CN108445406B - Power battery state of health estimation method - Google Patents
Power battery state of health estimation method Download PDFInfo
- Publication number
- CN108445406B CN108445406B CN201810205365.6A CN201810205365A CN108445406B CN 108445406 B CN108445406 B CN 108445406B CN 201810205365 A CN201810205365 A CN 201810205365A CN 108445406 B CN108445406 B CN 108445406B
- Authority
- CN
- China
- Prior art keywords
- battery
- capacity
- curve
- peak value
- current
- 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.)
- Expired - Fee Related
Links
Images
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
The invention discloses a method for estimating the health state of a power battery, which comprises the steps of acquiring the voltage V, the current I and the time t of the battery charged by constant current of the battery to obtain the charging capacity Q, establishing a V-Q relation curve, acquiring the peak value and the peak position information of a capacity increment curve, establishing an RBF neural network, training an RBF neural network model by a particle swarm algorithm, and estimating the health state of the battery by utilizing the generated RBF neural network. Under the condition that an equivalent circuit of a power battery of the electric automobile does not need to be established, the method establishes the mapping relation between the peak value and the peak position of the capacity increment curve of the constant-current charging and the health state of the battery in a data driving mode, improves the estimation precision, realizes online real-time estimation and can realize the integral estimation of the battery pack.
Description
Technical Field
The invention relates to the technical field of power batteries, in particular to a power battery health state estimation method.
Background
The lithium ion power battery is widely applied to the fields of electric automobiles, electric tools, base station standby power supplies and the like. The state of health (SOH) of the battery is one of the most important parameters in the battery management system, and reflects the aging condition of the battery caused by the environmental temperature, the charge-discharge cutoff voltage, the charge-discharge rate, the charge-discharge depth, the shelf condition and the operation condition. The self-ignition, explosion and other accidents of the electric automobile are caused by the fact that the health state of the battery is not monitored in place. Therefore, the SOH of the battery pack can be accurately mastered, the charging and discharging performance of the battery pack can be ensured, the estimation precision of the state of charge of the battery is improved, a basis is provided for the detection and diagnosis of the battery, and unnecessary faults and accidents are avoided.
For the detection of the state of health of the battery, the current common methods include:
(1) battery state of health definition: according to the method, in one definition of the battery health state, the current capacity of the battery is in the battery health state compared with the nominal capacity, and the battery is discharged to the cut-off voltage after being fully charged, so that the current capacity of the battery is accurately calculated.
(2) The impedance analysis method adopts a single-frequency alternating current signal to measure the SOH of the lithium battery, a signal generating circuit is required to be designed to input signals with different frequencies to the lithium battery, a feedback signal is acquired, the internal resistance of the battery is calculated, and the health state of the battery is solved according to the relation between the internal resistance of the battery capacity and the health state of the battery, the method is accurate, and the defect is that a hardware detection circuit of the signal generating circuit is required to be added, and the injection of the signal can generate noise influence on the battery;
(3) and (3) a cycle number folding algorithm: the method for estimating the service life of the battery according to the cycle number of the battery establishes the corresponding relation between the full charge and discharge cycle number of the battery and the health state of the battery, and has the defects that the charging and discharging depths of the battery are different every time when the battery is used on an automobile, the corresponding relation between the cycle number and the health state of the battery is difficult to establish, and the method is difficult to be applied in practice.
(4) An estimation algorithm based on a battery equivalent circuit model is as follows: the method has the defects that the number of single batteries in a battery pack in the electric automobile is large, and the single batteries are connected in series and parallel, so that the equivalent circuit model of the battery pack is complex, and an accurate equivalent model is difficult to establish.
