device for estimating a cruising range.

Background technique

With the development of new energy technology, new energy vehicles are becoming more and more common. Some new energy vehicles are powered by on-board power supplies, which have the advantages of low maintenance costs and little impact on the environment.

Due to the limited energy of the on-board power supply, the endurance of new energy vehicles powered by the on-board power supply is weak. Therefore, during driving, users need to check the endurance displayed by the electric vehicle in time to formulate a driving plan.

Therefore, there is an urgent need for a method to accurately estimate the cruising range.

Summary of the invention

The present application provides a battery cell and a manufacturing method and system thereof, a battery, and an electrical device, which can improve the assembly efficiency of the battery cell and enhance the safety of the battery cell.

In a first aspect, an embodiment of the present application provides a method for estimating a cruising range, including:

Obtain the vehicle's first driving path, remaining energy, and real-time driving behavior data;

Performing driving style recognition on the real-time driving behavior data to obtain a real-time driving style;

predicting a vehicle operating condition of the vehicle according to the first driving path and the real-time driving style;

Controlling the vehicle mirror model to drive the first driving path under the vehicle operating condition, and determining the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;

Determining a target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined according to historical data of the vehicle or simulation data under standard operating conditions;

The cruising range of the vehicle is estimated based on the remaining energy and the target energy consumption.

Optionally, the historical data of the vehicle is divided into different categories according to the historical driving style and characteristic parameters of the historical data;

Before determining the target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle, the method further includes:

When the historical data under the same category meets a preset condition, selecting a number of historical data whose driving style matches the real-time driving style from the historical data of the vehicle;

Acquire real-time driving data, and select from the plurality of historical data a plurality of historical data that match characteristic parameters of the real-time driving data, all of which are used as target historical data;

According to the several target historical data, the historical average energy consumption is determined.

Optionally, the preset condition is that the accumulated mileage of historical data under the same category reaches a preset mileage.

Optionally, before determining the target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle, the method further includes:

When the historical data under the same category does not satisfy the preset condition, the average energy consumption obtained by driving the vehicle mirror model under the standard working condition is determined as the historical average energy consumption.

Optionally, performing driving style recognition on the real-time driving behavior data to obtain the real-time driving style specifically includes:

Searching for a target data range that matches the real-time driving behavior data from a range of driving behavior data corresponding to a plurality of preset driving styles;

The preset driving style corresponding to the target data range is determined as the real-time driving style.

Optionally, the acquiring the first driving path of the vehicle specifically includes:

Acquiring real-time location information of the vehicle and some historical driving path data of the vehicle;

Constructing a historical driving path state transition probability matrix according to the plurality of historical driving path data;

According to the real-time position information and the historical driving path state transition probability matrix, a Monte Carlo algorithm is used to perform random prediction to obtain a first driving path of the vehicle.

Optionally, obtaining real-time operating condition data and real-time driving data of the vehicle within a preset time period;

Controlling the vehicle mirror model to travel according to the real-time operating condition data to obtain virtual driving data;

The vehicle mirror model is adjusted according to the real-time driving data and the virtual driving data.

Optionally, determining the target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle specifically includes:

Determining, according to the mileage of the first driving route, the remaining energy, and the predicted energy consumption, a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption;

A target energy consumption is determined according to the first weighted value, the predicted energy consumption, the second weighted value, and the historical average energy consumption.

Optionally, determining a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption according to the mileage of the first driving path, the remaining energy, and the predicted energy consumption specifically includes:

Determine the product of the remaining energy and the predicted energy consumption as the predicted mileage;

Determine a ratio of the mileage of the first driving path to the predicted mileage as a first weighted value;

A difference between the preset parameter and the first weighted value is determined as a second weighted value.

In a second aspect, an embodiment of the present application provides a device for estimating a cruising range, including:

An acquisition module, for acquiring a first driving path, remaining energy, and real-time driving behavior data of the vehicle;

An identification module, performing driving style identification on the real-time driving behavior data to obtain a real-time driving style;

a prediction module, for predicting a vehicle operating condition of the vehicle according to the first driving path and the real-time driving style;

A control module controls the vehicle mirror model to drive the first driving path under the vehicle operating condition, and determines the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;

a determination module, determining a target energy consumption of the vehicle according to the predicted energy consumption and a historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined according to historical data of the vehicle or simulation data under standard working conditions;

An estimation module estimates the cruising range of the vehicle based on the remaining energy and the target energy consumption.

A cruising range estimation device, characterized in that the device includes: a processor and a memory storing computer program instructions.

When the processor executes the computer program instructions, the cruising range estimation method as described in any one of claims 1-9 is implemented.

A computer-readable storage medium, characterized in that computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the cruising range estimation method according to any one of claims 1 to 9 is implemented.

A computer program product, characterized in that when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the cruising range estimation method as described in any one of claims 1-9.

