CN110135527A - A kind of dynamical unmanned plane charge states of lithium ion battery estimating system and method - Google Patents
A kind of dynamical unmanned plane charge states of lithium ion battery estimating system and method Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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Abstract
A kind of dynamical unmanned plane charge states of lithium ion battery estimating system and method, are related to technical field of battery management.The present invention is the blank in order to fill up the assessment of unmanned plane charge states of lithium ion battery.The present invention realizes the SOC On-line Estimation of lithium ion battery using the method that Kalman filtering and support vector machines merge, and compensates for traditional lower deficiency of SOC estimation method precision.Meanwhile even if the accurate estimation of SOC can be also realized in the case where SOC initial value is unknown.The acquisition of lithium ion battery data and SOC estimation that can be used for during unmanned plane execution task.
Description
Technical field
The invention belongs to technical field of battery management.
Background technique
Lithium ion battery because its light weight, energy density is big, output voltage is high the advantages that be widely used in consumer electronics, electricity
The fields such as electrical automobile, unmanned plane.In unmanned plane field, lithium ion battery can substantially reduce the weight and volume of its power supply system.
As unmanned plane execute task when sole energy source, lithium ion battery need to for unmanned plane all devices power, therefore lithium from
Sub- battery management has become the key technology of unmanned plane management.Charge states of lithium ion battery (State of State, SOC) is
One of the parameter of battery management most critical, SOC both can reflect the information of lithium ion battery dump energy, can be used for assessing
The reliability of battery.Accurate estimation to lithium ion battery SOC is to guarantee that unmanned plane safely and reliably executes the premise of task, because
This accurate evaluation unmanned plane lithium ion battery SOC has a very important significance.
However, only realize the acquisition of the voltage and electric current of lithium ion battery when existing unmanned plane executes task, lack pair
The estimation of its state-of-charge.At the same time, the weight, volume, power consumption for controlling it equipment due to unmanned plane have particular/special requirement,
The acquisition of its data is realized by embedded module.So realizing unmanned plane charge states of lithium ion battery using embedded module
It is urgently to be resolved to assess this technology.
Summary of the invention
The present invention is the blank in order to fill up the assessment of unmanned plane charge states of lithium ion battery, is now provided a kind of dynamical
Unmanned plane charge states of lithium ion battery estimating system and method.
A kind of dynamical unmanned plane charge states of lithium ion battery estimating system, comprising: data acquisition unit and data
Processing unit；
Data acquisition unit: for acquiring the status data of lithium ion battery during unmanned plane during flying, state data packets
Include voltage signal and current signal；
Data processing unit: for estimating the state-of-charge of lithium ion battery, the data processing unit according to status data
It comprises the following modules:
Training module: establishing training dataset using status data and be trained to supporting vector machine model,
Observation obtains module: the voltage signal at k moment and current signal are substituted into the supporting vector machine model after training,
Obtain the state-of-charge Z at k momentkAnd as Kalman filtering observation,
Predicted value obtains module: obtaining the pre- of k moment battery charge state according to the state transition equation of Kalman filtering
Measured value
State-of-charge obtains module: utilizing Kalman filtering observation Zk, the k moment Kalman filtering gain KgkWhen with k
Carve the predicted value of battery charge stateEstimate k moment battery charge state estimated value SOCk。
A kind of dynamical unmanned plane charge states of lithium ion battery estimation method, comprising the following steps:
Data collection steps: the status data of lithium ion battery during acquisition unmanned plane during flying, status data include electricity
Press signal and current signal；
Training step: establishing training dataset using status data and be trained to supporting vector machine model,
Observation obtains step: the voltage signal at k moment and current signal are substituted into the supporting vector machine model after training,
Obtain the state-of-charge Z at k momentkAnd as Kalman filtering observation,
Predicted value obtains step: obtaining the pre- of k moment battery charge state according to the state transition equation of Kalman filtering
Measured value
State-of-charge obtains step: utilizing Kalman filtering observation Zk, the k moment Kalman filtering gain KgkWhen with k
Carve the predicted value of battery charge stateEstimate k moment battery charge state estimated value SOCk。
The present invention is based on embedded controllers, devise a kind of dynamical unmanned plane charge states of lithium ion battery estimation
Embedded system and method, have many advantages, such as small in size, light-weight, low in energy consumption, can be used for during unmanned plane execution task
The acquisition of lithium ion battery data and SOC estimation.Compared with prior art, in addition to realizing the basic functions such as voltage and current acquisition, this
Invention also achieves lithium ion battery SOC and obtains On-line Estimation.The present invention uses Kalman filtering (KF) and support vector machines (SVM)
The method of fusion realizes the SOC On-line Estimation of lithium ion battery, compensates for traditional lower deficiency of SOC estimation method precision.Together
When, even if can also realize the accurate estimation of SOC in the case where SOC initial value is unknown.The present invention propose a kind of dynamical lithium from
Sub- battery charge state estimating system and method have many advantages, such as that miniaturization, low-power consumption, precision are high, hold for realizing unmanned plane
The accurate estimation of battery charge state during row task.
