CN106199443A - A kind of lithium battery degeneration discrimination method and degeneration warning system - Google Patents
A kind of lithium battery degeneration discrimination method and degeneration warning system Download PDFInfo
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- CN106199443A CN106199443A CN201610526170.2A CN201610526170A CN106199443A CN 106199443 A CN106199443 A CN 106199443A CN 201610526170 A CN201610526170 A CN 201610526170A CN 106199443 A CN106199443 A CN 106199443A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Abstract
The invention discloses a kind of lithium battery degeneration discrimination method and degeneration warning system.The method uses the analysis method of array dispersion degree, using some reference physical quantitys as an array, this array carries out dispersion degree discriminating, and described physical quantity includes the open-circuit voltage of battery and the ambient temperature of battery, the discharge-rate of battery, the capacity that can release.The battery of work or the electric current of set of cells, voltage, temperature are detected by this system by detecting device, calculate according to electric discharge average current and accumulation discharge time and release open-circuit voltage before capacity, electric discharge, ambient temperature；Send host computer to carry out degeneration factor calculating, send value of calculation back to slave computer, if degeneration factor exceedes threshold value, send warning.The evaluation of quick-charging circuit, equalizing circuit and management system that the present invention is battery provides quick and evaluates and identify, also cascade utilization, lifetime limitation for battery replace offer foundation.
Description
Technical field
The present invention relates to a kind of lithium battery degeneration discrimination method and degeneration warning system, belong to computer control system neck
Territory.
Background technology
Economic development is along with the generation of the problems such as the deterioration of environment and energy scarcity, and environmental protection and energy crisis are more
More govern expanding economy, countries in the world seek the outlet of clean energy resource one after another.Lithium battery is as a kind of clean energy resource, more
To be applied in production and the life of the people manyly.Safe and stable, operation efficiently is the principle of lithium battery management.At electricity
In pond management system (BMS), cell degradation degree detecting i.e. differentiates that battery damage degree and degree of aging are battery management technique
Bottleneck.Understand and grasp degree of degeneration and the degree of aging of set of cells, the battery that can change in time, it is to avoid unnecessary in time
Security incident.
The qualification in the aging and life-span of current battery, generally uses cycle-index as foundation.And cycle life is generally up to
To thousands of times even ten thousand times, so " cycle-index " is very difficult is confirmed at short notice.
Summary of the invention
The problems referred to above existed for the authentication method in the aging and life-span of battery in prior art and system, the present invention carries
For a kind of lithium battery degeneration discrimination method and degeneration warning system, by a neural network model, quickly draw cell decay
Index, thus differentiate cell degradation degree, and when degeneration reaches threshold value, report to the police.
Technical scheme is as follows:
A kind of lithium battery degeneration discrimination method, uses the analysis method of array dispersion degree, makees some with reference to physical quantity
Being an array, this array carries out dispersion degree discriminating, described physical quantity includes the open-circuit voltage of battery and the environment of battery
Temperature, the discharge-rate of battery, the capacity that can release.
Further, apply dispersion analyze time, it is considered to participate in data the coefficient of variation, by each with reference to physical quantity carry out as
Lower variation:
Multiplying power lower discharge time Time frame variation coefficient=actual discharge time/0.2C；
The capacity coefficient of variation=actual capacity/nominal capacity；
The open-circuit voltage coefficient of variation=actual open-circuit voltage/rated voltage；
Temperature variations coefficient=actual temperature/20 DEG C；
On this basis, according to the effect played in degenerating of each physical quantity, then it is multiplied by corresponding coefficient.
Further, set up neural network model and carry out degeneration factor estimation, comprise the steps:
Step 1: set up neural network model；
Fuzzy inference system in described neural network model uses Sugeno Fuzzy model, sets up according to experimental data
FIS；According to lithium ion battery external behavior Parameter analysis, filter out t discharge timePut, release capacity QPut, open-circuit voltage Uk, ring
Border temperature T is as the input of neural network model, and model is output as the degree of degeneration characterising parameter of lithium ion battery, i.e. degenerates
Factor beta:
β=f (tPut, QPut, Uk, T)
In formula, tPutRepresent discharge time；QPutRepresent and release capacity；UkRepresent open-circuit voltage；T represents ambient temperature；β be
Numerical value between 0～1, when β is close to 0, then cell degradation lesser extent；When β is close to 1, then cell degradation degree is serious.
