CN115097336A - Battery state of charge value estimation system, method, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a battery state of charge value estimation system, a method, electronic equipment and a medium, belonging to the technical field of battery management and comprising the following steps: the system comprises a vehicle terminal unit, an edge calculation unit and a cloud calculation unit. The patent provides a battery state of charge value estimation system, method, electronic equipment and medium, combines vehicle terminal unit, edge computing unit and cloud computing platform, utilizes the high real-time edge computing transmission of vehicle terminal unit low time delay, the powerful computing power of cloud computing to realize terminal, edge end, high in the clouds collaborative work, improves battery state estimation accuracy.
Description
Technical Field
The invention discloses a battery state of charge value estimation system, a battery state of charge value estimation method, electronic equipment and a medium, and belongs to the technical field of battery management.
Background
The State Of Charge (SOC) Of a battery is one Of core technologies Of a BMS, and the accuracy Of the SOC directly affects the driving mileage Of a vehicle and the safety Of charging and discharging the battery. How to ensure the high-precision estimation of the SOC of the full life cycle of the battery is always the focus of the core technology and industry attention of the BMS. BMS can produce a large amount of data every day, if adopt traditional cloud to calculate and vehicle terminal unit processing model hardly satisfy the demand that a large amount of data real-time transmission and real-time processing, in order to reduce data memory capacity and network load, traditional big data platform carries out BMS data transmission according to 10S intervals usually, and this time interval can receive to the SOH estimation of slow change, but has great influence to the higher SOC estimation precision of data real-time requirement.
The mainstream SOC algorithm in the industry mainly comprises an ampere-hour integral method, an open-circuit voltage method, a Kalman filtering method based on a battery model and the like, the ampere-hour integral has accumulated errors, the application of the open-circuit voltage method is limited by working conditions, the precision of the filtering algorithm based on the battery model is limited by the battery model and parameters, and a cloud SOC estimation method based on a large data platform is not limited by the battery model and is widely researched and applied at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a battery state of charge value estimation system, a battery state of charge value estimation method, electronic equipment and a battery state of charge value estimation medium.
The technical scheme of the invention is as follows:
according to a first aspect of embodiments of the present invention, there is provided a battery state of charge estimation system, including: the system comprises a vehicle terminal unit, an edge calculation unit and a cloud calculation unit;
the vehicle terminal unit is used for acquiring real-time battery data, estimating the state of charge of the battery in real time to obtain a real-time estimated battery state of charge value of the terminal, and sending the real-time battery data to the edge computing unit;
the edge computing unit is used for acquiring the real-time battery data sent by the vehicle terminal unit, estimating the state of charge of the battery in real time by adopting a neural network model to obtain an edge real-time estimated battery state of charge value, sending the edge real-time estimated battery state of charge value to the vehicle terminal unit, and sending the real-time battery data sent by the vehicle terminal unit to the cloud computing unit;
the cloud computing unit is used for acquiring battery real-time data improvement data conditions sent by the vehicle terminal unit and sent by the edge computing unit, respectively training the multi-neural-network model, and sending updated neural-network model algorithm data to the edge computing unit;
the vehicle terminal unit is further used for obtaining an edge real-time estimated battery state of charge value and fusing the edge real-time estimated battery state of charge value with the terminal real-time estimated battery state of charge value to obtain an accurate battery state of charge estimated value, and the edge calculation unit obtains updated neural network model algorithm data to update the existing neural network model.
Preferably, the vehicle terminal unit is further configured to obtain a current longitude and latitude of the vehicle, determine an edge computing unit matched with the vehicle terminal unit in the communication network according to the current longitude and latitude of the vehicle, and send matching connection request data to the edge computing unit, the edge computing unit is further configured to perform data confirmation and security verification after receiving the matching connection request data sent by the vehicle terminal unit, and feed back confirmation information to the vehicle terminal unit after the data confirmation and security verification is passed, and the vehicle terminal unit is further configured to obtain the confirmation information sent by the edge computing unit.
Preferably, the edge computing unit is further configured to determine, clean and filter the acquired battery real-time data sent by the vehicle terminal unit, and store the battery real-time data after cleaning and filtering at the edge.
