CN109633474A - A kind of lithium ion battery residual life prediction technique - Google Patents
A kind of lithium ion battery residual life prediction technique Download PDFInfo
- Publication number
- CN109633474A CN109633474A CN201811352261.4A CN201811352261A CN109633474A CN 109633474 A CN109633474 A CN 109633474A CN 201811352261 A CN201811352261 A CN 201811352261A CN 109633474 A CN109633474 A CN 109633474A
- Authority
- CN
- China
- Prior art keywords
- value
- lithium ion
- ion battery
- capacity
- prediction
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 34
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 34
- 239000002245 particle Substances 0.000 claims abstract description 50
- 238000009499 grossing Methods 0.000 claims abstract description 13
- 230000008859 change Effects 0.000 claims description 23
- 238000012952 Resampling Methods 0.000 claims description 16
- 238000002474 experimental method Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 230000007850 degeneration Effects 0.000 claims description 5
- 238000005315 distribution function Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims 1
- 150000002500 ions Chemical class 0.000 claims 1
- 229910052744 lithium Inorganic materials 0.000 claims 1
- 230000008676 import Effects 0.000 abstract 1
- 230000015556 catabolic process Effects 0.000 description 7
- 238000006731 degradation reaction Methods 0.000 description 7
- 238000005070 sampling Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a kind of lithium ion battery residual life prediction techniques, by the state parameter delta data for obtaining battery model with particle filter algorithm, it imports data to Smoothing Prediction model (ES) and obtains state parameter predicted value, it brings observational equation into again and obtains the observation predicted value of capacity, observation predicted value is finally fed back into particle filter, remaining battery life (RUL) is predicted.The problem of ES-PF prediction model of the invention, can solve particle filter algorithm cannot update in forecast period state parameter, cause prediction error elongated with predetermined period and become larger, effectively improves the precision of prediction of particle filter algorithm.
Description
Technical field
The invention belongs to technical field of lithium ion, are to be related to a kind of remaining longevity of lithium ion battery more specifically
Order prediction technique.
Background technique
Lithium ion battery due to high-energy density, low self-discharge rate, without the advantages such as memory effect, long circulation life, make
For most promising dynamic origin, it is widely used in consumer electronics, electric car even space industry.With ceaselessly
Charge and discharge cycles, the performance degradation of battery i.e. aging are inevitable.Decaying for battery will lead to performance decline, economy
Loss or even contingency.Therefore a kind of accurate reliable remaining battery life prediction technique is searched out, to monitor declining for battery
The reliability for losing and assessing it is very necessary.
Remaining battery life (RUL) prediction is prediction from previous cycle/moment, using how many circulation/time, battery
SOH value reach critical value (usually 70%-80%SOH).Invention improves the residue that many algorithms remove prediction battery at present
Service life, but cause due to the unreliability experimental data, model and the incompatible of algorithm, calculating excessively complexity etc. current
Until which kind of algorithm to be generally acknowledged best algorithm there are no.
Particle filter (PF) is the algorithms most in use of the prediction remaining battery life of rising in recent years, with Kalman filtering phase
Than it can be used not only for nonlinear system, and does not require to noise and (can be non-Gaussian noise).But particle filter exists
There is a serious defect on forecast function, exactly when forecast period does not have measured value, the state of particle filter can not be more
Newly, that is to say, that state value rests on the last one known point, goes the surplus of prediction battery with the point estimation of the last one known point
The remaining service life causes predetermined period longer, and precision of prediction is lower.
Summary of the invention
It is a kind of based on Smoothing Prediction (ES) and particle filter the purpose of the present invention is in view of the deficiencies of the prior art, proposing
The lithium ion battery residual life prediction technique of wave (PF) fusion, to improve the precision of prediction of particle filter algorithm.
