CN110324383A - Cloud Server, electric car and the wherein management system, method of power battery - Google Patents
Cloud Server, electric car and the wherein management system, method of power battery Download PDFInfo
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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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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The invention discloses a kind of Cloud Server, electric car and the wherein management systems, method of power battery.Battery management system includes Cloud Server and the BMS that is arranged on the electric car, wherein, BMS, for acquiring the status information of power battery in electric car, and according to multiple first reference curve clusters under multiple operating conditions of the state information acquisition power battery of power battery, and the status information of power battery and multiple first reference curve clusters are sent to Cloud Server, and receive and save the second reference curve cluster of Cloud Server transmission；Cloud Server, for saving the historical data of power battery, and according to historical data and multiple first reference curve fasciations at the second reference curve cluster, thus, the reference curve cluster in BMS can be constantly updated, every status information of power battery can be accurately estimated by updated reference curve cluster, convenient for effectively being managed power battery, be conducive to the service life for improving power battery.
Description
Technical field
The present invention relates to electric vehicle engineering field, in particular to the management system of power battery in a kind of electric car,
Management method, a kind of electric car and a kind of Cloud Server of power battery in a kind of electric car.
Background technique
Lithium ion battery is because its specific energy is high, have extended cycle life, retention of charge is strong, environmental pollution is low, memoryless effect
Many advantages, such as answering, it has also become most common energy storage device on electric car at present, therefore its performance and working condition are to vehicle
For be vital.For the superperformance for ensuring power battery pack, the energy of power battery is made full use of, and extends electricity
The service life in pond, effectively being managed and controlled to it will be particularly important.
Current existing BMS (Battery Management System, battery management system) is by BCU (Battery
Control Unit, battery control unit) and BIC (Battery Information Collector, battery information collector)
Composition, and each battery cell pack is provided with BIC and BCU.Wherein, BIC is used for the sampling and monitoring, electricity of battery cell voltage
Pond equilibrium, battery pack temperature sampling and monitoring, BCU is for bus current detection, system insulation monitoring, battery system up/down electricity
Management, the estimation of battery system heat management, battery charge state, cell health state estimation, battery power status estimation, failure are examined
Disconnected, vehicle communicates and in sequence of threads update, data record etc..
In the art, when estimating battery status, by calling OCV (the Open Circuit prestored in BCU
Voltage, open-circuit voltage)-SOC (State of Charge, state-of-charge) curve tables look-up and is corrected, then according in advance
The reference curve prestored, which is tabled look-up, obtains the state parameter of power battery, including Remainder Range of Electric Vehicle (kilometer kM), while real
The functions such as present condition monitoring, charge and discharge control, fault diagnosis, CAN communication.
Battery status parameter is influenced by factors such as temperature, charge-discharge magnification, degree of aging and battery usage histories.On and
State the OCV-SOC curve prestored in BCU in technology, usually under laboratory condition using same size battery specific temperature,
The curve measured under specific rate of charge, since the influence factor of introducing is cured into a constant, rather than reference variable,
Therefore the curve can not reflect battery status parameter with temperature, charge-discharge magnification, degree of aging and battery usage history it is each because
Variation relation between element, can not preestimating battery state parameter variation tendency, the electric car then estimated is in full work
There are biggish errors for battery status parameter within the scope of condition.And the intensification of the attenuation degree with battery pack, which can not
Disconnected accumulation expands, and causes the problem that SOC jump occurs in the process of moving in vehicle and continual mileage is not allowed.
Summary of the invention
The present invention is directed to solve one of the technical problem in above-mentioned technology at least to a certain extent.For this purpose, of the invention
One purpose is to propose a kind of management system of power battery in electric car, accurately to estimate every state of power battery
Information, realization effectively manage power battery.
Second object of the present invention is to propose a kind of management method of power battery in electric car.
Third object of the present invention is to propose a kind of electric car.
Fourth object of the present invention is to propose a kind of Cloud Server.
In order to achieve the above objectives, first aspect present invention embodiment proposes a kind of management of power battery in electric car
System, including Cloud Server and the battery management system BMS being arranged on the electric car, wherein the BMS is used for
Acquire the status information of power battery in the electric car, and the power according to the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions of battery, and by the status information of the power battery and the multiple
First reference curve cluster is sent to the Cloud Server, and receives and saves the second reference curve that the Cloud Server is sent
Cluster, to update the reference curve cluster in the BMS；The Cloud Server, for saving the historical data of the power battery, and
According to the historical data and the multiple first reference curve fasciation at the second reference curve cluster.
The management system of power battery in electric car according to an embodiment of the present invention, by BMS according to power battery
Multiple first reference curve clusters under multiple operating conditions of state information acquisition power battery, and then by server according to going through
History data and multiple first reference curve fasciations receive and save the second reference song at the second reference curve cluster, and by BMS
Line cluster, thereby, it is possible to constantly update the reference curve in BMS, convenient for the current every status information of accurate estimated driving force battery,
Be conducive to effectively manage power battery, improve the service life of power battery.
In addition, the management system of power battery can also have in the electric car proposed according to that above embodiment of the present invention
Following additional technical characteristic:
According to one embodiment of present invention, the BMS is according to the multiple first ginseng of multiple fitting algorithm estimation fitting
Examine set of curves.
According to one embodiment of present invention, the Cloud Server is also used to be generated according to the historical data described dynamic
The prediction curve of power battery, and the prediction curve is sent to the BMS, to update the reference curve cluster in the BMS.
According to one embodiment of present invention, the BMS includes: multiple battery information collector BIC, multiple BIC difference
It is corresponding with multiple battery cells in the power battery；Battery control unit BCU, BCU are connected with the multiple BIC, and
It is communicated with the Cloud Server, the BCU is used for the power battery according to the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions, and the multiple first reference curve cluster is sent to the cloud and is taken
Business device, and receive and save the second reference curve cluster that the Cloud Server is sent.
