CN116572797A - Battery management system control method, device, server and storage medium - Google Patents

Battery management system control method, device, server and storage medium Download PDF

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Publication number
CN116572797A
CN116572797A CN202310783286.4A CN202310783286A CN116572797A CN 116572797 A CN116572797 A CN 116572797A CN 202310783286 A CN202310783286 A CN 202310783286A CN 116572797 A CN116572797 A CN 116572797A
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vehicle
cloud
bms
soc
charging
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郑英
李东江
李宗华
朱骞
喻成
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Deep Blue Automotive Technology Co ltd
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Deep Blue Automotive Technology Co ltd
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Priority to CN202310783286.4A priority Critical patent/CN116572797A/en
Publication of CN116572797A publication Critical patent/CN116572797A/en
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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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/42—Control modes by adaptive correction
    • 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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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The application relates to the technical field of battery management systems, in particular to a battery management system control method, a device, a server and a storage medium, wherein the method comprises the following steps: acquiring historical data of a Battery Management System (BMS) uploaded by a vehicle; predicting cloud parameters of the BMS according to the historical data, and acquiring vehicle-end parameters uploaded by the vehicle, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack; and adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm. Therefore, the problems that the fusion of the vehicle-end local BMS and the cloud BMS is not considered to realize personalized difference control BMS and the like in the prior art are solved.

Description

Battery management system control method, device, server and storage medium
Technical Field
The application relates to the technical field of battery management systems, in particular to a battery management system control method, a device, a server and a storage medium.
Background
With the popularization of new energy automobiles and the increase of the occupancy rate of the new energy automobiles in the automobile market year by year, mass data of the cloud platform becomes the favorable capital of each whole automobile factory. Big data real-time recording car data, to massive data processing operation, fuses BMS (Battery Management System ) and big data high in the clouds algorithm, can break through the limit of car end BMS computational resource, give more functions for BMS, provide user individuation difference strategy, fully promote BMS and whole car performance.
The vehicle-end BMS algorithm is basically determined by testing the average battery use condition, but the vehicle-end BMS can not achieve thousands of people due to differences of actual user use, driving style and use habit. At present, the cloud BMS mostly designs a cloud algorithm based on cloud mass data, and the advantages of the vehicle-end local BMS are not considered.
Disclosure of Invention
The application aims to provide a battery management system control method which is used for solving the problem that the fusion of a vehicle-end local BMS and a cloud BMS is not considered in the prior art to realize personalized difference control BMS; the second objective is to provide a battery management system control device; a third object is to provide a server; a fourth object is to provide a computer-readable storage medium.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
a battery management system control method applied to a server, wherein the method comprises the steps of: acquiring historical data of a Battery Management System (BMS) uploaded by a vehicle; predicting cloud parameters of the BMS according to the historical data, and acquiring vehicle-end parameters uploaded by a vehicle, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack; and adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle-end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.
According to the technical means, the cloud parameters of the BMS can be predicted according to the historical data of the BMS of the vehicle, the current data of the battery pack is utilized to predict the vehicle-end parameters of the BMS, different user habits are fully considered, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-end parameters, the advantages of cloud computing resources and the advantages of the vehicle-end reserved user habits are fully utilized, the control management of the battery management system is realized, and the BMS and the overall vehicle performance are fully improved.
Further, the cloud parameters include cloud SOH, cloud actual power, cloud average driving mileage, cloud actual charging time, cloud charging SOC interval, cloud battery pack average temperature and cloud single battery maximum temperature, and the vehicle-end parameters include vehicle-end SOH, vehicle-end average driving mileage, vehicle-end actual charging time and vehicle-end charging SOC interval.
Further, the control algorithm includes one or more of a power algorithm, a charging algorithm, an SOC usage interval, and a thermal management strategy.
