Method and device for predicting battery capacity attenuation of new energy electric vehicle

Abstract

The invention relates to the technical field of new energy electric vehicles, and discloses a method for predicting battery capacity attenuation of a new energy electric vehicle, which comprises the following steps: acquiring a vehicle track of the new energy electric vehicle in a preset time period; acquiring geographical environment information according to the vehicle track; driving behavior information of the new energy electric vehicle in the preset time period is obtained according to the geographical environment information; and predicting the battery capacity attenuation condition of the new energy electric vehicle according to the driving behavior information. According to the method, the battery capacity attenuation condition of the new energy electric vehicle can be predicted through the smart phone, the battery capacity attenuation condition of the new energy electric vehicle without a battery capacity prediction function can be predicted, and the method is more convenient. The invention further discloses a device for predicting the battery capacity attenuation of the new energy electric vehicle and the smart phone.

Description

Technical Field

[0001] The present application relates to the technical field of new energy electric vehicles, for example, to a method and device for predicting battery capacity attenuation of new energy electric vehicles. Background Art

[0002] The battery of a new energy electric vehicle is the core component of the entire vehicle system. It not only determines the vehicle's endurance, but also directly affects the vehicle's performance and service life. An efficient and stable battery can provide the electric vehicle with long-lasting and strong power output, ensuring that the driver enjoys a worry-free travel experience. At the same time, the continuous innovation and progress of battery technology is also driving the new energy electric vehicle industry towards a more efficient and environmentally friendly direction.

[0003] However, the battery of a new energy electric vehicle always has a limited service life, and the battery's storage capacity and performance will gradually decline. This means that electric vehicle owners need to pay attention to the health of the battery and replace or maintain it when necessary to ensure the vehicle's endurance and overall performance. Although battery technology continues to advance, this limitation is still an issue that needs to be continuously addressed and solved in the electric vehicle field.

[0004] The capacity of new energy electric vehicle batteries will decay. In order to ensure safe driving, existing new energy electric vehicles usually predict the battery capacity of the electric vehicle to remind users to replace the battery.

[0005] There are many factors that affect the battery capacity of new energy electric vehicles. In the existing technology, the battery capacity of electric vehicles is usually predicted by considering factors such as battery type and structure, electrode material, electrolyte type, electrolyte concentration and electrolyte purity. However, this method requires the vehicle itself to have battery monitoring and battery capacity prediction functions. At the same time, it does not take into account that the battery is used in new energy electric vehicles and lacks consideration of the use of the electric vehicles. In this case, for those new energy electric vehicles that do not have the battery capacity prediction function, it is impossible to predict their battery capacity attenuation.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] The embodiments of the present disclosure provide a method and device for predicting the battery capacity attenuation of a new energy electric vehicle, and a smart phone, so as to more conveniently predict the battery capacity attenuation of a new energy electric vehicle.

[0009] In some embodiments, the method for predicting the battery capacity decay of a new energy electric vehicle is applied to a smart phone. The method includes: obtaining a vehicle trajectory of a new energy electric vehicle within a preset time period; obtaining geographic environment information according to the vehicle trajectory; obtaining driving behavior information of the new energy electric vehicle within the preset time period according to the geographic environment information; and predicting the battery capacity decay of the new energy electric vehicle according to the driving behavior information.

[0010] In some embodiments, the device for predicting the battery capacity decay of a new energy electric vehicle is applied to a smart phone. The device comprises: a vehicle trajectory acquisition module configured to acquire the vehicle trajectory of the new energy electric vehicle within a preset time period; a geographic environment information acquisition module configured to acquire geographic environment information according to the vehicle trajectory; a driving behavior information acquisition module configured to acquire the driving behavior information of the new energy electric vehicle within the preset time period according to the geographic environment information; and a prediction module configured to predict the battery capacity decay of the new energy electric vehicle according to the driving behavior information.

[0011] In some embodiments, the device for predicting the capacity decay of a new energy electric vehicle battery comprises a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned method for predicting the capacity decay of a new energy electric vehicle battery when running the program instructions.

