CN109870654A - The online method for dynamic estimation of accumulator capacity based on impact load response characteristic - Google Patents
The online method for dynamic estimation of accumulator capacity based on impact load response characteristic Download PDFInfo
- Publication number
- CN109870654A CN109870654A CN201910106341.XA CN201910106341A CN109870654A CN 109870654 A CN109870654 A CN 109870654A CN 201910106341 A CN201910106341 A CN 201910106341A CN 109870654 A CN109870654 A CN 109870654A
- Authority
- CN
- China
- Prior art keywords
- impact load
- energy
- battery
- accumulator capacity
- capacity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Secondary Cells (AREA)
Abstract
The present invention discloses a kind of online method for dynamic estimation of the accumulator capacity based on impact load response characteristic, comprising: keeps carrying out on-line real time monitoring to cell voltage, electric current and temperature；When there is impact load, i.e. when battery current moment, which increases amplitude, is greater than preset value, the accumulating voltage acquired during load sudden change, electric current and temperature data are delivered to On-line Estimation device, by merging the accumulator capacity On-line Estimation algorithm of wavelet analysis and Cerebellar Model Articulation Controller, real-time estimation remaining battery capacity.The present invention is not necessarily to put battery progress long-time Man Chongman, without actively injecting other harmonic signals to battery, directly using under impact load effect, the cell voltage of real-time monitoring, electric current and temperature data, the capacity of battery can be carried out estimating quickly, accurately, in real time, it is suitble to uninterruptible power system, battery maintenance cost is reduced, accumulator capacity reduction or Problem of Failure is found in time, improves the reliability of system.
Description
Technical field
The present invention relates to battery detecting fields, and in particular to a kind of accumulator capacity based on impact load response characteristic exists
Line method for dynamic estimation.
Background technique
Energy energy-storage units of the battery as uninterruptible power system become electric energy during electric network power-fail and uniquely provide
Person, reliability are most important to system.However, battery active volume is gradually with cycle-index increase and long-time aging
It reduces or even entirely ineffective.Therefore, the necessary ring that the battery virtual condition comprising active volume is battery service is inspected periodically
Section.There are mainly two types of existing battery condition detection technological means: first is that appraising and deciding the surplus of battery using regular charge and discharge system
Covolume amount, this method is reliably effective, but that there are labor intensives is more, the time is long, the big disadvantage of energy loss, is additionally present of when electric discharge
When low to electricity, there is grid power blackout, then without the risk of enough electric energy support load；Second is that being judged using internal resistance detection method
The state of battery, wherein off-line type, it is still desirable to a large amount of artificial routine tests, time-consuming work consuming, during existing simultaneously detection twice
Battery failure fails the risk found in time；Online inner walkway method, although it is a large amount of artificial to overcome off-line type consuming
Defect, but active harmonic, exist and cause dysgenic hidden danger to system load and other equipment.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of accumulator capacity based on impact load response characteristic is online
Method for dynamic estimation increases sensor and certain loads without additional, directly negative using impact also not to battery harmonic
Under load effect, battery current, the end data such as voltage and temperature are online real-time by trained machine learning intelligent algorithm
Estimate remaining battery capacity.
To achieve the above object, the present invention adopts the following technical scheme:
A kind of online method for dynamic estimation of accumulator capacity based on impact load response characteristic, providing a system includes electric power storage
Pond, sampling processing circuit, accumulator capacity On-line Estimation device, communication module, remote monitoring system, comprising the following steps:
Step S1: battery voltage, electric current and temperature are acquired in real time by sampling processing circuit；
Step S2: when there is impact load, i.e., electric current is more than preset threshold, by the battery acquired in real time during impact electricity
Pressure, electric current and temperature data are sent to remaining battery capacity On-line Estimation module；
Step S3: accumulator capacity On-line Estimation module is according to obtained data, by being built in accumulator capacity On-line Estimation mould
It is transferred to machine learning algorithm after wavelet analysis feature extraction in block, real-time remaining battery capacity estimation is carried out, obtains electric power storage
Pond residual capacity estimated value；
Step S4: remaining battery capacity estimated value is sent to remote monitoring system by communication module and is monitored in real time.