Further, the control algorithm for adaptively adjusting the BMS on the vehicle according to the cloud parameter and the vehicle-end parameter includes: determining a recharging reference table according to the cloud SOH, the cloud actual driving power and the cloud average driving mileage, adjusting the recharging reference table according to the vehicle-end SOH and the vehicle-end average driving mileage, and adjusting the power algorithm according to the adjusted recharging reference table; determining a reference charging meter according to the cloud SOH, the cloud average driving mileage, the cloud actual charging time and the cloud charging SOC interval, adjusting the reference charging meter according to the vehicle-end SOH, the vehicle-end actual charging time and the vehicle-end charging SOC interval, and adjusting the charging algorithm according to the adjusted reference charging meter; determining a reference SOC section according to the cloud end daily average driving mileage and the cloud end driving SOC section, adjusting the reference SOC section according to the actual attenuation of the vehicle end, the vehicle end daily average driving mileage and the vehicle end driving SOC section, and adjusting the SOC using section according to the adjusted reference SOC section; and adjusting the thermal management strategy according to the cloud SOH, the average temperature of the cloud battery pack and the highest temperature of the cloud single battery.
According to the technical means, the embodiment of the application can adaptively adjust the BMS algorithm on the vehicle according to different conditions, and adaptively control the vehicle end power algorithm, the charging algorithm, the SOC use interval and the thermal management strategy.
Further, the historical data includes battery pack temperature, state of charge, SOC, battery pack current, battery pack voltage, current accumulated total time t now Current accumulated throughput Ah now And one or more of the driving range.
Further, the calculation formula of SOH is:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature; i c Is the charging multiplying power; SOC1 is an SOC interval; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time.
Further, the vehicle predicts a vehicle-end parameter of the BMS according to the current data of the battery pack, including: and predicting the driving frequency and the habit of the vehicle according to the SOH, the accumulated driving mileage of the vehicle, the daily average driving mileage, the driving SOC interval and the driving actual power.
According to the technical means, the method and the device can predict the driving frequency and the habit of the vehicle according to the current data of the battery pack, and fully consider the use habits of different users.
A battery management system control apparatus, the apparatus being applied to a server, wherein the apparatus comprises: the acquisition module is used for acquiring historical data of the BMS uploaded by the vehicle; the prediction module is used for predicting cloud parameters of the BMS according to the historical data and obtaining vehicle-end parameters uploaded by a vehicle, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack; and the control module is used for adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle-end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.
Further, the cloud parameters include cloud SOH, cloud actual power, cloud average driving mileage, cloud actual charging time, cloud charging SOC interval, cloud battery pack average temperature and cloud single battery maximum temperature, and the vehicle-end parameters include vehicle-end SOH, vehicle-end average driving mileage, vehicle-end actual charging time and vehicle-end charging SOC interval.
Further, the control algorithm includes one or more of a power algorithm, a charging algorithm, an SOC usage interval, and a thermal management strategy.
Further, the control module is further to: determining a recharging reference table according to the cloud SOH, the cloud actual driving power and the cloud average driving mileage, adjusting the recharging reference table according to the vehicle-end SOH and the vehicle-end average driving mileage, and adjusting the power algorithm according to the adjusted recharging reference table; determining a reference charging meter according to the cloud SOH, the cloud average driving mileage, the cloud actual charging time and the cloud charging SOC interval, adjusting the reference charging meter according to the vehicle-end SOH, the vehicle-end actual charging time and the vehicle-end charging SOC interval, and adjusting the charging algorithm according to the adjusted reference charging meter; determining a reference SOC section according to the cloud end daily average driving mileage and the cloud end driving SOC section, adjusting the reference SOC section according to the actual attenuation of the vehicle end, the vehicle end daily average driving mileage and the vehicle end driving SOC section, and adjusting the SOC using section according to the adjusted reference SOC section; and adjusting the thermal management strategy according to the cloud SOH, the average temperature of the cloud battery pack and the highest temperature of the cloud single battery.
Further, the historical data includes battery pack temperature, state of charge, SOC, battery pack current, battery pack voltage, current accumulated total time t now Current accumulated throughput Ah now And one or more of the driving range.
Further, the calculation formula of SOH is:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature; i c Is the charging multiplying power; SOC1 isAn SOC section; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time.
Further, the prediction module is further to: and predicting the driving frequency and the habit of the vehicle according to the SOH, the accumulated driving mileage of the vehicle, the daily average driving mileage, the driving SOC interval and the driving actual power.
A server, comprising: the battery management system control device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the battery management system control method according to the embodiment.
A computer-readable storage medium having stored thereon a computer program that is executed by a processor for implementing the battery management system control method as described in the above embodiments.
