CN116304582A - Abnormal mark correction method for monotone data in power battery - Google Patents
Abnormal mark correction method for monotone data in power battery Download PDFInfo
- Publication number
- CN116304582A CN116304582A CN202310547808.0A CN202310547808A CN116304582A CN 116304582 A CN116304582 A CN 116304582A CN 202310547808 A CN202310547808 A CN 202310547808A CN 116304582 A CN116304582 A CN 116304582A
- Authority
- CN
- China
- Prior art keywords
- data
- abnormal
- slope
- power battery
- monotonic
- 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
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 51
- 238000012937 correction Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000005856 abnormality Effects 0.000 claims description 10
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 6
- 229910052744 lithium Inorganic materials 0.000 claims description 6
- QSNQXZYQEIKDPU-UHFFFAOYSA-N [Li].[Fe] Chemical compound [Li].[Fe] QSNQXZYQEIKDPU-UHFFFAOYSA-N 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000002203 pretreatment Methods 0.000 claims 1
- 238000012163 sequencing technique Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Secondary Cells (AREA)
Abstract
The invention belongs to the technical field of power batteries, and particularly relates to an anomaly marking correction method for monotone data in a power battery, which comprises the steps of analyzing and obtaining original data reported by the power battery, matching target data meeting monotone data characteristics in a set period in the original data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, taking each data in the vectorization data as a scattered point and connecting straight lines, and calculating the slope of the straight lines to construct a slope number set; marking the abnormal data after slope judgment, judging whether the data quantity of the abnormal data is smaller than the jump amplitude of the normal data, and judging whether scattered points in the abnormal data meet a preset second preset condition one by one; according to the invention, the slope judgment of a straight line formed by introducing scattered points to an abnormal mark in monotonic data is carried out, the scattered points of straight line segments with the slope not being 1 are marked as abnormal data, the abnormal data are stretched and changed, and the abnormal value in an actual data curve is smoothed, so that the data can be used further.
Description
Technical Field
The invention belongs to the technical field of power batteries, and particularly relates to an abnormality mark correction method for monotone data in a power battery.
Background
The power battery is used as a core component of the new energy automobile and is directly related to safe and stable running of the automobile. According to the requirements, the new energy automobile needs to send data to the big data platform according to the protocol format of the standard GBT32960 technical Specification of remote service and management System of electric automobile.
However, due to the fact that the working condition of the new energy automobile is complex and the problems of data storage and transmission of the T-BOX and the BMS, abnormal data exist in a message reported by the T-BOX, and for fields with monotonic non-decreasing or monotonic non-increasing requirements like mileage, discharging SOC, charging SOC and the like in the message, an abnormal data marking and correcting method is provided.
Disclosure of Invention
The invention aims to provide an abnormality mark correction method for monotone data in a power battery, so as to solve the problems in the background technology.
The invention realizes the above purpose through the following technical scheme:
an abnormality mark correction method of monotone data in a power battery is applied to processing a real-time message of a power battery related index reported to a TSP platform by a vehicle T-BOX, and comprises the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by a power battery, matching target data of which the original data meet monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed;
s2: taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity with the slopes not being 1 in the slope number set, marking the straight line section scattered points with the slopes not being 1 as abnormal data, marking the straight line section scattered points with the slopes being 1 as normal data, and executing step S3 on the abnormal data;
s3: judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value meeting the second preset condition as the corrected value;
s4: and ending the abnormal mark correction flow.
The further improvement is that in step S1, the first preset condition is specifically a data format and a requirement of national standard GB/T32960 for reporting real-time messages by the power battery, and the obtained raw data at least includes mileage data, discharge SOC data and charge SOC data of the power battery.
The further improvement is that step S1 further comprises, after obtaining the original data reported by the power battery, sequentially performing sorting and data cleaning on the original data according to the time sequence of the original data acquisition to remove the repeated data and the null data.
The further improvement is that the continuous de-duplication method in the step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing the de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation.
The further improvement is that the marking of the abnormal data in step S2 is specifically: and taking the tail index number and the head index number between the two sections of normal data, and marking the original data corresponding to the vectorized data between the tail index number and the head index number.