[0012] In some embodiments, the smart phone includes: a smart phone body; and the device for predicting the capacity attenuation of a new energy electric vehicle battery as described above is installed on the smart phone body.

[0013] The method and device for predicting the capacity decay of a new energy electric vehicle battery, and the smart phone provided in the embodiments of the present disclosure can achieve the following technical effects:

[0014] The geographic environment information is obtained through the vehicle trajectory of the new energy electric vehicle in a preset time period, and then the driving behavior information of the new energy electric vehicle in the preset time period is obtained based on the geographic environment information, so as to predict the battery capacity decay of the new energy electric vehicle. Since the driving behavior is taken into account through the geographical environment, it is more suitable for predicting the battery capacity decay of the new energy electric vehicle. In this case, the method of predicting the battery capacity decay of the new energy electric vehicle can be realized through a smart phone, and the battery capacity decay of those new energy electric vehicles that do not have the battery capacity prediction function can also be predicted, which is more convenient.

[0015] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0017] Figure 1 is a schematic diagram of a method for predicting battery capacity decay of a new energy electric vehicle provided by an embodiment of the present disclosure;

[0018] Figure 2 is a schematic diagram of a device for predicting battery capacity decay of a new energy electric vehicle provided by an embodiment of the present disclosure;

[0019] Figure 3 is a schematic diagram of another device for predicting battery capacity decay of a new energy electric vehicle provided by an embodiment of the present disclosure;

[0020] Figure 4 is a schematic diagram of the structure of a smart phone provided by an embodiment of the present disclosure;

[0021] Figure 5 It is a schematic diagram of the structure of another smart phone provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0023] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0024] Unless otherwise stated, the term "plurality" means two or more.

[0025] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0026] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0027] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0028] In the disclosed embodiment, the smartphone can be connected to the Internet and communicate with the server to upload the vehicle trajectory, or download the vehicle trajectory from the server. In the disclosed embodiment, the smartphone can also be connected to the vehicle via Bluetooth to bind and / or exchange data between the smartphone and the new energy electric vehicle. In some embodiments, the smartphone is provided with a positioning device, and the smartphone obtains the vehicle trajectory through the positioning device. The smartphone can also be installed with navigation software, and the smartphone obtains the vehicle trajectory through the navigation software. In some embodiments, the smartphone queries geographic environment information from the server, and in some embodiments, the smartphone presets a geographic environment information table to query the geographic environment information. In some embodiments, the smartphone queries driving behavior information from the server, and in some embodiments, the smartphone presets a driving behavior information table to query driving behavior information.

[0029] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for predicting the capacity decay of a new energy electric vehicle battery, comprising:

[0030] Step S101, the smart phone obtains the vehicle trajectory of the new energy electric vehicle within a preset time period.

[0031] Step S102: The smart phone obtains geographic environment information according to the vehicle trajectory.

[0032] Step S103, the smart phone obtains the driving behavior information of the new energy electric vehicle within a preset time period according to the geographical environment information.

[0033] Step S104: the smart phone predicts the battery capacity attenuation of the new energy electric vehicle based on the driving behavior information.

[0034] The method for predicting the battery capacity decay of new energy electric vehicles provided by the embodiment of the present disclosure can be used to predict the battery capacity decay of new energy electric vehicles. Geographical environment information is obtained through the vehicle trajectory of the new energy electric vehicle within a preset time period, and then the driving behavior information of the new energy electric vehicle within the preset time period is obtained based on the geographical environment information, thereby predicting the battery capacity decay of the new energy electric vehicle. Since the driving behavior is taken into account through the geographical environment, it is more suitable for predicting the battery capacity decay of new energy electric vehicles. In this case, this method of predicting the battery capacity decay of new energy electric vehicles can be achieved through a smart phone. For those new energy electric vehicles that do not have the battery capacity prediction function, their battery capacity decay can also be predicted, which is more convenient.