Detailed description of the invention
Fig. 1 is a kind of dynamical unmanned plane charge states of lithium ion battery estimating system described in specific embodiment one
Structural schematic diagram；
Fig. 2 is data processing unit IP kernel and each functional interface structural schematic diagram；
SOC estimation method functional block diagram of the Fig. 3 based on KF-SVM；
Fig. 4 is SOC estimated result curve graph under noise-free case；
Fig. 5 is SOC estimation error curve figure under noise-free case；
Fig. 6 is SOC estimated result curve graph under 40dB noise situations；
Fig. 7 is SOC estimation error curve figure under 40dB noise situations；
Fig. 8 is SOC estimated result curve graph under 50dB noise situations；
Fig. 9 is SOC estimation error curve figure under 50dB noise situations；
Figure 10 is SOC estimated result curve graph under 60dB noise situations；
Figure 11 is SOC estimation error curve figure under 60dB noise situations；
The experimental result curve graph that Figure 12 is SOC initial value when being 1；
The experimental result curve graph that Figure 13 is SOC initial value when being 0.8.
Specific embodiment
Specific embodiment 1: present embodiment is illustrated referring to figs. 1 to Fig. 3, it is a kind of high described in present embodiment
The unmanned plane charge states of lithium ion battery estimating system of efficiency, comprising: data acquisition unit, data processing unit and data are deposited
Storage unit.
Data acquisition unit: for acquiring the status data of lithium ion battery during unmanned plane during flying, state data packets
Include voltage signal and current signal；Specifically, data acquisition unit includes following submodule: experiment module: for simulating nobody
The charge and discharge process of lithium ion battery in machine flight course；Acquisition module: for acquiring lithium-ion electric in above-mentioned charge and discharge process
The voltage signal and current signal in pond.
When practical application, pass through Hall sensor ACS712 and TI (Texas Instrument) company's analog-digital converter
The acquisition of ADS1256 realization electric current, voltage signal；ACS712 can acquire twocouese current signal, will fill and (put) in electric process
Current signal is converted to voltage signal, and analog-digital converter ADS1256 is then input to together with battery voltage signal and is converted to number
Word signal completes acquisition.Data transmission is realized by spi bus interface between ADS1256 and data processing unit.
Data processing unit: it for estimating the state-of-charge of lithium ion battery according to status data, is also used to lithium ion
The state-of-charge of battery is converted to digital signal, and is sent to data storage cell；Specifically, above-mentioned data processing unit includes
Following submodule: training module: training dataset is established using status data and supporting vector machine model is trained；It sees
Measured value obtains module: the voltage signal at k moment and current signal being substituted into the supporting vector machine model after training, obtain the k moment
State-of-charge ZkAnd as Kalman filtering observation；Predicted value obtains module: according to the state transfer side of Kalman filtering
The predicted value of journey acquisition k moment battery charge stateState-of-charge obtains module: utilizing Kalman filtering observation Zk、k
The Kalman filtering gain Kg at momentkWith the predicted value of k moment battery charge stateEstimation k moment battery charge state is estimated
Evaluation SOCk。
When practical application, data processing unit controls data acquisition unit using the ZYNQ7045 of Xilinx company and realizes
SOC estimation.Program is write according to functional requirement, and by jtag interface by program Solidification to ZYNQ7045, to realize that data are adopted
The control of collection and SOC estimation.
