WO2018153116A1 - 计及非线性容量特性的分数阶KiBaM电池模型及参数辨识方法 - Google Patents
计及非线性容量特性的分数阶KiBaM电池模型及参数辨识方法 Download PDFInfo
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- WO2018153116A1 WO2018153116A1 PCT/CN2017/106912 CN2017106912W WO2018153116A1 WO 2018153116 A1 WO2018153116 A1 WO 2018153116A1 CN 2017106912 W CN2017106912 W CN 2017106912W WO 2018153116 A1 WO2018153116 A1 WO 2018153116A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- 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
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- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4285—Testing apparatus
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/40—The network being an on-board power network, i.e. within a vehicle
- H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Definitions
- the invention relates to a fractional-order KiBaM battery model and a parameter identification method which take into account nonlinear capacity characteristics.
- Lithium-ion batteries have become the most widely used power battery for electric vehicles due to their high energy density, long service life, good cost performance and high single-cell voltage.
- the battery model is of great significance for the rational design and safe operation of the power battery and its battery management system. It is the basis for battery state of charge (SOC) estimation, health status (SOH) estimation, and residual life (RUL) prediction.
- SOC battery state of charge
- SOH health status
- RUL residual life
- the battery model is developed to the present stage. According to different modeling mechanisms, it can be divided into electrochemical models that represent the internal characteristics of the battery, simplified electrochemical models, thermal models, etc., as well as stochastic models, neural network models, and equivalents that describe the external characteristics of the battery. Circuit model, etc.
- the electrochemical model uses complex nonlinear differential equations to describe the internal chemical process of the battery.
- the model is accurate, it is too abstract; the thermal model mainly studies the heat generation and heat transfer process of the battery; the stochastic model mainly focuses on the recovery characteristics of the battery, and the battery
- the behavior is described as a Markov process, which can describe the pulse discharge characteristics of the battery, but it can not be applied to the variable current situation.
- the neural network model has good nonlinear mapping ability, fast parallel processing ability, strong self-learning and self-organization. The advantages of ability, but a lot of experiments are needed to obtain training data, and the model error is susceptible to training data and training methods.
- the equivalent circuit model uses the physical characteristics of the battery, using different physical components such as voltage source, current source, capacitor and resistor to form an equivalent circuit to simulate the IV output characteristics of the battery, because of its simple and intuitive form, and suitable for electrical Design and simulation advantages have become the most widely used model.
- the equivalent circuit model can accurately describe the external characteristics of the battery's I-V output, it is difficult to express the internal characteristics such as the nonlinear capacity effect of the battery and the running time.
- the available power of a lithium-ion battery is not like the water in a bucket. How much electricity you use drops, but with some nonlinear characteristics. Therefore, it is impossible to take out all the power of the battery, and the specific amount of electricity is related to the battery load and usage.
- This nonlinear characteristic is mainly manifested in two aspects: capacity effect and recovery effect.
- the capacity effect means that the larger the discharge current, the smaller the amount of electricity obtained. For example, the current discharge with 1A is smaller than the total discharge with 0.5A current discharge; the recovery effect means that the battery is no longer discharged. The battery's power will pick up.
- KiBaM The KiBaM electrochemical model
- KiBaM fully known as the Kinetic Battery Model
- the KiBaM electrochemical model is a model based on perceptual knowledge, so the model is intuitive, easy to understand and simple. It is easy to use, which uses a reduced order equation to characterize the nonlinear capacity effect and running time of the battery, which can well describe the discharge characteristics of the battery.
- the KiBaM electrochemical model takes into account the recovery and capacity effects of the battery and accurately characterizes the internal characteristics of the battery.
- the internal electrochemical reaction process of the battery is extremely complicated, including conductive ion transfer, internal electrochemical reaction, charge and discharge hysteresis effect, and concentration diffusion effect. It exhibits strong nonlinear characteristics and is more suitable for simulation with fractional model. .
- the fractional model has more degrees of freedom, greater flexibility and newness in design.
- fluid motion characteristics, including lithium ion and electrons inside the battery still exhibit strong fractional calculus.
- the results of the KiBaM electrochemical model using fractional calculus have not been found yet.
- the present invention proposes a fractional-order KiBaM battery model and parameter identification method that takes into account nonlinear capacity characteristics.