[0035] Optionally, obtaining the vehicle trajectory of the new energy electric vehicle within a preset time period in step S101 includes: obtaining at least one vehicle trajectory of the new energy electric vehicle within a preset time period from a preset trajectory database, and the vehicle trajectory stored in the trajectory database is obtained in the following manner: determining whether the smart phone moves with the new energy electric vehicle; when the smart phone moves with the new energy electric vehicle, storing the movement trajectory corresponding to the smart phone as the vehicle trajectory of the new energy electric vehicle in the trajectory database. Wherein, determining whether the smart phone moves with the new energy electric vehicle includes: determining whether the smart phone is connected to the vehicle; when the smart phone is connected to the vehicle, determining that the smart phone moves with the new energy electric vehicle and obtaining the vehicle information connected to the smart phone; otherwise, determining that the smart phone does not move with the new energy electric vehicle. In this way, by determining that the smart phone is connected to the new energy electric vehicle, the movement trajectory of a target vehicle in the preset time period can be accurately obtained, thereby preparing for the subsequent prediction of the battery capacity attenuation of the new energy electric vehicle in a targeted manner.

[0036] Optionally, in step S102, the vehicle trajectory includes at least one position coordinate information. Obtaining geographic environment information according to the vehicle trajectory includes: using the position coordinate information corresponding to each acquired vehicle trajectory to perform a table lookup operation in a preset geographic environment information table to obtain the geographic environment information corresponding to each vehicle trajectory; the geographic environment information table stores the position coordinate information, the geographic environment information, and the correspondence between the position coordinate information and the geographic environment information. Each position coordinate corresponds to a position, and the vehicle trajectory includes at least one position. According to the position, the environment information of the corresponding position can be obtained, so as to facilitate the subsequent acquisition of the possible driving behavior of the vehicle.

[0037] Optionally, in step S103, the driving behavior information of the new energy electric vehicle within a preset time period is obtained according to the geographical environment information, including: using each geographical environment information to perform a table lookup operation in a preset driving behavior information table to obtain the driving behavior information corresponding to each geographical environment information; the driving behavior information table stores the geographical environment information, the driving behavior information, and the corresponding relationship between the geographical environment information and the driving behavior information. The geographical environment information includes one or more of the slope information, the pit information and the curve information. In some embodiments, the slope information includes steep slope, gentle slope, uphill, downhill and other information. In some embodiments, the slope information may also include the slope value, such as 24°, 45°, etc. In some embodiments, the pit information includes the presence of a pit, the absence of a pit, the pit depth value, etc. In some embodiments, the curve information includes continuous sharp bends, reverse bends, straight roads, etc. In some embodiments, the curve information includes the number of bends at a preset distance and the curvature of the bend. In some embodiments, the driving behavior information includes one or more of sudden acceleration, sudden braking, smooth driving, point braking, linear acceleration and linear deceleration. In actual driving, different geographical environments will affect the driver's driving behavior, and driving behavior has an important impact on the capacity decay of the electric vehicle's battery. For example, during sudden acceleration, the motor needs to output more power to quickly increase the speed of the vehicle, which will cause the battery's discharge current to increase sharply. High discharge current will accelerate the speed of chemical reactions inside the battery, generate more heat, and may cause slight changes in the battery's internal structure, such as the shedding of active substances or the decomposition of electrolytes. These changes may cause the battery capacity to gradually decrease. During sudden braking, the battery may be frequently charged and discharged with a large current in a short period of time, increasing the burden on the battery and accelerating its capacity decay. There are many factors that affect the battery's capacity decay, but due to the consideration of different geographical environments, different driving behaviors may be adopted, which is more in line with the use scenarios of new energy electric vehicles, and the battery capacity decay prediction for new energy electric vehicles is more accurate.

[0038] Optionally, in step S104, the battery capacity attenuation of the new energy electric vehicle is predicted based on the driving behavior information, including: obtaining a driving characteristic value corresponding to the driving behavior information, the driving characteristic value being used to characterize the degree of deviation between the driving behavior and a preset standard behavior; and predicting the battery capacity attenuation of the new energy electric vehicle based on the driving characteristic value.