The application has the beneficial effects that:
(1) According to the embodiment of the application, the cloud parameters of the BMS can be predicted according to the historical data of the BMS of the vehicle, the current data of the battery pack is utilized to predict the vehicle-end parameters of the BMS, different user habits are fully considered, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-end parameters, the advantages of computing resources of the cloud and the advantages of the vehicle-end reserved user habits are fully utilized, the control management of the battery management system is realized, and the BMS and the overall vehicle performance are fully improved.
(2) According to the embodiment of the application, the BMS algorithm on the vehicle can be adaptively adjusted according to different conditions, and the vehicle-end power algorithm, the charging algorithm, the SOC use interval and the thermal management strategy are adaptively controlled.
(3) The embodiment of the application can predict the driving frequency and the vehicle habit of the vehicle according to the current data of the battery pack, and fully considers the use habits of different users.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
Fig. 1 is a flowchart of a control method of a battery management system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a power limiting flow provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a charging rate limiting flow provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of a SOC usage window adjustment procedure according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating a cooling-on temperature adjustment according to an embodiment of the present application;
FIG. 6 is a flowchart of a method for controlling a battery management system according to an embodiment of the present application;
fig. 7 is a schematic diagram of a battery management system control device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
Further advantages and effects of the present application will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Specifically, fig. 1 is a flowchart of a control method of a battery management system according to an embodiment of the present application.
As shown in fig. 1, the battery management system control method includes the steps of:
in step S101, history data of the vehicle uploading the battery management system BMS is acquired.
Wherein the historical data comprises battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current accumulated total time t now Current accumulated throughput Ah now And one or more of the driving range.
It should be noted that, in the embodiment of the present application, data cleaning is performed on data uploaded by a battery management system to obtain historical data, including: the big data platform processes the temperature of the battery pack to obtain historical average temperature; processing the SOC signals to obtain a historical running condition SOC use average value and a historical standing condition SOC use average value; processing the current signal to obtain the average charging multiplying power of the historical working condition; and processing the battery pack current and the battery pack total voltage signal to obtain the historical driving actual power. The big data processing data comprises the functions of data acquisition, data cleaning pretreatment, data storage, data encryption, data downloading, data backup, data analysis modeling, data visualization display and the like, and meanwhile, the big data processing data is provided with an analysis engine, a machine learning algorithm database and the like required by data mining, so that modeling and analysis of a big data analysis scene are realized.
In step S102, cloud parameters of the BMS are predicted according to the historical data, and vehicle-end parameters uploaded by the vehicle are obtained, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack.
The cloud parameters include cloud SOH (State of Health), cloud actual driving power, cloud daily average driving mileage, cloud actual charging time, cloud charging SOC interval, cloud battery pack average temperature and cloud single battery maximum temperature, and the vehicle end parameters include vehicle end SOH, vehicle end daily average driving mileage, vehicle end actual charging time and vehicle end charging SOC interval.
It can be appreciated that the embodiment of the application can predict the cloud parameters of the BMS according to the historical data of the BMS and acquire the vehicle-end parameters uploaded by the vehicle for subsequent adjustment of the control algorithm of the BMS on the vehicle.
In the embodiment of the application, the calculation formula of SOH is:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature; i c Is the charging multiplying power; SOC1 is an SOC interval; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time.
In an embodiment of the present application, a vehicle predicts a vehicle-end parameter of a BMS according to current data of a battery pack, including: and predicting the driving frequency and the vehicle habit of the vehicle according to the SOH, the accumulated driving mileage of the vehicle, the daily average driving mileage, the driving SOC interval and the driving actual power.
It can be understood that the embodiment of the application can predict the current driving frequency and the using habit of the user according to SOH, the accumulated driving mileage of the vehicle, the average driving mileage of the day, the driving SOC interval and the driving actual power.
In step S103, a control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.
It can be appreciated that the embodiment of the application can adaptively adjust the BMS control algorithm on the vehicle according to the cloud parameters and the vehicle-end parameters, and the vehicle controls the BMS according to the adjusted control algorithm. Wherein the control algorithm includes one or more of a power algorithm, a charging algorithm, an SOC usage interval, and a thermal management strategy.
