CN110370983B - Battery management system for electric automobile based on 5G mobile communication - Google Patents
Battery management system for electric automobile based on 5G mobile communication Download PDFInfo
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- CN110370983B CN110370983B CN201910530973.9A CN201910530973A CN110370983B CN 110370983 B CN110370983 B CN 110370983B CN 201910530973 A CN201910530973 A CN 201910530973A CN 110370983 B CN110370983 B CN 110370983B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
Abstract
The invention discloses a battery management system for an electric vehicle based on 5G mobile communication, relates to the technical field of batteries, and mainly aims to ensure the high capacity, real-time performance and reliability of data communication when the battery management system transmits data with a cloud terminal. The system comprises: the system comprises a battery control system arranged at the electric automobile end, a 5G communication module and a cloud computing system arranged at the cloud end. The battery control system is used for detecting the running state parameters of the battery; the 5G communication module is used for modulating the running state parameters and sending the running state parameters to the cloud computing system; the cloud computing system is used for training a battery management model of the cloud according to the running state parameters and transmitting the battery management model back to the 5G communication module; the 5G communication module is also used for demodulating the updated model parameters and sending the model parameters to the battery control system; and the battery control system is also used for updating a local battery management model by using the updated model parameters and calculating the control state parameters of the battery so as to control the running state of the battery.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery management system for an electric automobile based on 5G mobile communication.
Background
The battery management system is used as the core of the energy management of the electric automobile, and estimates the running state, the remaining mileage, the safety state and the like of the battery by collecting the working information of the battery in the electric automobile, so as to control the charging and discharging of the battery, balance the battery, heat the battery and manage the safety of the battery. The battery management system can prolong the service life of the battery and ensure the safe operation of the battery.
At present, most of battery management systems for electric vehicles carry out battery management locally on the basis of embedded systems, and are limited by the operation resources and the operation speed of the embedded systems, the battery management systems mostly adopt linear models such as a voltage estimation algorithm, an ampere-hour integration method and a Kalman filtering method to estimate the residual capacity, the safety state and the like of a battery, but because the charging and discharging processes of the battery are complex chemical reactions, the problems of poor estimation precision, error accumulation, poor stability and the like can be caused by adopting simple linear models.
With the rapid development of technologies such as wireless communication and cloud computing, at present, electric vehicles can transmit a large amount of computing work to a cloud computing platform for processing through a wireless communication technology, and feed back a processed result to the electric vehicle through the wireless communication technology, and the electric vehicle performs related battery control and management according to information fed back by the platform. However, in the data transmission between the electric vehicle and the cloud computing platform, the existing wireless communication technologies such as 4G, wifi have the disadvantages of slow transmission rate, poor real-time performance, insufficient transmission distance and the like, and cannot meet the use requirement of real-time data interaction between the electric vehicle and the cloud computing platform.
Disclosure of Invention
In view of this, the present invention provides a battery management system for an electric vehicle based on 5G mobile communication, and mainly aims to ensure high capacity, real-time performance and reliability of data communication when the battery management system performs data transmission with a cloud.
According to an aspect of the present invention, there is provided a battery management system for an electric vehicle based on 5G mobile communication, including:
a battery control system arranged at the electric automobile end, a 5G communication module and a cloud computing system arranged at the cloud end,
the battery control system is used for detecting the running state parameters of the battery;
the 5G communication module is used for modulating the running state parameters and sending the running state parameters to the cloud computing system;
the cloud computing system is used for training a battery management model at the cloud end according to the running state parameters, obtaining updated model parameters and transmitting the updated model parameters back to the 5G communication module;
the 5G communication module is also used for demodulating the updated model parameters and sending the demodulated model parameters to the battery control system;
the battery control system is further configured to update a local battery management model by using the updated model parameters, and calculate control state parameters of the battery according to the operation state parameters and the updated local battery management model to control the operation state of the battery.