A further improvement is that in step S3, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
The invention has the beneficial effects that:
the method for marking the abnormal mark in the monotonic data is more accurate and rapid in marking the position, and in the process of the mileage data analyzed from the GBT32960 message, the conditions of mileage jump, multi-frame mileage abnormal value, mileage abnormal jump low, lasting for a period of time and the like possibly exist, the problems of low efficiency and easy marking dislocation exist by adopting a first-order difference or window judgment method in the prior art, and the method can be avoided;
the correction strategy for the abnormal data is simple and effective, is in accordance with the actual situation, trowells the abnormal value in the actual data curve through stretching change, increases the effectiveness of the data, and is convenient for further use of the data.
Drawings
FIG. 1 is a schematic diagram of the overall execution flow of an abnormality flag correction method for monotonic data in a power cell according to the present invention;
FIG. 2 is a schematic diagram of a continuous deduplication operation of target data in the present invention;
fig. 3 is a schematic diagram of the correction of the abnormal mark of the monotone data in the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, wherein it is to be understood that the following detailed description is for the purpose of illustration only and is not to be construed as limiting the scope of the invention, as various insubstantial modifications and adaptations of the invention to those skilled in the art may be made in light of the foregoing disclosure.
As shown in fig. 1, the invention provides an anomaly flag correction method for monotonic data in a power battery, which is applied to processing real-time messages of power battery related indexes reported to a TSP platform by a vehicle T-BOX, and comprises the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by the power battery, matching the original data with target data meeting monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed.
The setting period can be set as one day, namely, the original data of the related indexes of the power battery in one day; the monotonic data characteristic refers to data meeting the requirements of monotonic non-increasing or monotonic non-decreasing conditions in the power battery index data; the related data such as the voltage array and the temperature array of the power battery in the prior art do not meet the characteristics of monotone data defined in the invention due to the uncertainty of instantaneous change.
In the invention, the first preset condition in step S1 is specifically the data format and requirement of national standard GB/T32960 for reporting real-time messages by the power battery, and the obtained raw data at least includes mileage data, discharge SOC data and charge SOC data of the power battery.
In the invention, step S1 further includes, after obtaining the original data reported by the power battery, sequentially performing sorting and data cleaning on the original data according to the time sequence of the original data acquisition to remove the repeated data and the null data.
In addition, the continuous de-duplication method in step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing a de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation; the deduplication operation is specifically to remove duplicate data.
Referring specifically to fig. 2, a schematic diagram of a continuous deduplication operation of target data is shown.
S2: and (3) taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity of which the slopes in the slope number set are not 1, marking the straight line section scattered points of which the slopes are not 1 as abnormal data, marking the straight line section scattered points of which the slopes are 1 as normal data, and executing step S3 on the abnormal data.
In the invention, the marking of the abnormal data in the step S2 is specifically as follows: if the slope is all "1" in the period, normal data is determined. If the slope in the period is not all '1', the abnormal data are proved to exist, the tail index number and the head index number between two sections of normal data are taken, and the original data corresponding to the vectorized data between the tail index number and the head index number are marked.
S3: and (3) judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value which meets the second preset condition as the corrected value.
In the present invention, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
In the invention, if aiming at the mileage data in the original data, the jump amplitude in the step S3 refers to the difference value of the normal mileage data at two adjacent ends of the abnormal mileage data; if the mileage data is abnormal data from 13000km to 13200km, the jump amplitude of the normal data at two adjacent ends of the abnormal data is 200km.
Referring specifically to fig. 3, which is an abnormal mark correction schematic diagram of monotonic data; in the process of abnormality correction, the last value and the first value of the normal data segment before and after the abnormal data segment are recorded as followsy 1 ,y 2 Andx1,x2the data amount of the abnormal data is recorded asnThe method comprises the steps of carrying out a first treatment on the surface of the When scattered points in the abnormal data meet the compliance condition, performing straight line fitting on the previous normal data segment of the abnormal data, and marking the fitted straight line as
，/>
As an inverse function thereof; when->
When in use, correction is carried out; when->
In this case, no correction (data deletion) is performed. Wherein->
Correction value ∈1 satisfying the condition>
The method comprises the following steps: />
。
First, the
Correction value which does not satisfy the condition->
The method comprises the following steps:
wherein,,
for rounding function, ++>
The value of the most recent correction point satisfying the condition with a larger index number is taken.
In the invention, the data quantity and the jump amplitude of the abnormal data need to be judged preferentially in the correction process of the abnormal data, and if the data quantity is less than the jump amplitude, the abnormal data is not corrected; if the data quantity is more than or equal to the jump amplitude, judging whether the reference data meets the compliance condition or not during correction, if so, filling the scattered points meeting the condition in a stretching mode to obtain a correction value, and if not, taking the previous value meeting the compliance condition as the correction value; in the correction method, scattered points meeting the conditions are corrected in sequence according to the size of the index number.