A kind of lithium ion battery residual life that (ES-PF) being merged with particle filter based on Smoothing Prediction of the invention is pre-
Survey method realizes that process is as follows:
A kind of lithium ion battery residual life prediction technique, experiment obtain capacity of the lithium ion battery under different variables with
The change curve of cycle-index establishes capacity of lithium ion battery degeneration empirical model, tracks lithium-ion electric pool capacity known to early period
Magnitude obtains state parameter change curve, resettles Smoothing Prediction model, to obtain capacity observation predicted value, feedback
To particle filter, prediction obtains the remaining life of battery.
Further, the different variables include temperature, discharge cut-off voltage, charge cutoff voltage and cycle-index.
Further, the capacity is to test to obtain by lithium ion battery Life Cycle with the change curve of cycle-index
, it specifically includes:
(1) constant-current discharge is carried out to new battery, the stopping when voltage reaches discharge cut-off voltage is shelved；
(2) constant-current charge: the stopping when voltage reaches charge cutoff voltage；
(3) constant-voltage charge: stop when electric current is less than preset value, shelve；
(4) constant-current discharge: stop when voltage reaches discharge cut-off voltage (or setting value), shelve；
(5) (2)-(4) are repeated, until capacity stops experiment when dropping to the threshold value of setting.
Further, the capacity of lithium ion battery degeneration empirical model:
State equation are as follows: K (k)=[a (k) b (k) c (k) d (k)]T Observational equation
Are as follows: wherein X (k) is state vector, a (k), b to Q (k)=a (k) exp (b (k) k)+c (k) exp (d (k) k)+v (k)
(k), c (k), d (k) are state parameter, wa(k)、wb(k)、wc(k)、wdIt (k) is process noise, v (k) is observation noise, Q (k)
To observe capability value, k is cycle-index.
Further, capacity of lithium ion battery value known to the tracking early period is obtained by particle filter tracking algorithm
, specifically:
(1) parameter of set algorithm: population N, process noise, observation noise, largest loop value drive matrix, state
Initial value；
(2) it initializes particle collection: being that each particle assigns initial value according to original state, the weight of each particle is equal at this time；
(3) sample: selection distribution function calculates the state of current time each particle；
(4) weight of current time each particle is calculated；
(5) weight normalizes；
(6) resampling；
(7) step (3)-(6) are repeated, until cycle-index k=predicts starting point T；
(8) output state parameter with cycle-index change curve.
Further, the method for the resampling includes random resampling, multinomial resampling, residual error resampling, system weight
Sampling.
Further, the Smoothing Prediction model are as follows:WhereinFor predicted value, α
For parameter, xtFor true value.
Further, the obtaining step of the capacity observation predicted value are as follows:
(1) model initial value is set；
(2) model parameter: the selection principle of α ∈ (0,1) is set are as follows:
1. the value range of α is 0.1-0.3 if time series fluctuation is less；
2. the value range of α is 0.6-0.8 if time series has rapidly and apparent change is inclined to；
(3) recursion prediction obtains status predication value, and the state value of prediction substitution observational equation is obtained the observation of capacity
Predicted value.
The present invention by adopting the above technical scheme, exist it is following the utility model has the advantages that
The present invention for the first time calculates the Smoothing Prediction model in management science with the particle filter in engineer application field
Method fusion, establishes ES-PF prediction model；Particle filter algorithm is compensated for for predicting that state parameter can not when remaining battery life
It updates, the defect for causing precision of prediction to decline；Prediction model is established using exponential smoothing, historical data can be made full use of, obtained
To reasonable accurate prediction result.The method of the invention is compared to the intelligent prediction algorithms such as support vector machines, neural network, meter
It is small to calculate pressure, but precision is able to satisfy forecast demand in practical application almost without reduction.