In order to overcome the defects of the method, the invention patent with the publication number of CN106569136A 'a method and a system for online estimation of the state of health of a battery', proposes a method and a system for online estimation of the state of health of a battery, the method selects a voltage point at the maximum value of the ratio of the change of the electric quantity in the battery to the change of the open-circuit voltage in the charging and discharging process of the battery as a reference point, selects two voltages V1+ and V1-near the reference voltage point, obtains the change value of the internal capacity of the battery in the process that the terminal voltage of the battery changes from V1+ to V1-under the open-circuit voltage or constant current working condition, then filters the obtained change value, records the filtered change value as CT, records the actual capacity of the battery as CA, and establishes a linear model of the capacity of the battery: CA ═ a × CT + b. Although the method can realize online estimation of the state of health of the battery, the actual capacity model is nonlinear, so that the estimation result of the method has large errors.
Disclosure of Invention
The invention aims to solve the problems that the existing electric vehicle power battery health state estimation is difficult to realize and is low in precision, and provides a power battery health state estimation method.
In order to solve the problems, the invention is realized by the following technical scheme:
a power battery state of health estimation method includes the following steps:
step 2, establishing a relation curve of the battery voltage and the battery charge state in the charging process, obtaining a capacity increment curve by deriving the relation curve of the battery voltage and the battery charge state, and obtaining peak values and peak position data of the curve according to the capacity increment curve;
step 3, taking the peak value and the peak value position of the capacity increment curve as input characteristics and the battery health state as output, and establishing an RBF neural network model;
step 5, in actual use, acquiring current charging data, namely voltage, current and time data, of the battery to be estimated, and obtaining current peak value and peak value position data of the battery to be estimated on the basis; and (3) taking the current peak value and the peak value position of the battery to be estimated as input, and estimating the health state of the battery on line by using the RBF neural network model determined in the step (3).
In the step 1, the battery is circularly charged in a constant current mode.
In the step 2, the derivative obtained incremental curve needs to be filtered to obtain a smooth capacity incremental curve, and the peak value and the peak position data are obtained by comparison on the smooth capacity incremental curve.
Compared with the prior art, the invention has the following characteristics:
1. an RBF neural network model for SOH estimation is established in an off-line mode through data obtained by experiments, and SOH on-line estimation can be realized on a real vehicle by using the model; the neural network has strong fitting performance, and the accuracy of estimating the state of health of the battery is higher;
2. the battery equivalent circuit equivalent model does not need to be established, the problem that the whole battery pack equivalent circuit model is difficult to establish is solved, the health state of the single battery pack can be estimated, and the whole estimation of the health state of the battery pack can be realized in a battery management system with a good balancing effect.
Drawings
Fig. 1 is an overall flowchart of a method for estimating the state of health of a power battery.
FIG. 2 is a schematic diagram of a V-Q relationship curve of a voltage platform in a constant current charging process of a battery.
Fig. 3 is a schematic diagram of a capacity increment curve in the constant current charging process of a battery.
Fig. 4 is a schematic structural diagram of an RBF neural network model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings in conjunction with specific examples.
A method for estimating the state of health of a power battery is shown in FIG. 1, and specifically comprises the following steps:
step 1: and collecting the voltage, the current and the time of the battery cyclic charge-discharge aging experiment. The battery cycle charge-discharge aging experiment carries out charge-discharge in a constant current mode.
And carrying out a cyclic charge-discharge test on the battery, and acquiring voltage V, current I and time t data of each cycle until the cycle is finished. The battery cycle charge-discharge stop conditions are as follows: the state of health of the battery, defined as capacity, is less than 80%. The capacity-defined battery state of health expression is:
SOH＝Ci/C0*100％
in the formula, CiIs the current maximum available capacity, C, of the power battery0The nominal capacity of the power battery.
Step 2: and estimating the health state of the battery by using the data in the charging process, establishing a relation curve of the battery voltage V and the charging capacity Q in the charging process, and solving the peak value and the peak value position data of the capacity increment curve on the basis.
Step 2-1: the battery charging capacity Q is calculated by using data acquired in the constant current charging process, and a corresponding relation curve of the battery voltage V and Q is established, as shown in fig. 2.