According to one embodiment of present invention, the BCU includes: the first controller, for according to the power battery
Status information carries out full-vehicle control；Second controller, for being communicated with the Cloud Server, and according to the power battery
State information acquisition described in power battery multiple first reference curve clusters under multiple operating conditions, and by the multiple
One reference curve cluster is sent to the Cloud Server, and receives and saves the second reference curve that the Cloud Server is sent
Cluster.
According to one embodiment of present invention, the multiple operating condition include multiple battery temperatures, multiple charge-discharge magnifications or
Multiple degree of agings.
In order to achieve the above objectives, second aspect of the present invention embodiment proposes a kind of management of power battery in electric car
Method, wherein be provided with battery management system BMS on the electric car, the described method comprises the following steps: is described
BMS acquires the status information of power battery in the electric car, and according to the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions of power battery, and by the status information of the power battery and described
Multiple first reference curve clusters are sent to Cloud Server；The Cloud Server is according to the historical data of the power battery and described
Multiple first reference curve fasciations are at the second reference curve cluster；The BMS receives and saves what the Cloud Server was sent
Second reference curve cluster, to update the reference curve cluster in the BMS.
The management method of power battery in electric car according to an embodiment of the present invention, by BMS according to power battery
Multiple first reference curve clusters under multiple operating conditions of state information acquisition power battery, and then by server according to going through
History data and multiple first reference curve fasciations receive and save the second reference song at the second reference curve cluster, and by BMS
Line cluster, thereby, it is possible to constantly update the reference curve in BMS, convenient for the current every status information of accurate estimated driving force battery,
Be conducive to effectively manage power battery, improve the service life of power battery.
In addition, the management method of power battery can also have in the electric car proposed according to that above embodiment of the present invention
Following additional technical characteristic:
According to one embodiment of present invention, the BMS is according to the multiple first ginseng of multiple fitting algorithm estimation fitting
Examine set of curves.
According to one embodiment of present invention, the Cloud Server generates the power battery also according to the historical data
Prediction curve, and the prediction curve is sent to the BMS, to update the reference curve cluster in the BMS.
According to one embodiment of present invention, the multiple operating condition include multiple battery temperatures, multiple charge-discharge magnifications or
Multiple degree of agings.
In order to achieve the above objectives, third aspect present invention embodiment proposes a kind of electric car, in the electric car
On be provided with battery management system BMS, wherein the BMS is used for: acquire power battery in the electric car state letter
Breath, and multiple first references under multiple operating conditions of the power battery according to the state information acquisition of the power battery
Set of curves, and the status information of the power battery and the multiple first reference curve cluster are sent to the Cloud Server,
So that the Cloud Server according to the historical data of the power battery of preservation and the multiple first reference curve fasciation at
Second reference curve cluster；And the second reference curve cluster that the Cloud Server is sent is received and saved, described in updating
Reference curve cluster in BMS.
Electric car according to an embodiment of the present invention, by BMS according to the state information acquisition power battery of power battery
Multiple first reference curve clusters under multiple operating conditions, with by server according to historical data and multiple first with reference to bent
Line fasciation receives and saves the second reference curve cluster at the second reference curve cluster, and then by BMS.Thereby, it is possible to constantly more
Reference curve in new BMS convenient for the current every status information of accurate estimated driving force battery, and then is conducive to power battery
It is effectively managed, improves the service life of power battery.
In addition, the management method of power battery can also have in the electric car proposed according to that above embodiment of the present invention
Following additional technical characteristic:
According to one embodiment of present invention, the BMS is according to the multiple first ginseng of multiple fitting algorithm estimation fitting
Examine set of curves.
According to one embodiment of present invention, the BMS includes: multiple battery information collector BIC, multiple BIC difference
It is corresponding with multiple battery cells in the power battery；Battery control unit BCU, the BCU and the multiple BIC phase
Even, it and is communicated with the Cloud Server, the BCU is used for the power according to the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions of battery, and the multiple first reference curve cluster is sent to described
Cloud Server, and receive and save the second reference curve cluster that the Cloud Server is sent.
According to one embodiment of present invention, the BCU includes: the first controller, for according to the power battery
Status information carries out full-vehicle control；Second controller, for being communicated with the Cloud Server, and according to the power battery
State information acquisition described in power battery multiple first reference curve clusters under multiple operating conditions, and by the multiple
One reference curve cluster is sent to the Cloud Server, and receives and saves the second reference curve that the Cloud Server is sent
Cluster.
According to one embodiment of present invention, the multiple operating condition include multiple battery temperatures, multiple charge-discharge magnifications or
Multiple degree of agings.
In order to achieve the above objectives, fourth aspect present invention embodiment proposes a kind of Cloud Server, comprising: receiving module is used
In receiving the status information of power battery and multiple the in the electric car that battery management system BMS is sent in electric car
One reference curve cluster, wherein BMS power battery according to the state information acquisition of the power battery in multiple works
The multiple first reference curve cluster under condition；Memory module, for storing the status information of the power battery, using as
The historical data of the power battery；First generation module, for bent according to the historical data and the multiple first reference
Line fasciation is at the second reference curve cluster；Sending module, for the second reference curve cluster to be sent to the BMS, to update
Reference curve cluster in the BMS.
The Cloud Server of the embodiment of the present invention is received by receiving module in the electric car that BMS is sent in electric car
The status information of power battery and multiple first reference curve clusters, by the first generation module according to the history comprising power battery
The historical data of state parameter and multiple first reference curve fasciations pass through sending module for second at the second reference curve cluster
Reference curve cluster is sent to BMS, current convenient for accurate estimated driving force battery thereby, it is possible to constantly update the reference curve in BMS
Every status information, be conducive to effectively manage power battery, improve the service life of power battery.