In the embodiment of the application, a control algorithm for adaptively adjusting BMS on a vehicle according to cloud parameters and vehicle end parameters comprises the following steps: determining a recharging reference table according to the cloud SOH, the cloud actual driving power and the cloud average driving mileage, adjusting the recharging reference table according to the vehicle-end SOH and the vehicle-end average driving mileage, and adjusting a power algorithm according to the adjusted recharging reference table; determining a reference charging meter according to the cloud SOH, the cloud daily average driving mileage, the cloud actual charging time and the cloud charging SOC interval, adjusting the reference charging meter according to the vehicle end SOH, the vehicle end actual charging time and the vehicle end charging SOC interval, and adjusting a charging algorithm according to the adjusted reference charging meter; determining a reference SOC section according to the cloud average driving mileage and the cloud driving SOC section, adjusting the reference SOC section according to the actual attenuation of the vehicle end, the vehicle end average driving mileage and the vehicle end driving SOC section, and adjusting the SOC use section according to the adjusted reference SOC section; and adjusting a thermal management strategy according to the cloud SOH, the average temperature of the cloud battery pack and the highest temperature of the cloud single battery.
It can be appreciated that the embodiment of the present application can adaptively adjust the BMS control algorithm on the vehicle, including but not limited to, adaptive control on the vehicle-end power algorithm, the charging algorithm, the SOC use interval, and the thermal management policy, where the specific algorithm is:
power algorithm: and predicting the attenuation of the battery pack and the actual power and daily average driving mileage condition of the user according to the cloud big data, and adaptively controlling a vehicle end power algorithm. And (3) formulating a reference power allowed by recharging the battery pack, and on the basis of the MAP, carrying out certain adjustment on the recharging reference MAP according to the actual attenuation and daily average driving mileage of the user. And dividing the vehicle utilization frequency of the user according to the daily driving mileage. The regulation strategy diagram is shown in fig. 2 for limiting the scenes of severe working conditions and large battery attenuation of the high-frequency vehicle.
Charging algorithm: and predicting the attenuation of the battery pack and the conditions of the daily average driving mileage, the actual charging time and the charging SOC interval according to the cloud big data, and making a charging MAP of a battery pack reference, wherein on the basis of the MAP, the reference MAP is adjusted upwards or limited to a certain extent according to the actual attenuation of a user, the actual charging time and the charging SOC interval. The charging rate of the battery attenuation is limited, the battery is in a good state of health, the user is mainly charged quickly, the charging initial SOC is low, the charging rate is properly increased, and the rate coefficient is shown in fig. 3.
SOC usage interval: and (3) according to the user habits such as the average daily driving mileage, the driving SOC interval and the like of the user, a reference SOC interval of the battery pack is established, and based on the actual attenuation of the user, the average daily driving mileage and the driving SOC interval of the user, the reference SOC interval is shifted downwards. SOH decays, and if the user driving SOC interval is narrow, SOCmax is adjusted downwards, and if the user driving SOC interval is wide, SOCmax and SOCmin are both adjusted downwards. The adjustment amplitude should comprehensively consider the influence on the actual driving habit of the user, and the user instrument perception is not influenced, and the SOC use window adjustment schematic diagram is shown in fig. 4.
Thermal management strategies: predicting the attenuation of the battery pack and the average temperature and the highest temperature of the battery pack according to the cloud big data, and adaptively controlling a vehicle-end thermal management strategy; SOH decays, if the average temperature that the user used is greater than a certain value, the battery package cooling open temperature of dynamic adjustment, and the adjustment range should take into account the comprehensive influence to user's charging efficiency and driving energy consumption comprehensively, and cooling open temperature adjustment schematic diagram is shown in figure 5.
In summary, a specific flow of the battery system control method according to the embodiment of the present application is shown in fig. 6:
step 1: the cloud acquires temperature, SOC, current, voltage, service time and accumulated mileage information.
Step 2: the cloud calculates battery decay soh=cycle decay f (SOH 1) +calendar decay f (SOH 2) from the data.
And the cloud life prediction algorithm applies a cluster analysis and neural network modeling method according to cloud data, and comprehensively determines the battery life model as follows:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature (K); i c Is the charging multiplying power; SOC1 is an SOC interval; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time (days).
Step 3: and dynamically adjusting the recharging power according to the daily average driving mileage and the driving actual recharging power average value.
Step 4: and dynamically adjusting the charging strategy according to the SOC use interval, the charging mode and the charging time statistical result.
Step 5: and dynamically adjusting the SOC use window according to the daily average driving mileage and SOH attenuation.