The invention provides a battery management system for an electric automobile based on 5G mobile communication, compared with the prior art that data interaction between the battery management system and a cloud end is realized through communication modes such as 4G, WIFI and the like, the battery management system is provided with a battery control system and a 5G communication module at an electric automobile end, a cloud computing system is arranged at the cloud end, and the battery control system is used for detecting running state parameters of a battery; the 5G communication module is used for modulating the running state parameters and sending the running state parameters to the cloud computing system; the cloud computing system is used for training a battery management model at the cloud end according to the running state parameters, obtaining updated model parameters and transmitting the updated model parameters back to the 5G communication module; the 5G communication module is also used for demodulating the updated model parameters and sending the demodulated model parameters to the battery control system; the battery control system is further configured to update a local battery management model by using the updated model parameters, and calculate control state parameters of the battery according to the operation state parameters and the updated local battery management model to control the operation state of the battery. According to the invention, the 5G communication technology is utilized to realize data interaction between the battery management system and the cloud, so that the high capacity, real-time performance and reliability of data communication can be ensured, and the safe, stable and reliable operation of the electric automobile can be ensured.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic structural diagram of a battery management system for an electric vehicle based on 5G mobile communication according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating an overall framework of a battery management system for an electric vehicle based on 5G mobile communication according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another battery management system for an electric vehicle based on 5G mobile communication according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a battery control system provided by an embodiment of the invention;
fig. 5 is a schematic diagram of a 5G communication module according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a cloud computing system provided by an embodiment of the invention;
FIG. 7 is a schematic diagram illustrating a computation process of a deep learning training model according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As described in the background art, at present, an electric vehicle may transmit a large amount of computing work required by a battery management system to a cloud computing platform for processing through a wireless communication technology, and feed back a processed result to the electric vehicle through the wireless communication technology, and the electric vehicle performs related battery control and management according to information fed back by the platform. However, in the data transmission between the electric vehicle and the cloud computing platform, the existing wireless communication technologies such as 4G, wifi have the defects of slow transmission rate, poor real-time performance, insufficient transmission distance and the like, and the use requirements of the internet of vehicles cannot be met.
In order to solve the above problem, an embodiment of the present invention provides a battery management system for an electric vehicle based on 5G mobile communication, as shown in fig. 1, the system includes: the system comprises a battery control system 11 arranged at the electric automobile end, a 5G communication module 12 and a cloud computing system 13 arranged at the cloud end.
The battery control system 11 may be configured to detect an operating state parameter of the battery, and specifically may include a cell voltage, a total cell voltage, a cell charging/discharging current, a cell temperature, and a cell insulation resistance.
The battery control system 11 may be further configured to update a local battery management model by using the updated model parameter, and calculate a control state parameter of the battery according to the operation state parameter and the updated local battery management model, so as to control the operation state of the battery.
The 5G communication module 12 may be configured to modulate the operation state parameter and send the operation state parameter to the cloud computing system 13.
The 5G communication module 12 may be further configured to demodulate the updated model parameter and send the demodulated model parameter to the battery control system 11.
The cloud computing system 13 may be configured to train a battery management model at the cloud according to the operating state parameters, obtain updated model parameters, and transmit the updated model parameters back to the 5G communication module 12, as shown in fig. 2.
Further, another battery management system for an electric vehicle based on 5G mobile communication is provided in an embodiment of the present invention, as shown in fig. 3, the system includes: the system comprises a battery control system 21 arranged at the electric automobile end, a 5G communication module 22 and a cloud computing system 23 arranged at the cloud end.
The battery control system may specifically include: a battery detection control module 212 and a battery model execution module 211. As shown in fig. 3, a battery control system schematic is provided.
The battery detection control module 212 may be specifically configured to detect an operating state parameter of a battery, and upload the operating state parameter to the battery model execution module 211.
The battery detection control module 212 may be further configured to receive the control state parameter returned by the battery model executing module 211, so as to control the operation state of the battery.