Preferably, the cloud computing unit is further configured to request corresponding data from the edge computing unit according to a preset data dimension, the edge computing unit is further configured to receive and judge in real time data request information sent by the cloud computing unit to judge whether the locally stored data meets a data request of the cloud computing unit, and if the data request information meets the data requirement of the cloud computing unit, corresponding data meeting the requirement is selected according to the requirement and is sent to the cloud computing unit to improve the cloud data working condition.
Preferably, the cloud computing unit is further configured to monitor a cloud network load condition in real time, and perform time-sharing data transmission according to the network load condition.
According to a second aspect of the embodiments of the present invention, there is provided a battery state of charge value estimation method, including:
acquiring the battery real-time data and sending the battery real-time data to an edge computing unit;
the battery real-time data carries out real-time estimation on the battery charge state to obtain a terminal real-time estimated battery charge state value;
and acquiring an edge real-time estimation battery charge state value, and performing fusion processing according to the terminal real-time estimation battery charge state value to obtain an accurate battery charge state estimation value.
Preferably, the obtaining the edge real-time estimated battery state of charge value and performing fusion processing according to the terminal real-time estimated battery state of charge value to obtain an accurate battery state of charge estimated value includes:
acquiring an edge real-time estimation battery state of charge value and determining a confidence coefficient of the edge real-time estimation battery state of charge value;
determining a confidence coefficient of the battery state of charge value estimated by the terminal in real time according to the battery state of charge value estimated by the terminal in real time;
and determining an accurate estimated value of the battery state of charge according to the edge real-time estimation battery state of charge value confidence coefficient, the terminal real-time estimation battery state of charge value confidence coefficient, the edge real-time estimation battery state of charge value and the terminal real-time estimation battery state of charge value.
Preferably, the determining an accurate battery state of charge estimation value according to the edge real-time estimation battery state of charge value confidence coefficient, the terminal real-time estimation battery state of charge value confidence coefficient, the edge real-time estimation battery state of charge value and the terminal real-time estimation battery state of charge value includes:
determining the weight of the edge real-time estimation battery SOC value according to the edge real-time estimation battery SOC value confidence coefficient and the terminal real-time estimation battery SOC value confidence coefficient;
determining the weight of the battery state of charge value estimated by the terminal in real time according to the weight of the battery state of charge value estimated by the edge in real time;
the edge real-time estimation battery SOC value weight, the terminal real-time estimation battery SOC value weight, the edge real-time estimation battery SOC value and the terminal real-time estimation battery SOC value determine an accurate battery SOC estimation value according to a formula (1):
S extract (Chinese character of 'Jing') ＝S Edge ×W Edge +S Seed of a species of rice ×W Terminal (1)
Wherein: s Extract (Chinese character of 'Jing') For accurate battery state of charge estimation, S Edge Real-time estimation of battery state of charge, W, for edges Edge Estimating battery state of charge value weight for edges in real time, S Final (a Chinese character of 'gan') Estimating a battery state of charge, W, in real time for a terminal Final (a Chinese character of 'gan') Estimating battery state of charge values in real time for a terminalAnd (4) weighting.
According to a third aspect of embodiments of the present invention, there is provided a terminal, including:
one or more processors;
a memory for storing the one or more processor-executable instructions;
wherein the one or more processors are configured to:
the method of the first aspect of the embodiments of the present invention is performed.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium, wherein instructions, when executed by a processor of a terminal, enable the terminal to perform the method according to the first aspect of embodiments of the present invention.
According to a fifth aspect of embodiments of the present invention, there is provided an application program product, which, when running on a terminal, causes the terminal to perform the method of the first aspect of embodiments of the present invention.