- the present invention considers the recovery effect and specific capacity effect of the battery, and can accurately describe the nonlinear capacity effect of the battery. And the running time, the discharge characteristics of the battery are well described, and the accurate simulation of the internal characteristics of the power battery is realized, which has high application value. It is difficult to solve the shortcomings and shortcomings of the internal characteristics such as the nonlinear capacity effect of the battery and the running time of the existing battery model.
- the invention provides a fractional-order KiBaM battery model and parameter identification method which takes into account nonlinear capacity characteristics, and uses a fractional-order calculus principle to generalize the traditional KiBaM battery model to fractional order (non-integer order), so that the model obtains more
- fractional-order calculus principle to generalize the traditional KiBaM battery model to fractional order (non-integer order)
- the degree of freedom, greater flexibility and novelty, and the introduction of fractional order have also added many new phenomena and laws, which have the superiority that conventional integer-order battery models cannot achieve.
- a fractional-order KiBaM battery model that accounts for nonlinear capacity characteristics, including a temporary capacity and a achievable capacity portion for describing a nonlinear capacity characteristic of a battery, the temporary capacity portion indicating a quantity of electricity that can be directly obtained during discharge, The state of charge of the battery SOC; the portion of the available capacity represents the amount of power that cannot be directly obtained, and the two parts are connected.
- the load current i flows out from the temporary capacity portion, and at the same time, the rate of passage of the volume of the capacity portion is obtained.
- the non-linear capacity effect and battery recovery effect of the battery are expressed by the ratio of the temporary capacity to the height of the available capacity portion, combined with the size of the battery capacity characteristic fractional order.
- the sum of the temporary capacity portion and the available capacity portion is the total capacity of the battery.
- the height of the temporary capacity portion is zero.
- the temporary capacity is denoted by y 1 and represents the amount of electricity that can be directly obtained during discharge.
- the height is denoted by h 1 , indicating the state of charge SOC of the battery;
- the available capacity is denoted by y 2 , indicating that it cannot be directly obtained.
- the amount of electricity is recorded as h 2 ; and the sum of y1 and y 2 is the total capacity of the battery;
- c represents the distribution ratio of the battery capacity between the two parts, and the following relationship exists:
- the temporary capacity is denoted by y 1 , which represents the amount of electricity that can be directly obtained during discharge, and its height is denoted by h 1 , which represents the state of charge SOC of the battery;
- the available capacity is denoted by y 2 , indicating that it cannot be directly obtained.
- the amount of electricity is recorded as h 2 ; and the sum of y 1 and y 2 is the total capacity of the battery;
- c is the distribution ratio of the battery capacity between the two parts;
- k is the rate coefficient from the temporary capacity flow to the available capacity ;
- ⁇ represents the size of the battery capacity characteristic fractional order, and has: 0 ⁇ ⁇ ⁇ 1.
- the state of charge SOC of the power battery is expressed as:
- the battery's unavailable capacity C unavail is expressed as:
- the capacity relationship of the power battery is expressed as:
- C avail (t) C init - ⁇ i bat (t)dt-C unavail (t)
- c represents the distribution ratio of the battery capacity
- k represents the rate coefficient from the temporary capacity flow to the available capacity
- ⁇ represents the battery capacity characteristic
- An identification method for applying the above-described fractional-order KiBaM battery model includes the following steps:
- Step 1 Perform a constant current charge and discharge test on the power battery to restore the power battery to a fully charged state as the initial state of the battery;
- Step 2 Perform a small current constant current discharge test on the power battery to obtain an initial capacity of the power battery C init ;
- Step 3 Fully charge the power battery and conduct a large current constant current discharge test. Since the discharge current is large, the discharge cutoff voltage is discharged in a short time, and the capacity C 1 of the power battery under a large current is obtained, and the distribution ratio of the battery capacity is calculated. ;
- Step 4 Perform two sets of constant current discharge tests of different power rates on the power battery, obtain the data of the unusable capacity C unavail and the discharge time t d of the battery under the discharge rate, and calculate the parameter k according to the battery discharge end determination condition. 'and the size of the order ⁇ ;
- Step 5 Through the above tests and experiments, the parameters of the fractional-order KiBaM electrochemical model of the tested power battery are obtained.
- the identification method further comprises the step 6: performing a constant current discharge test on the power battery at other magnifications, obtaining the unavailable capacity and the discharge time data of the battery at the discharge rate; and comparing with the unusable capacity and the discharge time calculated by the model , verify the accuracy of the model.
