CN105445671A - Lithium ion battery service life prediction method based on traceless particle filtering - Google Patents

Lithium ion battery service life prediction method based on traceless particle filtering Download PDF

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CN105445671A
CN105445671A CN201511006033.8A CN201511006033A CN105445671A CN 105445671 A CN105445671 A CN 105445671A CN 201511006033 A CN201511006033 A CN 201511006033A CN 105445671 A CN105445671 A CN 105445671A
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capacity
lithium ion
ion battery
battery
state variable
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房红征
艾力
樊焕贞
李蕊
罗凯
熊毅
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Beijing Aerospace Measurement and Control Technology Co Ltd
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Beijing Aerospace Measurement and Control Technology Co Ltd
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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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC

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  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a lithium ion battery service life prediction method based on traceless particle filtering, and the method can achieve more accurate estimation of the capacity state of a battery and improves the prediction accuracy of the service life of the battery. The method comprises the steps: enabling a double-index capacity attenuation model as a lithium ion battery capacity degradation model, and obtaining a state transfer equation and measurement equation of the lithium ion battery capacity; obtaining the distribution of the initial value of a state variable of the double-index capacity attenuation model according to the known service life attenuation data of other batteries; determining a corresponding prediction starting point for a to-be-measured battery, wherein the service life of the to-be-measured battery needs to be predicted; carrying out the state tracking of to-be-measured battery capacity data of charging and discharging times through employing a traceless particle filtering method, updating the state variable in the capacity attenuation model, and obtaining a corresponding state variable after the charging; predicting the corresponding state variable and battery capacity after the charging and discharging, plotting a capacity prediction curve, and determining the service life of the to-be-measured battery.