In addition, the Cloud Server proposed according to that above embodiment of the present invention can also have the following additional technical features:
According to one embodiment of present invention, the Cloud Server, further includes: the second generation module, for according to
Historical data generates the prediction curve of the power battery, wherein the prediction curve is also sent to institute by the sending module
BMS is stated, to update the reference curve cluster in the BMS.
Detailed description of the invention
Fig. 1 is the structural block diagram of the management system of power battery in electric car according to an embodiment of the present invention；
Fig. 2 is the structural frames of the management system of power battery in electric car accord to a specific embodiment of that present invention
Figure；
Fig. 3 is the structural frames of the management system of power battery in the electric car of another specific embodiment according to the present invention
Figure；
Fig. 4 is the workflow of the management system of power battery in electric car accord to a specific embodiment of that present invention
Figure；
Fig. 5 is the flow chart of the management method of power battery in electric car according to an embodiment of the present invention；
Fig. 6 is the structural block diagram of electric car according to an embodiment of the present invention；
Fig. 7 is the structural block diagram of Cloud Server according to an embodiment of the invention；And
Fig. 8 is the structural block diagram of Cloud Server in accordance with another embodiment of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to is used to explain the present invention, and is not considered as limiting the invention.
The electric car and its battery management system and management method of the embodiment of the present invention described with reference to the accompanying drawing.
Fig. 1 is the block diagram according to the management system of power battery in the electric car of the embodiment of the present invention.Such as Fig. 1 institute
Show, the management system 100 of power battery includes Cloud Server 10 and the battery being arranged on electric car in the electric car
Management system BMS20.
Wherein, BMS20 is used to acquire the status information of power battery in electric car, and is believed according to the state of power battery
Breath obtains multiple first reference curve clusters under multiple operating conditions of power battery, and by the status information of power battery and more
A first reference curve cluster is sent to Cloud Server 10, and receives and saves the second reference curve of the transmission of Cloud Server 10
Cluster, and then the reference curve cluster in BMS20 is updated according to the second reference curve cluster.Cloud Server 10 is for saving power battery
Historical data, and according to historical data and multiple first reference curve fasciations at the second reference curve cluster.
Optionally, the status information of power battery includes the voltage, battery balanced of the total voltage of power battery, battery cell
Situation, the temperature of battery cell, bus current etc.；Historical data includes the historic state information and power battery of power battery
History reference set of curves.Wherein, the state letter for all power batteries that the historic state information of power battery, that is, BMS20 is sent
Breath, history reference set of curves may include all first reference curve clusters and cloud for the BMS20 transmission that Cloud Server 10 receives
All second reference curve clusters that server 10 generates.
In this embodiment, multiple operating conditions include multiple battery temperatures, multiple charge-discharge magnifications or multiple degree of agings, and
One reference curve cluster, the second reference curve cluster each mean battery cell in different temperatures, different charge-discharge magnifications or different agings
A plurality of state parameter change curve under degree.
Specifically, BMS20 can be every the status information of power battery in preset time t acquisition electric car, such as in t
The status information for collecting power battery (for the first time) is carved, then according to the corresponding power battery of the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions, and then by the status information of the power battery of t moment and corresponding
Multiple first reference curve clusters are sent to Cloud Server 10.Cloud Server 10 receive t moment power battery status information and
Corresponding multiple first reference curve clusters, and the status information of the power battery of t moment is put into historical data base and is saved, in turn
According in current historical data base historical data and multiple first reference curve fasciations at the second reference curve cluster, and by second
Reference curve cluster is back to BMS20, while Cloud Server 10 can also save the second reference curve cluster into historical data base.
BMS20 receives and saves the second reference curve cluster of the passback of Cloud Server 10, and reference curve cluster current in BMS20 is replaced
It is changed to the second reference curve cluster received, using the reference curve as battery predictive management.
Further, BMS20 collects the status information of power battery at the 2*t moment, according to the state of the power battery
Multiple first reference curve clusters under multiple operating conditions of the corresponding power battery of acquisition of information, and then moving the 2*t moment
The status information of power battery and corresponding multiple first reference curve clusters are sent to Cloud Server 10.Cloud Server 10 receives 2*t
The status information of the power battery at moment and corresponding multiple first reference curve clusters, and by the shape of the power battery at 2*t moment
State information be put into historical data base preservation, and then according in current historical data base historical data and multiple first reference curves
Fasciation is back to BMS20 at the second reference curve cluster, and by the second reference curve cluster, at the same Cloud Server 10 can also by this second
Reference curve cluster is saved into historical data base.BMS20 receives and saves the second reference curve cluster of the passback of Cloud Server 10, with
And reference curve cluster current in BMS20 is replaced with to the second reference curve cluster received, using as battery predictive management
Reference curve.
In this way, going deep into power battery charge and discharge cycles, BMS20 is constantly fitted estimation and obtains new first with reference to bent
Line cluster is simultaneously uploaded to Cloud Server 10, and Cloud Server 10 is continuously generated the second new reference curve cluster according to historical data and returns
To BMS10, continuous loop iteration, thereby, it is possible to make entire battery system prediction result closer to the true shape of power battery
State is conducive to effectively manage power battery, improves the service life of power battery.
In one embodiment of the invention, BMS20 is fitted multiple first reference curves according to the estimation of multiple fitting algorithm
Cluster.
For example, double estimations or joint estimate framework of the BMS20 based on target following filtering algorithm and battery model building
The estimation of BMS algorithm is fitted multiple first reference curve clusters.