Further, the wavelet analysis feature extraction specifically:
Step S21: using wavelet transformation extracted valid data feature, selects three rank Daubechies small echos, Decomposition order setting
It is 11 layers；
Step S22: being decomposed into approximation coefficient and detail coefficients for voltage signal, current signal and temperature signal, weight after decomposition
Then structure calculates the energy of every layer of detail coefficients;
Step S23: scaling function Ф j,k (t) and wavelet function ψ j,k (t) such as formula (1), shown in (2):
(1)
(2)
E dj Indicate thejLayer detail coefficients energy:
i =1, 2,…, N (3)
E a Indicate approximation coefficient portion of energy:
j =1, 2,…, n (4)
E total Indicate gross energy, i.e. detail coefficients energy part and approximation coefficient energy part summation:
(5)
P j Indicate thejLayer detail coefficients energy accounting:
(6)
N is data length in formula, and n is Decomposition order, PjFor jth layer detail coefficients energy accounting；d k,j It isjLayer detail coefficients
ThekNumber；a j,n It isnLayer approximation coefficient thejNumber.
Further, the machine learning algorithm uses Cerebellar Model Articulation Controller algorithm, wherein 8 layers of interlayer selecting,
By 1,3,9 input as neural network in 11 layers of energy of wavelet decomposition, neural network output is accumulator capacity.
Further, the accumulator capacity is divided into, and 100% ~ 90%, 90% ~ 80%, 80% ~ 70%, 70% ~ 60%, 60%
~ 50%, 50% ~ 0% 6 class；Accumulator electric-quantity SOC is 20% ~ 100%.
Further, the machine learning algorithm building specifically:
Step S31: the storage battery power supply system experimental platform containing impact load is built；
Step S32: under the effect of the same impact load, the response spy in the case of different batteries capacity and different SOC is acquired
Linearity curve；
Step S33: adjusting action time and the amplitude of impact load, and return step two obtains the response data under different loads；
Step S34: using collected battery voltage, electric current and temperature data, data sample library is established；
Step S35: using wavelet analysis, carries out data characteristics extraction；
Step S36: the data completed are extracted using data characteristics, add corresponding dash current amplitude and time width, electric power storage
Pond temperature carries out off-line training to Cerebellar Model Articulation Controller algorithm, until its estimated accuracy meets preset requirement.
Further, the Cerebellar Model Articulation Controller algorithm interbed selects 8 layers, and choose in voltage decomposition energy 1,
3,9 layers, electric current, temperature decompose the third layer energy in energy, input of totally five characteristic signals as neural network, nerve net
Network output is accumulator capacity.
Compared with the prior art, the invention has the following beneficial effects:
The present invention is not necessarily to carry out long-time Man Chongman to battery to put, without actively injecting other harmonic signals to battery,
Directly using under impact load effect, the cell voltage of real-time monitoring, electric current and temperature data, can be to the capacity of battery
Quickly, accurately, in real time estimate, be suitble to uninterruptible power system, reduce battery maintenance cost, it is timely to find electric power storage pool capacity
Amount reduces or Problem of Failure, improves the reliability of system.
Detailed description of the invention
Fig. 1 is that the present invention is based on the online dynamic estimation intelligent apparatus of the accumulator capacity of impact load response characteristic；
Fig. 2 is the method for the present invention flow chart；
Fig. 3 is battery impact load response characteristic in one embodiment of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides a kind of online dynamic estimation of the accumulator capacity based on impact load response characteristic
Intelligent method, the online dynamic estimation circuit of accumulator capacity as shown in Figure 1, the system include power grid, rectification circuit, battery,
Conventional load, impact load, sampling processing circuit, battery On-line Estimation module, communication network, remote monitoring system.
For the present embodiment impact load selection high-voltage circuitbreaker.When power grid works normally, by power grid to conventional load
It powers with high-voltage circuitbreaker；When electric network fault disconnects, powered by battery to constant load and high-voltage circuitbreaker.
Online dynamic estimation intelligent method specific implementation method the following steps are included:
Step S1: it as shown in figure, keeps to cell voltage, electric current and temperature online real-time monitoring；
Step S2: when the high-voltage circuitbreaker movement powered by direct current operative power source, the large impact electricity of a short time can be generated
Stream belongs to impact load.
Step S3:, will be during load sudden change when the amplitude for detecting that battery current moment increases is greater than preset value
Cell voltage, electric current and the temperature of acquisition are delivered to accumulator capacity On-line Estimation device, include impact load in On-line Estimation device
Response characteristic feature extraction and remaining battery capacity On-line Estimation algorithm, the data of acquisition are by being built in small wavelength-division in estimator
Machine learning algorithm, which is transferred to, after analysis feature extraction carries out accumulator capacity real-time estimation;
Step S4: by communication network, battery real time capacity is transmitted to remote monitoring end, carries out the processing of next step；It is no
Then continue to detect battery voltage, electric current and temperature；
In the present embodiment, impact load is acted on down collected voltage response curves data and electric current and temperature, use are small
Wave conversion extracted valid data feature.