Description

A kind of based on the lithium ion battery life-span prediction method without mark particle filter
Technical field
The invention belongs to lithium ion battery failure prognostics and health management technical field, be specifically related to a kind of based on the lithium ion battery life-span prediction method without mark particle filter.
Background technology
Lithium ion battery has larger application prospect as novel storage battery, and particularly in the electrical property of energy storage and the higher occasion of reliability requirement, the aerospace equipment such as such as Low Earth Orbit, geostationary orbit, space station have huge development prospect.
The remaining life of accumulator, also referred to as cycle life, refers under certain discharge and recharge system, the charge and discharge cycles number of times that before capacity drops to setting, battery stands.For many application of lithium ion battery, lithium ion battery, under fully charged state, is considered as when actual capacity drops to the 70%-80% of rated capacity losing efficacy.Life prediction is the key factor realizing lithium ion battery health control, and health control formulates maintenance plan, spares provisioning plan etc. according to life prediction.In order to prevent the generation of the catastrophic failure caused by lithium ion battery, the health control technology of lithium ion battery is subject to increasing attention.The life prediction of lithium ion battery is a study hotspot of battery management.But, there is certain limitation in actual applications in the method for the life prediction based on particle filter (PF) common in prior art, particularly: in the particle filter algorithm of standard, generally get prior distribution for suggestion distribution, this method does not consider up-to-date measurement information, when system model is inaccurate or measurement noise changes suddenly, predicts the outcome and effectively can not represent actual value.In addition, the electrochemical reaction process of inside lithium ion cell complexity is difficult to characterize, in conjunction with the whole life deterioration process of battery and the more difficult foundation of battery capacity attenuation model of degraded data feature, certain difficulty is brought to life prediction, cause the estimation out of true in lithium ion battery life-span thus, fail to reflect truly the rule of battery life, thus bring a lot of difficulty by the prognostic and health management of following use lithium ion battery.Therefore, need a kind of based on the lithium ion battery life-span prediction method without mark particle filter, to solve the above-mentioned technical matters existed in prior art.
Summary of the invention
In view of this, the invention provides a kind of based on the lithium ion battery life-span prediction method without mark particle filter, utilize improve without mark particle filter algorithm (UPF), raising life prediction accuracy.
Realizing implementer's case of the present invention is:
Based on the lithium ion battery life-span prediction method without mark particle filter, comprise the following steps:
Step one, using two index capacity attenuation model as capacity of lithium ion battery degradation model, and obtains state transition equation and the measurement equation of capacity of lithium ion battery further;
Two index capacity attenuation model: Q k=aexp (bk)+cexp (dk)
State transition equation: a k = a k - 1 + w a w a ~ N ( 0 , σ a ) b k = b k - 1 + w b w b ~ N ( 0 , σ b ) c k = c k - 1 + w c w c ~ N ( 0 , σ c ) d k = d k - 1 + w d w d ~ N ( 0 , σ d )
Measurement equation: Q k=a kexp (b kk)+c kexp (d kk)+vv ~ N (0, σ v)
Wherein, a k, b k, c kand d kfor the state variable corresponding to the lithium ion battery kth time charge and discharge cycles cycle, k gets natural number, Q krepresent the actual capacity value of battery during kth time charge and discharge cycles cycle, w a, w b, w cand w dfor process noise, w a, w b, w cand w dall obey N (0, σ a), N (0, σ b), N (0, σ c) and N (0, σ d) normal distribution, v is measurement noises, and v submits to N (0, σ v) normal distribution;
Step 2, according to the life time decay data that other batteries are known, obtains the state variable initial value a of two index capacity attenuation model 0, b 0, c 0, d 0distribution;
Step 3, for the mesuring battary of required bimetry, determines the prediction starting point k of its correspondence, and wherein k represents the discharge and recharge number of times that mesuring battary has carried out;
Step 4, utilizes and carries out status tracking without mark particle filter method to k the mesuring battary capacity data carrying out discharge and recharge, upgrades the state variable in capacity attenuation model, state variable a corresponding after obtaining kth time discharge and recharge k, b k, c k, d k;
Step 5, utilization state variable a k, b k, c k, d k, after state transition equation and time discharge and recharge of measurement equation prediction kth, the state variable corresponding to each discharge and recharge and battery capacity;
Step 6, with mesuring battary discharge and recharge number of times for horizontal ordinate, with mesuring battary capacity for ordinate, sets up capacity predict curve;
Step 7, according to the mesuring battary capacity threshold of setting, determines the charging times of mesuring battary, i.e. the life-span of mesuring battary from described capacity predict curve.
Further, the present invention also can utilize the capacity curve of the actual use of mesuring battary and capacity predict curve to contrast, analyze mean absolute error (MAE), the root-mean-square error (RMSE) of capacity predict curve, and the error of battery life predicting (RUL_Error), it can be used for the reliability evaluating the method.
Beneficial effect:
(1) the present invention is directed to the problem of sample degeneracy and the dilution existed in conventional particle filter forecasting process, used up-to-date measurement information more new state posterior probability Density Distribution, the residual life of lithium ion battery can be estimated more accurately.
(3) often concentrate on owing to the invention solves the suggestion distribution of particle existed in standard particle filtering method the problem that Posterior probability distribution afterbody easily occurs compared with high weight variance, therefore effectively improve precision and the efficiency of lithium ion battery life prediction.
(4) the present invention proposes a kind of two index capacity attenuation model, can the process of simulate lithium ion battery life deterioration, and have build easily, goodness of fit high, there is stronger using value.This invention engineering practical value is high, raising lithium ion battery failure prognostics and health management level is had to the meaning of outbalance.
Accompanying drawing explanation
Fig. 1 is the process flow diagram based on the lithium ion battery life-span prediction method without mark particle filter of the present invention;
Fig. 2 is the prediction of the model parameter based on the UPF algorithm process flow diagram in the life-span prediction method of lithium ion battery shown in Fig. 1;
Fig. 3 is the life prediction result figure of certain battery in the life-span prediction method of lithium ion battery shown in Fig. 1 when T=50Cycle;