[0039] In some embodiments, obtaining a driving characteristic value corresponding to driving behavior information includes: using the driving behavior information to perform a table lookup operation in a preset driving characteristic score table to obtain a driving characteristic score corresponding to each driving behavior information; the driving characteristic value table stores driving behavior information, driving characteristic scores, and the corresponding relationship between driving behavior information and driving characteristic scores. Obtain the number of connections between the smartphone and the vehicle within the preset time period, use the number of connections between the smartphone and the vehicle within the preset time period to correct the driving characteristic score corresponding to each driving behavior information, obtain a reference driving characteristic score corresponding to each driving behavior information, sum all types of reference driving characteristic scores corresponding to the preset time period, and obtain a target driving characteristic score. Obtain a preset driving characteristic score corresponding to the preset time period, find the difference between the target driving characteristic score and the preset driving characteristic score, and determine the absolute value of the difference between the target driving characteristic score and the preset driving characteristic score as the driving characteristic value corresponding to the driving behavior information. Wherein, by calculating The driving characteristic score corresponding to each driving behavior information is corrected by using the number of connections between the smartphone and the vehicle within the preset time period. s is the reference driving characteristic score corresponding to the s-th driving behavior information, SC is the number of connections between the smartphone and the vehicle within the preset time period, SCB is the preset number of mobile phone-vehicle connections, and C s is the number of occurrences of the s-th driving behavior within the preset time period, CB s is the number of driving behaviors corresponding to the preset time period, FS s is the driving characteristic score of the s-th driving behavior, s is a positive integer, and SCB and CB s Both are not 0.

[0040] The number of connections between a smartphone and a vehicle within a preset time period can reflect the relationship between the smartphone and the vehicle to a certain extent. Since smartphones are usually bound to users, for example, a smartphone needs to be inserted into a communication chip of an operator, i.e., a phone card, a smartphone is usually considered to correspond to a user. The more times a smartphone is connected to a vehicle within a preset time period, the closer the relationship between the user corresponding to the smartphone and the vehicle is, and the driving habits that the vehicle may face are more consistent. The fewer times a smartphone is connected to a vehicle within a preset time period, the looser the relationship between the user corresponding to the smartphone and the vehicle is, and the vehicle may have more users. The driving behavior corresponding to the new energy electric vehicle may vary greatly from user to user. By correcting the driving characteristic score corresponding to each driving behavior information by the number of connections between the smartphone and the vehicle within a preset time period, the driving characteristic value finally determined can fully consider the different driving behaviors caused by different driving habits of different users, and it is easier to be close to the actual situation, reducing the impact of different driving habits of different drivers of the same vehicle on the prediction accuracy, thereby achieving more accurate prediction of the battery capacity decay of the electric vehicle.

[0041] In some embodiments, obtaining the driving characteristic value corresponding to the driving behavior information includes: obtaining the number of occurrences corresponding to each type of driving behavior information.

[0042] The number of occurrences corresponding to each type of driving behavior information is used to perform a table lookup operation in a preset feature score table to obtain the driving feature score corresponding to the number of occurrences of each type of driving behavior information; the feature score table stores the number of occurrences of each type of driving behavior information, the driving feature score, and the corresponding relationship between the number of occurrences of each type of driving behavior information and the driving feature score. This acquisition method is simple and fast, and is convenient whether interacting with a server or presetting the feature score table in a smart phone.

[0043] In some embodiments, predicting the battery capacity attenuation of a new energy electric vehicle based on a driving characteristic value includes: obtaining a driving characteristic value corresponding to the new energy electric vehicle, a battery attenuation speed corresponding to the new energy electric vehicle, and a battery capacity corresponding to the current new energy electric vehicle, wherein the initial value of the battery capacity of the new energy electric vehicle is 100%. Optionally, the driving characteristic value corresponding to the new energy electric vehicle is obtained by looking up a table, and optionally, the driving characteristic value corresponding to the new energy electric vehicle is obtained by obtaining a driving characteristic value input by a user. Optionally, the driving characteristic value corresponding to the new energy electric vehicle is preset. Optionally, the battery attenuation speed corresponding to the new energy electric vehicle is preset, and optionally, the battery attenuation speed corresponding to the new energy electric vehicle is obtained by looking up a table, and optionally, the battery attenuation speed corresponding to the new energy electric vehicle is obtained by obtaining a attenuation speed value input by a user.