The battery charging capacity is:
Qk+1＝Qk+IkΔt
in the formula, Qk+1For the current battery capacity, QkFor previous battery capacity, IkΔ t is the sampling time interval for the current sampled last time.
Step 2-2: selecting Q/C from the established V-Q relation curveiAnd taking a curve in the range of 0.3-0.8 as an original curve for solving the capacity increment curve, wherein data in the range can be easily obtained in the charging process of the electric automobile under the common condition.
Step 2-3: and (4) performing derivation on the V-Q curve obtained in the step 2-2, namely obtaining a capacity increment curve from the dQ/dV. The capacity increment curve can highlight an unobvious voltage platform in the charging process of the lithium ion power battery, and the peak value of the capacity increment curve is reduced along with the reduction of the state of health of the battery and moves towards the voltage increasing direction, so the peak value (PVmax) and the peak value position (PPmax) can be used as characteristic parameters for estimating the state of health of the lithium ion power battery.
Since the capacity increment curve obtained in the above steps has large noise, a smooth capacity increment curve is obtained through a filtering algorithm (the filtering algorithm is not limited to filtering algorithms such as a mean value, a low pass, kalman, wavelet, and the like), as shown in fig. 3. Then, on the smoothed capacity increment curve, the peak value PVmax (i.e., the maximum value on the ordinate) and the peak position PPmax (i.e., the abscissa corresponding to the peak value) can be obtained by comparison as characteristic factors for characterizing the state of health of the battery.
And step 3: and (3) taking the peak value PVmax and the peak value position PPmax of the capacity increment curve as input characteristics, outputting the SOH of the battery, and establishing an RBF neural network model of the peak value, the peak value position and the SOH of the capacity increment curve. The RBF neural network model structure is determined, and the weight parameter is not determined.
The RBF neural network is a three-layer feedforward network with an input layer, a single hidden layer and an output layer, and the model is shown in FIG. 4, and the mapping relation is as follows:
wherein X ═ X1, X2]TIs an input sample; (x) is an output vector; phi is ai(X) is the ith hidden layer basis function; omega0Weight of constant 1, ωiIs the ith basis function weight; k is the number of centers of the hidden layer basis functions; z is a radical ofiIs the center of the i-th layer of the gaussian function; deltaiIs the width of the ith basis function.
And 4, step 4: and training the RBF neural network model by the particle swarm algorithm, determining undetermined weight parameters in the neural network model by an optimization method, and obtaining the RBF neural network model with determined structure and parameters. The RBF neural network model training data set comprises peaks, peak position matrixes and SOH matrixes obtained by all cyclic charge-discharge tests.
The particle swarm optimization is an optimization algorithm, provides an optimal solution for solving parameters of the RBF neural network model, and the specific implementation method is as follows: randomly initializing a group of particles, dynamically adjusting the speed of the particle group according to comprehensive analysis of flight experience of individuals and groups, searching in a solution space, and finding out an optimal solution through iteration. In the iterative process, the particle updates itself by tracking the individual extremum pbest and the global extremum gbest. Suppose that in the D object dimension search space, there are m particles grouped together, and the ith particle position is represented as Xi＝(xi1,xi2,...,xid) 1 ≦ i ≦ m, and its corresponding velocity denoted Vi(vi1, vi 2.., vid). The updated formula for the velocity and position of each particle is as follows:
wherein k is the current iteration number,
is the velocity position of the ith particle d-dimensional component at the first iteration,
for the position of the d-dimensional component of the ith particle at the kth iteration, PidIs the position of the individual extreme point of the ith particle in the d-dimension of the kth iteration, PgdIs the position of the global extreme point of the d dimension of the whole population; c. C1And c2Is a non-negative constant acceleration factor, omega (k) is an inertia weight, adjusts the searching capability of a solution space, rand () is a random function, and the value range is [0, 1%]And the search randomness is increased.