Fig. 4 is the life prediction result figure of certain battery in the life-span prediction method of lithium ion battery shown in Fig. 1 when T=60Cycle;
Fig. 5 is the life prediction result figure of certain battery in the life-span prediction method of lithium ion battery shown in Fig. 1 when T=70Cycle;
Fig. 6 is the life prediction result figure of certain battery in the life-span prediction method of lithium ion battery shown in Fig. 1 when T=80Cycle;
Embodiment
To develop simultaneously embodiment below in conjunction with accompanying drawing, describe the present invention.
The invention provides a kind of based on the lithium ion battery life-span prediction method without mark particle filter, for further setting forth the present invention for the technological means reaching predetermined object and take and effect, below in conjunction with accompanying drawing and preferred embodiment, the present invention is described in detail as after.
As shown in Figure 1, step one, using two index capacity attenuation model as capacity of lithium ion battery degradation model, utilize above-mentioned pair of index capacity attenuation model to describe the state space of lithium ion battery, and obtain state transition equation and the measurement equation of capacity of lithium ion battery further;
Two index capacity attenuation model: Q k=aexp (bk)+cexp (dk);
State transition equation: a k = a k - 1 + w a w a ~ N ( 0 , σ a ) b k = b k - 1 + w b w b ~ N ( 0 , σ b ) c k = c k - 1 + w c w c ~ N ( 0 , σ c ) d k = d k - 1 + w d w d ~ N ( 0 , σ d ) ;
Measurement equation: Q k=a kexp (b kk)+c kexp (d kk)+vv ~ N (0, σ v);
Wherein, a k, b k, c kand d kfor the state variable corresponding to the lithium ion battery kth time charge and discharge cycles cycle, k gets natural number, Q krepresent the actual capacity value of battery during kth time charge and discharge cycles cycle, w a, w b, w cand w dbe respectively process noise, w a, w b, w cand w dall obey N (0, σ a), N (0, σ b), N (0, σ c) and N (0, σ d) normal distribution, v is measurement noises, and v submits to N (0, σ v) normal distribution;
The present invention is directed to the foundation of capacity of lithium ion battery degradation model, life prediction in view of lithium ion battery is main in the face of problems such as historical data is less, the more difficult foundation of physical model, select the concrete manifestation form of analytic model as capacity attenuation, the analytic model built can be applicable to a small amount of historical data, in the indefinite situation of physical significance, still can obtain higher degree of accuracy, and be convenient to finally to utilize the methods such as Kalman filtering successfully to realize the life prediction of battery.
Therefore this step proposes a kind of pair of index capacity attenuation model for describing the attenuation trend of battery capacity: Q k=aexp (bk)+cexp (dk).Wherein Q krepresent the actual capacity value of battery during kth time charge and discharge cycles cycle, k represents the discharge and recharge number of times that mesuring battary has carried out, a 0, b 0, c 0, d 0for constant.This pair of index capacity attenuation model can correct the speed of different lifetime stage capacity of lithium ion battery decay, and model structure is simple, and the method easily through particle filter carries out the recurrence estimation of model parameter.
Step 2, according to the life time decay data that other batteries are known, obtains the state variable initial value a of two index capacity attenuation model 0, b 0, c 0, d 0distribution.
Namely the capacity attenuation data Q extracting battery to be analyzed is concentrated from battery data 0, utilize the given data of other batteries to obtain a to the initialization of two index capacity attenuation model parameter 0, b 0, c 0, d 0distribution.
Step 3, for the mesuring battary of required bimetry, determines the prediction starting point k of its correspondence.Data before k cycle period are known historical data, and the data after k cycle period are unknown data;
Step 4, utilizes and carries out status tracking without mark particle filter method to k the mesuring battary capacity data carrying out discharge and recharge, upgrades the state variable in degradation in capacity model, state variable a corresponding after obtaining k discharge and recharge k, b k, c k, d k.
Be prior art without mark particle filter, below it be briefly described.
As shown in Figure 2, without mark particle filter step, comprising: initialization step: during k=0, according to extract N number of particle the initial estimation of given system state and variance suggestion distribution is generated according to Unscented kalman filtering (UKF) method, for i=1 ..., N, by particle substitute in UKF algorithm, calculate and obtain utilize sequential importance sampling (SIS), obtain { x 0 : k ( i ) , W k ( i ) } i = 1 N = S I S &lsqb; { x 0 : k ( i ) , W k ( i ) } i = 1 N , y k &rsqb; ; Make i=1, according to sampling namely calculate and estimate importance weight normalization importance weight carry out algorithm and judge process, if i<N, make i=i+1 and return sampling step; Calculate effective sample number if N eff< N threshlod, N threshlodfor the threshold values of setting, then foundation carry out resampling, former sample set { x ~ 0 : k ( i ) , W ~ k ( i ) } i = 1 N Change into new samples collection { x 0 : k ( i ) , W k ( i ) } i = 1 N , W k ( i ) = 1 / N , I.e. basis Pr ( x k / k i - x k / k - 1 j ) = w ~ k j Carry out resampling; Export, provide state estimation result and variance evaluation result carry out algorithm and judge process, if k≤T (T is the number of known quantity measured value y), make k=k+1 and return suggestion distribution generation step, otherwise exiting.
Step 5, utilizes a k, b k, c k, d k, after state transition equation and time discharge and recharge of measurement equation prediction kth, the state variable corresponding to each discharge and recharge and battery capacity.
Step 6, to fill mesuring battary discharge time for horizontal ordinate, with mesuring battary capacity for ordinate, sets up capacity predict curve.
Step 7, according to the mesuring battary capacity threshold of setting, determines the charging times of mesuring battary, i.e. the life-span of mesuring battary from described capacity predict curve.
Measurement equation is utilized to carry out life prediction, the threshold values arranging end-of-life is 70% (or 80%) of battery rated capacity, the measurement equation that utilization state space obtains draws the curve of prediction, thinks battery end of life when the prediction curve of capacity arrives threshold values.
The present invention also can utilize the capacity curve of the actual use of mesuring battary and capacity predict curve to contrast, analyze mean absolute error (MAE), the root-mean-square error (RMSE) of capacity predict curve, and the error of battery life predicting (RUL_Error), it can be used for the reliability evaluating the method.
As seen in figures 3-6, according to different prediction starting point T, according to above-mentioned prediction flow process, certain battery is predicted, the life prediction result of acquisition, be respectively life prediction figure during T=50/60/70/80Cycle.Terminating point true lifetime of this battery is 109Cycle, and when selected different prediction starting point, the result of life prediction is not identical yet.
In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (2)