Step 2: model training and simulating, verifying；
Experimental data is divided into two groups, i.e. training group and check groups, input as model training with training data, with training
System model, the step-length arranging training is trained, and builds phantom；Using Data Processing in Experiment result as model
Input, respectively obtains the degeneration factor of corresponding date lithium ion battery.
A kind of lithium battery degeneration warning system, including host computer, communication interface and slave computer, system is by detecting device to work
The battery made or the electric current of set of cells, voltage, temperature detect, and calculate according to accumulating electric discharge average current and discharge time
Release the open-circuit voltage before capacity, electric discharge, ambient temperature；Send host computer to carry out degeneration factor calculating, value of calculation is sent back to down
Position machine, if degeneration factor exceedes threshold value, sends warning.
Further, the peripheral circuit of slave computer includes current detecting unit, voltage detection unit, A/D converting unit, temperature
Detector unit, clock acquisition unit, serial communication unit, power subsystem, display unit, alarm unit；Described current detecting list
Current information and the information of voltage of voltage detection unit detection that unit gathers are sent to slave computer by A/D converting unit, institute
The temporal information of the temperature information and clock acquisition unit collection of stating temperature detecting unit detection is sent to slave computer, described serial ports
Communication unit and power subsystem are not connected to slave computer, and the information of slave computer is respectively sent to display unit and alarm unit.
Further, calculate with degeneration factor with calculation of capacity and communication including current detecting；
Described current detecting comprises the steps: with calculation of capacity
The first step, current detecting subprogram starts；
Second step, when electric current exceedes the upper limit, returns upper level mastery routine；When electric current is not less than the upper limit, carry out electricity
Cumulative calculation, returns upper level mastery routine after calculating；
Described communication calculates with degeneration factor and comprises the steps:
The first step, serial ports initializes；
Second step, sends data acquisition command to slave computer；
3rd step, if data receiver terminates, then data loading, the degradation model program of Calling MATLAB, it is judged that degenerate
Coefficient, whether not less than threshold value, if more than or equal to threshold value, then sends alarm command to slave computer, if not more than or
Equal to threshold value, then it is back to second step；If still receiving data, then continue to data；
4th step, returns second step, continues to data until communication calculates complete with degeneration factor.
Beneficial effects of the present invention is as follows:
The present invention, by setting up the degradation function model of lithium battery, applies neutral net and discrete theory, sets up battery to decline
Subtract coefficient.By the analysis to battery relevant parameter, calculate the degeneration factor of battery, thus analyze cell degradation with aging
Degree.Evaluation for quick-charging circuit, equalizing circuit and the management system of battery provides quickly evaluation and identifies, is also
The cascade utilization of battery, lifetime limitation are replaced provides foundation.The present invention is suitable for other electricity the most existing for this model insertion
In pond management system, aging to evaluate battery status and prediction, and alarm is to avoid user to lose.
Accompanying drawing explanation
Fig. 1 is adaptive neural network Sugeno fuzzy model.
Fig. 2 is Artificial Neural Network Structures.
Fig. 3 is training result.
Fig. 4 is degeneration phantom.
Fig. 5 is that four Battery pack degeneration factors follow the tracks of situation.
Fig. 6 is degeneration warning system overall construction drawing.
Fig. 7 is slave computer and peripheral circuit.
Fig. 8 is current detecting and calculation of capacity subroutine flow chart.
Fig. 9 is that communication calculates alarm flow figure with degeneration factor.
Detailed description of the invention
Below in conjunction with the accompanying drawings the present invention is described in further detail.