And 5: in actual use, the current charging data of the battery is collected, the peak value and the peak position of the capacity increment curve are calculated as input in step 2, and the health state of the battery can be estimated on line by utilizing the determined RBF neural network model.
The invention discloses a method for estimating the health state of a power battery of an electric automobile by using a particle swarm RBF neural network, which comprises the steps of acquiring the voltage V, the current I and the time t of a battery which is charged by constant current to obtain the charging capacity Q, establishing a V-Q relation curve, acquiring the peak value and the peak position information of a capacity increment curve, establishing the RBF neural network, training a RBF neural network model by using a particle swarm algorithm, and estimating the health state of the battery by using the generated RBF neural network. Under the condition that an equivalent circuit of a power battery of the electric automobile does not need to be established, the method establishes the mapping relation between the peak value and the peak position of the capacity increment curve of the constant-current charging and the health state of the battery in a data driving mode, improves the estimation precision, realizes online real-time estimation and can realize the integral estimation of the battery pack.
It should be noted that, although the above-mentioned embodiments of the present invention are illustrative, the present invention is not limited thereto, and thus the present invention is not limited to the above-mentioned embodiments. Other embodiments, which can be made by those skilled in the art in light of the teachings of the present invention, are considered to be within the scope of the present invention without departing from its principles.
Claims (3)
1. A method for estimating the state of health of a power battery is characterized by comprising the following steps:
step 1, carrying out a cyclic charge-discharge test on a battery sample, and collecting voltage, current and time data of each cycle; the battery state of health, SOH, expression defined in terms of capacity is:
SOH＝Ci/C0*100％
in the formula, CiIs the current maximum available capacity, C, of the power battery0The nominal capacity of the power battery;
step 2, establishing a relation curve of the battery voltage and the battery charging capacity in the charging process, obtaining a capacity increment curve by deriving the relation curve of the battery voltage and the battery charging capacity, and obtaining a peak value and peak value position data of the curve according to the capacity increment curve; namely:
step 2-1, calculating the battery charging capacity by using data acquired in the constant current charging process, and establishing a relation curve between the battery voltage and the battery charging capacity, wherein the battery charging capacity is as follows:
Qk+1＝Qk+IkΔt
in the formula, Qk+1For the current battery capacity, QkFor previous battery capacity, IkThe current sampled at the previous time is delta t, and the sampling time interval is delta t;
2-2, selecting a curve with the battery charging capacity being 0.3-0.8 of the current maximum available capacity of the power battery, namely Q/Ci, as an original curve for solving a capacity increment curve from the established relation curve of the battery voltage and the battery charging capacity;
step 2-3, deriving the original curve obtained in the step 2-2 to obtain a capacity increment curve, and obtaining the peak value and peak value position data of the curve according to the capacity increment curve;
step 3, taking the peak value and the peak value position of the capacity increment curve as input characteristics and the battery health state as output, and establishing an RBF neural network model;
step 4, taking the peak values, the peak value positions and the battery health state matrix obtained by all the cyclic charge-discharge tests as a training data set, and training the RBF neural network model established in the step 3 by utilizing a particle swarm algorithm to obtain the RBF neural network model with determined structure and parameters;
step 5, in actual use, acquiring current charging data, namely voltage, current and time data, of the battery to be estimated, and obtaining current peak value and peak value position data of the battery to be estimated on the basis; and (3) taking the current peak value and the peak value position of the battery to be estimated as input, and estimating the health state of the battery on line by using the RBF neural network model determined in the step (3).
2. The method for estimating the state of health of a power battery as claimed in claim 1, wherein in step 1, the battery sample is subjected to a cyclic charge-discharge test in a constant current mode.