1., based on the lithium ion battery life-span prediction method without mark particle filter, it is characterized in that, the method comprises as follows:
Step one, using two index capacity attenuation model as capacity of lithium ion battery degradation model, and obtains state transition equation and the measurement equation of capacity of lithium ion battery further;
Two index capacity attenuation model: Q k=aexp (bk)+cexp (dk);
State transition equation: a k = a k - 1 + w a w a ~ N ( 0 , &sigma; a ) b k = b k - 1 + w b w b ~ N ( 0 , &sigma; b ) c k = c k - 1 + w c w c ~ N ( 0 , &sigma; c ) d k = d k - 1 + w d w d ~ N ( 0 , &sigma; d ) ;
Measurement equation: Q k=a kexp (b kk)+c kexp (d kk)+vv ~ N (0, σ v);
Wherein, a k, b k, c kand d kfor the state variable corresponding to the lithium ion battery kth time charge and discharge cycles cycle, k gets natural number, Q krepresent the actual capacity value of battery during kth time charge and discharge cycles cycle, w a, w b, w cand w dbe process noise, v is measurement noises;
Step 2, according to the life time decay data that other batteries are known, obtains the state variable initial value a of two index capacity attenuation model 0, b 0, c 0, d 0distribution;
Step 3, for the mesuring battary of required bimetry, determines the prediction starting point k of its correspondence, and wherein k represents the discharge and recharge number of times that mesuring battary has carried out;
Step 4, utilizes and carries out status tracking without mark particle filter method to k the mesuring battary capacity data carrying out discharge and recharge, upgrades the state variable in capacity attenuation model, state variable a corresponding after obtaining kth time discharge and recharge k, b k, c k, d k;
Step 5, utilization state variable a k, b k, c k, d k, after state transition equation and time discharge and recharge of measurement equation prediction kth, the state variable corresponding to each discharge and recharge and battery capacity;
Step 6, with mesuring battary discharge and recharge number of times for horizontal ordinate, with mesuring battary capacity for ordinate, sets up capacity predict curve;
Step 7, according to the mesuring battary capacity threshold of setting, determines the charging times of mesuring battary, i.e. the life-span of mesuring battary from described capacity predict curve.
2. a kind of based on the lithium ion battery life-span prediction method without mark particle filter as claimed in claim 1, it is characterized in that, utilize the capacity curve of the actual use of mesuring battary and capacity predict curve to contrast, analyze the error of the mean absolute error of capacity predict curve, root-mean-square error and battery life predicting.
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Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106055775A (en) * 2016-05-27 2016-10-26 哈尔滨工业大学 Prediction method for life of secondary battery based on particle filter and mechanism model
CN106291199A (en) * 2016-09-08 2017-01-04 厦门理工学院 Fake capacitance type ultracapacitor life-span prediction method based on particle filter
CN107238800A (en) * 2017-06-12 2017-10-10 北京理工大学 A kind of remaining probable life Forecasting Methodology of the electrokinetic cell system based on Method Using Relevance Vector Machine and particle filter
CN107479000A (en) * 2017-08-17 2017-12-15 北京理工大学 A kind of lithium-ion-power cell RUL Forecasting Methodologies based on Box Cox conversion with Monte-Carlo Simulation
CN107918103A (en) * 2018-01-05 2018-04-17 广西大学 A kind of lithium ion battery residual life Forecasting Methodology based on grey particle filter
CN107957562A (en) * 2018-01-05 2018-04-24 广西大学 A kind of lithium ion battery residual life on-line prediction method
CN108205114A (en) * 2017-12-29 2018-06-26 上海电气集团股份有限公司 The Forecasting Methodology and system of battery life
CN108414947A (en) * 2018-06-06 2018-08-17 哈尔滨工业大学 A kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales
CN108445421A (en) * 2018-06-06 2018-08-24 哈尔滨工业大学 A kind of health state of lithium ion battery On-line Estimation method of space-oriented application
CN109633474A (en) * 2018-11-14 2019-04-16 江苏大学 A kind of lithium ion battery residual life prediction technique
CN110133508A (en) * 2019-04-24 2019-08-16 上海博强微电子有限公司 The safe early warning method of electric automobile power battery
CN110457789A (en) * 2019-07-25 2019-11-15 桂林电子科技大学 A kind of lithium ion battery residual life prediction technique merged based on improvement particle filter with double exponential decay experience physical models
CN110531279A (en) * 2019-05-31 2019-12-03 西安工程大学 Lithium ion battery remaining life prediction technique based on IUPF
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CN111413619A (en) * 2020-03-30 2020-07-14 上海交通大学 Multi-working-condition battery residual capacity prediction method and system based on Gaussian process regression
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CN114779088A (en) * 2022-04-20 2022-07-22 哈尔滨工业大学 Battery remaining service life prediction method based on maximum expectation-unscented particle filtering
CN115575843A (en) * 2022-10-25 2023-01-06 楚能新能源股份有限公司 Lithium ion battery life prediction method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010002227A (en) * 2008-06-18 2010-01-07 Autonetworks Technologies Ltd Open-circuit voltage estimating method and power supply
CN103344923A (en) * 2013-08-01 2013-10-09 哈尔滨工业大学 EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery
CN103675706A (en) * 2013-12-13 2014-03-26 桂林电子科技大学 Power battery electric charge quantity estimation method
CN103744026A (en) * 2013-12-19 2014-04-23 广西科技大学 Storage battery state of charge estimation method based on self-adaptive unscented Kalman filtering
US20150349385A1 (en) * 2014-04-01 2015-12-03 Medtronic, Inc. Method and System for Predicting Useful Life of a Rechargeable Battery