The battery detection control module 212 may specifically include: a battery detection unit 2122 and a battery control unit 2121.
The battery detection unit 2122 may be specifically configured to detect an operation state parameter of the battery, where the operation state parameter includes a cell voltage, a total battery voltage, a battery charging/discharging current, a battery temperature, and a battery insulation resistance.
It should be noted that, the logical relationship between the cell voltage of the battery, the battery temperature and the control state parameter may be: taking the logic relationship between the cell voltage and the battery temperature and the control state parameter SOC as an example, the cell voltage may be an appearance of the SOC, and if the SOC increases, the cell voltage of the battery also increases. Similarly, the temperature of the battery and the charge-discharge state of the battery also affect the value of the SOC, e.g., the SOC increases as the temperature increases and decreases as the temperature decreases; the SOC increases when the battery is in a charged state and decreases when the battery is in a discharged state.
The battery detection unit 2122 may specifically include: a battery measurement unit 21221 and a high voltage unit 21222.
The battery measurement unit 21221 may specifically include a cell voltage detection circuit 212211, a battery temperature detection circuit 212212, a battery equalization circuit 212213, and a first control circuit 212214.
The first control circuit 212214 may be configured to convert the cell voltage detected by the cell voltage detection circuit 212211 and the cell temperature detected by the cell temperature detection circuit 212212, and then upload the converted cell voltage and the cell temperature to the cell control unit 2121. The conversion processing mode may specifically be: the communication protocol analysis or Analog-to-Digital conversion (AD conversion) may specifically include: SPI communication protocol, IIC communication protocol, etc. The process of uploading to the battery control unit 2121 may be performed in a Controller Area Network (CAN) communication manner.
The battery equalization circuit 212213 may be configured to perform equalization discharge on the battery after receiving an equalization command returned by the battery control unit 2121.
The high voltage unit 21222 may specifically include a total voltage detection circuit 212221, an insulation resistance measurement circuit 212222, a charge/discharge current detection circuit 212223, and a second control circuit 212224.
The total voltage detection circuit 212221 can be used to collect the total voltage of the battery by means of resistance voltage division and insulation operational amplifier.
The insulation resistance measurement circuit 212222 can be used for respectively collecting insulation resistance between the total positive and negative of the battery and the ground of the automobile through double high-voltage insulation MOS switches.
The charging and discharging current detecting circuit 212223 can be used for measuring the charging and discharging currents of the battery through the dual-range hall sensor.
The second control circuit 212224 may be configured to convert the total battery voltage, the insulation resistance, and the charging/discharging current, and then transmit the converted voltage, the insulation resistance, and the charging/discharging current to the battery control unit 2121. The uploading to the battery control unit 2121 may be performed in a communication manner of a CAN.
The battery control unit 2121 may be specifically configured to control the operation state of the battery by using the control state parameter, where the control state parameter includes a battery remaining capacity, a battery health state, a battery remaining power, and a battery remaining capacity.
The battery control unit 2121 may be further configured to prompt a user of the electric vehicle in advance through the battery management interface when the remaining battery power is insufficient, and perform path planning according to a mileage input by the user through the battery management interface and the control state parameter. The battery control unit 2121 may also be used to turn off some devices in the electric vehicle to save power when the battery voltage is too low.
The battery control unit 2121 may specifically include: a communication circuit 21211, a relay control circuit 21212, a fault detection and safety protection circuit 21213, and a third control circuit 21214.
The communication circuit 21211 may be specifically configured to communicate with the battery detection unit 21221 to obtain the operation state parameter, establish communication with a charger to charge the battery, communicate with the battery model execution module 211, upload the operation state parameter to the battery model execution module 211, and receive the control state parameter.
The relay control circuit 21212 may be specifically configured to control a charging relay, a discharging relay, a heating relay, and a fan relay of the battery, so as to manage the energy state of the battery.
The fault detection and safety protection circuit 21213 may be specifically configured to, when it is determined that the battery has a fault according to the operation state information, output an alarm message and disconnect a corresponding relay under a high risk level condition, so as to ensure safety of the battery.
The third control circuit 21214 may be specifically configured to control operations performed by the communication circuit 21211, the relay control circuit 21212, and the fault detection and safety protection circuit 21213.
The battery model executing module 211 may be specifically configured to clean the operating state parameters, and send the cleaned operating state parameters to the 5G communication module 22.
The 5G communication module 22 may be specifically configured to modulate the cleaned operation state parameters and send the modulated operation state parameters to the cloud computing system 23.
The battery model executing module 211 may be further configured to update a local battery management model by using the updated model parameter, and calculate a control state parameter of the battery according to the operating state parameter and the updated local battery management model.
The battery model executing module 211 may specifically include: a physical layer 2111 and an application layer 2112.
The data cleansing unit 21121 and the model execution unit 21122 are respectively disposed at the application layer 2112.
The data washing unit 21121 may be specifically configured to wash the operating state parameters, and forward the washed operating state parameters to the model execution unit 21122 and the 5G communication module 22. The cleaning process may include: deduplication, error correction, denoising, and the like.
The model executing unit 21122 may be specifically configured to calculate a control state parameter of the battery according to the cleaned operation state parameter and the updated local battery management model, and transmit the control state parameter back to the battery detection control module 212. The control state parameters of the battery may specifically include: a battery remaining capacity (SOC), a battery State of Health (SOH), a battery remaining Power (SOP), a battery remaining capacity (SOE), and the like.
The physical layer 2111 may specifically include an embedded ARM CPU21111 and an embedded GPU21112, where the embedded ARM CPU21111 may be used to support the data cleaning unit 21121 to perform data cleaning, and the embedded GPU21112 may be used to support the model execution unit 21122 to perform calculation of control state parameters.
The embedded ARM CPU21111 has strong logic branch processing capability, and can be particularly used for controlling system execution, communication, man-machine interaction and the like. The system may perform control, including controlling the operation flow of the whole battery model execution module 211, such as data acquisition, cleaning, forwarding control, model operation control, and the like; the communication control may include acquiring data from the battery detection control module 212 and sending the processed data to the 5G communication module 22; the human-computer interaction control process may specifically include: a user at the electric automobile end can check battery operation parameter information and control parameter information through a battery management interface and send an instruction to control the battery control system through the battery management interface.
The embedded GPU21112 has strong parallel computing capability, and may be specifically configured to compute the control state parameters of the battery according to the operating state parameters and the local battery management model.
The 5G communication module 22 may specifically include: a 5G baseband chip 221 and a 5G antenna array 222. As shown in fig. 5, a schematic diagram of a 5G communication module is provided.
The 5G communication module 22 may be specifically configured to convert the operating state parameter into an electromagnetic wave signal through the 5G antenna array 222 after being modulated by the 5G baseband chip 221, and transmit the electromagnetic wave signal to a 5G base station, where the 5G base station sends the electromagnetic wave signal to the cloud computing center 23 through a network.
The 5G communication module 22 may be further configured to convert the electromagnetic wave signal sent by the 5G base station into an electrical signal through the 5G antenna array 222, and transmit the electrical signal back to the battery control system 21 after being modulated by the 5G baseband chip 221.
The 5G baseband chip 221 may be specifically configured to modulate the operating state parameter into a first electrical signal;
the 5G baseband chip 221 is further configured to demodulate the second electrical signal to obtain the updated model parameter.
The 5G baseband chip 221 may specifically include: a baseband section 2211 and a radio frequency section 2212.
The baseband portion 2211 may be specifically configured to modulate the operation state parameter into a first baseband signal. The modulation process may specifically include channel coding of signals, signal modulation, digital signal processing, interface control, and the like.
The radio frequency part 2212 may be specifically configured to convert the first baseband signal into a first radio frequency signal and convert the first radio frequency signal into the first electrical signal.
The radio frequency part 2212 is further configured to convert the second electrical signal into a second radio frequency signal, and convert the second radio frequency signal into a second baseband signal.
The baseband part 2211 is further configured to demodulate the second baseband signal to obtain the updated model parameter. The demodulation process may specifically include channel decoding of the signal, signal demodulation, digital signal processing, interface control, and the like.
The 5G antenna array 222 may be specifically configured to receive the first electrical signal, convert the first electrical signal into a first electromagnetic wave signal, and send the first electromagnetic wave signal to a 5G base station, where the 5G base station is configured to send the operation state parameter to the cloud computing system 23 through the first electromagnetic wave signal.
The 5G antenna array 222 may be further specifically configured to receive a second electromagnetic wave signal sent by the cloud computing system 23 through the 5G base station, and convert the second electromagnetic wave signal into a second electrical signal.
The 5G antenna array 222 may specifically adopt an n × n antenna array, for example, a 5 × 4 antenna array, as shown in fig. 5.
The 5G antenna array 222 may be closely attached to the 5G baseband chip 221, or the 5G antenna array 222 may be directly integrated into the 5G baseband chip 221, in order to reduce signal attenuation and reduce the trace length.
The cloud computing system 23 may specifically include: a data storage center 231 and a model training center 232. As shown in fig. 6, a cloud computing system schematic is provided.
The data storage center 231 may be specifically configured to store the operation state parameters uploaded by the 5G communication module 22.
The model training center 232 may be specifically configured to update a training battery management model in real time according to the operating state parameter, and upload the obtained updated model parameter to the 5G communication module 22 to send to the battery control system 21.
The model training center 232 may be further configured to train a battery management model of the training cloud. For example, as shown in fig. 7, a deep learning calculation method is used to perform model training and updating, the deep learning network input layer is the cell voltage Vi, k of the battery, the total voltage Vtotal, k of the battery, the charge-discharge current Tk, and the cycle number Ncycle, the intermediate layer is a deep belief network DBN method (divided into an RBM layer and a BP layer) for the network, and the final output layer SOC and SOH result values are calculated according to the parameter information of each node returned by the cloud computing system 23. The Deep Belief Network (DBN) training mode adopts a multilayer Restricted Boltzmann Machine (RBM) and a layer of Back Propagation (BP), and two adjacent hidden layers form an RBM. The training process of the DBN is as follows: setting parameters such as the number of network layers, the number of hidden layer units and the like, and randomly initializing the whole DBN network parameter; training the RBM by using training sample data to a first RBM and adopting a continuous learning algorithm (CD algorithm) to store network parameters; training the next RBM by taking the hidden layer output of the next layer of RBM as input data until all RBMs are trained, and obtaining the whole DBN network parameters through unsupervised pre-training; and carrying out supervised training by using the BP network of the last layer, and reversely adjusting the RBMs of all layers to obtain the adjusted DBN network parameters. In the training process of the DBN, the core of RBM during the training realizes the initialization of DBN parameters through the layer-by-layer training of the RBM, the network parameters are not optimal parameters, but the network parameters usually fall near the optimal value, and the problems that the BP algorithm falls into a local optimal solution or the training time is too long due to the random initialization of the network parameters when a classifier is trained can be effectively avoided.
The model training center 232 may be configured to train and update various machine learning models stored in the cloud, such as data mining, deep learning, and neural networks, according to the operating state parameters stored in the data storage center 231, so as to obtain more accurate model parameters. The model parameters may be results obtained by the model training center 232 updating and training the battery management model in real time according to the operating state parameters, taking a deep learning manner as an example: the model parameters may be a w value (weight value) and a b value (offset value) of each node in the deep learning process, and the model parameters are usually transmitted back to the battery model executing module 211 in the form of a matrix, that is, the cloud computing system 23 sends the model parameters to the battery model executing module 211, where the form of the model parameters includes a w matrix and a b matrix.
According to the technical scheme, when the electric automobile and the cloud computing platform perform data transmission, the high capacity, the real-time performance and the reliability of data communication can be ensured, the electric automobile can run safely, stably and reliably, and the use requirement of performing real-time data interaction between the electric automobile and the cloud computing platform is met.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the method and apparatus described above are referred to one another. In addition, "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent merits of the embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (8)
1. A battery management system for an electric vehicle based on 5G mobile communication is characterized by comprising: a battery control system arranged at the electric automobile end, a 5G communication module and a cloud computing system arranged at the cloud end,
the battery control system is used for detecting the running state parameters of the battery;
the 5G communication module is used for modulating the running state parameters and sending the running state parameters to the cloud computing system;
the cloud computing system is used for training a battery management model at the cloud end according to the running state parameters, obtaining updated model parameters and transmitting the updated model parameters back to the 5G communication module;
the 5G communication module is also used for demodulating the updated model parameters and sending the demodulated model parameters to the battery control system;
the battery control system is further configured to update a local battery management model by using the updated model parameters, and calculate control state parameters of the battery according to the operation state parameters and the updated local battery management model to control the operation state of the battery;
the battery control system includes: a battery detection control module and a battery model execution module,
the battery detection control module is used for detecting the running state parameters of the battery and uploading the running state parameters to the battery model execution module;
the battery model execution module is used for cleaning the running state parameters and sending the cleaned running state parameters to the 5G communication module;
the 5G communication module is specifically used for modulating the cleaned running state parameters and sending the modulated running state parameters to the cloud computing system;
the battery model execution module is further configured to update a local battery management model by using the updated model parameters, and calculate control state parameters of the battery according to the operating state parameters and the updated local battery management model;
the battery detection control module is further configured to receive the control state parameter returned by the battery model execution module, so as to control the running state of the battery;
the battery detection control module includes: a battery detection unit and a battery control unit,
the battery detection unit is used for detecting the running state parameters of the battery, wherein the running state parameters comprise the voltage of a single battery, the total voltage of the battery, the charging and discharging current of the battery, the temperature of the battery and the insulation resistance of the battery;
the battery control unit is used for controlling the running state of the battery by using the control state parameters, the control state parameters comprise battery residual capacity, battery health state, battery residual power and battery residual capacity, can also be used for prompting an electric vehicle user in advance through a battery management interface under the condition that the battery residual capacity is insufficient, and can also be used for carrying out path planning according to mileage input by the user through the battery management interface and the control state parameters, and can also be used for closing partial equipment in the electric vehicle to save electric quantity when the battery voltage is too low.
2. The system of claim 1, wherein the 5G communication module comprises:
the 5G baseband chip is used for modulating the operation state parameters into first electric signals;
the 5G antenna array is used for receiving the first electric signal, converting the first electric signal into a first electromagnetic wave signal and sending the first electromagnetic wave signal to a 5G base station, and the 5G base station is used for sending the running state parameter to the cloud computing system through the first electromagnetic wave signal;
the 5G antenna array is further used for receiving a second electromagnetic wave signal sent by the cloud computing system through the 5G base station and converting the second electromagnetic wave signal into a second electric signal;
and the 5G baseband chip is also used for demodulating the second electric signal to obtain the updated model parameters.
3. The system of claim 2, wherein the 5G baseband chip comprises:
a baseband part for modulating the operation state parameter into a first baseband signal;
a radio frequency part for converting the first baseband signal into a first radio frequency signal and converting the first radio frequency signal into the first electrical signal;
the radio frequency part is further used for converting the second electric signal into a second radio frequency signal and converting the second radio frequency signal into a second baseband signal;
the baseband part is further configured to demodulate the second baseband signal to obtain the updated model parameter.
4. The system of claim 1, wherein the battery model execution module comprises: a data cleaning unit and a model executing unit,
the data cleaning unit is used for cleaning the running state parameters and forwarding the cleaned running state parameters to the model execution unit and the 5G communication module;
and the model execution unit is used for calculating the control state parameters of the battery according to the cleaned running state parameters and the updated local battery management model, and transmitting the control state parameters back to the battery detection control module.
5. The system of claim 4, wherein the battery model execution module comprises: a physical layer and an application layer, and,
the data cleaning unit and the model execution unit are respectively provided with the application layer;
the physical layer comprises an embedded ARM CPU and an embedded GPU, the embedded ARM CPU is used for supporting the data cleaning unit to clean data, and the embedded GPU is used for supporting the model execution unit to calculate control state parameters.
6. The system of claim 1, wherein the battery detection unit comprises: a battery measuring unit and a high voltage unit,
the battery measuring unit comprises a single battery voltage detection circuit, a battery temperature detection circuit, a battery equalization circuit and a first control circuit, wherein the first control circuit is used for converting the single battery voltage detected by the battery voltage detection circuit and the battery temperature detected by the battery temperature detection circuit and then uploading the converted single battery voltage and the converted battery temperature to the battery control unit;
the high voltage unit comprises a total voltage detection circuit, an insulation resistance measurement circuit, a charging and discharging current detection circuit and a second control circuit, wherein the second control circuit is used for transmitting the total voltage of the battery detected by the total voltage detection circuit, the insulation resistance detected by the insulation resistance measurement circuit and the charging and discharging current detected by the charging and discharging current detection circuit to the battery control unit after conversion processing.
7. The system of claim 1, wherein the battery control unit comprises:
the communication circuit is used for communicating with the battery detection unit to acquire the running state parameters, establishing communication with a charger to charge the battery, communicating with the battery model execution module to upload the running state parameters to the battery model execution module and receive the control state parameters;
the relay control circuit is used for controlling a charging relay, a discharging relay, a heating relay and a fan relay of the battery so as to manage the energy state of the battery;
the fault detection and safety protection circuit is used for outputting alarm information and disconnecting a corresponding relay under the condition of high danger level when the battery is determined to be in fault according to the running state information so as to ensure the safety of the battery;
and the third control circuit is used for controlling the work executed by the communication circuit, the relay control circuit and the fault detection and safety protection circuit.
8. The system of claim 1, wherein the cloud computing system comprises:
the data storage center is used for storing the running state parameters uploaded by the 5G communication module;
and the model training center is used for updating and training a battery management model in real time according to the running state parameters, and uploading the obtained updated model parameters to the 5G communication module so as to send the updated model parameters to the battery control system.
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| CN112073117A (en) * | 2020-09-18 | 2020-12-11 | 四川爱联科技股份有限公司 | 5G communication module and 5G communication system |
| CN112331941B (en) * | 2020-11-20 | 2024-01-05 | 中国科学技术大学 | Cloud auxiliary battery management system and method |
| CN112693342A (en) * | 2020-12-31 | 2021-04-23 | 湖北追日电气股份有限公司 | Electric automobile self-service charging device based on artificial intelligence |
| CN112819203B (en) * | 2021-01-12 | 2022-12-20 | 湖北追日新能源技术有限公司 | Charging management system and method based on deep learning |
| CN112829632B (en) * | 2021-01-14 | 2022-10-14 | 广州橙行智动汽车科技有限公司 | Operation control method and device of power battery, vehicle and storage medium |
| CN113246796A (en) * | 2021-05-24 | 2021-08-13 | 广东技术师范大学 | Cloud vehicle-mounted battery information database based on 5G network |
| CN113655384A (en) * | 2021-08-19 | 2021-11-16 | 山东浪潮科学研究院有限公司 | Battery state computing method for cloud edge side computing |
| CN114755524B (en) * | 2022-06-16 | 2022-09-13 | 深圳市明珞锋科技有限责任公司 | Vehicle-mounted bidirectional charger measurement and control system applying RBM algorithm model |
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