3. The method as claimed in claim 1, wherein in step 2, the derivative incremental curve is filtered to obtain a smooth capacity incremental curve, and the peak value and the peak position data are obtained by comparing on the smooth capacity incremental curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810205365.6A CN108445406B (en) | 2018-03-13 | 2018-03-13 | Power battery state of health estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810205365.6A CN108445406B (en) | 2018-03-13 | 2018-03-13 | Power battery state of health estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108445406A CN108445406A (en) | 2018-08-24 |
| CN108445406B true CN108445406B (en) | 2021-05-25 |
Family
ID=63195048
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810205365.6A Expired - Fee Related CN108445406B (en) | 2018-03-13 | 2018-03-13 | Power battery state of health estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108445406B (en) |
Families Citing this family (43)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109061514B (en) * | 2018-09-30 | 2020-11-06 | 北京理工大学 | Big data-based power battery degradation condition analysis method and system |
| CN109031153B (en) * | 2018-10-16 | 2020-01-24 | 北京交通大学 | Online health state estimation method for lithium ion battery |
| CN109802190B (en) * | 2019-01-31 | 2022-04-08 | 南京理工大学 | Multi-target charging method for battery pack |
| CN109799765A (en) * | 2019-02-02 | 2019-05-24 | 中国民航大学 | Steering engine electrohydraulic servo system intelligent control method |
| CN109975713B (en) * | 2019-04-12 | 2021-11-02 | 苏州正力新能源科技有限公司 | Power battery SOH estimation method considering multi-factor influence |
| CN110146822B (en) * | 2019-04-25 | 2020-07-28 | 同济大学 | Vehicle power battery capacity online estimation method based on constant-current charging process |
| CN110058178A (en) * | 2019-05-14 | 2019-07-26 | 郑州大学 | A kind of lithium battery method for detecting health status and system |
| CN110967631B (en) | 2019-05-17 | 2021-03-30 | 宁德时代新能源科技股份有限公司 | SOH correction method and apparatus, battery management system, and storage medium |
| CN110441706A (en) * | 2019-08-23 | 2019-11-12 | 优必爱信息技术（北京）有限公司 | A kind of battery SOH estimation method and equipment |
| CN110635187B (en) * | 2019-09-01 | 2021-02-12 | 南京理工大学 | Lithium battery charging method considering aging |
| CN110703101B (en) * | 2019-09-12 | 2021-01-05 | 北京交通大学 | Lithium ion battery inter-partition cycle capacity decline prediction method |
| CN110728360B (en) * | 2019-10-12 | 2020-07-03 | 联合微电子中心有限责任公司 | Micro-energy device energy identification method based on BP neural network |
| CN110632528B (en) * | 2019-11-04 | 2021-08-31 | 桂林电子科技大学 | Lithium battery SOH estimation method based on internal resistance detection |
| JP7151689B2 (en) * | 2019-11-05 | 2022-10-12 | トヨタ自動車株式会社 | BATTERY MANAGEMENT SYSTEM, BATTERY MANAGEMENT METHOD, AND BATTERY MANUFACTURING METHOD |
| CN110794308B (en) * | 2019-11-07 | 2021-09-10 | 交控科技股份有限公司 | Method and device for predicting train battery capacity |
| CN110927591B (en) * | 2019-12-11 | 2020-10-02 | 北京理工大学 | Battery capacity estimation method, computer readable medium and vehicle |
| CN111142036B (en) * | 2019-12-18 | 2021-02-02 | 同济大学 | Lithium ion battery online rapid capacity estimation method based on capacity increment analysis |
| DE102020100668B4 (en) * | 2020-01-14 | 2021-07-22 | TWAICE Technologies GmbH | Characterization of rechargeable batteries with machine-learned algorithms |
| CN111308378B (en) * | 2020-03-10 | 2021-05-18 | 北京理工大学 | Battery pack health state detection method and system based on capacity increment curve |
| CN111398833B (en) * | 2020-03-13 | 2021-08-31 | 浙江大学 | Battery health state assessment method |