[0044] The driving characteristic value is divided by the driving characteristic value corresponding to the new energy electric vehicle to obtain the weight.

[0045] The weight is multiplied by the battery decay rate corresponding to the new energy electric vehicle to obtain the predicted decay rate.

[0046] The predicted attenuation speed is multiplied by the battery capacity corresponding to the current new energy electric vehicle to obtain the battery capacity attenuation amount corresponding to the new energy electric vehicle. The battery capacity attenuation amount is the battery capacity attenuation situation.

[0047] In some embodiments, predicting the battery capacity attenuation of a new energy electric vehicle based on a driving characteristic value includes: using the driving characteristic value to perform a table lookup operation in a preset attenuation condition table to obtain the battery capacity attenuation corresponding to the driving characteristic value; the attenuation condition table stores the driving characteristic value, the battery capacity attenuation condition, and the corresponding relationship between the driving characteristic value and the battery capacity attenuation condition. The battery capacity attenuation condition includes one or more of the battery capacity attenuation amount, the battery capacity attenuation speed, and the battery remaining capacity.

[0048] Combination Figure 2 As shown, an embodiment of the present disclosure provides a device 200 for predicting the attenuation of the battery capacity of a new energy electric vehicle, which is applied to a smart phone. The device includes a vehicle trajectory acquisition module 201, a geographic environment information acquisition module 202, a driving behavior information acquisition module 203 and a prediction module 204. The vehicle trajectory acquisition module 201 is configured to obtain the vehicle trajectory of the new energy electric vehicle within a preset time period. The geographic environment information acquisition module 202 is configured to obtain geographic environment information based on the vehicle trajectory. The driving behavior information acquisition module 203 is configured to obtain the driving behavior information of the new energy electric vehicle within the preset time period based on the geographic environment information. The prediction module 204 is configured to predict the battery capacity attenuation of the new energy electric vehicle based on the driving behavior information.

[0049] The device for predicting the battery capacity decay of new energy electric vehicles provided by the embodiment of the present disclosure can predict the battery capacity decay of new energy electric vehicles. The geographical environment information is obtained through the vehicle trajectory of the new energy electric vehicle within a preset time period, and then the driving behavior information of the new energy electric vehicle within the preset time period is obtained based on the geographical environment information, so as to predict the battery capacity decay of the new energy electric vehicle. Since the driving behavior is taken into account through the geographical environment, it is more suitable for predicting the battery capacity decay of new energy electric vehicles. In this case, this method of predicting the battery capacity decay of new energy electric vehicles can be achieved through a smart phone. For those new energy electric vehicles that do not have the battery capacity prediction function, their battery capacity decay can also be predicted, which is more convenient.

[0050] The vehicle trajectory acquisition module is configured to acquire the vehicle trajectory of the new energy electric vehicle within a preset time period in the following manner: acquiring at least one vehicle trajectory of the new energy electric vehicle within a preset time period from a preset trajectory database, and the vehicle trajectory stored in the trajectory database is acquired in the following manner: determining whether a smart phone follows the new energy electric vehicle; in the case where the smart phone follows the new energy electric vehicle, storing the movement trajectory corresponding to the smart phone as the vehicle trajectory of the new energy electric vehicle in the trajectory database.

[0051] The vehicle trajectory acquisition module is configured to determine whether the smartphone is connected to the vehicle in the following manner: when the smartphone is connected to the vehicle, determine that the smartphone follows the new energy electric vehicle and obtain the vehicle information to which the smartphone is connected; otherwise, determine that the smartphone does not follow the new energy electric vehicle.