Detailed description of the invention
Fig. 1 is a kind of lithium ion battery residual life prediction technique flow chart of the present invention；
Fig. 2 is change curve of the obtained battery capacity of experiment with cycle-index；
Fig. 3 is change curve of the obtained state parameter of particle filter tracking algorithm with cycle-index, and Fig. 3 (a) is grain
For the state parameter a (k) that sub- filter tracking algorithm obtains with the change curve of cycle-index, Fig. 3 (b) is particle filter tracking calculation
For the state parameter b (k) that method obtains with the change curve of cycle-index, Fig. 3 (c) is the state that particle filter tracking algorithm obtains
For parameter c (k) with the change curve of cycle-index, Fig. 3 (d) is the obtained state parameter d (k) of particle filter tracking algorithm with following
The change curve of ring number；
Fig. 4 is the prediction graph for the state parameter that ES prediction model obtains, and Fig. 4 (a) is the shape that ES prediction model obtains
The prediction graph of state parameter a (k), Fig. 4 (b) are the prediction graph for the state parameter b (k) that ES prediction model obtains, Fig. 4
(c) prediction graph of the state parameter c (k) obtained for ES prediction model, Fig. 4 (d) are the state ginseng that ES prediction model obtains
The prediction graph of number d (k)；
Fig. 5 is the prediction result comparison diagram of prediction technique provided by the present invention and the prediction of standard particle filtering algorithm.
Specific embodiment
Further more detailed description is made to technical solution of the present invention With reference to embodiment.
Battery data used by the present embodiment is Jiangsu University's automobile engineering research institute power battery laboratory data, is made
With 18650 size batteries, rated capacity 2600mAh, charge cutoff voltage 4.2V, discharge cut-off voltage 2.75V, 4 section of battery number.
The present invention provides a kind of lithium ion battery for merging (ES-PF) with particle filter based on Smoothing Prediction is remaining
Life-span prediction method realizes that process is as follows:
Step 1: Life Cycle experiment is carried out to lithium ion battery, processing data obtain lithium ion battery in different variables
Under capacity with cycle-index change curve；Wherein, different variables include at least temperature, discharge cut-off voltage, charge cutoff
Voltage, cycle-index；
Wherein, the specific steps of lithium ion battery Life Cycle experiment are as follows:
1. carrying out 1C constant-current discharge to new battery, stops when voltage reaches 2.75V, shelve 10 minutes；
2. 1C constant-current charge: stopping when voltage reaches 4.2V；
3. 4.2V constant-voltage charge: when electric current less than 20mA stop, shelving 30 minutes；
4. 1C constant-current discharge: when voltage reach 2.4V, 2.5V, 2.6V, 2.7 stop, shelving 30 minutes；
5. repeat step 2. -4., until capacity of lithium ion battery drops to the 80% of rated capacity, stop experiment；Experiment
As a result as shown in Fig. 2, wherein 1 degradation in capacity rate of battery is too fast, 3 deterioration velocity of battery is very fast, 4 battery of battery, 2 degradation in capacity
Rate is more normal.
Step 2: capacity of lithium ion battery degeneration empirical model is established
The capacity obtained according to step 1 establishes capacity of lithium ion battery degeneration empirical model with cycle-index change curve:
State equation are as follows: X (k)=[a (k) b (k) c (k) d (k)]T
Wherein
Observational equation are as follows: Q (k)=a (k) exp (b (k) k)+c (k) exp (d (k) k)+v (k) (2)
Wherein, X (k) is state vector, and a (k), b (k), c (k), d (k) are state parameter, wa(k)、wb(k)、wc(k)、wd
It (k) is process noise, v (k) is observation noise, and q (k) is observation capability value, and k is cycle-index.
Step 3: particle filter algorithm tracks capacity of lithium ion battery value known to early period, and it is bent to obtain state parameter variation
Line
It is tested according to the Life Cycle of step 1, the complete degradation in capacity data of 4 batteries can be obtained, select battery 4
Degradation in capacity data as prediction object, the degradation in capacity data of other batteries as particle filter initial state value setting according to
According to, it is assumed that data before predicting the T times of object circulation it is known that the data after recycling for the T time are unknown, can use known
Data obtain state parameter with the change curve of cycle-index by particle filter tracking algorithm.