Wherein, target following filtering algorithm can be Kalman filtering algorithm；Battery model, the i.e. reference of power battery are bent
Line initial value, the initial value can be a plurality of reference curve cluster measured in laboratory conditions, in the reference curve cluster, that is, shadow
Factor (such as electric current I, temperature T, state of charge SOC, health status SOH) is rung to battery model parameter (internal resistance of cell DCIR, resistance
R0, resistance R1, capacitor C1) and battery status amount (Cap capacity, SOC, SOH, SOP, SOE) function relation curve.Optionally,
The reference curve cluster is stored in advance in BMS20 when can be electric car factory.
For example, BMS20 can be according to the current state information and battery model of power battery collected, and utilizes card
The estimation fitting of Kalman Filtering algorithm obtains multiple first reference curve clusters under multiple operating conditions.Further, server 10 can root
It is corresponding with multiple first reference curve clusters according to the historical data (including at least the historic state information of power battery) of power battery
Multiple second reference curve clusters are generated, and send it to BMS20, BMS20 receives the second reference curve cluster, and to BMS20
In currently stored reference curve cluster be updated, and updated reference curve cluster is used for battery status parameter estimating algorithm
Input of tabling look-up.It should be noted that the reference curve cluster in BMS20 is to constantly update.
In one embodiment of the invention, the fitting algorithm that multinomial, neural network model etc. combine also can be used
Estimation fitting obtains the second reference curve cluster.
It is appreciated that the reference curve initial value stored in BMS20 is existing always, it is for estimating the first ginseng of fitting
Set of curves is examined, the reference curve cluster of storage is updated after the second reference curve cluster for receiving the transmission of Cloud Server 10
's.Wherein, whenever the data that Cloud Server 10 is uploaded according to BMS20 continue when fitting obtains the second reference curve cluster by second
Reference curve cluster is back to BMS for updating reference curve cluster currently stored in BMS20.
Further, BMS20 can estimate the power rating SOP (State of power battery according to updated reference curve cluster
Of Power), and maximum power of the power battery under current working is estimated, to improve the discharging efficiency of power battery.BMS20
It can estimate the energy state SOE (State of Energy) of power battery, according to updated reference curve cluster also accurately to estimate
The remaining mileage for calculating electric car, which provides, directly to be referred to.
In one embodiment of the invention, Cloud Server 10 is also used to generate the prediction of power battery according to historical data
Curve, and prediction curve is sent to BMS20.
Specifically, Cloud Server 10 can historical data (historic state parameter, the history of such as power battery to power battery
Reference curve cluster etc.) big data analysis is carried out, for example, power vehicle current driving on expressway, then can obtain under the operating condition
All historical datas, and the future state of power battery, i.e. prediction curve are predicted accordingly, with the control strategy for BMS20
(such as estimating power rating SOP, the energy state SOE of power battery) provides important reference.
As can be seen that compared to battery management system in the related technology, power battery in electric car of the invention
Management system also has battery status forecast function not only with the battery status management function of tradition BMS, can be by dividing
The historical variations curve for analysing power battery status information, while accurately monitoring power battery current state, moreover it is possible to accurate pre-
Estimate the future state of power battery.
In this embodiment, BMS20 has quickly identification, accurately tracks and monitor each cell-state information and state
The ability of parameter, Cloud Server 10 have the function of cloud data collection, big data statistical analysis, personalized real-time update etc..By
This, which is able to record and monitors the historic state and current state of all battery cell pack, and according to mathematical statistics
Algorithm estimates future state, to optimize performance and the service life of power battery, and provides data for its classified utilization and supports.
In one particular embodiment of the present invention, as shown in Fig. 2, BMS20 includes multiple battery information collector BIC21
With battery control unit BCU22.
Wherein, multiple BIC21 are corresponding with multiple battery cells in power battery respectively；BCU22 and multiple BIC21 phases
Even, it and is communicated with Cloud Server 10, BCU22 is used for according to the state information acquisition power battery of power battery multiple
Multiple first reference curve clusters under operating condition, and multiple first reference curve clusters are sent to Cloud Server 10, and receive
And save the second reference curve cluster of the transmission of Cloud Server 10.It should be noted that BCU22 can protect the second reference curve cluster
The corresponding position of current reference set of curves into BMS20 is deposited, to update the reference curve cluster in BMS20.
Optionally, each BIC21 can pass through CAN (Controller Area Network controller local area network), vehicle
Support grid network FlexRay or Daisy Chain (daisy chain) sends data to BCU22.
In this embodiment, BCU22 and all BIC21 can be assemblied in electronic vapour together with all battery cell pack
Inside the cabin of vehicle.
Specifically, BIC21 is used for battery cell voltage sampling and monitoring, battery balanced, battery pack temperature sampling and monitoring,
BCU22 is for bus current detection, system insulation monitoring, battery system up/down fulgurite reason, battery system heat management, battery lotus
Electricity condition SOC (State of Charge) estimation, cell health state SOH (State of Health) estimation, the power of battery
State SOP (State of Power) estimation, fault diagnosis, vehicle communicate and in sequence of threads update, data record etc..
Further, as shown in figure 3, BCU22 includes the first controller 22a and second controller 22b.Wherein, the first control
Device 22a processed is used to carry out full-vehicle control according to the status information of power battery；Second controller 22b is used for and Cloud Server 10
It is communicated, and according to multiple first under multiple operating conditions of the state information acquisition power battery of power battery with reference to bent
Line cluster, and multiple first reference curve clusters are sent to Cloud Server 10, and receive and save the second of Cloud Server transmission
Reference curve cluster.It should be noted that second controller 22b the second reference curve cluster can be saved it is current into BMS20
The corresponding position of reference curve cluster, to update the reference curve cluster in BMS20.