[0052] The vehicle track includes at least one position coordinate information. The geographic environment information acquisition module is configured to acquire the geographic environment information according to the vehicle track in the following manner: using the position coordinate information corresponding to each acquired vehicle track to perform a table lookup operation in a preset geographic environment information table to obtain the geographic environment information corresponding to each vehicle track; the geographic environment information table stores the position coordinate information, the geographic environment information, and the corresponding relationship between the position coordinate information and the geographic environment information.

[0053] The driving behavior information acquisition module is configured to acquire the driving behavior information of the new energy electric vehicle within the preset time period according to the geographic environment information: using each geographic environment information to perform a table lookup operation in a preset driving behavior information table to obtain the driving behavior information corresponding to each geographic environment information; the driving behavior information table stores the geographic environment information, the driving behavior information, and the correspondence between the geographic environment information and the driving behavior information.

[0054] The prediction module is configured to predict the battery capacity attenuation of the new energy electric vehicle based on the driving behavior information in the following manner: obtaining a driving characteristic value corresponding to the driving behavior information, wherein the driving characteristic value is used to characterize the degree of deviation between the driving behavior and a preset standard behavior; and predicting the battery capacity attenuation of the new energy electric vehicle based on the driving characteristic value.

[0055] Combination Figure 3As shown, an embodiment of the present disclosure provides a device 300 for predicting the capacity decay of a new energy electric vehicle battery, including a processor (processor) 304 and a memory (memory) 301 storing program instructions. Optionally, the device may also include a communication interface (Communication Interface) 302 and a bus 303. Among them, the processor 304, the communication interface 302, and the memory 301 can communicate with each other through the bus 303. The communication interface 302 can be used for information transmission. The processor 304 can call the program instructions in the memory 301 to execute the method for predicting the capacity decay of a new energy electric vehicle battery of the above embodiment.

[0056] In addition, the logic instructions in the memory 301 described above can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0057] The memory 301 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 304 executes the function application and data processing by running the program instructions / modules stored in the memory 301, that is, the method for predicting the capacity decay of the battery of the new energy electric vehicle in the above embodiment is realized.

[0058] The memory 301 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 301 may include a high-speed random access memory and may also include a non-volatile memory.

[0059] Combination Figure 4 As shown, an embodiment of the present disclosure provides a smart phone 400, including: a smart phone body, and the above-mentioned device 200 for predicting the capacity decay of a new energy electric vehicle battery. The device 200 for predicting the capacity decay of a new energy electric vehicle battery is installed on the smart phone body. The installation relationship described here is not limited to placement inside a smart phone, but also includes installation connections with other components of the smart phone, including but not limited to physical connections, electrical connections, or signal transmission connections. It can be understood by those skilled in the art that the device 200 for predicting the capacity decay of a new energy electric vehicle battery can be adapted to a feasible smart phone body, thereby realizing other feasible embodiments.

[0060] Combination Figure 5As shown, an embodiment of the present disclosure provides a smart phone 400, including: a smart phone body, and the above-mentioned device 300 for predicting the capacity decay of a new energy electric vehicle battery. The device 300 for predicting the capacity decay of a new energy electric vehicle battery is installed on the smart phone body. The installation relationship described here is not limited to placement inside a smart phone, but also includes installation connections with other components of the smart phone, including but not limited to physical connections, electrical connections, or signal transmission connections. It can be understood by those skilled in the art that the device 300 for predicting the capacity decay of a new energy electric vehicle battery can be adapted to a feasible smart phone body, thereby realizing other feasible embodiments.

[0061] The smart phone provided by the embodiment of the present disclosure can be used to predict the battery capacity decay of new energy electric vehicles. Geographical environment information is obtained through the vehicle trajectory of the new energy electric vehicle within a preset time period, and then the driving behavior information of the new energy electric vehicle within the preset time period is obtained based on the geographical environment information, thereby predicting the battery capacity decay of the new energy electric vehicle. Since the driving behavior is taken into account through the geographical environment, it is more suitable for predicting the battery capacity decay of the new energy electric vehicle. In this case, this method of predicting the battery capacity decay of the new energy electric vehicle can be achieved through a smart phone. For those new energy electric vehicles that do not have the battery capacity prediction function, their battery capacity decay can also be predicted, which is more convenient.