Due to 1 degradation in capacity excessive velocities of battery, particle filter original state is obtained by battery 2 and 3 curve matching of battery
State parameter average value determine, as shown in table 1.
The determination of 1 particle filter original state parameter of table
| a(1) | b(1) | c(1) | d(1) | |
| Battery 2 | -8.867e-7 | 0.05802 | 0.9002 | -0.000841 |
| Battery 3 | -2.155e-5 | 0.06093 | 0.8778 | -0.0009009 |
| Average value | -1.122e-5 | 0.059475 | 0.889 | -0.00087095 |
Wherein, specific step is as follows for particle filter tracking algorithm:
1. the parameter (including but not limited to) of set algorithm: population N=200, process noise covariance battle arrayObservation noise covariance R=0.001, largest loop value 160 are driven
Dynamic matrixState initial value X0=[- 1.122 × 10-5 0.059475 0.889 -0.00087095]T；
2. initializing particle collection: being that each particle assigns initial value according to original state, the weight of each particle is equal at this time
3. sampling: selection significance distribution function(it can be the prior distribution of state?
Can be other APPROXIMATE DISTRIBUTIONs), calculate the state of current time each particle；
4. calculating the weight of current time each particle:
5. weight normalizes:
6. resampling: resampling is in order to avoid sample degeneracy problem, and the method for sampling includes random resampling, multinomial weight
Sampling, residual error resampling, system resampling etc.；
7. repeat step 3. -6., until cycle-index k=T (T is prediction starting point)；
8. output: state parameter a (k), b (k), c (k), d (k) with cycle-index change curve, as Fig. 3 (a), (b),
(c), shown in (d).
Indicate the status parameter values of i-th of particle kth time circulation, zkIndicate the capacity observation of kth time circulation,
Indicate the weight of i-th of particle kth time circulation,Weight after indicating normalization.
Step 4: onset index smoothing prediction model
The state parameter obtained according to step 3 can establish exponential smoothing with the change curve of cycle-index
(Exponential Smoothing, ES) prediction model:
Wherein,For predicted value, α is parameter, xtFor true value.
1. model initial value is arranged: can choose the average value of two initial time series status parameter values as ES mould
The initial value of type.
The determination of 2 ES prediction model initial value of table
2. model parameter is arranged: the selection principle of α ∈ (0,1) are as follows:
If a, less, relatively steady, then α should take a little bit smaller for time series fluctuation, such as 0.1-0.3, to reduce amendment width
Degree, enabling prediction model includes the information of long period sequence；
If b, time series have rapidly and it is apparent change tendency, α should take larger, such as 0.6-0.8, make to predict
Model sensitivity is higher, to keep up with the variation of data rapidly；
The present embodiment α=0.5.
3. recursion prediction obtains status predication value, (Fig. 4 (a), (b), (c), (d) show state parameter a (k), b (k), c
(k), the predicted value of d (k)), and the state value of prediction substitution observational equation (formula (2)) is obtained into capacity observation predicted value.
Step 5: ES-PF algorithm predicts battery RUL
According to the observation predicted value for the capacity that step 4 obtains, particle filter is fed back to, prediction obtains the remaining longevity of battery
Order (RUL), the step of particle filter algorithm in step 3 the step of particle filter algorithm it is unanimous on the whole；Difference 1 are as follows: weight meter
Calculate the likelihood function in formula (3)It is replaced using the predicted value (formula (5)) that ES prediction model obtains in step 4；Area
Other 2 are as follows: algorithm stop condition is the threshold value that the capability value of all particle predictions has all reached setting；Difference 3 are as follows: export as RUL
Predicted value.
Step 6: prediction result is compared with the prediction result of standard particle filtering algorithm, as shown in Figure 5 and Figure 6.