The invention has the beneficial effects that:
the patent provides a battery state of charge value estimation system, method, electronic equipment and medium, combines vehicle terminal unit, edge computing unit and cloud computing platform, utilizes the high real-time edge computing transmission of vehicle terminal unit low time delay, the powerful computing power of cloud computing to realize terminal, edge end, high in the clouds collaborative work, improves battery state estimation accuracy.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
FIG. 1 is a block diagram illustrating a battery state of charge estimation system according to an exemplary embodiment;
FIG. 2 is a comprehensive data interaction diagram illustrating a battery state of charge estimation system in accordance with an exemplary embodiment;
FIG. 3 is a flow chart illustrating a method of estimating a state of charge value of a battery according to an exemplary embodiment;
fig. 4 is a schematic block diagram of a terminal structure shown in accordance with an example embodiment.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example one
Fig. 1-2 are block diagrams illustrating a battery state of charge estimation system according to an exemplary embodiment, including: the system comprises a vehicle terminal unit, an edge calculation unit and a cloud calculation unit;
the vehicle terminal unit is used for acquiring real-time battery data, estimating the state of charge of the battery in real time to obtain a real-time estimated battery state of charge value of the terminal, and sending the real-time battery data to the edge computing unit;
the edge calculation unit is used for acquiring the real-time battery data sent by the vehicle terminal unit, estimating the battery charge state in real time by adopting a neural network model to obtain an edge real-time estimated battery charge state value, sending the edge real-time estimated battery charge state value to the vehicle terminal unit, and sending the real-time battery data sent by the vehicle terminal unit to the cloud calculation unit;
the cloud computing unit can be used for respectively carrying out neural network model training on the batteries of different types aiming at the characteristic difference of the batteries of different types, the batteries of different types can adopt different neural network training model algorithms, different characteristic parameter sets are set, and training is respectively carried out, so that the model training precision is improved. The cloud computing unit obtains battery real-time data sent by the vehicle terminal unit and sent by the edge computing unit, perfects data working conditions, respectively trains the multi-neural-network model, and sends updated neural-network model algorithm data to the edge computing unit.
The vehicle terminal unit is also used for acquiring an edge real-time estimated battery state-of-charge value and fusing the edge real-time estimated battery state-of-charge value with the terminal real-time estimated battery state-of-charge value to obtain an accurate battery state-of-charge estimated value, and the edge computing unit acquires updated neural network model algorithm data to update the existing neural network model.
Before the vehicle terminal unit sends the battery real-time data to the edge computing unit, the vehicle terminal unit is further used for obtaining the current longitude and latitude of the vehicle, judging the edge computing unit matched with the vehicle terminal unit in the communication network according to the current longitude and latitude of the vehicle and sending matching connection request data to the edge computing unit, so that the edge computing unit is further used for receiving the matching connection request data sent by the vehicle terminal unit and then carrying out data confirmation and safety verification, and after the data confirmation and safety verification are passed, the vehicle terminal unit feeds back confirmation information to the vehicle terminal unit, and therefore the vehicle terminal unit is further used for obtaining the confirmation information sent by the edge computing unit.
Before the edge computing unit sends the battery real-time data sent by the vehicle terminal unit to the cloud computing unit, the problems of data loss, data errors and the like easily affect the model training precision due to the fact that data asynchronization exists in big data, and the edge computing unit is further used for judging, cleaning and filtering the acquired battery real-time data sent by the vehicle terminal unit, eliminating invalid data so as to reduce occupation of storage space of the edge computing unit, and storing the cleaned and filtered battery real-time data at an edge end.
Then the cloud computing unit requests corresponding data to the edge computing unit according to preset data dimensionality, if the data are requested in a communication mode according to dimensionality of different vehicle mileage, different areas corresponding to a certain mileage, different altitudes, different environmental temperatures, different battery temperatures and the like and edge end modules in advance, the edge computing unit is allowed to feed back the corresponding data according to requirements, the cloud data amount and the operation amount can be reduced by requesting the corresponding data according to the preset dimensionality, meanwhile, different types of data can be obtained quickly to conduct model training, the model precision is improved, and the operation efficiency is improved. The edge computing unit receives and judges data request information sent by the cloud computing unit in real time to judge whether locally stored data meet the data request of the cloud computing unit, and if the locally stored data meet the data request of the cloud computing unit, corresponding battery real-time data which meet requirements and are obtained after cleaning and filtering are selected according to requirements and sent to the cloud computing unit to be used for improving the working condition of cloud data.
However, the cloud computing unit is also used for monitoring the load condition of the cloud network in real time and performing time-sharing data transmission according to the network load condition. The network load is reduced, the communication reliability is improved, the data effectiveness is guaranteed, the cloud computing unit does not request the required data when judging that the current network load is higher, the current network load is lower than a preset value when judging that the current network load is lower than the preset value, and the data request can be started.