Voltage responsive characteristic is as shown in figure 3, three rank Daubechies small echo of case column selection, Decomposition order are set as 11 layers.
Wherein scaling function Ф j,k (t) and wavelet function ψ j,k (t) such as formula (1), shown in (2), respectively as low pass and high-pass filter.
Voltage signal is broken down into approximation coefficienta j And detail coefficientsd j ,It is reconstructed after decomposition, then calculates the energy of every layer of detail coefficients.
Electric current is consistent with voltage decomposition process with temperature signal decomposable process.
(1)
(2)
E dj Indicate thejLayer detail coefficients energy:
i =1, 2,…, N (3)
E a Indicate approximation coefficient portion of energy:
j =1, 2,…, n (4)
E total Indicate gross energy, i.e. detail coefficients energy part and approximation coefficient energy part summation:
(5)
P j Indicate thejLayer detail coefficients energy accounting:
(6)
In the present embodiment, the remaining battery capacity On-line Estimation algorithm, the feature extracted using wavelet analysis, in conjunction with punching
Size of current, duration and impact moment battery temp are hit, trained machine learning intelligent algorithm is inputted, completes electric power storage
The estimation of pond residual capacity, and the estimated capacity value result of output is transferred to remote monitoring system by communication network.
In the present embodiment, the trained machine learning intelligent algorithm, the trained machine learning intelligent algorithm,
It is to be completed in advance by off-line training, as shown in Fig. 2, steps are as follows for machine learning intelligent algorithm off-line training:
Step 1: the storage battery power supply system experimental platform containing impact load is built；
Step 2: under the effect of the same impact load, the response characteristic in the case of different batteries capacity and different SOC is acquired
Curve；
Step 3: adjusting action time and the amplitude of impact load, and return step two obtains the response data under different loads；
Step 4: using step 2 and the collected battery voltage of step 3, electric current and temperature data, data sample is established
Library；
Step 5: using wavelet analysis, carries out data characteristics extraction；
Step 6: the data completed are extracted using data characteristics, add corresponding dash current amplitude and time width, battery
Temperature carries out off-line training to machine learning algorithm, until its estimated accuracy is met the requirements.
In the present embodiment, the online dynamic estimation intelligence side of the accumulator capacity based on impact load response characteristic
Method, which is characterized in that step 2 accumulator capacity and electrical parameter, wherein accumulator capacity is divided into, 100% ~ 90%, 90% ~
80%, 80% ~ 70%, 70% ~ 60%, 60% ~ 50%, 50% ~ 0% 6 class；Accumulator electric-quantity SOC is 20% ~ 100%.
In the present embodiment, the online dynamic estimation intelligence side of the accumulator capacity based on impact load response characteristic
Method, which is characterized in that machine learning algorithm described in step 6 selects Cerebellar Model Articulation Controller.Wherein 8 layers of interlayer selecting, and
1,3,9 layers in voltage decomposition energy are chosen, electric current, temperature decompose the third layer energy in energy, and totally five characteristic signals are made
For the input of neural network, neural network output is accumulator capacity.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (6)
1. a kind of online method for dynamic estimation of accumulator capacity based on impact load response characteristic, providing a system includes electric power storage
Pond, sampling processing circuit, accumulator capacity On-line Estimation device, communication module, remote monitoring system, which is characterized in that including with
Lower step:
Step S1: battery voltage, electric current and temperature are acquired in real time by sampling processing circuit；
Step S2: when there is impact load, i.e., electric current is more than preset threshold, by the battery acquired in real time during impact electricity
Pressure, electric current and temperature data are sent to remaining battery capacity On-line Estimation module；
Step S3: accumulator capacity On-line Estimation module is according to obtained data, by being built in accumulator capacity On-line Estimation mould
It is transferred to machine learning algorithm after wavelet analysis feature extraction in block, real-time remaining battery capacity estimation is carried out, obtains electric power storage
Pond residual capacity estimated value；
Step S4: remaining battery capacity estimated value is sent to remote monitoring system by communication module and is monitored in real time.