The comparison of 3 prediction result of table
As can be drawn from Table 3, ES-PF method provided by the present invention reduces 7.2% than standard PF method relative error,
Significantly improve precision of prediction.From fig. 5, it can be seen that standard PF prediction, which exists, to be deviated considerably from really after predetermined period is elongated
The case where value, this is exactly the defect for the PF algorithm being previously mentioned in background technique, and prediction technique provided by the present invention does not have
This case also demonstrates effectiveness of the invention.
It may be noted that the purpose of the present embodiment is in order to preferably explain the present invention, rather than to limit protection model of the invention
It encloses.The parameter value for the algorithm being arranged in embodiment and specific battery parameter are needed for this experimental verification, based in the present invention
Embodiment, other all embodiments of those skilled in the art under the premise of not making innovative labor all should belong to
Protection scope of the present invention.
Claims (8)
1. a kind of lithium ion battery residual life prediction technique, it is characterised in that: experiment obtains lithium ion battery in different variables
Under capacity with the change curve of cycle-index, establish capacity of lithium ion battery degeneration empirical model, track lithium known to early period
Ion battery capacity value obtains state parameter change curve, resettles Smoothing Prediction model, so that it is pre- to obtain capacity observation
Measured value, feeds back to particle filter, and prediction obtains the remaining life of battery.
2. remaining battery life prediction technique according to claim 1, it is characterised in that: the difference variable includes temperature
Degree, discharge cut-off voltage, charge cutoff voltage and cycle-index.
3. remaining battery life prediction technique according to claim 1, it is characterised in that: the capacity is with cycle-index
Change curve is to test to obtain by lithium ion battery Life Cycle, is specifically included:
(1) constant-current discharge is carried out to new battery, the stopping when voltage reaches discharge cut-off voltage is shelved；
(2) constant-current charge: the stopping when voltage reaches charge cutoff voltage；
(3) constant-voltage charge: stop when electric current is less than preset value, shelve；
(4) constant-current discharge: the stopping when voltage reaches discharge cut-off voltage is shelved；
(5) (2)-(4) are repeated, until capacity stops experiment when dropping to the threshold value of setting.
4. remaining battery life prediction technique according to claim 1, it is characterised in that: the capacity of lithium ion battery moves back
Change empirical model:
State equation are as follows: X (k)=[a (k) b (k) c (k) d (k)]T,Observational equation are as follows: Q
(k)=a (k) exp (b (k) k)+c (k) exp (d (k) k)+v (k)；Wherein X (k) be state vector, a (k), b (k),
C (k), d (k) are state parameter, wa(k)、wb(k)、wc(k)、wdIt (k) is process noise, v (k) is observation noise, and Q (k) is to see
Capability value is surveyed, k is cycle-index.
5. lithium ion battery residual life prediction technique according to claim 1, it is characterised in that: the tracking early period is
The capacity of lithium ion battery value known is obtained by particle filter tracking algorithm, specifically:
(1) parameter of set algorithm: population N, process noise, observation noise, largest loop value drive matrix, state initial value；
(2) it initializes particle collection: being that each particle assigns initial value according to original state, the weight of each particle is equal at this time；
(3) sample: selection distribution function calculates the state of current time each particle；
(4) weight of current time each particle is calculated；
(5) weight normalizes；
(6) resampling；
(7) step (3)-(6) are repeated, until cycle-index k=predicts starting point T；
(8) output state parameter with cycle-index change curve.
6. lithium ion battery residual life prediction technique according to claim 5, it is characterised in that: the side of the resampling
Method includes random resampling, multinomial resampling, residual error resampling, system resampling.
7. lithium ion battery residual life prediction technique according to claim 1, it is characterised in that: the exponential smoothing is pre-
Survey model are as follows:WhereinFor predicted value, α is parameter, xtFor true value.