- the battery discharge end determination condition is:
- a fractional-order KiBaM electrochemical model of a power battery according to the present invention taking into account the recovery effect and specific capacity effect of the battery, comprehensively considering characteristics such as dynamic characteristics and electrochemical mechanism, and establishing a fractional-order electrochemical model of the power battery. Accurately capture the nonlinear capacity characteristics of the power battery and the battery discharge characteristics; it can well describe the internal characteristics of the battery's nonlinear capacity effect and running time;
- 1 is a schematic structural view of a fractional-order KiBaM electrochemical model of a power battery of the present invention.
- the fractional-order KiBaM battery model includes two parts, which can be regarded as two containers having a volume with a connecting path , for example, a well.
- temporary capacity the non-linear capacity characteristics of the battery, which are called “temporary capacity” and “acquired capacity” respectively;
- temporary capacity is denoted by y 1 , which indicates the amount of electricity that can be directly obtained during discharge, and its height is recorded as h 1 indicating the SOC state of charge of the battery;
- available capacity denoted y 2
- c represents the distribution ratio of battery capacity between the two "wells”, and obviously has the following relationship:
- the fractional-order KiBaM battery model the relationship between "temporary capacity" y 1 and “available capacity” y 2 and h 1 and h 2 representing the state of charge SOC of the battery can be expressed as:
- temporary capacity is denoted by y 1 and represents the amount of electricity directly obtainable at the time of discharge, the height of which is denoted by h 1 , which indicates the state of charge SOC of the battery; and the said "obtainable capacity” is denoted by y 2 , indicating The amount of electricity that cannot be directly obtained is recorded as h 2 ; and the sum of y 1 and y 2 is the total capacity of the battery; c represents the distribution ratio of the battery capacity between the two "wells"; k represents the "temporary capacity” The rate coefficient flowing to the "available capacity"; ⁇ represents the size of the battery capacity characteristic fractional order, and has: 0 ⁇ ⁇ ⁇ 1.
- the battery's unavailable capacity can be expressed as:
- ⁇ ( ⁇ ) and E ⁇ , ⁇ (z) are the commonly used Gamma functions and Mittag-Leffler functions in fractional calculus calculations;
- the battery's unavailable capacity C unavail can be expressed as:
- the capacity relationship of the power battery can be expressed as:
- C max, C avail, C unavail representing initial capacity of the battery, the capacity of available capacity and unavailable
- C unavail wherein unavailable capacity SOC represents the nonlinear variable battery characteristics of the battery capacity due to the nonlinear effects
- the fractional-order KiBaM battery model can obtain the current total battery remaining capacity y(t), available capacity C avail (t), unavailable capacity C unavail (t), and battery state of charge SOC, thereby accurately capturing battery operation Time and power battery nonlinear capacity characteristics.
- the above-mentioned fractional-order KiBaM battery model and its identification method are known.
- the battery model shows that the parameter identification mainly includes the initial capacity y 0 of the battery, representing the distribution ratio c of the battery capacity between the two “wells”, indicating the “temporary capacity”.
- the rate coefficient k flowing to the "capable capacity”; and the size ⁇ indicating the fractional order of the battery capacity characteristics mainly includes the following steps:
- Step 1 Perform a constant current charge and discharge test on the power battery to restore the power battery to a fully charged state as the initial state of the battery;
- Step 2 Perform a small current constant current discharge test on the power battery to obtain an initial capacity of the power battery C init ;
- Step 3 Fully charge the power battery and conduct a large current constant current discharge experiment. Since the discharge current is large, the discharge cutoff voltage is discharged to a short time, and the capacity C 1 of the power battery under a large current is obtained; then the parameters of the battery model
- Step 4 Perform two sets of constant current discharge tests of different power rates on the power battery, obtain the data such as the unavailable capacity C unavail and the discharge time t d of the battery at the discharge rate; and formula (8) according to the end of the discharge of the battery.
- the parameter k' and the size ⁇ of the order can be identified;
- Step 5 Through the above tests and experiments, the parameters of the fractional-order KiBaM electrochemical model of the tested power battery can be obtained;
- Step 6 Perform constant current discharge test on the power battery at other magnifications, obtain the data such as the unavailable capacity and discharge time of the battery under the discharge rate; and compare with the unusable capacity and discharge time calculated by the model to verify the accuracy of the model. degree.