In this embodiment, BCU22 has double MCU of powerful data space and high-speed data processing speed (i.e.
First controller 22a and second controller 22b), there is off-line data processing capacity, and can by wireless communication module, by
Wireless communication mode and Cloud Server 10 carry out data interaction.And then by Cloud Server 10 to power battery whole life cycle
Battery status information and state parameter carry out cloud computing and big data analysis, realize current state management to power battery with not
Carry out status predication.
For ease of understanding in the electric car of the embodiment of the present invention management system of power battery workflow, it is combinable
Fig. 4 is illustrated:
As shown in figure 4, firstly, the status information of starting BMS20 acquisition power battery, the power electric including BIC21 acquisition
The voltage V in pondAlways, each battery cell voltage Vcell, power battery temperature T and BCU22 acquisition bus current IAlways,
And BIC21 is by VAlways、Vcell, T be sent to BCU22.
Then, the first controller 22a in BCU22 is according to VAlways、Vcell, T execute integrated vehicle control tactics, including control is external
The movement of high-low pressure component, fault diagnosis overcharge protection, Cross prevention, overheat protector, Balance route etc.；In BCU22
Two controller 22b on the one hand can be using dual model algorithm according to VAlways、Vcell, T and IAlwaysEstimation is fitted the first ginseng under different operating conditions
Set of curves is examined, it on the other hand, can be according to VAlways、Vcell, T and IAlwaysThe state parameters such as SOC, SOH, SOP, SOE, RM are estimated
And prediction.In turn, can by the wireless communication techniques such as 2/3/4/5G or bluetooth (bluetooth), by the first reference curve cluster with
And state parameter estimation result is uploaded to Cloud Server 10.
Further, Cloud Server 10 integrates the first reference curve cluster and state parameter estimation result, and root
According to historical data (status information and the state ginseng received as before of the first reference curve cluster and stored power battery
Number), it is fitted to the second reference curve cluster closest to power battery current state, the second reference curve cluster is then passed through 2/3/
The wireless communication techniques such as 4/5G or bluetooth are back to BCU22, for updating storage the reference in second controller 22b
Set of curves.
In this way, the reference curve cluster in BMS20 can be updated continuous iteration through the above way, so that the prediction of battery system
As a result closer to the time of day of battery, convenient for the accurate management to power battery.
Meanwhile Cloud Server 10 can also historic state information to power battery and state parameter etc. analyze, with pre-
Battery future state is surveyed, provides important reference for BMS control strategy.
It is appreciated that next circulation can be entered after terminating this data processing.
To sum up, in electric car according to an embodiment of the present invention power battery management system, can not only be more accurate
Preestimating battery items state parameter, the maximum value including SOC, SOH, SOE, SOP and SOP, convenient for the surplus of estimation electric car
Remaining mileage, guiding vehicle are released energy or are limited the releasable maximum work of current time vehicle with maximum power in discharge regime
Rate improves the service life of power battery so that power battery is effectively protected, also predictable battery future state, convenient for electricity
Electrical automobile and power battery state propose the maintenance suggestion or maintenance of foresight.In addition, passing through cloud computing and big data
Analysis can accurately understand the power battery characteristic performance under different type, different batches, different conditions of mixture ratios, be power electric
Pond design provides important design reference；And when power battery on electric car when reaching retired condition, using cloud number
It is analyzed and is screened according to retired battery pack, utilized convenient for the classification of retired power battery and echelon.
Fig. 5 is the flow chart of the management method of power battery in electric car according to an embodiment of the present invention.
In an embodiment of the present invention, BMS is provided on electric car.
As shown in figure 5, in the electric car power battery management method, method the following steps are included:
S101, BMS acquire the status information of power battery in electric car, and according to the state information acquisition of power battery
Multiple first reference curve clusters under multiple operating conditions of power battery, and by the status information of power battery and multiple first
Reference curve cluster is sent to Cloud Server.
Wherein, multiple operating conditions include multiple battery temperatures, multiple charge-discharge magnifications or multiple degree of agings.
In one embodiment of the invention, BMS is according to the multiple first reference curve clusters of multiple fitting algorithm evaluation and simulation.
S102, Cloud Server is according to the historical data and multiple first reference curve fasciations of power battery at second with reference to bent
Line cluster.
S103, BMS receive and save the second reference curve cluster of Cloud Server transmission, to update the reference curve in BMS
Cluster.
In one embodiment of the invention, Cloud Server generates the prediction curve of power battery also according to historical data,
And prediction curve is sent to BMS, to update the reference curve cluster in BMS.
It should be noted that in the electric car of the embodiment of the present invention management method of power battery other specific implementation
Mode can refer to the specific embodiment of the management system of power battery in the electric car of above-described embodiment.
The management method of power battery in electric car according to an embodiment of the present invention, can not only more accurately estimate
Battery items state parameter, the maximum value including SOC, SOH, SOE, SOP and SOP, convenient for estimate electric car remaining mileage,
Guiding vehicle is released energy or is limited the releasable maximum power of current time vehicle with maximum power in discharge regime, with effective
Protection power battery, improve the service life of power battery, also predictable battery future state, convenient for electric car and
The maintenance suggestion or maintenance of power battery state proposition foresight.In addition, by cloud computing and big data analysis, Ke Yizhun
Really understand the power battery characteristic performance under different type, different batches, different conditions of mixture ratios, provides weight for power battery design
The design reference wanted；And when power battery on electric car when reaching retired condition, using cloud data to retired electricity
Chi Bao is analyzed and is screened, and is utilized convenient for the classification of retired power battery and echelon.
Fig. 6 is the structural block diagram of electric car according to an embodiment of the present invention.