The range estimation method, device, equipment and computer storage medium of the embodiments of the present application can obtain predicted energy consumption through digital twin technology, and combine the predicted energy consumption with the historical average energy consumption obtained through the historical data of the vehicle to determine the target energy consumption, and estimate the range more accurately based on the target energy consumption.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

FIG1 is a schematic diagram of a flow chart of a method for estimating a range applicable to an embodiment of the present application;

FIG2 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG3 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG4 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG5 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG6 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG7 is a flow chart of a method for estimating a range applicable to another embodiment of the present application;

FIG8 is a schematic diagram of the structure of a cruising range estimation device applicable to an embodiment of the present application;

FIG9 is a schematic diagram of the structure of a cruising range estimation device applicable to an embodiment of the present application;

In the drawings, the drawings are not drawn to scale.

Detailed ways

In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

Unless otherwise defined, all technical and scientific terms used in this application have the same meanings as those commonly understood by technicians in the technical field of this application; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order or a primary and secondary relationship.

Reference to "embodiment" in this application means that a particular feature, structure or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments.

In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "attached" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

The term "and/or" in this application is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character "/" in this application generally indicates that the associated objects before and after are in an "or" relationship.

In the embodiments of the present application, the same reference numerals represent the same components, and for the sake of brevity, detailed descriptions of the same components are omitted in different embodiments. It should be understood that the thickness, length, width and other dimensions of various components in the embodiments of the present application shown in the drawings, as well as the overall thickness, length, width and other dimensions of the integrated device are only exemplary descriptions and should not constitute any limitation to the present application.

The term "plurality" used in the present application refers to two or more (including two).

In the present application, the battery cell may include a lithium-ion secondary battery cell, a lithium-ion primary battery cell, a lithium-sulfur battery cell, a sodium-lithium-ion battery cell, a sodium-ion battery cell or a magnesium-ion battery cell, etc., and the embodiments of the present application do not limit this. The battery cell may be cylindrical, flat, rectangular or other shapes, etc., and the embodiments of the present application do not limit this.

With the development of new energy technologies, new energy vehicles are becoming more and more common. Some new energy vehicles powered by on-board power supplies are favored by users because of their low maintenance costs and little impact on the environment.

However, since the energy of the vehicle power supply is limited, in order to reduce the probability of the vehicle power supply running out of energy during driving, the user usually needs to refer to the mileage displayed by the vehicle when making a driving plan. Therefore, in order to facilitate the estimation of the mileage, this manual provides a mileage estimation method.

FIG1 is a flow chart showing a method for estimating a range of mileage applicable to an embodiment of the present application.

As shown in FIG. 1 , the cruising range estimation method 100 may include the following steps.

Step 110: Obtain the first driving path, remaining energy, and real-time driving behavior data of the vehicle.

Step 120: Perform driving style recognition on the real-time driving behavior data to obtain a real-time driving style.

Step 130: Predicting the vehicle operating condition of the vehicle based on the first driving path and the real-time driving style.

Step 140: Control the vehicle mirror model to travel the first driving path under the vehicle operating condition, and determine the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed based on the digital twin algorithm.

Step 150: Determine the target energy consumption of the vehicle based on the predicted energy consumption and the historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined based on the historical data of the vehicle or simulation data under standard operating conditions.

Step 160: Estimate the cruising range of the vehicle based on the remaining energy and the target energy consumption.

In an embodiment of the present application, a method for estimating a cruising range is provided, which can obtain a first driving path, remaining energy, and real-time driving behavior data of a vehicle. The real-time driving behavior data is then used to identify the driving style to obtain the real-time driving style, and the vehicle operating condition of the vehicle is predicted based on the first driving path and the real-time driving style. The vehicle mirror model is controlled to drive under the vehicle operating condition to determine the predicted energy consumption. Among them, the vehicle mirror model is constructed according to the digital twin algorithm. And based on the predicted energy consumption and the historical average energy consumption of the vehicle, the target energy consumption is determined. Finally, based on the remaining energy and the target energy consumption, the cruising range of the vehicle is estimated. It can be seen that the predicted energy consumption is determined by digital twin technology, and the target energy consumption is determined in combination with the historical average energy consumption, so as to more accurately estimate the cruising range.

In one or more embodiments of the present specification, the cruising range estimation method may be executed by a vehicle control system or a cloud server. Therefore, for ease of description, the present application takes the vehicle control system executing the cruising range estimation method as an example for description.

Specifically, in step 110, the digital twin technology is a technology that makes full use of data such as physical models, sensor updates, and operation history, integrates multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, and completes mapping in virtual space, thereby reflecting the full life cycle process of the corresponding physical equipment. Therefore, in order to estimate the vehicle's cruising range through the digital twin algorithm, the vehicle control system can obtain data such as the first driving path and remaining energy.