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Abstract
Description
Claims (10)
- 一种计及非线性容量特性的分数阶KiBaM电池模型,其特征是:包括用于描述电池的非线性容量特性的临时容量与可获得容量部分,所述的临时容量部分表示放电时可直接获得的电量,表示电池的荷电状态SOC;所述的可获得容量部分表示不能直接获取的电量,两部分相连通,当电池放电时,负载电流i从临时容量部分流出,同时获得容量部分的电量通过速率系数,利用临时容量与可获得容量部分的高度比、结合电池容量特性分数阶阶次的大小,表达电池的非线性容量效应和电池恢复效应。
- 如权利要求1所述的一种计及非线性容量特性的分数阶KiBaM电池模型,其特征是:所述的临时容量部分与可获得容量部分之和为电池的总容量。
- 如权利要求1所述的一种计及非线性容量特性的分数阶KiBaM电池模型,其特征是:当电池完全放电结束后,临时容量部分的高度为零。
- 如权利要求1所述的一种计及非线性容量特性的分数阶KiBaM电池模型,其特征是:通过建立的分数阶KiBaM电池模型,获取当前的电池总剩余容量y(t)、可用容量Cavail(t)、不 可用容量Cunavail(t)和电池荷电状态SOC,以捕获电池运行时间和动力电池非线性容量内特征。
- 一种应用如权利要求1-7中任一项所述的分数阶KiBaM电池模型的参数辨识方法,其特征是:包括以下步骤:步骤一:对动力电池进行恒流充放电实验,使动力电池恢复到充满电的状态,作为电池的初始状态;步骤二:对动力电池进行小电流恒流放电实验,得到动力电池的初始容量Cinit;步骤三:对动力电池充满电,进行大电流恒流放电实验,由于放电电流很大,很短时间就放电到放电截止电压,得到大电流下动力电池的容量C1,计算电池容量的分配比例;步骤四:对动力电池进行两组不同倍率的恒流放电测试,获取此放电倍率下电池的不可用容量Cunavail、放电时间td数据,依据电池放电结束判定条件,计算得到可辨识出参数k'和阶次的大小α;步骤五:通过以上测试和实验,得到被测动力电池的分数阶KiBaM电化学模型的参数。
- 如权利要求8所述的参数辨识方法,其特征是:还包括步骤六对动力电池进行其他倍率的恒流放电测试,获取此放电倍率下电池的不可用容量、放电时间数据;并与模型计算得 到的不可用容量、放电时间做比较,验证模型的精确度。
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Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130241467A1 (en) * | 2012-03-13 | 2013-09-19 | Zafer Sahinoglu | Method and System for Charging Batteries Using a Kinetic Model |
CN103473446A (zh) * | 2013-08-29 | 2013-12-25 | 国家电网公司 | 用于有源配电网可靠性评估的负荷削减模型及其实现方法 |
CN104112036A (zh) * | 2014-06-12 | 2014-10-22 | 湖南文理学院 | 混联式混合动力电动汽车的仿真方法 |
CN104361405A (zh) * | 2014-10-28 | 2015-02-18 | 广东电网有限责任公司电力科学研究院 | 一种基于容量限值约束的微网储能装置设计方法 |
CN104392080A (zh) * | 2014-12-19 | 2015-03-04 | 山东大学 | 一种锂电池分数阶变阶等效电路模型及其辨识方法 |
CN106855612A (zh) * | 2017-02-21 | 2017-06-16 | 山东大学 | 计及非线性容量特性的分数阶KiBaM电池模型及参数辨识方法 |
CN106896327A (zh) * | 2017-03-10 | 2017-06-27 | 山东大学 | 分数阶KiBaM‑等效电路综合特征电池模型及其参数辨识方法 |
Family Cites Families (45)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE10021161A1 (de) * | 2000-04-29 | 2001-10-31 | Vb Autobatterie Gmbh | Verfahren zur Ermittlung des Ladezustands und der Belastbarkeit eines elektrischen Akkumulators |
DE10106508A1 (de) * | 2001-02-13 | 2002-08-29 | Bosch Gmbh Robert | Verfahren und Anordnung zur Bestimmung der Leistungsfähigkeit einer Batterie |
US6441586B1 (en) * | 2001-03-23 | 2002-08-27 | General Motors Corporation | State of charge prediction method and apparatus for a battery |
US6534954B1 (en) * | 2002-01-10 | 2003-03-18 | Compact Power Inc. | Method and apparatus for a battery state of charge estimator |
JP3771526B2 (ja) * | 2002-10-21 | 2006-04-26 | 株式会社日立製作所 | 二次電池評価方法および蓄電装置 |
US7199557B2 (en) * | 2003-07-01 | 2007-04-03 | Eaton Power Quality Company | Apparatus, methods and computer program products for estimation of battery reserve life using adaptively modified state of health indicator-based reserve life models |
US7321220B2 (en) * | 2003-11-20 | 2008-01-22 | Lg Chem, Ltd. | Method for calculating power capability of battery packs using advanced cell model predictive techniques |