Example two
Fig. 3 is a flowchart illustrating a battery state of charge value estimation method for use in a terminal according to an exemplary embodiment, the method comprising the steps of:
102, carrying out real-time estimation on the battery charge state by the battery real-time data to obtain a terminal real-time estimated battery charge state value;
103, acquiring an edge real-time estimated battery state of charge value, and performing fusion processing according to the terminal real-time estimated battery state of charge value to obtain an accurate battery state of charge estimated value, wherein the specific contents comprise:
acquiring a marginal real-time estimation battery charge state value and determining a marginal real-time estimation battery charge state value confidence coefficient, wherein the marginal real-time estimation battery charge state value confidence coefficient range is 0 to 1, the marginal real-time estimation battery charge state value confidence coefficient is related to historical data quantity, vehicle type data quantity of different regions is related, and the confidence coefficient is low when the historical quantity is small; the data quantity of the vehicle models in different areas is small, and the confidence coefficient is low.
Determining a confidence coefficient of a terminal real-time estimated battery state of charge value according to the terminal real-time estimated battery state of charge value, wherein the confidence coefficient of the terminal real-time estimated battery state of charge value is related to accumulated charging and discharging electric quantity after battery open-circuit voltage correction or full-power correction, the confidence coefficient of the terminal real-time estimated battery state of charge value after battery open-circuit voltage correction or full-power correction is 1, and the confidence coefficient of the terminal real-time estimated battery state of charge value gradually decreases as the accumulated charging and discharging electric quantity after correction increases; meanwhile, the confidence coefficient of the real-time estimated battery state of charge value of the terminal is related to the range section and the temperature of the battery state of charge, taking a ternary lithium ion battery as an example, the accuracy of the estimation model of the low state of charge section is low, and the confidence coefficient of the real-time estimated battery state of charge value of the terminal is reduced; the confidence coefficient of the low-temperature state of charge estimation model for estimating the state of charge value of the battery in real time at a low precision is reduced.
Determining an accurate battery SOC estimation value according to the edge real-time estimation battery SOC value confidence coefficient, the terminal real-time estimation battery SOC value confidence coefficient, the edge real-time estimation battery SOC value and the terminal real-time estimation battery SOC value, wherein the specific contents are as follows:
the edge real-time estimation battery state of charge value confidence coefficient and the terminal real-time estimation battery state of charge value confidence coefficient determine the edge real-time estimation battery state of charge value weight according to the formula (1):
W edge ＝C Edge /(C Edge +C Final (a Chinese character of 'gan') ) (1)
Wherein, W Edge Estimating battery state of charge value weight, C, in real time for an edge Edge Estimating a battery state of charge value confidence coefficient, C, in real time for an edge In And estimating a confidence coefficient of the state of charge value of the battery in real time for the terminal.
The edge real-time estimation battery state of charge value weight determines the terminal real-time estimation battery state of charge value weight according to the formula (2):
W final (a Chinese character of 'gan') ＝1-W Edge (2)
Wherein, W Terminal And estimating the weight of the state of charge value of the battery in real time for the terminal.
The method comprises the following steps of edge real-time estimation of battery state of charge value weight, terminal real-time estimation of battery state of charge value weight, edge real-time estimation of battery state of charge value and terminal real-time estimation of battery state of charge value, and determination of accurate battery state of charge estimation value according to a formula (1):
S extract of Chinese medicinal materials ＝S Edge ×W Edge +S Seed of a plant ×W Terminal (3)
Wherein: s. the Extract (Chinese character of 'Jing') For accurate battery state of charge estimation, S Edge Estimating battery state of charge, S, in real time for an edge Terminal And estimating the state of charge value of the battery for the terminal in real time.
EXAMPLE III
Fig. 4 is a block diagram of a terminal according to an embodiment of the present application, where the terminal may be the terminal in the foregoing embodiment. The terminal 200 may be a portable mobile terminal such as: smart phones, tablet computers. The terminal 200 may also be referred to by other names such as user equipment, portable terminal, etc.
Generally, the terminal 200 includes: a processor 201 and a memory 202.