First, the vehicle control system can construct a vehicle mirror model through a digital twin algorithm in a virtual space. Among them, there are relatively mature technologies on how to construct a vehicle mirror model through a digital twin algorithm, and this application will not go into details here. Of course, since some data needs to be obtained before estimating the vehicle's cruising range through the vehicle mirror model, in one or more embodiments of the present application, step 140 estimates the vehicle's cruising range through the vehicle mirror model. Therefore, in one or more embodiments of the present application, the vehicle mirror model may not be constructed temporarily in step 110, but the vehicle mirror model may be constructed through a digital twin algorithm in step 140 or in any step performed before step 140.

Secondly, the vehicle control system can obtain the first driving path, the remaining energy and the real-time driving behavior data. Among them, the real-time driving behavior data includes the accelerator pedal opening, the accelerator pedal opening change rate, the real-time vehicle speed, the vehicle horizontal driving angle (the slope of the road on which the vehicle is traveling), the current road section speed limit data, etc. The real-time driving behavior data can be obtained in a variety of ways. In one or more embodiments of this specification, the real-time driving behavior data can be obtained by sensors such as speed sensors and gyroscopes configured by the vehicle. In one or more embodiments of this specification, the real-time driving behavior data can also be obtained by other existing technologies. How to obtain the real-time driving behavior data is not limited by this specification and can be set as needed. In addition, in one or more embodiments of the present application, when obtaining the first driving path, the vehicle control system can randomly obtain the road data of a road, and the road data includes the number of lanes, the distribution of traffic lights, the road distance, the road slope change, etc., and the road data is used as the first driving path.

Specifically, in step 120, because in general, when users with different driving styles drive the same vehicle on the same road section and the same distance, the energy consumption of the vehicle is also different. For example, user A and user B drive the same vehicle for 200 kilometers on a road with a minimum speed limit of 80 kilometers per hour and a maximum speed limit of 120 kilometers per hour. User A drives at a speed of 85 kilometers per hour and user B drives at a speed of 115 kilometers per hour. User A consumes 40% of the total energy of the vehicle and user B consumes 48% of the total energy of the vehicle. Therefore, in order to more accurately estimate the vehicle's cruising range, the vehicle control system can determine the driving style of the current user.

First, the vehicle control system can obtain several preset driving styles and the driving behavior data range corresponding to each driving style, and according to the real-time driving behavior data, from the driving behavior data ranges corresponding to several driving styles, find the driving behavior data range corresponding to the driving behavior data as the target data range. Then determine the driving style corresponding to the target data range as the real-time driving style. Among them, the driving styles may include conservative, mild, standard, active, aggressive, etc., and the driving behavior data range corresponding to each driving style can be set as needed. The specific setting is not limited in this manual and can be set as needed.

For example, the real-time driving behavior data includes the current road section speed limit data of 80 km/h to 120 km/h, the real-time vehicle speed of 119 km/h, the vehicle horizontal driving angle of 0 degrees, the accelerator pedal opening of 5, and the accelerator pedal opening rate of 0. The driving behavior data corresponding to the aggressive type is that the accelerator pedal opening is greater than or equal to 4 and the accelerator pedal opening rate is greater than or equal to 0 and the real-time vehicle speed is greater than or equal to 100 km/h and the horizontal driving angle is greater than negative 10 degrees. The driving behavior data corresponding to the active type is that the accelerator pedal opening is between 3 and 4 and the accelerator pedal opening rate is greater than or equal to 0 and the real-time vehicle speed is between 80 km/h and 100 km/h and the horizontal driving angle is greater than negative 10 degrees. Therefore, the vehicle control system can determine that the real-time driving style is aggressive.

By adopting the above method, the vehicle driving platform can determine the real-time driving style based on the real-time driving behavior data acquired in real time and through the driving behavior data ranges corresponding to several preset driving styles, so as to more accurately estimate the vehicle's cruising range based on the real-time driving style.

In one or more embodiments of the present specification, after the vehicle control system determines the first driving path and the real-time driving style, it can predict the vehicle operating condition and control the vehicle mirror model to drive the first driving path under the vehicle operating condition in the virtual space, thereby obtaining driving data and determining the predicted energy consumption, so as to estimate the vehicle's cruising range. Specifically, in steps 130 to 140, first, the vehicle control system can predict the vehicle operating condition of the vehicle based on the first driving path and the real-time driving style, wherein the vehicle operating condition is the relationship between speed and time, and the relationship between horizontal driving angle and time. For example, the whole journey takes 10 minutes, with a speed of 30 kilometers per hour at 1 minute and a horizontal driving angle of 0 degrees; a speed of 50 kilometers per hour at 2 minutes and a horizontal driving angle of 0 degrees; a speed of 110 kilometers per hour at 3 minutes and a horizontal driving angle of 0 degrees; a speed of 115 kilometers per hour at 4 minutes and a horizontal driving angle of 0 degrees; a speed of 115 kilometers per hour at 5 minutes and a horizontal driving angle of 0 degrees; a speed of 115 kilometers per hour at 6 minutes and a horizontal driving angle of 10 degrees; a speed of 100 kilometers per hour at 7 minutes and a horizontal driving angle of 10 degrees; a speed of 115 kilometers per hour at 9 minutes and a horizontal driving angle of 10 degrees; a speed of 80 kilometers per hour at 9 minutes and a horizontal driving angle of 10 degrees; and a speed of 0 kilometers per hour at 9 minutes and a horizontal driving angle of 10 degrees.