US8103485B2 (en) * | 2004-11-11 | 2012-01-24 | Lg Chem, Ltd. | State and parameter estimation for an electrochemical cell |
JP5460943B2 (ja) * | 2005-08-19 | 2014-04-02 | 株式会社Nttファシリティーズ | 劣化判定装置、劣化判定方法、コンピュータプログラム |
US7446504B2 (en) * | 2005-11-10 | 2008-11-04 | Lg Chem, Ltd. | System, method, and article of manufacture for determining an estimated battery state vector |
US7723957B2 (en) * | 2005-11-30 | 2010-05-25 | Lg Chem, Ltd. | System, method, and article of manufacture for determining an estimated battery parameter vector |
US7957921B2 (en) * | 2008-02-19 | 2011-06-07 | GM Global Technology Operations LLC | Model-based estimation of battery hysteresis |
US8754614B2 (en) * | 2009-07-17 | 2014-06-17 | Tesla Motors, Inc. | Fast charging of battery using adjustable voltage control |
US8207706B2 (en) * | 2009-08-04 | 2012-06-26 | Honda Motor Co., Ltd. | Method of estimating battery state of charge |
US9366732B2 (en) * | 2009-09-04 | 2016-06-14 | Board Of Regents, The University Of Texas System | Estimation of state-of-health in batteries |
DE102009049589A1 (de) * | 2009-10-16 | 2011-04-21 | Bayerische Motoren Werke Aktiengesellschaft | Verfahren zur Bestimmung und/oder Vorhersage der maximalen Leistungsfähigkeit einer Batterie |
US8643342B2 (en) * | 2009-12-31 | 2014-02-04 | Tesla Motors, Inc. | Fast charging with negative ramped current profile |
JP5691592B2 (ja) * | 2010-02-18 | 2015-04-01 | 日産自動車株式会社 | 電池状態推定装置 |
US20110208453A1 (en) * | 2010-02-23 | 2011-08-25 | Song Ci | Circuit-based method for estimating battery capacity |
US8341449B2 (en) * | 2010-04-16 | 2012-12-25 | Lg Chem, Ltd. | Battery management system and method for transferring data within the battery management system |
US10687150B2 (en) * | 2010-11-23 | 2020-06-16 | Audiotoniq, Inc. | Battery life monitor system and method |
US9374787B2 (en) * | 2011-02-10 | 2016-06-21 | Alcatel Lucent | Method and apparatus of smart power management for mobile communication terminals using power thresholds |
KR20130121143A (ko) * | 2011-03-07 | 2013-11-05 | 가부시끼가이샤 히다치 세이사꾸쇼 | 전지 상태 추정 방법 및 전지 관리 시스템 |
US8449998B2 (en) * | 2011-04-25 | 2013-05-28 | Lg Chem, Ltd. | Battery system and method for increasing an operational life of a battery cell |
US8719195B2 (en) * | 2011-10-10 | 2014-05-06 | The Boeing Company | Battery adaptive learning management system |
KR101852322B1 (ko) * | 2011-11-30 | 2018-04-27 | 주식회사 실리콘웍스 | 배터리 파라미터 관리시스템 및 배터리 파라미터 추정방법 |
US9176198B2 (en) * | 2012-02-17 | 2015-11-03 | GM Global Technology Operations LLC | Battery state estimator with overpotential-based variable resistors |
KR101551088B1 (ko) * | 2014-05-09 | 2015-09-07 | 현대자동차주식회사 | 배터리 승온 시스템 및 릴레이 고장 검출 장치 및 그 방법 |
US10288691B2 (en) * | 2014-06-05 | 2019-05-14 | Ford Global Technologies, Llc | Method and system for estimating battery model parameters to update battery models used for controls |
JP6176632B2 (ja) * | 2014-06-30 | 2017-08-09 | 東洋ゴム工業株式会社 | 組電池の異常判定方法及び組電池の異常判定装置 |