Data storage cell: for storing the status data and state-of-charge of lithium ion battery.When practical application, data are deposited
Storage unit stores the historical state data of unmanned plane lithium ion battery, while the lithium of storing data processing unit output using SD card
Ion battery SOC estimated result.Using TXS02612 realize in SD card interface and data processing unit ZYNQ7045 processor it
Between logic level transition, to realize the reading and storage of data.
Further include 1 tunnel UART interface in the present embodiment, is beaten for the debugging of module allomeric function, state data acquisition result
Print, SOC estimated result is shown and configuration information is shown.
Further, the Kalman filtering gain Kg at k moment is obtained according to the following formulak:
Wherein,For the evaluated error covariance at k moment, RkFor the measurement noise covariance at k moment.
Further, k moment battery charge state estimated value SOC is obtained according to the following formulak:
Specific embodiment 2: a kind of dynamical unmanned plane charge states of lithium ion battery described in present embodiment is estimated
Meter method, SOC estimation is using based on Kalman filtering (Kalman Filter, KF) and support vector machines (Support Vector
Machine, SVM) fusion method, estimating step is as follows:
Data collection steps: the charge and discharge process of lithium ion battery during simulation unmanned plane during flying acquires the charge and discharge
The voltage signal and current signal of lithium ion battery in the process acquires the voltage and current data in experimentation using module,
And corresponding SOC of each moment is calculated, using the voltage signal of lithium ion battery, current signal and corresponding SOC as physical training condition
Data.
Training step: establishing training dataset using status data, which concentrates voltage signal and current signal
As list entries X1 and X2,
Wherein, N is list entries length, Vn(n=1,2 ..., N) and In(n=1,2 ... N) respectively indicates the electricity of battery
Pressure and electric current；
Training data concentrates SOC as output sequence Y,
Y=[SOC1,SOC2,...,SOCN]
Wherein, SOCn(n=1,2 ..., N) indicate battery SOC；
List entries X1, X2 and output sequence Y are substituted into SVM model, SVM model is trained.
Observation obtains step: due in actual operation, the acquisition of battery data has a noise, therefore by training data
Concentrate the noise of battery data addition varying strength to simulate unmanned plane lithium ion battery data actual acquisition situation.By the k moment
Battery data VkAnd IkIn SVM model after substituting into training, the corresponding output result Z of k moment voltage, current data is obtainedk=
SVM(Vk,Ik) and as KF observation, carry out the accurate evaluation of unmanned plane lithium ion battery SOC.
Predicted value obtains step: the predicted value of k moment battery charge state is obtained according to the state transition equation of KF
The state transition equation of the KF are as follows:
Wherein, SOCk-1It is sampling time interval, C for the estimated value of k-1 moment battery charge state, Δ trateFor lithium from
The rated capacity of sub- battery, wkThe noise of process is predicted for k moment SOC.
State-of-charge obtains step: firstly, obtaining the Kalman filtering gain Kg at k moment according to the following formulak:
Wherein,For the evaluated error covariance at k moment, RkFor the measurement noise covariance at k moment；
Then, Kalman filtering observation Z is utilizedk, the k moment Kalman filtering gain KgkWith k moment battery charge shape
The predicted value of stateTo estimate k moment battery charge state estimated value SOCk, specific formula is as follows:
Data storing steps, specifically: the state-of-charge of lithium ion battery is converted into digital signal, and is stored；
The status data and state-of-charge of lithium ion battery can also be stored.
In present embodiment, after the above step, in order to estimate the battery charge state of subsequent time, it is also necessary to pass through
Following formula updates the evaluated error covariance at k+1 moment
Wherein, PkFor the covariance at k moment,Qk+1For the systematic procedure noise covariance at k+1 moment.
SOC estimated result is obtained according to the above method and draws curve, referring to Fig. 4 to Figure 13.
1 SOC evaluation of result index of table
Present embodiment simulates unmanned plane actual operating mode to realize unmanned plane lithium ion battery SOC On-line Estimation
It obtains battery data (voltage, electric current), uses the relationship between SVM building voltage, electric current and battery SOC, it is contemplated that practical to survey
There are errors during amount, realize SOC On-line Estimation using the method that Kalman filtering is merged with SVM.Compared with prior art,
In certain error range and in the case that SOC initial value is unknown, the method merged based on data-driven and physical model can
Realize the accurate estimation of SOC.