As shown in fig. 6, being provided with BMS20 on the electric car 200, wherein BMS20 is for acquiring in electric car
The status information of power battery, and according to the multiple under multiple operating conditions of the state information acquisition power battery of power battery
First reference curve cluster, and the status information of power battery and multiple first reference curve clusters are sent to Cloud Server, so that
Cloud Server according to the historical data of the power battery of preservation and multiple first reference curve fasciations at the second reference curve cluster, into
And BMS20 also receives and saves the second reference curve cluster of Cloud Server transmission, to update the reference curve cluster in BMS.
Wherein, multiple operating conditions include multiple battery temperatures, multiple charge-discharge magnifications or multiple degree of agings.
Specifically, in one embodiment of the invention, BMS20 is according to multiple first ginsengs of multiple fitting algorithm estimation fitting
Examine set of curves.
In one embodiment of the invention, referring to Fig. 2, BMS20 includes: multiple battery information collector BIC21 and electricity
Pond control unit BCU22.
Wherein, multiple BIC21 are corresponding with multiple battery cells in power battery respectively.BCU22 and multiple BIC21 phases
Even, it and is communicated with Cloud Server, BCU22 is used for according to the state information acquisition power battery of power battery in multiple works
Multiple first reference curve clusters under condition, and multiple first reference curve clusters are sent to Cloud Server, and receive and protect
Deposit the second reference curve cluster of Cloud Server transmission.
Further, referring to Fig. 3, BCU22 includes: the first controller 22a and second controller 22b.
Wherein, the first controller 22a is used to carry out full-vehicle control according to the status information of power battery.Second controller
22b for being communicated with Cloud Server, and according to the state information acquisition power battery of power battery multiple operating conditions it
Under multiple first reference curve clusters, and multiple first reference curve clusters are sent to Cloud Server, and receive and save cloud
The second reference curve cluster that server is sent.
It should be noted that other specific embodiments of the electric car of the embodiment of the present invention can refer to above-described embodiment
Electric car in power battery management system specific embodiment.
Electric car according to an embodiment of the present invention, can not only more accurate preestimating battery items status information, packet
The maximum value of SOC, SOH, SOE, SOP and SOP are included, convenient for estimating the remaining mileage of electric car, guiding vehicle in discharge regime
The releasable maximum power of current time vehicle is released energy or limited with maximum power to mention so that power battery is effectively protected
The service life of high power battery.
In addition, electric car according to an embodiment of the present invention other constitute and its act on to those skilled in the art and
Speech be it is known, for reduce redundancy, be not repeated herein.
Fig. 7 is the structural block diagram of Cloud Server according to an embodiment of the invention.As shown in fig. 7, the Cloud Server 10
It include: receiving module 11, memory module 12, the first generation module 13 and sending module 14.
Wherein, receiving module 11 is used to receive the state letter of power battery in the electric car that BMS is sent in electric car
Breath and multiple first reference curve clusters, wherein BMS is according to the state information acquisition power battery of power battery in multiple operating conditions
Under multiple first reference curve clusters.Memory module 12 is used to store the status information of power battery, using as power battery
Historical data.First generation module 13 is used for according to historical data and multiple first reference curve fasciations into the second reference curve
Cluster.Sending module 14 is used to the second reference curve cluster being sent to BMS, to update the reference curve cluster in BMS.
In one embodiment of the invention, as shown in figure 8, Cloud Server 10 further includes the second generation module 15.Second
Generation module 15 is used to generate the prediction curve of power battery according to historical data.
In this embodiment, prediction curve is also sent to BMS by sending module 14, to update the reference curve cluster in BMS.
It should be noted that other specific embodiments of the Cloud Server 10 of the embodiment of the present invention can refer in the present invention
State the specific embodiment of Cloud Server 10 in the management system of power battery in the electric car of embodiment.
Cloud Server according to an embodiment of the present invention generates the second ginseng by the analysis constantly to battery history state parameter
Set of curves is examined, to constantly update the reference curve cluster in BMS, convenient for the current every state letter of the accurate estimated driving force battery of BMS
Breath, is conducive to effectively manage power battery, improves the service life of power battery.In addition, passing through cloud computing and big number
According to analysis, it can accurately understand the power battery characteristic performance under different type, different batches, different conditions of mixture ratios, be power
Battery design provides important design reference；And when power battery on electric car when reaching retired condition, using cloud
Data are analyzed and are screened to retired battery pack, are utilized convenient for the classification of retired power battery and echelon.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outside", " up time
The orientation or positional relationship of the instructions such as needle ", " counterclockwise ", " axial direction ", " radial direction ", " circumferential direction " be orientation based on the figure or
Positional relationship is merely for convenience of description of the present invention and simplification of the description, rather than the device or element of indication or suggestion meaning must
There must be specific orientation, be constructed and operated in a specific orientation, therefore be not considered as limiting the invention.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more,
Unless otherwise specifically defined.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
In the present invention unless specifically defined or limited otherwise, fisrt feature in the second feature " on " or " down " can be with
It is that the first and second features directly contact or the first and second features pass through intermediary mediate contact.Moreover, fisrt feature exists
Second feature " on ", " top " and " above " but fisrt feature be directly above or diagonally above the second feature, or be merely representative of
First feature horizontal height is higher than second feature.Fisrt feature can be under the second feature " below ", " below " and " below "
One feature is directly under or diagonally below the second feature, or is merely representative of first feature horizontal height less than second feature.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art within the scope of the invention can be to above-mentioned
Embodiment is changed, modifies, replacement and variant.