US20160018469A1 (en) * | 2014-07-21 | 2016-01-21 | Richtek Technology Corporation | Method of estimating the state of charge of a battery and system thereof |
US10408880B2 (en) * | 2014-08-19 | 2019-09-10 | Fca Us Llc | Techniques for robust battery state estimation |
CN104181470B (zh) * | 2014-09-10 | 2017-04-26 | 山东大学 | 一种基于非线性预测扩展卡尔曼滤波的电池soc估计方法 |
US9843069B2 (en) * | 2014-09-26 | 2017-12-12 | Ford Global Technologies, Llc | Battery capacity degradation resolution methods and systems |
DE112016000834T5 (de) * | 2015-02-19 | 2017-11-30 | Mitsubishi Electric Corporation | Vorrichtung zum einschätzen eines batteriestatus |
JP6455409B2 (ja) * | 2015-03-06 | 2019-01-23 | 株式会社デンソー | 電池状態推定装置 |
EP3279679B1 (en) * | 2015-03-31 | 2022-06-29 | Vehicle Energy Japan Inc. | Battery control device and electric vehicle system |
KR102441335B1 (ko) * | 2015-08-13 | 2022-09-06 | 삼성전자주식회사 | 배터리의 soc 추정 장치 및 방법 |
KR102515829B1 (ko) * | 2015-11-02 | 2023-03-29 | 삼성전자주식회사 | 배터리 초기값 추정 장치 및 방법 |
JP2017168422A (ja) * | 2016-03-15 | 2017-09-21 | 東洋ゴム工業株式会社 | 密閉型二次電池の残容量予測方法及び残容量予測システム |
JP2018029029A (ja) * | 2016-08-19 | 2018-02-22 | 東洋ゴム工業株式会社 | 使用済み電池を用いた組電池の製造方法及び組電池 |
KR102634815B1 (ko) * | 2016-11-22 | 2024-02-07 | 삼성전자주식회사 | 오차 보정에 기초한 배터리 상태 추정 방법 및 장치 |
US10677848B2 (en) * | 2017-06-02 | 2020-06-09 | Total S.A. | Apparatus, circuit model, and method for battery modelling |
CN107390138B (zh) * | 2017-09-13 | 2019-08-27 | 山东大学 | 动力电池等效电路模型参数迭代辨识新方法 |
US20190190091A1 (en) * | 2017-12-18 | 2019-06-20 | Samsung Electronics Co., Ltd. | Method and apparatus estimating a state of battery |
-
2017
- 2017-02-21 CN CN201710093350.0A patent/CN106855612B/zh active Active
- 2017-10-19 US US16/493,547 patent/US11526639B2/en active Active
- 2017-10-19 WO PCT/CN2017/106912 patent/WO2018153116A1/zh active Application Filing
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130241467A1 (en) * | 2012-03-13 | 2013-09-19 | Zafer Sahinoglu | Method and System for Charging Batteries Using a Kinetic Model |
CN103473446A (zh) * | 2013-08-29 | 2013-12-25 | 国家电网公司 | 用于有源配电网可靠性评估的负荷削减模型及其实现方法 |
CN104112036A (zh) * | 2014-06-12 | 2014-10-22 | 湖南文理学院 | 混联式混合动力电动汽车的仿真方法 |
CN104361405A (zh) * | 2014-10-28 | 2015-02-18 | 广东电网有限责任公司电力科学研究院 | 一种基于容量限值约束的微网储能装置设计方法 |
CN104392080A (zh) * | 2014-12-19 | 2015-03-04 | 山东大学 | 一种锂电池分数阶变阶等效电路模型及其辨识方法 |
CN106855612A (zh) * | 2017-02-21 | 2017-06-16 | 山东大学 | 计及非线性容量特性的分数阶KiBaM电池模型及参数辨识方法 |
CN106896327A (zh) * | 2017-03-10 | 2017-06-27 | 山东大学 | 分数阶KiBaM‑等效电路综合特征电池模型及其参数辨识方法 |
Non-Patent Citations (1)
Title |
---|
JONGERDEN, M. R. ET AL.: "Which Battery Model to Use?", IET SOFTW., vol. 3, no. 6, 31 December 2009 (2009-12-31), pages 445 - 457, XP006034262, ISSN: 1751-8806 * |
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Publication number | Priority date | Publication date | Assignee | Title |
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