2. the online method for dynamic estimation of the accumulator capacity according to claim 1 based on impact load response characteristic,
It is characterized in that: the wavelet analysis feature extraction specifically:
Step S21: using wavelet transformation extracted valid data feature, selects three rank Daubechies small echos, Decomposition order setting
It is 11 layers；
Step S22: being decomposed into approximation coefficient and detail coefficients for voltage signal, current signal and temperature signal, weight after decomposition
Then structure calculates the energy of every layer of detail coefficients;
Step S23: scaling function Ф j,k (t) and wavelet function ψ j,k (t) such as formula (1), shown in (2):
(1)
(2)
E dj Indicate thejLayer detail coefficients energy:
i =1, 2,…, N (3)
E a Indicate approximation coefficient portion of energy:
j =1, 2,…, n (4)
E total Indicate gross energy, i.e. detail coefficients energy part and approximation coefficient energy part summation:
(5)
P j Indicate thejLayer detail coefficients energy accounting:
(6)
N is data length in formula, and n is Decomposition order, PjFor jth layer detail coefficients energy accounting；d k,j It isjLayer detail coefficients thekNumber；a j,n It isnLayer approximation coefficient thejNumber.
3. the online method for dynamic estimation of the accumulator capacity according to claim 2 based on impact load response characteristic,
Be characterized in that: the machine learning algorithm uses Cerebellar Model Articulation Controller algorithm, wherein 8 layers of interlayer selecting, by small wavelength-division
1,3,9 input as neural network in 11 layers of energy of solution, neural network output are accumulator capacity.
4. the online method for dynamic estimation of the accumulator capacity according to claim 1 based on impact load response characteristic,
Be characterized in that: the accumulator capacity is divided into, and 100% ~ 90%, 90% ~ 80%, 80% ~ 70%, 70% ~ 60%, 60% ~ 50%, 50%
~ 0% six class；Accumulator electric-quantity SOC is 20% ~ 100%.
5. the online method for dynamic estimation of the accumulator capacity according to claim 3 based on impact load response characteristic,
It is characterized in that: the machine learning algorithm building specifically:
Step S31: the storage battery power supply system experimental platform containing impact load is built；
Step S32: under the effect of the same impact load, the response spy in the case of different batteries capacity and different SOC is acquired
Linearity curve；
Step S33: adjusting action time and the amplitude of impact load, and return step two obtains the response data under different loads；
Step S34: using collected battery voltage, electric current and temperature data, data sample library is established；
Step S35: using wavelet analysis, carries out data characteristics extraction；
Step S36: the data completed are extracted using data characteristics, add corresponding dash current amplitude and time width, electric power storage
Pond temperature carries out off-line training to Cerebellar Model Articulation Controller algorithm, until its estimated accuracy meets preset requirement.
6. the online method for dynamic estimation of the accumulator capacity according to claim 5 based on impact load response characteristic,
Be characterized in that: the Cerebellar Model Articulation Controller algorithm interbed selects 8 layers, and chooses 1,3,9 layers in voltage decomposition energy, electricity
Stream, temperature decompose the third layer energy in energy, input of totally five characteristic signals as neural network, and neural network output is
Accumulator capacity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910106341.XA CN109870654B (en) | 2019-02-02 | 2019-02-02 | Storage battery capacity online dynamic estimation method based on impact load response characteristic |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910106341.XA CN109870654B (en) | 2019-02-02 | 2019-02-02 | Storage battery capacity online dynamic estimation method based on impact load response characteristic |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109870654A true CN109870654A (en) | 2019-06-11 |
| CN109870654B CN109870654B (en) | 2021-07-13 |
Family
ID=66918605
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910106341.XA Active CN109870654B (en) | 2019-02-02 | 2019-02-02 | Storage battery capacity online dynamic estimation method based on impact load response characteristic |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109870654B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991076A (en) * | 2019-12-16 | 2020-04-10 | 天津凯发电气股份有限公司 | Method for predicting data storage state of NVRAM (non-volatile random Access memory) of relay protection device |