The basic ideas of the design of the present invention are as follows:
Cell degradation degree State of Degeneration (english abbreviation SOD) generally can be from the open circuit electricity of battery
The parametric synthesis such as pressure and the ambient temperature of battery, the discharge-rate of battery, the capacity that can release are analyzed and are judged.But, above several
Plant factor coupling each other very strong, and be difficult to peel off.Therefore it is considered as adaptive neural network fuzzy system and sets up them
And the relation between SOD.
In view of corresponding to a battery completely filled, have under certain open-circuit voltage, ambient temperature and discharge-rate
Release capacity accordingly, and this is released capacity and can embody the degree of degeneration of battery, it may be assumed that the capacity of releasing is the most, cell degradation
Degree is the lightest；Otherwise, cell degradation is the most serious.
The present invention uses the analysis method of array dispersion degree, using above each parameter physical quantity as an array,
Under certain condition, this array is carried out dispersion degree discriminating.Such as: when ambient temperature raises, then a corresponding full charge pond its
Open-circuit voltage is the highest, under certain discharge-rate, it will make discharge time the longest, releases capacity the most.Namely it is correlated with
Physical quantity can synchronize to raise.Otherwise, related physical quantity can synchronize to reduce.For the battery that degree of aging is more serious, above-mentioned situation
Under, the change of each physical quantity then can difference, therefore, the dispersion degree of array can increase.Therefore, divided by array dispersion
Analysis i.e. may determine that cell degradation degree.
When applying dispersion to analyze, it is necessary to considering to participate in the coefficient of variation of data, therefore, the present invention is by each reference physics
Amount has all carried out corresponding variation, to reduce the absolute figure impact on array dispersion.
1, Time frame variation coefficient=actual discharge time/0.2C multiplying power lower discharge time；
2, the capacity coefficient of variation=actual capacity/nominal capacity；
3, the open-circuit voltage coefficient of variation=actual open-circuit voltage/rated voltage；
4, temperature variations coefficient=actual temperature/20 DEG C.
(2) set up neural network model and carry out degeneration factor estimation
Below by setting up model, model training, three aspects of simulating, verifying carry out the explanation of SOD discrimination method.
1) neural network model is set up
Fuzzy inference system (Fuzzy Inference System, FIS) in this model, uses Sugeno Fuzzy mould
Type, sets up FIS according to a large amount of experimental datas reliably so that the model of structure is more objective, thus has also avoided because of every ginseng
Number highly couples the problem being difficult to peel off.
According to lithium ion battery external behavior Parameter analysis, finishing screen selects t discharge timePut, release capacity QPut, open circuit electricity
Pressure Uk, ambient temperature T as the input of neural network model, model is output as the degree of degeneration of lithium ion battery and describes ginseng
Number degeneration factor β, is shown in formula (1).
β=f (tPut, QPut, Uk, T) and (1)
T in formulaPutRepresenting discharge time, unit is hour (h)；
QPutRepresenting and release capacity, unit is ampere-hour (Ah)；
UkRepresenting open-circuit voltage, unit is volt (V)；
T represents ambient temperature, and unit is degree Celsius (DEG C).
Adaptive neural network fuzzy system in application MATLAB, sets up Sugeno Fuzzy model automatically as it is shown in figure 1, four
Individual input quantity is respectively discharge time, discharge capacity, open-circuit voltage, ambient temperature.Output variable i.e. degeneration factor β.β is 0
～the numerical value between 1, when β is close to 0, then cell degradation lesser extent；When β is close to 1, then cell degradation degree is serious.
2) lithium battery group experiment
This experiment uses four groups of Li-ion batteries piles samples, and (every Battery pack group is in series with three sheet lithium ion batteries, single
Body battery size is INCMP58145155N-I, and rated voltage is 3.7V, rated capacity 10Ah) implement four kinds of different filling simultaneously
Discharge system, concrete discharge and recharge parameter is arranged as shown in table 1.
Table 1 battery set charge/discharge parameter
Experiment uses host computer and slave computer to combine collection relevant experimental data, and slave computer is by new University of Science and Technology, Shijiazhuang energy
The accumulator comprehensive parameters ATE (model is BTS-M 300A/12V) that source development corporation, Ltd. produces, related experiment
Data are recorded by uploading to host computer computer terminal.