Claims (17)
1. the management system of power battery in a kind of electric car, which is characterized in that including Cloud Server and be arranged in the electricity
Battery management system BMS on electrical automobile, wherein
The BMS, for acquiring the status information of power battery in the electric car, and according to the state of the power battery
Multiple first reference curve clusters under multiple operating conditions of power battery described in acquisition of information, and by the shape of the power battery
State information and the multiple first reference curve cluster are sent to the Cloud Server, and receive and save the Cloud Server hair
The the second reference curve cluster sent, to update the reference curve cluster in the BMS；
The Cloud Server, for saving the historical data of the power battery, and according to the historical data and the multiple
First reference curve fasciation is at the second reference curve cluster.
2. the management system of power battery in electric car as described in claim 1, which is characterized in that the BMS is according to more
It reconstructs hop algorithm estimation and is fitted the multiple first reference curve cluster.
3. the management system of power battery in electric car as described in claim 1, which is characterized in that the Cloud Server,
It is also used to generate the prediction curve of the power battery according to the historical data, and the prediction curve is sent to described
BMS, to update the reference curve cluster in the BMS.
4. the management system of power battery in electric car as described in claim 1, which is characterized in that the BMS includes:
Multiple battery information collector BIC, the multiple BIC are opposite with multiple battery cells in the power battery respectively
It answers；
Battery control unit BCU, the BCU is connected with the multiple BIC, and is communicated with the Cloud Server, the BCU
Multiple first references under multiple operating conditions for the power battery according to the state information acquisition of the power battery
Set of curves, and the multiple first reference curve cluster is sent to the Cloud Server, and receive and save the cloud service
The second reference curve cluster that device is sent.
5. the management system of power battery in electric car as claimed in claim 4, which is characterized in that the BCU includes:
First controller, for carrying out full-vehicle control according to the status information of the power battery；
Second controller, for being communicated with the Cloud Server, and according to the state information acquisition of power battery institute
Multiple first reference curve clusters under multiple operating conditions of power battery are stated, and the multiple first reference curve cluster is sent
The extremely Cloud Server, and receive and save the second reference curve cluster that the Cloud Server is sent.
6. the management system of power battery in electric car as described in claim 1, which is characterized in that the multiple operating condition packet
Include multiple battery temperatures, multiple charge-discharge magnifications or multiple degree of agings.
7. the management method of power battery in a kind of electric car, which is characterized in that wherein, be arranged on the electric car
There is battery management system BMS, the described method comprises the following steps:
The BMS acquires the status information of power battery in the electric car, and according to the status information of the power battery
Obtain multiple first reference curve clusters under multiple operating conditions of the power battery, and by the multiple first reference curve
Cluster is sent to Cloud Server；
The Cloud Server is according to the historical data and the multiple first reference curve fasciation of the power battery at described
Two reference curve clusters；
The BMS receives and saves the second reference curve cluster that the Cloud Server is sent, to update the song of the reference in the BMS
Line cluster.
8. the management method of power battery in electric car as claimed in claim 7, which is characterized in that the BMS is according to more
It reconstructs hop algorithm estimation and is fitted the multiple first reference curve cluster.
9. the management method of power battery in electric car as claimed in claim 7, which is characterized in that the Cloud Server is also
The prediction curve of the power battery is generated according to the historical data, and the prediction curve is sent to the BMS, with more
Reference curve cluster in the new BMS.
10. the management method of power battery in electric car as claimed in claim 7, which is characterized in that the multiple operating condition
Including multiple battery temperatures, multiple charge-discharge magnifications or multiple degree of agings.
11. a kind of electric car, which is characterized in that be provided with battery management system BMS on the electric car, wherein
The BMS is used for:
The status information of power battery in the electric car is acquired, and according to the state information acquisition of the power battery
Multiple first reference curve clusters under multiple operating conditions of power battery, and the multiple first reference curve cluster is sent to
The Cloud Server, so that historical data and the multiple first ginseng of the Cloud Server according to the power battery of preservation
It examines set of curves and generates the second reference curve cluster；And
The second reference curve cluster that the Cloud Server is sent is received and saved, to update the reference curve in the BMS
Cluster.
12. electric car as claimed in claim 11, which is characterized in that the BMS is estimated according to multiple fitting algorithm and is fitted
The multiple first reference curve cluster.
13. electric car as described in claim 1, which is characterized in that the BMS includes:
Multiple battery information collector BIC, multiple BIC are corresponding with multiple battery cells in the power battery respectively；
Battery control unit BCU, the BCU is connected with the multiple BIC, and is communicated with the Cloud Server, the BCU
Multiple first references under multiple operating conditions for the power battery according to the state information acquisition of the power battery
Set of curves, and the multiple first reference curve cluster is sent to the Cloud Server, and receive and save the cloud service
The second reference curve cluster that device is sent.
14. electric car as claimed in claim 13, which is characterized in that the BCU includes:
First controller, for carrying out full-vehicle control according to the status information of the power battery；
Second controller, for being communicated with the Cloud Server, and according to the state information acquisition of power battery institute
Multiple first reference curve clusters under multiple operating conditions of power battery are stated, and the multiple first reference curve cluster is sent
The extremely Cloud Server, and receive and save the second reference curve cluster that the Cloud Server is sent.
15. electric car as claimed in claim 11, which is characterized in that the multiple operating condition includes multiple battery temperatures, more
A charge-discharge magnification or multiple degree of agings.
16. a kind of Cloud Server characterized by comprising
Receiving module, for receiving power battery in the electric car that battery management system BMS is sent in electric car
Status information and multiple first reference curve clusters, wherein the BMS is moved according to the state information acquisition of the power battery
The multiple first reference curve cluster under multiple operating conditions of power battery；
Memory module, for storing the status information of the power battery, using the historical data as the power battery；
First generation module is used for according to the historical data and the multiple first reference curve fasciation into the second reference curve
Cluster；
Sending module, for the second reference curve cluster to be sent to the BMS.