Secondly, the vehicle control system can control the vehicle mirror model in the virtual space, drive on the first driving path according to the vehicle operating conditions, and collect simulated driving data of the vehicle, wherein the driving data includes driving distance, driving speed, remaining energy at the start of driving, and remaining energy at the end of driving, etc. Finally, the vehicle control system can determine the predicted energy consumption based on the simulated driving data. In one or more embodiments of the present application, the vehicle control system can determine the difference between the remaining energy at the start of driving and the remaining energy at the end of driving as the consumed energy. And determine the predicted energy consumption based on the consumed energy and the driving distance. Of course, the vehicle control system can also use other methods to determine the predicted energy consumption. The specific determination method can be set as needed, and this specification does not limit it here.

Using the above method, the vehicle control system can perform simulation based on the vehicle mirror model constructed by the digital twin algorithm in the virtual space, and the vehicle operating conditions predicted by the first driving path and real-time driving style, and determine the predicted energy consumption based on the simulated driving data obtained by the simulation, so as to more accurately estimate the vehicle's cruising range.

Specifically, in the above steps 150 to 160, since the first driving path is the driving path predicted by the vehicle control system, it may be significantly different from the actual driving path. Therefore, in order to more accurately estimate the cruising range of the vehicle, the vehicle control system may also determine the historical average energy consumption of the vehicle based on the historical data of the vehicle or the simulation data obtained by the vehicle mirror model of the vehicle under standard working conditions, thereby estimating the cruising range of the vehicle based on the predicted energy consumption, the historical average energy consumption and the remaining energy. Among them, the standard working condition can be the New European Driving Cycle (NEDC), the China light-duty vehicle test cycle (CLTC), etc.

First, the vehicle control system can obtain historical data and determine whether the acquisition is successful. If so, the vehicle control system can determine the average energy consumption corresponding to the historical data based on the historical data as the historical average energy consumption. If not, the vehicle control system can obtain the standard working condition, and control the vehicle mirror model in the virtual space to travel according to the standard working condition, determine the standard simulation data, and determine the standard simulation average energy consumption based on the standard simulation data, and use the standard simulation average energy consumption as the historical average energy consumption.

Secondly, the vehicle control system can determine the average value of the predicted energy consumption and the historical average energy consumption as the target energy consumption. Finally, the vehicle control system can estimate the cruising range of the vehicle based on the target energy consumption and the remaining energy.

Using the above method, the vehicle control system can combine the predicted energy consumption obtained through digital twin technology and the historical average energy consumption obtained through historical data to more accurately estimate the vehicle's range.

In the embodiment provided in FIG. 1 above, the first driving path, remaining energy, and real-time driving behavior data of the vehicle can be obtained. The real-time driving behavior data is then subjected to driving style recognition to obtain a real-time driving style, and the vehicle operating condition of the vehicle is predicted based on the first driving path and the real-time driving style. The vehicle mirror model is controlled to travel under the vehicle operating condition to determine the predicted energy consumption, wherein the vehicle mirror model is constructed based on the digital twin algorithm. And based on the predicted energy consumption and the historical average energy consumption of the vehicle, the target energy consumption is determined. Finally, based on the remaining energy and the target energy consumption, the cruising range of the vehicle is estimated. It can be seen that the above content combines the predicted energy consumption obtained by digital twin technology with the historical average energy consumption obtained by the historical data of the vehicle to determine the target energy consumption, and the cruising range is more accurately estimated based on the target energy consumption.

FIG2 shows a flow chart of a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 2 , the cruising range estimation method 200 includes the following steps in addition to the above steps 110 to 160 .

Step 210: When the historical data under the same category meets a preset condition, a target historical driving style matching the real-time driving style is selected from the historical data of the vehicle.

Step 220: Acquire real-time driving data, and select a target historical data category that matches the characteristic parameters of the real-time driving data from different historical driving data of the target historical driving style.

Step 230: Determine the historical average energy consumption according to the target historical data category.

In general, weather, temperature and other data can affect the energy consumption of a vehicle. For example, vehicle A travels on the same road in summer and winter. The temperature causes the thermal efficiency to change. The lower the temperature, the lower the thermal efficiency. Therefore, the energy consumption is higher in winter and lower in summer. Therefore, the vehicle control system can determine the categories of each historical data respectively, so as to determine the historical average energy consumption.

Specifically, in the above steps 210 to 230, after acquiring a number of historical driving data, the vehicle control system may filter out a number of historical driving data whose driving style matches the real-time driving style from the number of historical driving data according to the determined real-time driving style and the driving style carried in each historical driving data, wherein each historical data includes a driving style.