The processor 201 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 201 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 201 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 201 may be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, the processor 201 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
In some embodiments, the terminal 200 may further optionally include: a peripheral interface 203 and at least one peripheral. Specifically, the peripheral device includes: at least one of radio frequency circuitry 204, touch display screen 205, camera 206, audio circuitry 207, positioning components 208, and power supply 209.
The peripheral interface 203 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 201 and the memory 202. In some embodiments, the processor 201, memory 202, and peripheral interface 203 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 201, the memory 202, and the peripheral device interface 203 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 204 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 204 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 204 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 204 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 204 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, generations of mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 204 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The touch display screen 205 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. The touch display screen 205 also has the ability to capture touch signals on or over the surface of the touch display screen 205. The touch signal may be input to the processor 201 as a control signal for processing. The touch screen display 205 is used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the touch display screen 205 may be one, providing the front panel of the terminal 200; in other embodiments, the touch display screen 205 may be at least two, respectively disposed on different surfaces of the terminal 200 or in a folded design; in still other embodiments, the touch display 205 may be a flexible display disposed on a curved surface or on a folded surface of the terminal 200. Even more, the touch screen display 205 can be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The touch Display screen 205 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The audio circuit 207 is used to provide an audio interface between the user and the terminal 200. The audio circuitry 207 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals into the processor 201 for processing or inputting the electric signals into the radio frequency circuit 204 to realize voice communication. The microphones may be plural and respectively provided at different portions of the terminal 200 for the purpose of stereo sound collection or noise reduction. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is then used to convert the electrical signals from the processor 201 or the radio frequency circuitry 204 into sound waves. The loudspeaker can be a traditional film loudspeaker and can also be a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, and converting the electric signal into a sound wave inaudible to the human being to measure a distance. In some embodiments, the audio circuitry 207 may also include a headphone jack.
The positioning component 208 is used to locate the current geographic Location of the terminal 200 to implement navigation or LBS (Location Based Service). The Positioning component 208 may be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.
The power supply 209 is used to supply power to the various components in the terminal 200. The power supply 209 may be alternating current, direct current, disposable or rechargeable. When the power supply 209 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
Those skilled in the art will appreciate that the configuration shown in fig. 4 is not intended to be limiting of terminal 200, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
Example four
In an exemplary embodiment, a computer readable storage medium is further provided, on which a computer program is stored, which when executed by a processor, implements a battery state of charge value estimation method as provided by all inventive embodiments of the present application.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
EXAMPLE five
In an exemplary embodiment, an application program product is also provided, which includes one or more instructions executable by the processor 201 of the apparatus to perform the battery soc estimation method.
Although the embodiments of the present invention have been disclosed above, it is not limited to the applications listed in the description and the embodiments. It can be applied to various fields in which the present invention is suitable. Additional modifications will readily occur to those skilled in the art. It is therefore intended that the invention not be limited to the exact details and illustrations described and illustrated herein, but fall within the scope of the appended claims and equivalents thereof.
Claims (10)
1. A battery state of charge estimation system, comprising: the system comprises a vehicle terminal unit, an edge calculation unit and a cloud calculation unit;
the vehicle terminal unit is used for acquiring real-time battery data, estimating the state of charge of the battery in real time to obtain a real-time estimated battery state of charge value of the terminal, and sending the real-time battery data to the edge computing unit;
the edge computing unit is used for acquiring the battery real-time data sent by the vehicle terminal unit, estimating the battery charge state in real time by adopting a neural network model to obtain an edge real-time estimated battery charge state value, sending the edge real-time estimated battery charge state value to the vehicle terminal unit, and sending the battery real-time data sent by the vehicle terminal unit to the cloud computing unit;
the cloud computing unit is used for acquiring battery real-time data sent by the vehicle terminal unit and sent by the edge computing unit, perfecting data working conditions, respectively training a multi-neural-network model, and sending updated neural-network model algorithm data to the edge computing unit;
the vehicle terminal unit is further used for obtaining an edge real-time estimated battery state of charge value and fusing the edge real-time estimated battery state of charge value with the terminal real-time estimated battery state of charge value to obtain an accurate battery state of charge estimated value, and the edge calculation unit obtains updated neural network model algorithm data to update the existing neural network model.