3) data training and emulation
The relative discrete degree that the present invention is considered as between input variable parameter is to consider the degeneration journey of lithium ion battery
Degree (State of Degeneration), degree of degeneration is the least, and the dispersion degree between input quantity will be the least, degenerates on the contrary
Degree is the biggest, and its dispersion degree will be the biggest.
Set up Artificial Neural Network Structures as shown in Figure 2.
Experimental data is divided into two groups of training groups and check groups.Input as model training with training data, with instruction
Practice system model.The step-length arranging training is 30, and training result is as shown in Figure 3.
From the lower left corner of Fig. 3 it can be seen that the error of training is 0.00021859, tentatively judge that this systematic function is good.Point
Not testing system with test data set and inspection data set, its result display test data set mean error is
0.00082952, inspection data set mean error is: 0.0025974, and it is good that such error display goes out systematic function.
According to above-mentioned training, building phantom as shown in Figure 4, input data set variable is e, and output variable is h.
Using Data Processing in Experiment result as the input of model, corresponding date lithium ion battery can be respectively obtained
Degeneration factor.Apply this model degeneration factor result of calculation to certain concrete battery parameter.Fig. 5 is to degenerate in four Battery pack experiments
The tracking situation of coefficient, from the figure, it can be seen that the change of other degeneration factors of three groups is relatively slower, and 4# Battery pack degenerate system
Number change ratio is very fast, illustrates that its catagen speed is accelerated.And 4# Battery pack uses 0.5C discharge and recharge.Can be concluded that big
Current charge-discharge electricity, can accelerate cell degradation speed.
(3) degeneration warning system
1) composition of system
Degeneration warning system overall construction design is as shown in Figure 6.
According to Fig. 6, System Working Principle is as follows: system is by portions such as host computer and slave computer and detection display alarms
Divide and constitute.By detection device, the electric current of battery (group), voltage, the temperature of work are detected, according to electric discharge average current with
Discharge time, accumulation calculated releasing capacity, the open-circuit voltage before electric discharge, ambient temperature.Host computer is sent to carry out degeneration factor meter
Calculate, send value of calculation back to slave computer, if degeneration factor exceedes threshold value, send warning.
2) slave computer and peripheral circuit
Slave computer chooses STC89C52, and its peripheral circuit is by crystal oscillating circuit, reset circuit, voltage x current Acquisition Circuit, temperature
The part compositions such as degree testing circuit, warning circuit, clock acquisition.The structure of slave computer and peripheral circuit is as shown in Figure 7.
3) software design
A. calculation of capacity
The design uses ampere-hour method to carry out actual capacity detection, if the capacity Q of lithium battery represents, its unit is
Ah.Available formula (2) calculating capacity:
In formula: i is battery discharge current；
T is battery discharge time.
The algorithm of cumulative summation can realize integral operation.
QPut=∑ Ia×Δt (3)
In formula: IaIt it is load current；Δ t is the sampling period (i.e. 1 millisecond) of main control chip.
Current detecting is with calculation of capacity flow chart as shown in Figure 8.
B. communication calculates with degeneration factor and reports to the police
Fig. 9 is that communication calculates alarm flow figure with degeneration factor.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention.All essences in the present invention
Any amendment, equivalent and the improvement etc. made within god and principle, should be included within the scope of the present invention.
Claims (6)
1. a lithium battery degeneration discrimination method, it is characterised in that: use the analysis method of array dispersion degree, by some references
Physical quantity, as an array, carries out dispersion degree discriminating to this array, and described physical quantity includes open-circuit voltage and the electricity of battery
The ambient temperature in pond, the discharge-rate of battery, the capacity that can release.
A kind of lithium battery degeneration discrimination method the most according to claim 1, it is characterised in that: in application dispersion analysis
Time, it is considered to participate in the coefficient of variation of data, carry out each such as lower variation with reference to physical quantity:
Multiplying power lower discharge time Time frame variation coefficient=actual discharge time/0.2C；
The capacity coefficient of variation=actual capacity/nominal capacity；
The open-circuit voltage coefficient of variation=actual open-circuit voltage/rated voltage；
Temperature variations coefficient=actual temperature/20 DEG C；
On this basis, according to the effect played in degenerating of each physical quantity, then it is multiplied by corresponding coefficient.
A kind of lithium battery degeneration discrimination method the most according to claim 1 and 2, it is characterised in that: set up neutral net mould
Type carries out degeneration factor estimation, comprises the steps:
Step 1: set up neural network model；
Fuzzy inference system in described neural network model uses Sugeno Fuzzy model, sets up FIS according to experimental data；
According to lithium ion battery external behavior Parameter analysis, filter out t discharge timePut, release capacity QPut, open-circuit voltage Uk, environment temperature
Degree T is as the input of neural network model, and model is output as the degree of degeneration characterising parameter of lithium ion battery, i.e. degeneration factor
β:
β=f (tPut, QPut, Uk, T)
In formula, tPutRepresent discharge time；QPutRepresent and release capacity；UkRepresent open-circuit voltage；T represents ambient temperature；β is 0～1
Between numerical value, when β is close to 0, then cell degradation lesser extent；When β is close to 1, then cell degradation degree is serious.
Step 2: model training and simulating, verifying；
Experimental data is divided into two groups, i.e. training group and check groups, input as model training with training data, with training system
Model, the step-length arranging training is trained, and builds phantom；Defeated using Data Processing in Experiment result as model
Enter, respectively obtain the degeneration factor of corresponding date lithium ion battery.
4. a lithium battery degeneration warning system, including host computer, communication interface and slave computer, it is characterised in that: system is by examining
Survey device to detect, the battery of work or the electric current of set of cells, voltage, temperature according to electric discharge average current and discharge time
Accumulation calculates the open-circuit voltage before releasing capacity, electric discharge, ambient temperature；Host computer is sent to carry out degeneration factor calculating, will meter
Calculation value sends slave computer back to, if degeneration factor exceedes threshold value, sends warning.
A kind of lithium battery degeneration warning system the most according to claim 4, it is characterised in that: the peripheral circuit bag of slave computer
Include current detecting unit, voltage detection unit, A/D converting unit, temperature detecting unit, clock acquisition unit, serial communication list
Unit, power subsystem, display unit, alarm unit；The current information of described current detecting unit collection and voltage detection unit
The information of voltage of detection is sent to slave computer by A/D converting unit, temperature information that described temperature detecting unit detects and time
The temporal information of clock collecting unit collection is sent to slave computer, and described serial communication unit and power subsystem are not connected to bottom
Machine, the information of slave computer is respectively sent to display unit and alarm unit.
6. according to a kind of lithium battery degeneration warning system described in claim 4 or 5, it is characterised in that: include current detecting with
Calculation of capacity and communication calculate with degeneration factor；
Described current detecting comprises the steps: with calculation of capacity
The first step, current detecting subprogram starts；
Second step, when electric current exceedes the upper limit, returns upper level mastery routine；When electric current is not less than the upper limit, carry out electric quantity accumulation
Calculate, after calculating, return upper level mastery routine；
Described communication calculates with degeneration factor and comprises the steps:
The first step, serial ports initializes；
Second step, sends data acquisition command to slave computer；
3rd step, if data receiver terminates, then data loading, the degradation model program of Calling MATLAB, it is judged that degeneration factor
Whether not less than threshold value, if more than or equal to threshold value, then send alarm command to slave computer, if not being more than or equal to
Threshold value, then be back to second step；If still receiving data, then continue to data；
4th step, returns second step, continues to data until communication calculates complete with degeneration factor.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646259A (en) * | 2016-12-26 | 2017-05-10 | 上海思致汽车工程技术有限公司 | Battery power attenuation degree detection method |
| CN106950507A (en) * | 2017-05-12 | 2017-07-14 | 国家电网公司 | A kind of intelligent clock battery high reliability lifetime estimation method |
| CN107015159A (en) * | 2017-04-16 | 2017-08-04 | 知豆电动汽车有限公司 | Battery heating rate alarm method |
| CN109100652A (en) * | 2018-06-05 | 2018-12-28 | 中国电力科学研究院有限公司 | It is a kind of for predict echelon using power battery dispersion method and system |
| CN112946483A (en) * | 2021-02-05 | 2021-06-11 | 重庆长安新能源汽车科技有限公司 | Comprehensive evaluation method for battery health of electric vehicle and storage medium |
| CN114200330A (en) * | 2022-02-16 | 2022-03-18 | 广东电网有限责任公司中山供电局 | Method and device for detecting running condition of storage battery pack |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5598087A (en) * | 1993-06-08 | 1997-01-28 | Honda Giken Kogyo Kabushiki Kaisha | System for monitoring residual capacity of battery |
| CN102749589A (en) * | 2012-07-13 | 2012-10-24 | 哈尔滨工业大学深圳研究生院 | Recession-mode predicting method of power battery of electric automobile |
| US20130043876A1 (en) * | 2010-12-24 | 2013-02-21 | Huizhou Epower Electronics Co., Ltd. | Method for estimating state-of-charge of lithium ion battery |
| CN103954913A (en) * | 2014-05-05 | 2014-07-30 | 哈尔滨工业大学深圳研究生院 | Predication method of electric vehicle power battery service life |
-
2016
- 2016-07-05 CN CN201610526170.2A patent/CN106199443B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5598087A (en) * | 1993-06-08 | 1997-01-28 | Honda Giken Kogyo Kabushiki Kaisha | System for monitoring residual capacity of battery |
| US20130043876A1 (en) * | 2010-12-24 | 2013-02-21 | Huizhou Epower Electronics Co., Ltd. | Method for estimating state-of-charge of lithium ion battery |
| CN102749589A (en) * | 2012-07-13 | 2012-10-24 | 哈尔滨工业大学深圳研究生院 | Recession-mode predicting method of power battery of electric automobile |
| CN103954913A (en) * | 2014-05-05 | 2014-07-30 | 哈尔滨工业大学深圳研究生院 | Predication method of electric vehicle power battery service life |
Non-Patent Citations (2)
| Title |
|---|
| 汤露曦: "电动汽车动力电池SOH在线实时估计算法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑（月刊）》 * |
| 邓大为等: "一种基于单片机的剩余容量在线检测新方法", 《应用能源技术》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646259A (en) * | 2016-12-26 | 2017-05-10 | 上海思致汽车工程技术有限公司 | Battery power attenuation degree detection method |
| CN106646259B (en) * | 2016-12-26 | 2017-11-17 | 上海思致汽车工程技术有限公司 | A kind of detection method of power of battery attenuation degree |
| CN107015159A (en) * | 2017-04-16 | 2017-08-04 | 知豆电动汽车有限公司 | Battery heating rate alarm method |
| CN106950507A (en) * | 2017-05-12 | 2017-07-14 | 国家电网公司 | A kind of intelligent clock battery high reliability lifetime estimation method |
| CN109100652A (en) * | 2018-06-05 | 2018-12-28 | 中国电力科学研究院有限公司 | It is a kind of for predict echelon using power battery dispersion method and system |
| CN109100652B (en) * | 2018-06-05 | 2022-04-26 | 中国电力科学研究院有限公司 | Method and system for predicting dispersion of power battery used in echelon |
| CN112946483A (en) * | 2021-02-05 | 2021-06-11 | 重庆长安新能源汽车科技有限公司 | Comprehensive evaluation method for battery health of electric vehicle and storage medium |
| CN112946483B (en) * | 2021-02-05 | 2022-05-06 | 重庆长安新能源汽车科技有限公司 | Comprehensive evaluation method for battery health of electric vehicle and storage medium |
| CN114200330A (en) * | 2022-02-16 | 2022-03-18 | 广东电网有限责任公司中山供电局 | Method and device for detecting running condition of storage battery pack |
| CN114200330B (en) * | 2022-02-16 | 2022-05-03 | 广东电网有限责任公司中山供电局 | Method and device for detecting running condition of storage battery pack |
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