8. lithium ion battery residual life prediction technique according to claim 7, it is characterised in that: the capacity observation is pre-
The obtaining step of measured value are as follows:
(1) model initial value is set；
(2) model parameter: the selection principle of α ∈ (0,1) is set are as follows:
1. the value range of α is 0.1-0.3 if time series fluctuation is less；
2. the value range of α is 0.6-0.8 if time series has rapidly and apparent change is inclined to；
(3) recursion prediction obtains status predication value, and the observation that the state value of prediction substitution observational equation obtains capacity is predicted
Value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811352261.4A CN109633474B (en) | 2018-11-14 | 2018-11-14 | Lithium ion battery residual life prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811352261.4A CN109633474B (en) | 2018-11-14 | 2018-11-14 | Lithium ion battery residual life prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109633474A true CN109633474A (en) | 2019-04-16 |
| CN109633474B CN109633474B (en) | 2021-09-10 |
Family
ID=66068003
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811352261.4A Active CN109633474B (en) | 2018-11-14 | 2018-11-14 | Lithium ion battery residual life prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109633474B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110095731A (en) * | 2019-05-06 | 2019-08-06 | 哈尔滨工业大学 | Remaining life DIRECT FORECASTING METHOD applied to long-life space lithium ion battery |
| CN110222426A (en) * | 2019-06-06 | 2019-09-10 | 南京航空航天大学 | Aircraft battery method for predicting residual useful life under discontinuous operating mode |
| CN110221222A (en) * | 2019-04-30 | 2019-09-10 | 蜂巢能源科技有限公司 | Cell safety blanking voltage prediction technique, device and battery management system |
| CN110310714A (en) * | 2019-06-14 | 2019-10-08 | 中北大学 | A kind of coal fired power plant denitrating catalyst method for predicting residual useful life |
| CN110442941A (en) * | 2019-07-25 | 2019-11-12 | 桂林电子科技大学 | It is a kind of to be tracked and RUL prediction technique based on the battery status for improving particle filter and process noise features fusion algorithm |
| CN110531279A (en) * | 2019-05-31 | 2019-12-03 | 西安工程大学 | Lithium ion battery remaining life prediction technique based on IUPF |
| CN110824375A (en) * | 2019-10-18 | 2020-02-21 | 广东电网有限责任公司 | Method for calculating actual capacity of retired storage battery |
| CN111007418A (en) * | 2019-12-30 | 2020-04-14 | 电子科技大学 | Lithium battery residual life prediction method based on expansibility index distribution |
| CN111044926A (en) * | 2019-12-16 | 2020-04-21 | 北京航天智造科技发展有限公司 | Method for predicting service life of proton exchange membrane fuel cell |
| CN111103544A (en) * | 2019-12-26 | 2020-05-05 | 江苏大学 | Lithium ion battery remaining service life prediction method based on long-time and short-time memory LSTM and particle filter PF |
| CN112345954A (en) * | 2020-10-21 | 2021-02-09 | 一汽解放汽车有限公司 | Battery life prediction method, device, electronic device and medium |
| CN113030760A (en) * | 2021-03-25 | 2021-06-25 | 昆山宝创新能源科技有限公司 | Battery life prediction method, device and storage medium |
| CN113657012A (en) * | 2021-07-21 | 2021-11-16 | 西安理工大学 | TCN and particle filter-based method for predicting residual life of key equipment |
| CN115015769A (en) * | 2022-08-10 | 2022-09-06 | 深圳市杰成镍钴新能源科技有限公司 | Power battery residual value estimation method, device, equipment and medium |
| 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)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855349A (en) * | 2012-08-06 | 2013-01-02 | 南京航空航天大学 | Quick prototype design method and platform for gas path fault diagnosis for aeroengine |
| CN103778323A (en) * | 2014-01-03 | 2014-05-07 | 株洲南车时代电气股份有限公司 | Method and system for forecasting traffic flow data based on exponential smoothing |