17. Cloud Server as claimed in claim 16, which is characterized in that further include:
Second generation module, for generating the prediction curve of the power battery according to the historical data, wherein
The prediction curve is also sent to the BMS by the sending module.
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| CN201810287724.7A CN110324383B (en) | 2018-03-30 | 2018-03-30 | Cloud server, electric automobile and management system and method of power battery in electric automobile |
| PCT/CN2019/079446 WO2019184841A1 (en) | 2018-03-30 | 2019-03-25 | Electric vehicle, and management system and method for power battery therein |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113848430A (en) * | 2021-10-20 | 2021-12-28 | 广东电网有限责任公司广州供电局 | Electric energy fault monitoring method and device |
| CN114152884A (en) * | 2021-12-29 | 2022-03-08 | 联方云天科技(北京)有限公司 | Intelligent evaluation and dynamic adjustment method based on BMS big data of lithium battery pack |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113306412A (en) * | 2021-06-17 | 2021-08-27 | 江苏云快充新能源科技有限公司 | Vehicle charging safety protection method and device, charging platform and storage medium |
| CN114035098A (en) * | 2021-12-14 | 2022-02-11 | 北京航空航天大学 | Lithium battery health state prediction method integrating future working condition information and historical state information |
| CN114629905B (en) * | 2022-02-16 | 2022-10-28 | 福建时代星云科技有限公司 | Energy storage system SOP optimization method and device based on cloud data |
| DE102022115777A1 (en) | 2022-06-24 | 2024-01-04 | Accure Battery Intelligence Gmbh | Method and system for the early detection of critical errors in a battery |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102862490A (en) * | 2012-09-19 | 2013-01-09 | 合肥工业大学 | Self-adaptive control method for battery management system for electric automobile |
| DE102013112533A1 (en) * | 2012-12-12 | 2014-06-12 | GM Global Technology Operations LLC | Plug-in Charge Capacity Estimation Method for Lithium Iron Phosphate Batteries |
| CN105789716A (en) * | 2016-03-03 | 2016-07-20 | 北京交通大学 | Generalized battery management system |
| KR101653700B1 (en) * | 2013-09-24 | 2016-09-02 | (주)아이비티 | Battery Management System united with I/O module |
| CN106080251A (en) * | 2016-08-05 | 2016-11-09 | 合肥工业大学 | There is the electric automobile power battery management system of complete fault self-diagnosis function |
| US20160339794A1 (en) * | 2014-01-16 | 2016-11-24 | Robert Bosch Gmbh | Method for monitoring a battery |
| CN106998086A (en) * | 2017-03-10 | 2017-08-01 | 常州新慧能电力服务有限公司 | MW class energy-accumulating power station battery management method and its system |
| WO2018025276A1 (en) * | 2016-08-05 | 2018-02-08 | Mahindra & Mahindra Limited | Estimation of soc of a lead-acid battery |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105676139B (en) * | 2016-01-22 | 2018-04-06 | 深圳市智锂能源科技有限公司 | It is a kind of that intelligent management and the method and device of retrospect are carried out to electrokinetic cell |
| CN106772101B (en) * | 2017-02-16 | 2019-05-17 | 欣旺达电子股份有限公司 | Modification method, correcting device and the battery SOH evaluation method of battery SOC |
| CN107831442A (en) * | 2017-10-18 | 2018-03-23 | 苏州协鑫集成储能科技有限公司 | Long-range estimation SOC method, apparatus, storage medium and computer equipment |
-
2018
- 2018-03-30 CN CN201810287724.7A patent/CN110324383B/en active Active
-
2019
- 2019-03-25 WO PCT/CN2019/079446 patent/WO2019184841A1/en active Application Filing
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102862490A (en) * | 2012-09-19 | 2013-01-09 | 合肥工业大学 | Self-adaptive control method for battery management system for electric automobile |
| DE102013112533A1 (en) * | 2012-12-12 | 2014-06-12 | GM Global Technology Operations LLC | Plug-in Charge Capacity Estimation Method for Lithium Iron Phosphate Batteries |
| KR101653700B1 (en) * | 2013-09-24 | 2016-09-02 | (주)아이비티 | Battery Management System united with I/O module |
| US20160339794A1 (en) * | 2014-01-16 | 2016-11-24 | Robert Bosch Gmbh | Method for monitoring a battery |
| CN105789716A (en) * | 2016-03-03 | 2016-07-20 | 北京交通大学 | Generalized battery management system |
| CN106080251A (en) * | 2016-08-05 | 2016-11-09 | 合肥工业大学 | There is the electric automobile power battery management system of complete fault self-diagnosis function |
| WO2018025276A1 (en) * | 2016-08-05 | 2018-02-08 | Mahindra & Mahindra Limited | Estimation of soc of a lead-acid battery |
| CN106998086A (en) * | 2017-03-10 | 2017-08-01 | 常州新慧能电力服务有限公司 | MW class energy-accumulating power station battery management method and its system |
Non-Patent Citations (2)
| Title |
|---|
| CHAOYONG HOU; JIZHONG CHEN: ""An online calibration algorithm of SOC for LiFePO4 battery by using characteristic curve"", 《2015 5TH INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES (DRPT)》 * |
| 张娟: ""基于远程监控系统的纯电动汽车锂离子电池SOC估算算法研究"", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113848430A (en) * | 2021-10-20 | 2021-12-28 | 广东电网有限责任公司广州供电局 | Electric energy fault monitoring method and device |
| CN114152884A (en) * | 2021-12-29 | 2022-03-08 | 联方云天科技(北京)有限公司 | Intelligent evaluation and dynamic adjustment method based on BMS big data of lithium battery pack |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110324383B (en) | 2021-09-03 |
| WO2019184841A1 (en) | 2019-10-03 |
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