CN106405434B - 电池荷电状态的估计方法 - Google Patents
电池荷电状态的估计方法 Download PDFInfo
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- CN106405434B CN106405434B CN201610972650.1A CN201610972650A CN106405434B CN 106405434 B CN106405434 B CN 106405434B CN 201610972650 A CN201610972650 A CN 201610972650A CN 106405434 B CN106405434 B CN 106405434B
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- battery
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 63
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- 238000005259 measurement Methods 0.000 claims description 13
- 230000005611 electricity Effects 0.000 claims description 7
- 230000010287 polarization Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 3
- 240000002853 Nelumbo nucifera Species 0.000 claims 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 239000003990 capacitor Substances 0.000 description 3
- 230000003862 health status Effects 0.000 description 3
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical group [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical group [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
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- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005530 etching Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 229910052987 metal hydride Inorganic materials 0.000 description 1
- 150000004681 metal hydrides Chemical class 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 229910052759 nickel Inorganic materials 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
Description
Claims (8)
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CN201610972650.1A CN106405434B (zh) | 2016-10-28 | 2016-10-28 | 电池荷电状态的估计方法 |
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CN201610972650.1A CN106405434B (zh) | 2016-10-28 | 2016-10-28 | 电池荷电状态的估计方法 |
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CN106405434A CN106405434A (zh) | 2017-02-15 |
CN106405434B true CN106405434B (zh) | 2019-09-10 |
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Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109031142B (zh) * | 2018-07-19 | 2020-09-25 | 电子科技大学 | 一种基于分段线性插值的二次电池模型及状态估计方法 |
CN109986997B (zh) * | 2019-03-26 | 2022-05-03 | 芜湖职业技术学院 | 一种动力电池soc预测装置、汽车及方法 |
CN110286324B (zh) * | 2019-07-18 | 2021-07-09 | 北京碧水润城水务咨询有限公司 | 一种电池荷电状态估算方法及电池健康状态估算方法 |
CN112034351A (zh) * | 2020-08-28 | 2020-12-04 | 厦门科灿信息技术有限公司 | 电池剩余容量确定方法及终端设备 |
CN112067998A (zh) * | 2020-09-10 | 2020-12-11 | 昆明理工大学 | 一种基于深度神经网络的锂离子电池荷电状态估计方法 |
CN112327183B (zh) * | 2020-09-18 | 2023-11-28 | 国联汽车动力电池研究院有限责任公司 | 一种锂离子电池soc估算方法和装置 |
CN112462282B (zh) * | 2020-11-09 | 2022-03-18 | 西南大学 | 基于机理模型的用于确定电池组实时荷电状态的方法 |
JP7297339B2 (ja) * | 2021-05-19 | 2023-06-26 | 東洋システム株式会社 | 電池状態判定方法および電池状態判定装置 |
JP7090949B1 (ja) * | 2021-05-19 | 2022-06-27 | 東洋システム株式会社 | 電池状態判定方法および電池状態判定装置 |
Citations (4)
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JP2013072677A (ja) * | 2011-09-27 | 2013-04-22 | Primearth Ev Energy Co Ltd | 二次電池の充電状態推定装置 |
CN105093122A (zh) * | 2015-07-09 | 2015-11-25 | 宁波飞拓电器有限公司 | 基于强跟踪自适应sqkf的应急灯电池soc估计方法 |
CN105182245A (zh) * | 2015-09-08 | 2015-12-23 | 盐城工学院 | 基于无迹卡尔曼滤波的大容量电池系统荷电状态估计方法 |
CN106019164A (zh) * | 2016-07-07 | 2016-10-12 | 武汉理工大学 | 基于双重自适应无际卡尔曼滤波器的锂电池soc估计算法 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
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US20150081237A1 (en) * | 2013-09-19 | 2015-03-19 | Seeo, Inc | Data driven/physical hybrid model for soc determination in lithium batteries |
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2016
- 2016-10-28 CN CN201610972650.1A patent/CN106405434B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2013072677A (ja) * | 2011-09-27 | 2013-04-22 | Primearth Ev Energy Co Ltd | 二次電池の充電状態推定装置 |
CN105093122A (zh) * | 2015-07-09 | 2015-11-25 | 宁波飞拓电器有限公司 | 基于强跟踪自适应sqkf的应急灯电池soc估计方法 |
CN105182245A (zh) * | 2015-09-08 | 2015-12-23 | 盐城工学院 | 基于无迹卡尔曼滤波的大容量电池系统荷电状态估计方法 |
CN106019164A (zh) * | 2016-07-07 | 2016-10-12 | 武汉理工大学 | 基于双重自适应无际卡尔曼滤波器的锂电池soc估计算法 |
Non-Patent Citations (1)
Title |
---|
动力电池组SOC估算及均衡控制方法研究;张金龙;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20120715(第07期);C035-22 |
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