| CN111323719A (en) * | 2020-03-18 | 2020-06-23 | 北京理工大学 | Method and system for online determination of health state of power battery pack of electric automobile |
| CN111443293A (en) * | 2020-03-30 | 2020-07-24 | 青岛大学 | Lithium battery state of health (SOH) estimation method based on data driving |
| CN111308381A (en) * | 2020-04-07 | 2020-06-19 | 国网江苏省电力有限公司苏州供电分公司 | Health state evaluation method for power battery of pure electric bus |
| CN115461635A (en) * | 2020-05-11 | 2022-12-09 | 东莞新能德科技有限公司 | Battery capacity estimation method, electronic device, and storage medium |
| CN111707958B (en) * | 2020-05-26 | 2021-09-03 | 同济大学 | Battery internal short circuit detection method based on capacity increment curve characteristics |
| CN111766524B (en) * | 2020-07-16 | 2023-03-10 | 浙江吉智新能源汽车科技有限公司 | Method, device, equipment and storage medium for determining battery health state |
| CN111965559B (en) * | 2020-08-17 | 2023-06-16 | 西安理工大学 | On-line estimation method for SOH of lithium ion battery |
| CN111965560B (en) * | 2020-08-24 | 2023-03-28 | 重庆大学 | Battery health state estimation method oriented to general discharge working condition |
| CN112014757A (en) * | 2020-08-27 | 2020-12-01 | 湖北工业大学 | Battery SOH estimation method integrating capacity increment analysis and genetic wavelet neural network |
| CN112444749B (en) * | 2020-11-06 | 2021-11-05 | 南京航空航天大学 | Lithium battery state of charge joint estimation method based on temperature correction model |
| CN112563591B (en) * | 2020-12-11 | 2021-10-01 | 吉林大学 | Storage battery pack balance control method |
| CN112698217B (en) * | 2020-12-25 | 2023-11-03 | 江苏省特种设备安全监督检验研究院 | Battery monomer capacity estimation method based on particle swarm optimization algorithm |
| CN112731162B (en) * | 2021-02-08 | 2024-05-03 | 武汉蔚能电池资产有限公司 | Battery health degree detection method based on V2G use scene |
| CN113406520B (en) * | 2021-05-21 | 2022-11-15 | 北京理工大学 | Battery health state estimation method for real new energy automobile |
| CN113219343A (en) * | 2021-06-02 | 2021-08-06 | 上海玫克生储能科技有限公司 | Lithium battery health state prediction method, system, equipment and medium based on elastic network |
| US11422199B1 (en) | 2021-06-17 | 2022-08-23 | Hong Kong Applied Science and Technology Research Institute Company Limited | State of health evaluation of retired lithium-ion batteries and battery modules |
| CN113325317A (en) * | 2021-07-08 | 2021-08-31 | 山东大学 | Power battery fault diagnosis method and system based on improved RBF neural network |
| CN113627671A (en) * | 2021-08-11 | 2021-11-09 | 万克能源科技有限公司 | SOH prediction calculation method for single battery in energy storage scene |
| CN113900028B (en) * | 2021-10-09 | 2022-07-08 | 山东大学 | Battery health state estimation method and system considering initial charge state and charge-discharge path |
| CN113884912A (en) * | 2021-10-18 | 2022-01-04 | 苏州元启动力科技有限公司 | Interpolation fitting method and device for dQ/dV curve of lithium battery based on simulated annealing algorithm |
| CN113759269B (en) * | 2021-11-10 | 2022-02-25 | 武汉理工大学 | Method and system for monitoring health state of battery of electric vehicle |
| CN114742297B (en) * | 2022-04-11 | 2024-05-24 | 中国第一汽车股份有限公司 | Method for processing power battery |
| CN115184805A (en) * | 2022-06-21 | 2022-10-14 | 东莞新能安科技有限公司 | Battery health state acquisition method, device, equipment and computer program product |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2444511B (en) * | 2006-12-06 | 2008-10-22 | Iti Scotland Ltd | Battery Management System |
| CN102680903B (en) * | 2012-05-11 | 2015-01-28 | 齐鲁工业大学 | Portable storage battery state detection system and method |
| CN202693771U (en) * | 2012-05-11 | 2013-01-23 | 山东轻工业学院 | Portable storage battery state detection system |