[0062] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0063] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0064] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0065] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0066] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for predicting the capacity attenuation of a new energy electric vehicle battery, applied to a smart phone, characterized in that: The method comprises: Obtain the vehicle trajectory of the new energy electric vehicle within a preset time period; Acquiring geographic environment information according to the vehicle trajectory; Acquiring driving behavior information of the new energy electric vehicle within the preset time period according to the geographic environment information; The battery capacity attenuation of the new energy electric vehicle is predicted based on the driving behavior information.

2. The method according to claim 1, characterized in that Get the vehicle trajectory of the new energy electric vehicle within the preset time period, including: At least one vehicle trajectory of the new energy electric vehicle within a preset time period is obtained from a preset trajectory database, wherein the vehicle trajectory stored in the trajectory database is obtained in the following manner: Determine whether the smartphone moves with the new energy electric vehicle; When the smart phone moves along with the new energy electric vehicle, the movement trajectory corresponding to the smart phone is stored in the trajectory database as the vehicle trajectory of the new energy electric vehicle.

3. The method according to claim 2, characterized in that Determine whether the smartphone is following the new energy electric vehicle, including: Determine if the smartphone is connected to the vehicle; When the smartphone is connected to the vehicle, it is determined that the smartphone moves following the new energy electric vehicle, and the vehicle information to which the smartphone is connected is obtained; otherwise, it is determined that the smartphone does not move following the new energy electric vehicle.

4. The method according to claim 1, characterized in that The vehicle trajectory includes at least one position coordinate information; Acquiring geographic environment information according to the vehicle trajectory, including: Using the position coordinate information corresponding to each acquired vehicle track to perform a table lookup operation in a preset geographic environment information table, the geographic environment information corresponding to each vehicle track is obtained; The geographic environment information table stores location coordinate information, geographic environment information, and the corresponding relationship between the location coordinate information and the geographic environment information.

5. The method according to claim 1, characterized in that Acquiring driving behavior information of the new energy electric vehicle within the preset time period according to the geographic environment information includes: Each geographical environment information is used to perform a table lookup operation in a preset driving behavior information table to obtain driving behavior information corresponding to each geographical environment information; the driving behavior information table stores geographical environment information, driving behavior information, and the corresponding relationship between the geographical environment information and the driving behavior information.

6. The method according to claim 1, characterized in that Predicting the battery capacity attenuation of the new energy electric vehicle according to the driving behavior information includes: Acquiring a driving characteristic value corresponding to the driving behavior information, wherein the driving characteristic value is used to characterize a degree of deviation between the driving behavior and a preset standard behavior; Predict the battery capacity attenuation of new energy electric vehicles based on driving characteristic values.

7. A device for predicting the capacity decay of a new energy electric vehicle battery, applied to a smart phone, characterized in that: The device comprises: A vehicle trajectory acquisition module is configured to acquire the vehicle trajectory of the new energy electric vehicle within a preset time period; A geographic environment information acquisition module, configured to acquire geographic environment information according to the vehicle trajectory; A driving behavior information acquisition module is configured to acquire the driving behavior information of the new energy electric vehicle within the preset time period according to the geographical environment information; The prediction module is configured to predict the battery capacity attenuation of the new energy electric vehicle based on the driving behavior information.

8. A device for predicting the capacity decay of a new energy electric vehicle battery, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for predicting the capacity decay of a new energy electric vehicle battery as described in any one of claims 1 to 6 when running the program instructions.

9. A smart phone, characterized in that: include: Smartphone body; The device for predicting the capacity attenuation of a new energy electric vehicle battery as described in claim 7 or 8 is installed on the smartphone body.

Images