Claims (10)
1. a kind of dynamical unmanned plane charge states of lithium ion battery estimating system characterized by comprising data acquisition is single
Member and data processing unit；
Data acquisition unit: for acquiring the status data of lithium ion battery during unmanned plane during flying, status data includes electricity
Press signal and current signal；
Data processing unit: for the state-of-charge according to status data estimation lithium ion battery, which includes
With lower module:
Training module: establishing training dataset using status data and be trained to supporting vector machine model,
Observation obtains module: the voltage signal at k moment and current signal being substituted into the supporting vector machine model after training, obtained
The state-of-charge Z at k momentkAnd as Kalman filtering observation,
Predicted value obtains module: the predicted value of k moment battery charge state is obtained according to the state transition equation of Kalman filtering
State-of-charge obtains module: utilizing Kalman filtering observation Zk, the k moment Kalman filtering gain KgkWith k moment electricity
The predicted value of pond state-of-chargeEstimate k moment battery charge state estimated value SOCk。
2. a kind of dynamical unmanned plane charge states of lithium ion battery estimating system according to claim 1, feature
It is, it further includes data storage cell,
Above-mentioned data processing unit is also used to the state-of-charge of lithium ion battery being converted to digital signal, and is sent to data and deposits
Storage unit；
Data storage cell is used to store the status data and state-of-charge of lithium ion battery.
3. a kind of dynamical unmanned plane charge states of lithium ion battery estimating system according to claim 1 or 2, special
Sign is that data acquisition unit comprises the following modules:
Experiment module: for simulating the charge and discharge process of lithium ion battery during unmanned plane during flying,
Acquisition module: for acquiring the voltage signal of lithium ion battery, current signal and state of charge in above-mentioned charge and discharge process.
4. a kind of dynamical unmanned plane charge states of lithium ion battery estimating system according to claim 3, feature
It is, obtains the Kalman filtering gain Kg at k moment according to the following formulak:
Wherein,For the evaluated error covariance at k moment, RkFor the measurement noise covariance at k moment.
5. a kind of dynamical unmanned plane charge states of lithium ion battery estimating system according to claim 3, feature
It is, obtains k moment battery charge state estimated value SOC according to the following formulak:
6. a kind of dynamical unmanned plane charge states of lithium ion battery estimation method, which comprises the following steps:
Data collection steps: the status data of lithium ion battery during acquisition unmanned plane during flying, status data include voltage letter
Number and current signal；
Training step: establishing training dataset using status data and be trained to supporting vector machine model,
Observation obtains step: the voltage signal at k moment and current signal being substituted into the supporting vector machine model after training, obtained
The state-of-charge Z at k momentkAnd as Kalman filtering observation,
Predicted value obtains step: the predicted value of k moment battery charge state is obtained according to the state transition equation of Kalman filtering
State-of-charge obtains step: utilizing Kalman filtering observation Zk, the k moment Kalman filtering gain KgkWith k moment electricity
The predicted value of pond state-of-chargeEstimate k moment battery charge state estimated value SOCk。
7. a kind of dynamical unmanned plane charge states of lithium ion battery estimation method according to claim 6, feature
It is, it further includes data storing steps, specifically:
The state-of-charge of lithium ion battery is converted into digital signal, and is stored；
Store the status data and state-of-charge of lithium ion battery.
8. a kind of dynamical unmanned plane charge states of lithium ion battery estimation method according to claim 5 or 6, special
Sign is, data collection steps specifically:
The charge and discharge process of lithium ion battery, acquires lithium ion battery in the charge and discharge process during simulation unmanned plane during flying
Voltage signal and current signal.
9. a kind of dynamical unmanned plane charge states of lithium ion battery estimation method according to claim 8, feature
It is, obtains the Kalman filtering gain Kg at k moment according to the following formulak:
Wherein,For the evaluated error covariance at k moment, RkFor the measurement noise covariance at k moment.
10. a kind of dynamical unmanned plane charge states of lithium ion battery estimation method according to claim 8, feature
It is, obtains k moment battery charge state estimated value SOC according to the following formulak:
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