| CN103675702B (en) * | 2013-12-04 | 2016-05-25 | 清华大学 | A kind of method of real-time assessment cell health state |
| CN103698714B (en) * | 2014-01-02 | 2016-06-29 | 清华大学 | Battery capacity decay mechanism discrimination method and system |
| CN106339755B (en) * | 2016-08-29 | 2018-09-21 | 深圳市计量质量检测研究院 | Lithium battery health status prediction technique based on neural network Yu period kernel function GPR |
| CN106980091B (en) * | 2017-03-29 | 2019-09-17 | 北京理工大学 | A kind of electrokinetic cell system health status estimation method based on fractional model |
| CN107482266B (en) * | 2017-08-28 | 2019-07-02 | 江西优特汽车技术有限公司 | A kind of power battery pack method for group matching |
-
2018
- 2018-03-13 CN CN201810205365.6A patent/CN108445406B/en not_active Expired - Fee Related
Non-Patent Citations (2)
| Title |
|---|
| 基于PSO-RBF神经网络的锂离子电池健康状态预测;张任等;《中国机械工程》;20161130;第27卷(第21期);2975-2981 * |
| 粒子群优化RBF神经网络光伏电池建模研究;张军朝等;《计算机工程与科学》;20111231;第33卷(第9期);169-173 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108445406A (en) | 2018-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108445406B (en) | Power battery state of health estimation method | |
| WO2021259196A1 (en) | Battery pack consistency evaluation method and system | |
| Xiong et al. | Modeling for lithium-ion battery used in electric vehicles | |
| Sangwan et al. | State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF) | |
| CN109358293B (en) | Lithium ion battery SOC estimation method based on IPF | |
| CN106872899B (en) | A kind of power battery SOC estimation method based on reduced dimension observer | |
| Stroe et al. | Lithium-ion battery dynamic model for wide range of operating conditions | |
| CN112345939B (en) | Lithium ion battery model parameter identification method based on continuous impulse response | |
| CN113238157B (en) | Method for screening through AI detection on retired batteries of electric vehicles | |
| Li et al. | A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles | |
| CN106772104A (en) | A kind of electrokinetic cell SOC value evaluation method | |
| Qiuting et al. | State of health estimation for lithium-ion battery based on D-UKF | |
| CN112578298A (en) | Battery temperature estimation method, device, electronic equipment and storage medium | |
| CN110632520A (en) | Estimation device and estimation method for SOC of power battery | |
| Ye et al. | Model-based state-of-charge estimation approach of the Lithium-ion battery using an improved adaptive particle filter | |
| CN112883637A (en) | Chaos time sequence nonlinear combination prediction method for lithium ion battery RUL | |
| Rosca et al. | On-line parameter, state-of-charge and aging estimation of Li-ion batteries | |
| Chen et al. | An approach for state of charge estimation of Li-ion battery based on Thevenin equivalent circuit model | |
| CN111751750A (en) | Multi-stage closed-loop lithium battery SOC estimation method based on fuzzy EKF | |
| Alsabari et al. | Modeling and validation of lithium-ion battery with initial state of charge estimation | |
| Livint et al. | Algorithm for estimating soc of lithium-ion batteries used for electric vehicles | |
| CN109782182B (en) | Online estimation method and device for energy state of series battery pack | |
| CN113759258B (en) | Power battery SOC estimation method and device and pure electric vehicle | |
| CN111400935A (en) | Lithium battery third-order model parameter identification method and system based on likelihood function | |
| Duong et al. | Novel estimation technique for the state-of-charge of the lead-acid battery by using EKF considering diffusion and hysteresis phenomenon |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210525 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |