CN107234982B - A kind of power battery charging method based on big data statistics - Google Patents
A kind of power battery charging method based on big data statistics Download PDFInfo
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- CN107234982B CN107234982B CN201710692215.8A CN201710692215A CN107234982B CN 107234982 B CN107234982 B CN 107234982B CN 201710692215 A CN201710692215 A CN 201710692215A CN 107234982 B CN107234982 B CN 107234982B
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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
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- 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
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
-
- 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
-
- 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
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- 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
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
-
- 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
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a kind of power battery charging methods based on big data statistics, and this method includes input signal processing, big data statistic algorithm, charge control parameter setting and four steps of charge control；The charging ability of power battery is obtained through excessive data statistics algorithm after input signal processing, charge control parameter is set later, carries out charge control.The present invention is based on existing battery management system, by in battery complete lifecycle, statistics application is carried out in power battery practical application charging process, one avoids the difference of different application scene charging, secondly avoiding caused inconsistent application under different application scene.
Description
Technical field
The present invention relates to new-energy automobile field, in particular to a kind of power battery charging side based on big data statistics
Method.
Background technique
Vehicular dynamic battery charge control at present mainly uses single charge control method.And this method does not adapt to
Different application scene, and be applicable in also and non-fully for different operating conditions.Such as fixed its charging of operation client and traveling road
Line is fixed, and has the good charging time, and on-fixed operation client has charging rate within a certain period of time and needs faster
It asks.
Design method relatively good at present can provide different software versions for different operation scenes, this mode increases
Complicated version management and matching and calibration work.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide one kind to be based on existing battery management system, leads to
It crosses in battery complete lifecycle, the power battery charging side of statistics application is carried out in power battery practical application charging process
Method.
The purpose of the present invention is achieved through the following technical solutions: it is a kind of based on big data statistics power battery fill
Method for electrically, this method include input signal processing, big data statistic algorithm, charge control parameter setting and charge control four steps
Suddenly；The charging ability of power battery is obtained through excessive data statistics algorithm after input signal processing, charge control is set later and is joined
Number carries out charge control.
It is preferred that input signal is handled: required for processing big data statistics and charge control parameter setting most
Polymeric monomer voltage and maximum temperature, battery charge ability parameter.
It is preferred that big data statistic algorithm includes: charging times and the charging time counted in certain time.It is excellent
Selection of land counts intraday charging times and charging time.
It is preferred that charge control parameter setting: charge cutoff monomer voltage is arranged in setting charge-current demands.
It is preferred that charge control includes: to charge in practical charging process according to charge-current demands, when monomer electricity
Pressure reaches cut-off monomer voltage and then stops charging.
It is preferred that input signal processing is comprising steps of calculate maximum monomer voltage MaxCellU；Calculate maximum electricity
Pond temperature MatT；It tables look-up to obtain power battery charging ability ChrgI by temperature；
Big data statistic algorithm is comprising steps of record daily charging times ChrgN；Record daily charging time ChrgT
(h)；Be input in big data algorithm by ChrgN and ChrgT, obtain big data count daily charging times trend parameter A and
Big data counts daily charging time trend parameter B；
Charge control parameter setting method comprising steps of if parameter A > A (t-1) and parameter B > B (t-1) is set up simultaneously,
Then illustrate that trend increases when the daily demand charging times of the user and charging, then (preferably, a takes phase ReqChrgI=ChrgI*a
1.2), increase charging demand current ReqChrgI；Otherwise judge A < A (t-1) and parameter B < B (t-1) while setting up, then explanation should
The daily demand charging times of user and charging time trend are reduced, then (preferably, 0.8) b takes ReqChrgI=ChrgI*b, subtracts
Small charging demand current ReqChrgI；If the Rule of judgment of front two is not satisfied, ReqChrgI=ChrgI, charging is needed
Electric current ReqChrgI is asked to charge according to power battery ability；
Charge control method is comprising steps of be more than or equal to charging if voltage if maximum monomer voltage MaxCellU
Stop charging, otherwise charges normal.
It is preferred that the table that temperature is tabled look-up is determined by power battery characteristic.
It is preferred that big number is calculated according to daily charging times ChrgN and daily charging time ChrgT (h)
Daily charging times trend parameter A and big data, which count daily charging time trend parameter B, according to statistics is counted on cloud backstage
It calculates.
The beneficial effects of the present invention are: the present invention is to pass through the complete Life Cycle of battery based on existing battery management system
In phase, statistics application is carried out in power battery practical application charging process, one avoids the charging of different application scene not
Together, secondly avoiding caused inconsistent application under different application scene.
Detailed description of the invention
Fig. 1 is control method block schematic illustration in the present invention；
Fig. 2 is input signal processing schematic in the present invention；
Fig. 3 is big data statistic algorithm schematic diagram in the present invention；
Fig. 4 is charge control parameter setting schematic diagram in the present invention；
Fig. 5 is charge control schematic diagram in the present invention.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing, but protection scope of the present invention is not limited to
It is as described below.
As shown in fig. 1~fig. 5, a kind of power battery charging method based on big data statistics, this method include input letter
Number processing, big data statistic algorithm, charge control parameter setting and four steps of charge control；Through excessive after input signal processing
Data statistics algorithm obtains the charging ability of power battery, and charge control parameter is arranged later, carries out charge control.
It is preferred that input signal is handled: required for processing big data statistics and charge control parameter setting most
Polymeric monomer voltage and maximum temperature, battery charge ability parameter.
It is preferred that big data statistic algorithm includes: charging times and the charging time counted in certain time.It is excellent
Selection of land counts intraday charging times and charging time.
It is preferred that charge control parameter setting: charge cutoff monomer voltage is arranged in setting charge-current demands.
It is preferred that charge control includes: to charge in practical charging process according to charge-current demands, when monomer electricity
Pressure reaches cut-off monomer voltage and then stops charging.
It is preferred that input signal processing is comprising steps of calculate maximum monomer voltage MaxCellU；Calculate maximum electricity
Pond temperature MatT；It tables look-up to obtain power battery charging ability ChrgI by temperature；
Big data statistic algorithm is comprising steps of record daily charging times ChrgN；Record daily charging time ChrgT
(h)；Be input in big data algorithm by ChrgN and ChrgT, obtain big data count daily charging times trend parameter A and
Big data counts daily charging time trend parameter B, and big data algorithm includes recurrence/cluster/decision tree etc.；
Charge control parameter setting method comprising steps of if parameter A > A (t-1) and parameter B > B (t-1) is set up simultaneously,
Then illustrate that trend increases when the daily demand charging times of the user and charging, then (preferably, a takes phase ReqChrgI=ChrgI*a
1.2), increase charging demand current ReqChrgI；Otherwise judge A < A (t-1) and parameter B < B (t-1) while setting up, then explanation should
The daily demand charging times of user and charging time trend are reduced, then (preferably, 0.8) b takes ReqChrgI=ChrgI*b, subtracts
Small charging demand current ReqChrgI；If the Rule of judgment of front two is not satisfied, ReqChrgI=ChrgI, charging is needed
Electric current ReqChrgI is asked to charge according to power battery ability；
Charge control method is comprising steps of if maximum monomer voltage MaxCellU is more than or equal to charging by voltage
(different battery chargings then stop charging by voltage by voltage difference, such as ternary lithium ion battery for 4.2V), otherwise just
Often charging.
It is preferred that the table that temperature is tabled look-up is determined by power battery characteristic, different temperatures battery allows electric current different:
Such as 20 DEG C to 40 DEG C are 1C, it is 0 that 0 DEG C, which is 0,65 DEG C,.
It is preferred that big number is calculated according to daily charging times ChrgN and daily charging time ChrgT (h)
Daily charging times trend parameter A and big data, which count daily charging time trend parameter B, according to statistics is counted on cloud backstage
It calculates.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, it is noted that all
Made any modifications, equivalent replacements, and improvements etc. within the spirit and principles in the present invention should be included in guarantor of the invention
Within the scope of shield.
Claims (5)
1. it is a kind of based on big data statistics power battery charging method, it is characterised in that: this method include input signal processing,
Four big data statistic algorithm, charge control parameter setting and charge control steps；It unites after input signal processing by big data
Calculating method obtains the charging ability of power battery, and charge control parameter is arranged later, carries out charge control；
Input signal processing: maximum monomer voltage and maximum temperature required for processing big data statistics and charge control parameter setting
Degree, battery charge ability parameter；
Big data statistic algorithm includes: charging times and the charging time counted in certain time；
Charge control parameter setting: charge cutoff monomer voltage is arranged in setting charge-current demands；
Charge control includes: to charge in practical charging process according to charge-current demands, when monomer voltage reaches cut-off monomer electricity
Pressure then stops charging；
Input signal processing is comprising steps of calculate maximum monomer voltage MaxCellU；Calculate largest battery temperature MatT；Pass through temperature
Degree tables look-up to obtain power battery charging ability ChrgI；
Big data statistic algorithm is comprising steps of record daily charging times ChrgN；Record daily charging time ChrgT (h)；It is logical
It crosses ChrgN and ChrgT (h) is input in big data algorithm, obtain big data and count daily charging times trend parameter A and big number
Daily charging time trend parameter B according to statistics；
Charge control parameter setting method is said comprising steps of if parameter A > A (t-1) and parameter B > B (t-1) is set up simultaneously
The daily demand charging times of the bright user and charging time trend increase, then ReqChrgI=ChrgI*a, increase charge requirement electricity
Flow ReqChrgI；Otherwise judge A < A (t-1) and parameter B < B (t-1) while setting up, then illustrate the daily demand charging time of the user
Several and charging time trend is reduced, then ReqChrgI=ChrgI*b, reduces charging demand current ReqChrgI；If parameter A > A
(t-1) condition with parameter B>B (t-1) and A<A (t-1) and parameter B<B (t-1) is not satisfied, then ReqChrgI=
ChrgI, charging demand current ReqChrgI charge according to power battery charging ability；
T indicates that number of days, a and b indicate constant；
Charge control method stops if voltage comprising steps of being more than or equal to charging if maximum monomer voltage MaxCellU
Charging, otherwise charges normal.
2. a kind of power battery charging method based on big data statistics according to claim 1, which is characterized in that statistics
Intraday charging times and charging time.
3. a kind of power battery charging method based on big data statistics according to claim 1, it is characterised in that: temperature
The table tabled look-up is determined by power battery characteristic.
4. a kind of power battery charging method based on big data statistics according to claim 1 or 3, it is characterised in that:
Big data is calculated according to daily charging times ChrgN and daily charging time ChrgT (h) and counts daily charging times
Trend parameter A and big data, which count daily charging time trend parameter B, to be calculated on cloud backstage.
5. a kind of power battery charging method based on big data statistics according to claim 1, it is characterised in that: a takes
1.2, b take 0.8.
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| CN201710692215.8A CN107234982B (en) | 2017-08-14 | 2017-08-14 | A kind of power battery charging method based on big data statistics |
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| CN107234982B true CN107234982B (en) | 2019-05-07 |
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| CN115257449B (en) * | 2022-08-08 | 2024-09-10 | 中联重科股份有限公司 | Charging control method and system for mixer truck |
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|---|---|---|---|---|
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| CN103872398A (en) * | 2012-12-13 | 2014-06-18 | 财团法人工业技术研究院 | Charging method of rechargeable battery and related charging structure |
| CN203660620U (en) * | 2014-01-28 | 2014-06-18 | 林家宏 | Charging control circuit for battery of mobile device |
| CN104852435A (en) * | 2015-05-22 | 2015-08-19 | 聊城大学 | Electric automobile serial lithium battery management system and a management method thereof |
| CN105826976A (en) * | 2016-03-30 | 2016-08-03 | 维沃移动通信有限公司 | Mobile terminal charging method and mobile terminal |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090243549A1 (en) * | 2008-03-31 | 2009-10-01 | Naoki Matsumura | Intelligent battery charging rate management |
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2017
- 2017-08-14 CN CN201710692215.8A patent/CN107234982B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0711016A2 (en) * | 1994-11-04 | 1996-05-08 | Mitsubishi Denki Kabushiki Kaisha | Parameter measuring method, charge/discharge control method and apparatus and life predicting method for secondary batteries and power storage apparatus using the same |
| CN103872398A (en) * | 2012-12-13 | 2014-06-18 | 财团法人工业技术研究院 | Charging method of rechargeable battery and related charging structure |
| CN203660620U (en) * | 2014-01-28 | 2014-06-18 | 林家宏 | Charging control circuit for battery of mobile device |
| CN104852435A (en) * | 2015-05-22 | 2015-08-19 | 聊城大学 | Electric automobile serial lithium battery management system and a management method thereof |
| CN105826976A (en) * | 2016-03-30 | 2016-08-03 | 维沃移动通信有限公司 | Mobile terminal charging method and mobile terminal |
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Effective date of registration: 20210427 Address after: 406, 4th floor, building 9, no.388, section 3, Chenglong Avenue, Chengdu Economic and Technological Development Zone (Longquanyi District), Sichuan 610000 Patentee after: Chengdu Yajun New Energy Technology Co.,Ltd. Address before: 610000 Sichuan city of Chengdu province Tianfu New Street Youfang village nine Group No. 300 emerging industrial park building B1 1-3 Patentee before: CHENGDU RAJA NEW ENERGY AUTOMOBILE SCIENCE AND TECHNOLOGY Co.,Ltd. |
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