Step 6: and dynamically adjusting the cooling start temperature according to the average temperature and SOH attenuation.
According to the battery management system control method provided by the embodiment of the application, cloud parameters of the BMS can be predicted according to the historical data of the BMS of the vehicle, the current data of the battery pack is utilized to predict the vehicle-end parameters of the BMS, different user habits are fully considered, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-end parameters, the advantages of computing resources of the cloud and the advantages of the vehicle-end reserved user habits are fully utilized, the control management of the battery management system is realized, and the BMS and the overall vehicle performance are fully improved.
Next, a battery management system control device according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 7 is a block diagram of a battery management system control apparatus according to an embodiment of the present application.
As shown in fig. 7, the battery management system control apparatus 10 is applied to a server, and includes: an acquisition module 100, a prediction module 200, and a control module 300.
The acquiring module 100 is configured to acquire historical data of the battery management system BMS uploaded by the vehicle; the prediction module 200 is configured to predict cloud parameters of the BMS according to the historical data, and obtain vehicle-end parameters uploaded by the vehicle, where the vehicle predicts the vehicle-end parameters of the BMS according to current data of the battery pack; the control module 300 is configured to adaptively adjust a control algorithm of the BMS on the vehicle according to the cloud parameter and the vehicle-end parameter, wherein the vehicle controls the BMS according to the adjusted control algorithm.
In the embodiment of the application, the cloud parameters include cloud SOH, cloud actual driving power, cloud daily average driving mileage, cloud actual charging time, cloud charging SOC interval, cloud battery pack average temperature and cloud single battery highest temperature, and the vehicle-end parameters include vehicle-end SOH, vehicle-end daily average driving mileage, vehicle-end actual charging time and vehicle-end charging SOC interval.
In an embodiment of the application, the control algorithm includes one or more of a power algorithm, a charging algorithm, an SOC usage interval, and a thermal management strategy.
In an embodiment of the present application, the control module 300 is further configured to: determining a recharging reference table according to the cloud SOH, the cloud actual driving power and the cloud average driving mileage, adjusting the recharging reference table according to the vehicle-end SOH and the vehicle-end average driving mileage, and adjusting a power algorithm according to the adjusted recharging reference table; determining a reference charging meter according to the cloud SOH, the cloud daily average driving mileage, the cloud actual charging time and the cloud charging SOC interval, adjusting the reference charging meter according to the vehicle end SOH, the vehicle end actual charging time and the vehicle end charging SOC interval, and adjusting a charging algorithm according to the adjusted reference charging meter; determining a reference SOC section according to the cloud average driving mileage and the cloud driving SOC section, adjusting the reference SOC section according to the actual attenuation of the vehicle end, the vehicle end average driving mileage and the vehicle end driving SOC section, and adjusting the SOC use section according to the adjusted reference SOC section; and adjusting a thermal management strategy according to the cloud SOH, the average temperature of the cloud battery pack and the highest temperature of the cloud single battery.
In an embodiment of the present application, the historical data includes a battery pack temperature, a state of charge SOC, a battery pack current, a battery pack voltage, a current accumulated total time t now Current accumulated throughput Ah now And one or more of the driving range.
In the embodiment of the application, the calculation formula of SOH is:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature; i c Is the charging multiplying power; SOC1 is an SOC interval; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time.
In an embodiment of the present application, the prediction module 200 is further configured to: and predicting the driving frequency and the vehicle habit of the vehicle according to the SOH, the accumulated driving mileage of the vehicle, the daily average driving mileage, the driving SOC interval and the driving actual power.
It should be noted that the foregoing explanation of the embodiment of the method for controlling a battery management system is also applicable to the battery management system control device of this embodiment, and will not be repeated here.
According to the battery management system control device provided by the embodiment of the application, cloud parameters of the BMS can be predicted according to the historical data of the BMS of the vehicle, the current data of the battery pack is utilized to predict the vehicle-end parameters of the BMS, different user habits are fully considered, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-end parameters, the advantages of computing resources of the cloud and the advantages of the vehicle-end reserved user habits are fully utilized, the control management of the battery management system is realized, and the BMS and the overall vehicle performance are fully improved.
Fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
a memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.
The processor 802 implements the battery management system control method provided in the above-described embodiment when executing a program.
Further, the vehicle further includes:
a communication interface 803 for communication between the memory 801 and the processor 802.