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010002227A (en) * 2008-06-18 2010-01-07 Autonetworks Technologies Ltd Open-circuit voltage estimating method and power supply
CN103344923A (en) * 2013-08-01 2013-10-09 哈尔滨工业大学 EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery
CN103675706A (en) * 2013-12-13 2014-03-26 桂林电子科技大学 Power battery electric charge quantity estimation method
CN103744026A (en) * 2013-12-19 2014-04-23 广西科技大学 Storage battery state of charge estimation method based on self-adaptive unscented Kalman filtering
US20150349385A1 (en) * 2014-04-01 2015-12-03 Medtronic, Inc. Method and System for Predicting Useful Life of a Rechargeable Battery

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
QIANG MIAO等: "Remaining useful life prediction of lithium-ion battery with unscented particle", 《MICROELECTRONICS RELIABILITY》 *
刘大同等: "锂离子电池健康评估和寿命预测综述", 《仪器仪表学报》 *
艾力等: "基于数据驱动的卫星锂离子电池寿命预测方法", 《计算机测量与控制》 *

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CN107957562A (en) * 2018-01-05 2018-04-24 广西大学 A kind of lithium ion battery residual life on-line prediction method
CN107957562B (en) * 2018-01-05 2023-06-09 广西大学 Online prediction method for residual life of lithium ion battery
CN107918103A (en) * 2018-01-05 2018-04-17 广西大学 A kind of lithium ion battery residual life Forecasting Methodology based on grey particle filter
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CN112986831A (en) * 2021-04-30 2021-06-18 上海海事大学 Lithium ion battery life prediction method based on correlation coefficient particle filtering
CN114460484A (en) * 2021-07-15 2022-05-10 崔跃芹 Rechargeable battery service life prediction method and device based on accumulated loss
CN114460484B (en) * 2021-07-15 2024-01-09 崔跃芹 Rechargeable battery life prediction method and device based on accumulated wear quantity
CN114779088A (en) * 2022-04-20 2022-07-22 哈尔滨工业大学 Battery remaining service life prediction method based on maximum expectation-unscented particle filtering
CN115575843A (en) * 2022-10-25 2023-01-06 楚能新能源股份有限公司 Lithium ion battery life prediction method

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Application publication date: 20160330