It is emphasized that the monotone characteristic of the original data is ensured by the abnormal marking of the monotone data and the index number generation mode in the correction process, and scientificity and rationality of the corrected data are also ensured by adding preset compliance conditions.
The foregoing detailed description of the embodiments is specific and detailed, but is not therefore to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (6)
1. The abnormal mark correction method of monotonic data in the power battery is applied to processing real-time messages of power battery related indexes reported to a TSP platform by a vehicle T-BOX, and is characterized by comprising the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by a power battery, matching target data of which the original data meet monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed;
s2: taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity with the slopes not being 1 in the slope number set, marking the straight line section scattered points with the slopes not being 1 as abnormal data, marking the straight line section scattered points with the slopes being 1 as normal data, and executing step S3 on the abnormal data;
s3: judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value meeting the second preset condition as the corrected value;
s4: and ending the abnormal mark correction flow.
2. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: in step S1, the first preset condition is specifically a data format and a requirement of national standard GB/T32960 of a real-time report of the power battery, and the obtained raw data at least includes mileage data, discharge SOC data, and charge SOC data of the power battery.
3. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: and step S1, after the original data reported by the power battery is obtained, sequencing and data cleaning are sequentially carried out on the original data according to the time sequence of the original data acquisition so as to remove the repeated data and the null data.
4. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: the pretreatment method in the step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing the de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation.
5. The abnormality flag correction method for monotonic data in a power cell as defined in claim 4, wherein: in step S2, the marking of the abnormal data specifically includes: and taking the tail index number and the head index number between the two sections of normal data, and marking the original data corresponding to the vectorized data between the tail index number and the head index number.
6. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: in step S3, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310547808.0A CN116304582B (en) | 2023-05-16 | 2023-05-16 | Abnormal mark correction method for monotone data in power battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310547808.0A CN116304582B (en) | 2023-05-16 | 2023-05-16 | Abnormal mark correction method for monotone data in power battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116304582A true CN116304582A (en) | 2023-06-23 |
| CN116304582B CN116304582B (en) | 2023-08-08 |
Family
ID=86796273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310547808.0A Active CN116304582B (en) | 2023-05-16 | 2023-05-16 | Abnormal mark correction method for monotone data in power battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116304582B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013206656A (en) * | 2012-03-28 | 2013-10-07 | Mitsubishi Motors Corp | Battery abnormality determination device |
| JP5342708B1 (en) * | 2013-06-19 | 2013-11-13 | 株式会社日立パワーソリューションズ | Anomaly detection method and apparatus |
| CN107703132A (en) * | 2017-09-30 | 2018-02-16 | 深圳迈瑞生物医疗电子股份有限公司 | Response curve abnormality eliminating method and device, Biochemical Analyzer, storage medium |
| CN109239360A (en) * | 2018-09-14 | 2019-01-18 | 深圳开立生物医疗科技股份有限公司 | A kind of response curve method for detecting abnormality and device |
| CN110737874A (en) * | 2019-09-02 | 2020-01-31 | 中国科学院地理科学与资源研究所 | watershed water quality monitoring abnormal value detection method based on spatial relationship |
| WO2022213597A1 (en) * | 2021-04-07 | 2022-10-13 | 北京理工大学 | New energy vehicle eic system safety feature database construction method |
| CN115454980A (en) * | 2022-08-30 | 2022-12-09 | 重庆长安汽车股份有限公司 | Data quality diagnosis method and device for battery safety monitoring and storage medium |
| CN115718262A (en) * | 2022-11-25 | 2023-02-28 | 中国汽车工程研究院股份有限公司 | Power battery sampling abnormal risk identification method and fault judgment method |
-
2023
- 2023-05-16 CN CN202310547808.0A patent/CN116304582B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013206656A (en) * | 2012-03-28 | 2013-10-07 | Mitsubishi Motors Corp | Battery abnormality determination device |
| JP5342708B1 (en) * | 2013-06-19 | 2013-11-13 | 株式会社日立パワーソリューションズ | Anomaly detection method and apparatus |
| CN107703132A (en) * | 2017-09-30 | 2018-02-16 | 深圳迈瑞生物医疗电子股份有限公司 | Response curve abnormality eliminating method and device, Biochemical Analyzer, storage medium |
| CN109239360A (en) * | 2018-09-14 | 2019-01-18 | 深圳开立生物医疗科技股份有限公司 | A kind of response curve method for detecting abnormality and device |
| CN110737874A (en) * | 2019-09-02 | 2020-01-31 | 中国科学院地理科学与资源研究所 | watershed water quality monitoring abnormal value detection method based on spatial relationship |
| WO2022213597A1 (en) * | 2021-04-07 | 2022-10-13 | 北京理工大学 | New energy vehicle eic system safety feature database construction method |
| CN115454980A (en) * | 2022-08-30 | 2022-12-09 | 重庆长安汽车股份有限公司 | Data quality diagnosis method and device for battery safety monitoring and storage medium |
| CN115718262A (en) * | 2022-11-25 | 2023-02-28 | 中国汽车工程研究院股份有限公司 | Power battery sampling abnormal risk identification method and fault judgment method |
Non-Patent Citations (3)
| Title |
|---|
| KIRAN BHASKAR: "Detecting synthetic anomalies using median-based residuals in lithium-ion cell groups", 《IEEE XPLORE》 * |
| 仪坤: "动力电池安全性诊断研究及其平台开发", 《万方数据库》 * |
| 辛欣;周娜;王震;: "数据异常值检测及修正方法研究", 现代电子技术, no. 11 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116304582B (en) | 2023-08-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103969587B (en) | A kind of hybrid vehicle electrokinetic cell SOC estimation method | |
| CN109655754B (en) | Battery performance evaluation method based on multi-dimensional grading of charging process | |
| CN112564109A (en) | Frequency modulation optimization operation method based on participation of energy storage system in large-scale offshore wind power | |
| CN113030758B (en) | Aging early warning method and system based on lithium ion battery capacity water jump point, automobile and computer storage medium | |
| CN111077465A (en) | Battery characteristic parameter extraction method and device, computer equipment and storage medium | |
| CN110045291B (en) | Lithium battery capacity estimation method | |
| CN117996807A (en) | Charging and discharging method for energy storage battery of light storage charging station | |
| CN110311451A (en) | Control method, system, equipment and the storage medium of the charge and discharge of echelon battery | |
| CN111639866B (en) | Method for configuring energy storage capacity of energy storage charging station based on genetic algorithm | |
| CN111007411B (en) | Electric quantity state correction method of energy storage battery system | |
| CN114722329A (en) | Electric vehicle carbon emission reduction accounting method and device and electronic equipment | |
| CN104066165A (en) | Wireless communication power allocation method based on energy collection mode | |
| CN114994541A (en) | Lithium ion battery SOH estimation method based on multi-strategy fusion | |
| CN113352946B (en) | Energy management method of fuel cell automobile power system | |
| CN116304582B (en) | Abnormal mark correction method for monotone data in power battery | |
| CN113642988A (en) | Multi-working-condition multi-type energy storage power station cost benefit analysis method and setting system | |
| CN116579902B (en) | Digital park electric carbon data mapping method, system, equipment and storage medium | |
| CN110470993B (en) | SOC algorithm for starting and stopping battery | |
| CN105070964A (en) | Lithium ion battery optimization charging technology based on charging voltage curve fixation control | |
| CN106936145B (en) | Life optimization control method for energy storage power station | |
| CN114142470B (en) | Method and system for measuring and calculating reasonable scale of energy storage matched with new energy power station of electric power system | |
| CN111369159A (en) | Reliability assessment method for electric power traffic coupling network | |
| CN117872195A (en) | Method for detecting and early warning health state of power battery | |
| CN115395545A (en) | Method for participating in power grid frequency modulation by lithium iron phosphate battery considering environment correction model parameters | |
| CN114186811A (en) | Method for constructing real-time scheduling model of micro-grid system and application |
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 | ||
| CP03 | Change of name, title or address |
Address after: No. 15 Hengyang Road, Guxian Street, Yantai Economic and Technological Development Zone, Shandong Province, China 265503 Patentee after: Ligao (Shandong) New Energy Technology Co.,Ltd. Country or region after: China Address before: Room 501, No.8, No.300, Changjiang Road, Yantai Economic and Technological Development Zone, Shandong Province 264000 Patentee before: Ligao (Shandong) New Energy Technology Co.,Ltd. Country or region before: China |
|
| CP03 | Change of name, title or address |