Secondly, the vehicle control system can obtain real-time driving data, which includes temperature characteristic parameters, weather characteristic parameters, vehicle air conditioning characteristic parameters, etc. And from the selected historical data, select several historical data whose characteristic parameters match the characteristic parameters (temperature characteristic parameters, weather characteristic parameters, vehicle air conditioning characteristic parameters, etc.) included in the real-time driving data, and use the selected historical data as target historical data. Among them, the several characteristic parameters included in the real-time driving data can be obtained through several sensors configured on the vehicle.

Finally, the vehicle control system can use the average energy consumption of the determined target historical data as the historical average energy consumption when it is determined that the determined target historical data meet the preset conditions. The preset conditions can be that the cumulative mileage reaches the preset mileage, the mileage corresponding to any historical data reaches the preset single mileage, etc. The specific conditions of the preset conditions can be set as needed, and this manual does not limit them here.

Using the above method, when determining the historical average energy consumption, the vehicle control system can screen out several historical data with characteristic parameters similar to the characteristic parameters of the real-time driving data currently obtained from multiple historical data as target historical data, thereby determining the historical average energy consumption based on several target historical data and more accurately estimating the vehicle's cruising range.

Specifically, other related technical solutions in the above steps can refer to the relevant descriptions of steps 240 to 260 above, and will not be elaborated here.

FIG3 shows a flow chart of a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 3 , the cruising range estimation method 300 includes the following steps in addition to the above steps 110 to 160 .

Step 310: When the historical data under the same category does not satisfy the preset condition, the average energy consumption obtained by driving the vehicle mirror model under standard working conditions is determined as the historical average energy consumption.

In one or more embodiments of the present specification, the historical average energy consumption needs to be determined through historical data stored in the vehicle, and the newly purchased vehicles often do not store historical data, so the historical average energy consumption can also be determined based on standard operating conditions.

Specifically, in step 310, the vehicle control system may obtain a standard operating condition when it is determined that a number of historical data of the same category as the acquired real-time driving data do not meet the preset condition. Then, the vehicle mirror model in the virtual space is controlled to drive under the standard operating condition to obtain standard simulation data. The average energy consumption carried in the standard simulation data is used as the historical average energy consumption.

By adopting the above method, the vehicle control system can determine the historical average energy consumption through standard operating conditions and vehicle mirror models when it is difficult to obtain historical data, or when the historical data obtained in the same category as the real-time driving data does not meet the preset conditions, thereby more accurately estimating the vehicle's cruising range.

FIG4 shows a flow chart of a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 4 , the cruising range estimation method 200 includes the following steps in addition to the above steps 110 to 160 .

Step 410: Acquire the real-time location information of the vehicle and some historical driving path data of the vehicle.

Step 420: construct a historical driving path state transition probability matrix based on the plurality of historical driving path data.

Step 430: Based on the real-time position information and the historical driving path state transition probability matrix, a Monte Carlo algorithm is used to perform random prediction to obtain a first driving path of the vehicle.

Since in one or more embodiments of the present application, the vehicle control system needs to construct a vehicle mirror model based on a digital twin algorithm in a virtual space, and control the vehicle mirror model to travel on a simulated road, and the predicted energy consumption is used to estimate the vehicle's range. In general, due to the different characteristics of traffic lights, number of lanes, etc. on different roads, even if the same vehicle travels the same distance (kilometers) on different roads, the final average energy consumption may be different. Therefore, when the vehicle control system does not obtain the navigation path based on the navigation information, the real-time location information provided by the Global Navigation Satellite System (GNSS) and several historical driving path data of the vehicle can be obtained. And from several historical driving path data, determine several historical driving path data with the real-time location information as the starting point or waypoint (the location point where the historical driving path data passes), and determine the first driving path from the several historical driving path data. Thereby, the vehicle's range is estimated more accurately.

Specifically, in step 410 to step 430, first, the vehicle control system may obtain the real-time location information of the vehicle and some historical driving path data of the vehicle, and then construct a historical driving path state transfer matrix through a Markov chain according to the some historical driving path data.

Secondly, the vehicle control system can perform random prediction through the Monte Carlo algorithm according to the real-time position information and the historical driving path state transition probability matrix, and determine the prediction result as the first driving path of the vehicle.

By adopting the above method, the vehicle control system can predict the actual future driving path of the vehicle based on the real-time position information provided by GNSS and some historical driving path data without obtaining navigation data, and determine the prediction result as the first driving path. This makes the predicted energy consumption based on the vehicle mirror model in the virtual space and the first driving path more accurate, thereby more accurately estimating the vehicle's cruising range.

FIG5 is a flow chart showing a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 5 , the cruising range estimation method 500 includes the following steps in addition to the above steps 110 to 160 .

Step 510: Obtain navigation data, and use the navigation path in the navigation data as the first driving path.

In one or more embodiments of the present application, the vehicle control system needs to construct a vehicle mirror model based on a digital twin algorithm in a virtual space, and control the vehicle mirror model to travel on a simulated road, and the predicted energy consumption is used to estimate the vehicle's range. In general, due to the different characteristics of traffic lights, number of lanes, etc. on different roads, even if the same vehicle travels the same distance (kilometers) on different roads, the final average energy consumption may be different. Therefore, the vehicle control system can obtain navigation data.

Specifically, in step 510, the vehicle control system may obtain navigation data, and determine a navigation path in the navigation data, and use the navigation path as the first driving path.

By adopting the above method, the vehicle control system can use the navigation path in the navigation data as the first driving path, so that the predicted energy consumption obtained based on the vehicle mirror model in the virtual space and the first driving path is more accurate and closer to the energy consumption data obtained by the actual driving of the vehicle, thereby more accurately estimating the vehicle's cruising range based on the predicted energy consumption.

FIG6 shows a flow chart of a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 6 , the cruising range estimation method 600 includes the following steps in addition to the above steps 110 to 160 .

Step 610: Acquire real-time operating condition data and real-time driving data of the vehicle within a preset time period.

Step 620: Control the vehicle mirror model to travel according to the real-time operating condition data to obtain virtual driving data.

Step 630: Adjust the vehicle mirror model according to the real-time driving data and the virtual driving data.

Since the vehicle mirror model is a model constructed in a virtual space through a digital twin algorithm, it may be significantly different from the actual vehicle. For example, the tires, power supplies and other components of the actual vehicle may be worn out and their performance may decline due to long-term use, resulting in high energy consumption. Therefore, in order to more accurately estimate the vehicle's range, the vehicle control system can adjust the virtual mirror model based on the vehicle's real-time driving data.

Specifically, in step 610 to step 630, first, the vehicle control system can obtain the real-time operating condition data and real-time driving data of the vehicle within a preset time period. The time period length of the preset time period is preset, and the preset time period is determined based on the current time obtained in real time and the time period length of the preset time period, and the current time is used as the time period end. For example, if the current time is 10:30 and the time period length is 10 minutes, the time period start point is 10:20 and the time period end point is 10:30.

Secondly, the vehicle control system can control the vehicle mirror model in the virtual space to drive according to the real-time operating condition data, and obtain the virtual driving data of the vehicle mirror model after the vehicle mirror model has finished driving.

Finally, the vehicle control platform can adjust the parameters of the vehicle mirror model based on the real-time driving data and the virtual driving data.

Using the above method, the vehicle control platform can adjust the vehicle mirror model based on the real-time driving data collected during the driving of the actual vehicle and the virtual driving data collected by the vehicle mirror model under the same working conditions, so that the vehicle mirror model is more similar to the vehicle, thereby more accurately determining the virtual energy consumption and more accurately estimating the vehicle's range.

FIG. 7 shows a flow chart of a method for estimating a range applicable to another embodiment of the present application.

As shown in FIG. 7 , the cruising range estimation method 700 includes the following steps in addition to the above steps 110 to 160 .

Step 710: Determine a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption based on the mileage of the first driving route, the remaining energy, and the predicted energy consumption.

Step 720: Determine the target energy consumption according to the first weighted value, the predicted energy consumption, the second weighted value and the historical average energy consumption.

In one or more embodiments of the present specification, since the first driving route is a predicted driving route, the mileage of the driving route may deviate greatly from the actual mileage of the vehicle, resulting in a decrease in the accuracy of the predicted energy consumption.

For example, the first driving path is a driving path obtained through navigation, which is the same as the actual driving path of the vehicle. However, the mileage of the first driving path is 200 kilometers, and the first half of the first driving path (90 kilometers) is an uphill route with a higher average energy consumption of 9 kilometers/kWh, and the second half of the first driving path (110 kilometers) is a downhill route with a lower average energy consumption of 9 kilometers/kWh. Therefore, the predicted energy consumption of the first driving path is 11 kilometers/kWh. The remaining energy of the vehicle is 10 kWh, that is, the estimated cruising range is 100 kilometers. Even if the virtual data obtained by the vehicle mirror model is exactly the same as the data of the actual driving of the vehicle, since the first half of the vehicle needs to travel is an uphill route, the actual cruising range is the product of 9 kilometers/kWh and 10 kWh, that is, 90 kWh. Therefore, even if the virtual data obtained by the vehicle mirror model is exactly the same as the actual driving data of the vehicle, the mileage of the first driving path is significantly different from the actual mileage that the vehicle can travel, and the estimated cruising range may be inaccurate. To this end, the vehicle control platform can determine the first weighted value and the second weighted value based on the mileage of the first driving path, the remaining energy, and the predicted energy consumption.

Specifically, in steps 710 to 720, the vehicle control platform can determine the product of the remaining energy and the predicted energy consumption based on the mileage of the first driving path, the remaining energy, and the predicted energy consumption, as the predicted mileage, and then determine the ratio of the mileage of the first driving path to the predicted mileage as the first weighted value corresponding to the predicted energy consumption.

The above calculation process can be expressed by the following formula:

Wherein, α is the first weighted value, A is the mileage of the first driving route, B is the remaining energy, and C is the predicted energy consumption.

Secondly, the vehicle control platform can determine the difference between the preset parameter and the first weighted value as the second weighted value.

Finally, the vehicle control platform can determine that the product of the predicted energy consumption and the first weighted value is the first weight, the product of the historical average energy consumption and the second weighted value is the second weight, and the sum of the first weight and the second weight is the target energy consumption.

By adopting the above method, the vehicle control platform can determine the weight of the predicted energy consumption in the target energy consumption according to the mileage of the first driving path, so as to more accurately determine the target energy consumption and more accurately estimate the vehicle's cruising range.

FIG8 shows a schematic structural diagram of a cruising range estimation device applicable to an embodiment of the present application.

As shown in FIG8 , the cruising range estimation device 800 includes the following modules.

An acquisition module 810 is used to acquire a first driving path, remaining energy, and real-time driving behavior data of the vehicle;

The recognition module 820 performs driving style recognition on the real-time driving behavior data to obtain a real-time driving style;

A prediction module 830, predicting a vehicle operating condition of the vehicle according to the first driving path and the real-time driving style;

A control module 840 controls the vehicle mirror model to drive the first driving path under the vehicle operating condition, and determines the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;

A determination module 850, which determines a target energy consumption of the vehicle according to the predicted energy consumption and a historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined according to historical data of the vehicle or simulation data under a standard operating condition;

The estimation module 860 estimates the cruising range of the vehicle according to the remaining energy and the target energy consumption.

Optionally, the determination module 850 is also used to filter out several historical data whose driving style matches the real-time driving style from the historical data of the vehicle when the historical data under the same category meets a preset condition, obtain real-time driving data, and filter out several historical data that match the characteristic parameters of the real-time driving data from the several historical data, all of which are used as target historical data, and determine the historical average energy consumption based on the several target historical data.

Optionally, in the determination module 850, the preset condition is that the accumulated mileage of historical data under the same category reaches a preset mileage.

Optionally, the determination module 850 is further used to determine the average energy consumption of the vehicle mirror model when driving under standard conditions as the historical average energy consumption when the historical data under the same category does not meet the preset condition.

Optionally, the identification module 820 is further used to search for a target data range that matches the real-time driving behavior data from a range of driving behavior data corresponding to several preset driving styles, and determine the preset driving style corresponding to the target data range as the real-time driving style.

Optionally, the acquisition module 810 is also used to obtain the real-time position information of the vehicle and several historical driving path data of the vehicle, construct a historical driving path state transition probability matrix based on the several historical driving path data, and use the Monte Carlo algorithm to perform random prediction based on the real-time position information and the historical driving path state transition probability matrix to obtain the first driving path of the vehicle.

Optionally, the acquisition module 810 is also used to obtain real-time operating condition data and real-time driving data of the vehicle within a preset time period, control the vehicle mirror model to drive according to the real-time operating condition data, obtain virtual driving data, and adjust the vehicle mirror model according to the real-time driving data and the virtual driving data.

Optionally, the determination module 850 is also used to determine a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption based on the mileage of the first driving path, the remaining energy and the predicted energy consumption, and to determine the target energy consumption based on the first weighted value, the predicted energy consumption, the second weighted value and the historical average energy consumption.

Optionally, the determination module 850 is also used to determine the product of the remaining energy and the predicted energy consumption as the predicted mileage, determine the ratio of the mileage of the first driving path to the predicted mileage as the first weighted value, and determine the difference between the preset parameter and the first weighted value as the second weighted value.

FIG9 shows a schematic structural diagram of a cruising range estimation device applicable to an embodiment of the present application.

As shown in FIG. 9 , the cruising range estimation device may include a processor 901 and a memory 902 storing computer program instructions.

Specifically, the above-mentioned processor 901 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

The memory 902 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 902 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 902 may include removable or non-removable (or fixed) media. Where appropriate, the memory 902 may be inside or outside the integrated gateway disaster recovery device. In a particular embodiment, the memory 902 is a non-volatile solid-state memory.

In certain embodiments, memory 902 includes a read-only memory (ROM). The ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of the above, where appropriate.

The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical/tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

The processor 901 implements any one of the cruising range estimation methods in the above embodiments by reading and executing computer program instructions stored in the memory 902 .

In one example, the cruising range estimation device may further include a communication interface 903 and a bus 910. The processor 901, the memory 902, and the communication interface 909 are connected via the bus 910 and communicate with each other.

The communication interface 903 is mainly used to implement communication between various modules, devices, units and/or equipment in the embodiments of the present application.

Bus 910 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 910 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

The cruising range estimation device can execute the cruising range estimation method in the embodiment of the present application based on the currently intercepted spam text messages and text messages reported by users, thereby realizing the cruising range estimation method and device described in combination with Figures 1 and 8.

In addition, in combination with the range estimation method in the above embodiments, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the range estimation methods in the above embodiments is implemented.

It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

Aspects of the present disclosure are described above with reference to the flowchart and/or block diagram of the method, device and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and/or block diagram and the combination of each box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function/action specified in one or more boxes of the flowchart and/or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It can also be understood that each box in the block diagram and/or flowchart and the combination of boxes in the block diagram and/or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein, but these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

A method for estimating a cruising range, characterized by comprising:

Obtain the vehicle's first driving path, remaining energy, and real-time driving behavior data;

Performing driving style recognition on the real-time driving behavior data to obtain a real-time driving style;

predicting a vehicle operating condition of the vehicle according to the first driving path and the real-time driving style;

Controlling the vehicle mirror model to drive the first driving path under the vehicle operating condition, and determining the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;

Determining a target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined according to historical data of the vehicle or simulation data under standard operating conditions;

The cruising range of the vehicle is estimated based on the remaining energy and the target energy consumption.
The method according to claim 1, characterized in that the historical data of the vehicle is divided into different categories according to the historical driving style and the characteristic parameters of the historical data;

Before determining the target energy consumption of the vehicle according to the predicted energy consumption and the historical average energy consumption of the vehicle, the method further includes:

When the historical data under the same category meets a preset condition, selecting a number of historical data whose driving style matches the real-time driving style from the historical data of the vehicle;

Acquire real-time driving data, and select from the plurality of historical data a plurality of historical data that match characteristic parameters of the real-time driving data, all of which are used as target historical data;

According to the several target historical data, the historical average energy consumption is determined.
The method according to claim 2 is characterized in that the preset condition is that the accumulated mileage of historical data under the same category reaches a preset mileage.
The method according to any one of claims 2 or 3, characterized in that, before determining the target energy consumption of the vehicle based on the predicted energy consumption and the historical average energy consumption of the vehicle, the method further comprises:

When the historical data under the same category does not satisfy the preset condition, the average energy consumption obtained by driving the vehicle mirror model under the standard working condition is determined as the historical average energy consumption.
The method according to claim 1 is characterized in that the step of performing driving style recognition on the real-time driving behavior data to obtain the real-time driving style specifically comprises:

Searching for a target data range that matches the real-time driving behavior data from a range of driving behavior data corresponding to a plurality of preset driving styles;

The preset driving style corresponding to the target data range is determined as the real-time driving style.
The method according to claim 1 is characterized in that the obtaining of the first driving path of the vehicle specifically comprises:

Acquiring real-time location information of the vehicle and some historical driving path data of the vehicle;

Constructing a historical driving path state transition probability matrix according to the plurality of historical driving path data;

According to the real-time position information and the historical driving path state transition probability matrix, a Monte Carlo algorithm is used to perform random prediction to obtain a first driving path of the vehicle.
The method according to claim 1, characterized in that the method further comprises:

Acquiring real-time operating condition data and real-time driving data of the vehicle within a preset time period;

Controlling the vehicle mirror model to travel according to the real-time operating condition data to obtain virtual driving data;

The vehicle mirror model is adjusted according to the real-time driving data and the virtual driving data.
The method according to claim 1, characterized in that the step of determining the target energy consumption of the vehicle based on the predicted energy consumption and the historical average energy consumption of the vehicle specifically comprises:

Determining, according to the mileage of the first driving route, the remaining energy, and the predicted energy consumption, a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption;

A target energy consumption is determined according to the first weighted value, the predicted energy consumption, the second weighted value, and the historical average energy consumption.
The method according to claim 8, characterized in that the determining, based on the mileage of the first driving path, the remaining energy, and the predicted energy consumption, a first weighted value corresponding to the predicted energy consumption and a second weighted value corresponding to the historical average energy consumption specifically comprises:

Determine the product of the remaining energy and the predicted energy consumption as the predicted mileage;

Determine a ratio of the mileage of the first driving path to the predicted mileage as a first weighted value;

A difference between the preset parameter and the first weighted value is determined as a second weighted value.
A cruising range estimation device, comprising:

An acquisition module, for acquiring the first driving path, remaining energy and real-time driving behavior data of the vehicle;

An identification module performs driving style identification on the real-time driving behavior data to obtain a real-time driving style;

A prediction module, for predicting a vehicle operating condition of the vehicle according to the first driving path and the real-time driving style;

A control module controls the vehicle mirror model to drive the first driving path under the vehicle operating condition, and determines the predicted energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;

a determination module, determining a target energy consumption of the vehicle according to the predicted energy consumption and a historical average energy consumption of the vehicle, wherein the historical average energy consumption is determined according to historical data of the vehicle or simulation data under standard working conditions;

An estimation module estimates the cruising range of the vehicle based on the remaining energy and the target energy consumption.
A cruising range estimation device, characterized in that the device includes: a processor and a memory storing computer program instructions.

When the processor executes the computer program instructions, the cruising range estimation method as described in any one of claims 1-9 is implemented.
A computer-readable storage medium, characterized in that computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the cruising range estimation method according to any one of claims 1 to 9 is implemented.