| CN105445671A (en) * | 2015-12-29 | 2016-03-30 | 北京航天测控技术有限公司 | Lithium ion battery service life prediction method based on traceless particle filtering |
| CN105938575A (en) * | 2016-04-13 | 2016-09-14 | 山东毅康科技股份有限公司 | Multivariable-grey-neural-network-based prediction system for residual life of industrial equipment |
| 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 |
-
2018
- 2018-11-14 CN CN201811352261.4A patent/CN109633474B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855349A (en) * | 2012-08-06 | 2013-01-02 | 南京航空航天大学 | Quick prototype design method and platform for gas path fault diagnosis for aeroengine |
| CN103778323A (en) * | 2014-01-03 | 2014-05-07 | 株洲南车时代电气股份有限公司 | Method and system for forecasting traffic flow data based on exponential smoothing |
| CN105445671A (en) * | 2015-12-29 | 2016-03-30 | 北京航天测控技术有限公司 | Lithium ion battery service life prediction method based on traceless particle filtering |
| CN105938575A (en) * | 2016-04-13 | 2016-09-14 | 山东毅康科技股份有限公司 | Multivariable-grey-neural-network-based prediction system for residual life of industrial equipment |
| 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 |
Non-Patent Citations (4)
| Title |
|---|
| 周东华等: "《可靠性预测与最优维护技术》", 31 December 2013, 中国科学技术大学出版社 * |
| 姜琳: "锂离子电池荷电状态估计与寿命预测技术研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 张凝等: "基于粒子滤波算法的锂离子电池剩余寿命预测方法研究", 《高技术通讯》 * |
| 耿晓宇等: "基于云模型的锂电池指数平滑寿命预测方法", 《PROCEEDINGS OF 2014 IEEE CHINESE GUIDANCE，NAVIGATION AND CONTROL CONFERENCE》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110221222B (en) * | 2019-04-30 | 2022-03-29 | 蜂巢能源科技有限公司 | Battery safety cut-off voltage prediction method and device and battery management system |
| CN110221222A (en) * | 2019-04-30 | 2019-09-10 | 蜂巢能源科技有限公司 | Cell safety blanking voltage prediction technique, device and battery management system |
| CN110095731A (en) * | 2019-05-06 | 2019-08-06 | 哈尔滨工业大学 | Remaining life DIRECT FORECASTING METHOD applied to long-life space lithium ion battery |
| CN110095731B (en) * | 2019-05-06 | 2021-06-22 | 哈尔滨工业大学 | Method for directly predicting residual life of lithium ion battery applied to long-life space |
| CN110531279A (en) * | 2019-05-31 | 2019-12-03 | 西安工程大学 | Lithium ion battery remaining life prediction technique based on IUPF |
| CN110222426A (en) * | 2019-06-06 | 2019-09-10 | 南京航空航天大学 | Aircraft battery method for predicting residual useful life under discontinuous operating mode |
| CN110310714A (en) * | 2019-06-14 | 2019-10-08 | 中北大学 | A kind of coal fired power plant denitrating catalyst method for predicting residual useful life |
| CN110442941A (en) * | 2019-07-25 | 2019-11-12 | 桂林电子科技大学 | It is a kind of to be tracked and RUL prediction technique based on the battery status for improving particle filter and process noise features fusion algorithm |
| CN110824375A (en) * | 2019-10-18 | 2020-02-21 | 广东电网有限责任公司 | Method for calculating actual capacity of retired storage battery |
| CN111044926A (en) * | 2019-12-16 | 2020-04-21 | 北京航天智造科技发展有限公司 | Method for predicting service life of proton exchange membrane fuel cell |
| CN111103544A (en) * | 2019-12-26 | 2020-05-05 | 江苏大学 | Lithium ion battery remaining service life prediction method based on long-time and short-time memory LSTM and particle filter PF |
| CN111103544B (en) * | 2019-12-26 | 2021-12-21 | 江苏大学 | Lithium ion battery remaining service life prediction method based on long-time and short-time memory LSTM and particle filter PF |
| CN111007418A (en) * | 2019-12-30 | 2020-04-14 | 电子科技大学 | Lithium battery residual life prediction method based on expansibility index distribution |
| CN112345954A (en) * | 2020-10-21 | 2021-02-09 | 一汽解放汽车有限公司 | Battery life prediction method, device, electronic device and medium |
| CN112345954B (en) * | 2020-10-21 | 2024-04-09 | 一汽解放汽车有限公司 | Battery life prediction method, device, electronic equipment and medium |
| CN113030760B (en) * | 2021-03-25 | 2023-02-03 | 昆山宝创新能源科技有限公司 | Battery life prediction method, device and storage medium |