2. The system according to claim 1, wherein the vehicle terminal unit is further configured to obtain a current longitude and latitude of the vehicle, determine an edge computing unit matched with the vehicle terminal unit in the communication network according to the current longitude and latitude of the vehicle, and send matching connection request data to the edge computing unit, the edge computing unit is further configured to perform data confirmation and security check after receiving the matching connection request data sent by the vehicle terminal unit, and feed back confirmation information to the vehicle terminal unit after the data confirmation and security check is passed, and the vehicle terminal unit is further configured to obtain confirmation information sent by the edge computing unit.
3. The system of claim 2, wherein the edge computing unit is further configured to determine and clean and filter the acquired real-time battery data sent by the vehicle terminal unit, and store the cleaned and filtered real-time battery data at the edge.
4. The battery state-of-charge value estimation system of claim 3, wherein the cloud computing unit is further configured to request corresponding data from the edge computing unit according to a preset data dimension, the edge computing unit is further configured to receive and judge data request information sent by the cloud computing unit in real time to determine whether the locally stored data meet the data request of the cloud computing unit, and if the data request of the cloud computing unit is met, the corresponding data meeting the requirement is selected according to the requirement and is transmitted to the cloud computing unit for completing the cloud data working condition.
5. The battery soc estimation system of claim 4, wherein the cloud computing unit is further configured to monitor a cloud network load condition in real time, and perform time-sharing data transmission according to the network load condition.
6. A method of estimating a state of charge value of a battery, comprising:
acquiring the battery real-time data and sending the battery real-time data to an edge computing unit;
the battery real-time data carries out real-time estimation on the battery charge state to obtain a terminal real-time estimated battery charge state value;
and acquiring an edge real-time estimation battery state-of-charge value, and performing fusion processing according to the terminal real-time estimation battery state-of-charge value to obtain an accurate battery state-of-charge estimation value.
7. The method of claim 6, wherein the obtaining the edge real-time estimated battery soc value and performing the fusion process according to the terminal real-time estimated battery soc value to obtain the accurate battery soc estimation value comprises:
acquiring an edge real-time estimation battery state of charge value and determining a confidence coefficient of the edge real-time estimation battery state of charge value;
determining a confidence coefficient of the real-time estimated battery state of charge value of the terminal according to the real-time estimated battery state of charge value of the terminal;
and determining an accurate estimated value of the battery state of charge according to the edge real-time estimation battery state of charge value confidence coefficient, the terminal real-time estimation battery state of charge value confidence coefficient, the edge real-time estimation battery state of charge value and the terminal real-time estimation battery state of charge value.
8. The method of claim 7, wherein determining an accurate state of charge estimate based on the edge real-time estimated battery state of charge value confidence coefficient, the terminal real-time estimated battery state of charge value confidence coefficient, the edge real-time estimated battery state of charge value, and the terminal real-time estimated battery state of charge value comprises:
determining the weight of the edge real-time estimation battery SOC value according to the edge real-time estimation battery SOC value confidence coefficient and the terminal real-time estimation battery SOC value confidence coefficient;
determining the weight of the battery state of charge value estimated by the terminal in real time according to the weight of the battery state of charge value estimated by the edge in real time;
the edge real-time estimation battery SOC value weight, the terminal real-time estimation battery SOC value weight, the edge real-time estimation battery SOC value and the terminal real-time estimation battery SOC value determine an accurate battery SOC estimation value according to a formula (1):
S extract of Chinese medicinal materials ＝S Edge ×W Edge +S Seed of a plant ×W Final (a Chinese character of 'gan') (1)
Wherein: s Extract of Chinese medicinal materials For accurate battery state of charge estimation, S Edge Estimating battery state of charge, W, in real time for an edge Edge Estimating battery state of charge value weight, S, in real time for an edge Final (a Chinese character of 'gan') Estimating a battery state of charge, W, in real time for a terminal Final (a Chinese character of 'gan') And estimating the weight of the state of charge value of the battery in real time for the terminal.
9. An electronic device comprising a memory and a processor, the memory being coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the battery state of charge value estimation method of any of claims 6-8.
10. A computer-readable storage medium, in which a computer program is stored, which when executed, implements the battery state of charge value estimation method according to any one of claims 6 to 8.
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