A memory 801 for storing a computer program executable on the processor 802.
The memory 801 may include high-speed RAM (Random Access Memory ) memory, and may also include non-volatile memory, such as at least one disk memory.
If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component, external device interconnect) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a chip, the memory 801, the processor 802, and the communication interface 803 may communicate with each other through internal interfaces.
The processor 802 may be a CPU (Central Processing Unit ) or ASIC (Application Specific Integrated Circuit, application specific integrated circuit) or one or more integrated circuits configured to implement embodiments of the present application.
The embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the battery management system control method as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable gate arrays, field programmable gate arrays, and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (10)

1. A battery management system control method, wherein the method is applied to a server, and wherein the method comprises the steps of:
acquiring historical data of a Battery Management System (BMS) uploaded by a vehicle;
predicting cloud parameters of the BMS according to the historical data, and acquiring vehicle-end parameters uploaded by a vehicle, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack;
and adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle-end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.
2. The method according to claim 1, wherein the cloud parameters include cloud SOH, cloud actual power, cloud average driving distance, cloud actual charging time, cloud charging SOC interval, cloud battery pack average temperature, and cloud battery cell maximum temperature, and the vehicle parameters include vehicle SOH, vehicle average driving distance, vehicle actual charging time, and vehicle charging SOC interval.
3. The battery management system control method of claim 2, wherein the control algorithm comprises one or more of a power algorithm, a charging algorithm, an SOC usage interval, and a thermal management strategy.
4. The battery management system control method according to claim 3, wherein the control algorithm for adaptively adjusting the BMS on the vehicle according to the cloud parameter and the vehicle-end parameter comprises:
determining a recharging reference table according to the cloud SOH, the cloud actual driving power and the cloud average driving mileage, adjusting the recharging reference table according to the vehicle-end SOH and the vehicle-end average driving mileage, and adjusting the power algorithm according to the adjusted recharging reference table;
determining a reference charging meter according to the cloud SOH, the cloud average driving mileage, the cloud actual charging time and the cloud charging SOC interval, adjusting the reference charging meter according to the vehicle-end SOH, the vehicle-end actual charging time and the vehicle-end charging SOC interval, and adjusting the charging algorithm according to the adjusted reference charging meter;
determining a reference SOC section according to the cloud end daily average driving mileage and the cloud end driving SOC section, adjusting the reference SOC section according to the actual attenuation of the vehicle end, the vehicle end daily average driving mileage and the vehicle end driving SOC section, and adjusting the SOC using section according to the adjusted reference SOC section;
and adjusting the thermal management strategy according to the cloud SOH, the average temperature of the cloud battery pack and the highest temperature of the cloud single battery.
5. The battery management system control method of claim 1 wherein the historical data includes battery pack temperature, state of charge SOC, battery pack current, battery pack voltage, current accumulated total time t now Current accumulated throughput Ah now And one or more of the driving range.
6. The battery management system control method according to claim 2, wherein the SOH is calculated by the formula:
SOH＝f 1 (T,I c ,OC1,Ah)+ 2 (T,SOC2,t)，
wherein f 1 Is a cyclic decay; f (f) 2 Is calendar decay; t is the temperature; i c Is the charging multiplying power; SOC1 is an SOC interval; ah is the accumulated charge throughput; SOC2 is the storage SOC; t is the storage time.
7. The battery management system control method according to claim 2, wherein the vehicle predicts a vehicle-end parameter of the BMS according to current data of the battery pack, comprising:
and predicting the driving frequency and the habit of the vehicle according to the SOH, the accumulated driving mileage of the vehicle, the daily average driving mileage, the driving SOC interval and the driving actual power.
8. A battery management system control apparatus, the apparatus being applied to a server, wherein the apparatus comprises:
the acquisition module is used for acquiring historical data of the BMS uploaded by the vehicle;
the prediction module is used for predicting cloud parameters of the BMS according to the historical data and obtaining vehicle-end parameters uploaded by a vehicle, wherein the vehicle predicts the vehicle-end parameters of the BMS according to the current data of the battery pack;
and the control module is used for adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle-end parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.
9. A server, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the battery management system control method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for realizing the battery management system control method according to any one of claims 1 to 7.
CN202310783286.4A 2023-06-28 2023-06-28 Battery management system control method, device, server and storage medium Pending CN116572797A (en)

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