| CN113030760A (en) * | 2021-03-25 | 2021-06-25 | 昆山宝创新能源科技有限公司 | Battery life prediction method, device and storage medium |
| CN113657012A (en) * | 2021-07-21 | 2021-11-16 | 西安理工大学 | TCN and particle filter-based method for predicting residual life of key equipment |
| CN115015769B (en) * | 2022-08-10 | 2022-11-11 | 深圳市杰成镍钴新能源科技有限公司 | Power battery residual value estimation method, device, equipment and medium |
| CN115015769A (en) * | 2022-08-10 | 2022-09-06 | 深圳市杰成镍钴新能源科技有限公司 | Power battery residual value estimation method, device, equipment and medium |
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109633474B (en) | 2021-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109633474A (en) | A kind of lithium ion battery residual life prediction technique | |
| CN107064800B (en) | The real-time predicting method of lithium ion battery remaining life | |
| CN110031770B (en) | Method for rapidly obtaining capacity of all single batteries in battery pack | |
| CN107015157B (en) | The lithium battery cycles left service life online fast test method of segment is risen based on constant current equipressure | |
| CN106291372A (en) | A kind of new lithium-ion-power cell method for predicting residual useful life | |
| CN107728072A (en) | A kind of method for quick predicting of cycle life of lithium ion battery | |
| CN106885994B (en) | Lithium battery remaining life fast detection method based on constant-voltage charge segment | |
| CN107015158B (en) | Lithium battery remaining life on-line quick detection method based on constant-current discharge segment | |
| CN113406523B (en) | Energy storage battery state evaluation method and device, electronic equipment and storage system | |
| CN107167739B (en) | A kind of power battery life-span prediction method | |
| CN115656857A (en) | Multi-scale fusion prediction method for remaining service life of lithium ion battery | |
| CN116953547A (en) | Energy storage battery health evaluation method, system, equipment and storage medium | |
| CN115267555A (en) | Battery SOH (State of health) evaluation system of energy storage system based on battery multipoint temperature measurement | |
| CN114675187A (en) | Lithium ion battery service life prediction method integrating physical mechanism and machine learning | |
| CN112710955A (en) | Algorithm for improving battery capacity estimation precision | |
| CN111639442A (en) | Method and system for screening influence factors of residual service life of power lithium battery | |
| CN114545275A (en) | Indirect prediction method for remaining service life of lithium ion battery | |
| Eleftheriadis et al. | Comparative study of machine learning techniques for the state of health estimation of li-ion batteries | |
| CN113376541A (en) | Lithium ion battery health state prediction method based on CRJ network | |
| CN116500475B (en) | Energy storage acquisition method and system with real-time SOC correction compensation | |
| Avadhanula et al. | Comparative study of mathematical models and data driven models for battery performance parameter estimation | |
| CN117110884A (en) | Lithium battery remaining service life prediction method based on multi-core correlation vector machine | |
| CN116029183A (en) | Power battery temperature prediction method based on iPSO-LSTM model | |
| CN115312895A (en) | Method for monitoring steady state of battery pack of new energy vehicle | |
| CN114970734A (en) | Abnormal battery determination method, abnormal battery determination device, computer equipment and storage medium |
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 | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231219 Address after: Room 2086, Building 1, No. 17 Cangjingguan Hutong, Dongcheng District, Beijing, 100000 Patentee after: AVIC JINLIN TECHNOLOGY (BEIJING) Co.,Ltd. Address before: Zhenjiang City, Jiangsu Province, 212013 Jingkou District Road No. 301 Patentee before: JIANGSU University |