| CN111611747A (en) * | 2020-05-25 | 2020-09-01 | 福州大学 | Online state estimation method and device for hybrid energy storage system |
| CN114114052A (en) * | 2021-11-25 | 2022-03-01 | 福州大学 | Method for rapidly estimating SOH and SOC of battery based on shock response characteristic |
| CN116643178A (en) * | 2023-07-27 | 2023-08-25 | 深圳凌奈智控有限公司 | SOC estimation method and related device of battery management system |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1809758A (en) * | 2003-06-23 | 2006-07-26 | 矢崎总业株式会社 | Method and apparatus for judging deterioration of battery |
| US20090033277A1 (en) * | 2007-07-31 | 2009-02-05 | Apple Inc. | Battery charging system and mobile and accessory devices |
| CN102023283A (en) * | 2009-09-16 | 2011-04-20 | 徐先 | On-line measuring method for capacity of accumulator |
| CN102967831A (en) * | 2012-09-17 | 2013-03-13 | 常州大学 | On-line detection system and detection method of lead-acid storage battery performance |
| CN103344921A (en) * | 2013-07-08 | 2013-10-09 | 华南师范大学 | Lithium-ion power battery health state evaluating system and method |
| CN104049213A (en) * | 2013-03-12 | 2014-09-17 | 三星Sdi株式会社 | Apparatus for predicting state of health of battery pack by using discrete wavelet transform |
| CN105021995A (en) * | 2015-07-28 | 2015-11-04 | 王树华 | Portable intelligent storage battery parameter test method and tester |
| CN105807230A (en) * | 2016-03-11 | 2016-07-27 | 郑贵林 | Storage battery residual capacity and health state rapid detection method and device |
| CN106599777A (en) * | 2016-11-02 | 2017-04-26 | 华南理工大学 | Cable partial discharge signal identification method based on energy percentage |
| CN106918789A (en) * | 2017-05-10 | 2017-07-04 | 成都理工大学 | A kind of SOC SOH combine online real-time estimation and on-line amending method |
| CN107329089A (en) * | 2017-05-25 | 2017-11-07 | 中国电力科学研究院 | A kind of rigid package lithium ion battery explosion propagation effect appraisal procedure and device |
| CN108845264A (en) * | 2018-06-01 | 2018-11-20 | 西安交通大学 | A kind of cell health state estimation method based on small echo |
| CN108896926A (en) * | 2018-07-18 | 2018-11-27 | 湖南宏迅亿安新能源科技有限公司 | A kind of appraisal procedure, assessment system and the associated component of lithium battery health status |
| CN109004696A (en) * | 2018-04-18 | 2018-12-14 | 国家电网公司 | A kind of substation battery multifunction control system and its control method |
-
2019
- 2019-02-02 CN CN201910106341.XA patent/CN109870654B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1809758A (en) * | 2003-06-23 | 2006-07-26 | 矢崎总业株式会社 | Method and apparatus for judging deterioration of battery |
| US20090033277A1 (en) * | 2007-07-31 | 2009-02-05 | Apple Inc. | Battery charging system and mobile and accessory devices |
| CN102023283A (en) * | 2009-09-16 | 2011-04-20 | 徐先 | On-line measuring method for capacity of accumulator |
| CN102967831A (en) * | 2012-09-17 | 2013-03-13 | 常州大学 | On-line detection system and detection method of lead-acid storage battery performance |
| CN104049213A (en) * | 2013-03-12 | 2014-09-17 | 三星Sdi株式会社 | Apparatus for predicting state of health of battery pack by using discrete wavelet transform |
| CN103344921A (en) * | 2013-07-08 | 2013-10-09 | 华南师范大学 | Lithium-ion power battery health state evaluating system and method |
| CN105021995A (en) * | 2015-07-28 | 2015-11-04 | 王树华 | Portable intelligent storage battery parameter test method and tester |
| CN105807230A (en) * | 2016-03-11 | 2016-07-27 | 郑贵林 | Storage battery residual capacity and health state rapid detection method and device |
| CN106599777A (en) * | 2016-11-02 | 2017-04-26 | 华南理工大学 | Cable partial discharge signal identification method based on energy percentage |
| CN106918789A (en) * | 2017-05-10 | 2017-07-04 | 成都理工大学 | A kind of SOC SOH combine online real-time estimation and on-line amending method |
| CN107329089A (en) * | 2017-05-25 | 2017-11-07 | 中国电力科学研究院 | A kind of rigid package lithium ion battery explosion propagation effect appraisal procedure and device |
| CN109004696A (en) * | 2018-04-18 | 2018-12-14 | 国家电网公司 | A kind of substation battery multifunction control system and its control method |
| CN108845264A (en) * | 2018-06-01 | 2018-11-20 | 西安交通大学 | A kind of cell health state estimation method based on small echo |
| CN108896926A (en) * | 2018-07-18 | 2018-11-27 | 湖南宏迅亿安新能源科技有限公司 | A kind of appraisal procedure, assessment system and the associated component of lithium battery health status |
Non-Patent Citations (4)
| Title |
|---|
| ANUPAMA. KEELI: ""Optimal use of second life battery for peak load management and improving the life of the battery"", 《2012 IEEE INTERNATIONAL ELECTRIC VEHICLE CONFERENCE》 * |
| 刘金枝: ""一种基于能量建模的锂离子电池电量估算方法"", 《电工技术学报》 * |
| 季迎旭: ""蓄电池SOC估算方法综述"", 《电测与仪表》 * |
| 李学斌: ""基于容量分解的电力直流电源系统蓄电池容量计算方法"", 《电网技术》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991076A (en) * | 2019-12-16 | 2020-04-10 | 天津凯发电气股份有限公司 | Method for predicting data storage state of NVRAM (non-volatile random Access memory) of relay protection device |
| CN110991076B (en) * | 2019-12-16 | 2023-10-31 | 天津凯发电气股份有限公司 | NVRAM data storage state prediction method of relay protection device |
| CN111611747A (en) * | 2020-05-25 | 2020-09-01 | 福州大学 | Online state estimation method and device for hybrid energy storage system |
| CN111611747B (en) * | 2020-05-25 | 2022-09-09 | 福州大学 | Online state estimation method and device for hybrid energy storage system |
| CN114114052A (en) * | 2021-11-25 | 2022-03-01 | 福州大学 | Method for rapidly estimating SOH and SOC of battery based on shock response characteristic |
| CN116643178A (en) * | 2023-07-27 | 2023-08-25 | 深圳凌奈智控有限公司 | SOC estimation method and related device of battery management system |
| CN116643178B (en) * | 2023-07-27 | 2023-09-22 | 深圳凌奈智控有限公司 | SOC estimation method and related device of battery management system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109870654B (en) | 2021-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109870654A (en) | The online method for dynamic estimation of accumulator capacity based on impact load response characteristic | |
| Shen et al. | A new battery available capacity indicator for electric vehicles using neural network | |
| CN115663312B (en) | Battery operation monitoring system and method based on battery protection | |
| CN107656216A (en) | A kind of lead-acid accumulator on-line monitoring is safeguarded and early warning system and performance estimating method | |
| CN104156889B (en) | A kind of wind power plant performance evaluation system and its appraisal procedure based on WAMS data | |
| CN104318485A (en) | Power transmission line fault identification method based on nerve network and fuzzy logic | |
| CN103439631A (en) | Method and system for detecting corrosion state of grounding grid | |
| CN108983104B (en) | Online capacity calculation method based on battery open circuit voltage method | |
| CN115425737B (en) | Intelligent control system of high-voltage fixed-frequency voltage-regulating stabilized power supply | |
| CN108988782A (en) | A kind of photovoltaic generating system health state evaluation method based on variable weight analytic hierarchy process (AHP) | |
| CN106515480B (en) | A kind of automobile storage battery intelligent control method based on car networking big data technology | |
| CN117078113B (en) | Outdoor battery production quality management system based on data analysis | |
| CN103824124B (en) | A kind of energy potential evaluation method for grid company | |
| Yang | Bounds of supercapacitor open-circuit voltage change after constant power experiments | |
| CN102930408B (en) | A kind of 750kV electric grid secondary equipment state appraisal procedure based on information fusion | |
| CN113659659A (en) | Data processing method and device based on sensor energy supply system | |
| Sharif et al. | Fault detection and location in dc microgrids by recurrent neural networks and decision tree classifier | |
| Yan et al. | A simplified current feature extraction and deployment method for DC series arc fault detection | |
| CN107086636A (en) | Batteries verification property discharge system and method | |
| CN103997041B (en) | A kind of on-Line Voltage stabilizing determination system of alternating current-direct current combined hybrid system | |
| CN117706399A (en) | Distributed energy storage operation health state analysis method considering battery consistency | |
| CN111611747B (en) | Online state estimation method and device for hybrid energy storage system | |
| CN116780707A (en) | Energy storage battery system and power supply method | |
| CN116523681A (en) | Load decomposition method and device for electric automobile, electronic equipment and storage medium | |
| CN116680598A (en) | Fault diagnosis and residual life prediction method for intelligent distribution box of construction site |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |