CN114636930B - Battery self-discharge fault early warning method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000001174 ascending effect Effects 0.000 claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 239000000178 monomer Substances 0.000 claims description 10
- 239000000463 material Substances 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 5
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 claims description 5
- 230000009286 beneficial effect Effects 0.000 description 6
- 238000007726 management method Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004873 anchoring Methods 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007086 side reaction Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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Abstract
The invention provides a battery self-discharge fault early warning method, and belongs to the technical field of batteries. The method comprises two judging conditions, and judging that the battery has self-discharge faults as long as one of the two judging conditions is met; wherein, the first judgment condition is: under the condition that the delta SOC value of the battery is more than or equal to the delta SOC trigger threshold, the delta SOC value of the battery is in an ascending trend in a first set time before reaching the delta SOC trigger threshold; the judgment condition II is as follows: and under the condition that the low-voltage core occupation ratio is more than or equal to the low-voltage core occupation ratio trigger threshold, the low-voltage core occupation ratio is in an ascending trend in a second set time before the low-voltage core occupation ratio trigger threshold is reached. According to the method, on the basis of preliminary fault judgment by utilizing the low-voltage core occupation ratio and the delta SOC value, further fault recognition is performed based on the change trend in the corresponding set time before the low-voltage core occupation ratio and the delta SOC value reach the corresponding trigger threshold, the accuracy of fault recognition is improved, and a fault recognition result has a certain advance.
Description
Technical Field
The invention relates to a battery self-discharge fault early warning method, and belongs to the technical field of batteries.
Background
At present, two main methods for early warning the self-discharge fault of a battery are provided: one is to measure the self-discharge of the battery by an open-circuit voltage decay rate measurement method, but the open-circuit voltage decay rate measurement method is to estimate the self-discharge result of the battery based on the fitted OCV (open-circuit voltage) -SOC relationship, the estimation accuracy of the self-discharge result of the battery depends on experimental simulation results, and is easily influenced by working conditions such as temperature, multiplying power and the like, and the calculation accuracy is difficult; one is to build a nonlinear model (for example, fourier fitting model) of the discharge voltage and the battery capacity by a nonlinear fitting method to perform the self-discharge measurement of the battery, and although the accuracy of the self-discharge measurement model can be improved, the self-discharge fault identification is performed simply according to the estimated self-discharge rate, the accuracy is not sufficient, and the severity of the fault is difficult to define.
Disclosure of Invention
The invention aims to provide a battery self-discharge fault early warning method which is used for solving the problem that the accuracy of battery self-discharge fault identification is insufficient by only using a self-discharge rate estimated value in the prior art.
In order to achieve the above object, the present invention provides a battery self-discharge fault early warning method, which includes two judgment conditions, and judges that a battery has a self-discharge fault as long as one of the two judgment conditions is satisfied; wherein, the first judgment condition is: under the condition that the delta SOC value of the battery is more than or equal to the delta SOC trigger threshold, the delta SOC value of the battery is in an ascending trend in a first set time before reaching the delta SOC trigger threshold; the judgment condition II is as follows: under the condition that the low-voltage core occupation ratio is more than or equal to the low-voltage core occupation ratio trigger threshold, the low-voltage core occupation ratio is in an ascending trend in a second set time before reaching the low-voltage core occupation ratio trigger threshold;
determining a delta SOC value of the battery according to the highest voltage of the single body and the lowest voltage of the single body in the battery;
the low-voltage core ratio is obtained through the following steps: calculating the times of occurrence of low-voltage states of each single battery cell in the battery in a third set time based on the voltage data of each single battery cell reported by the battery in real time, taking the battery cell with the largest times of occurrence of the low-voltage states as the low-voltage battery cell, and calculating the ratio of the times of occurrence of the low-voltage states of the low-voltage battery cell to the total times of occurrence of the low-voltage states of all single battery cells in the battery in the third set time as the ratio of the low-voltage battery cells; when the voltage of the single battery cell is smaller than the set voltage value, the single battery cell is in a low-voltage state.
The beneficial effects of the invention are as follows: (1) The low-voltage battery core occupation ratio and the delta SOC value (delta SOC value for short) of the battery are used as key indexes for identifying the self-discharge faults of the battery, the faults are further identified based on the change trend in the corresponding set time before the low-voltage battery core occupation ratio and the delta SOC value reach the corresponding trigger threshold on the basis of the initial fault judgment by utilizing the low-voltage battery core occupation ratio and the delta SOC value, the accuracy of fault identification is improved, and the problem that the accuracy of the self-discharge fault identification of the battery is insufficient by only utilizing the self-discharge rate measuring and calculating value in the prior art is solved; the fault recognition based on the key index change trend ensures that the fault recognition result has certain advance, the fault problem can be found more than 10 days in advance, and the coverage of the fault recognition is improved on the premise of ensuring the recognition accuracy; (2) And fault identification is carried out from the 2 dimensions of the ratio of the piezoelectric core to the delta SOC value, and the identification results of the 2 dimensions play a role in mutual complementation, so that the accuracy and coverage of model identification are further improved.
Further, in the above method, the method further includes a step of performing a step of early warning on the self-discharge fault of the battery, the step of early warning includes: when the judging condition is met, judging that the self-discharge fault of the battery is a serious fault; and when the judging condition II is met, judging whether the delta SOC value is larger than or equal to the delta SOC trigger threshold, if so, judging that the self-discharge fault of the battery is an important fault, otherwise, judging that the self-discharge fault of the battery is a general fault.
The beneficial effects of doing so are: and the fault early warning grades are classified, so that the subsequent application management is facilitated.
Further, in the method, the delta SOC value of the battery is calculated according to an established delta SOC nonlinear index calculation model by combining the highest voltage of the monomer and the lowest voltage of the monomer in the battery, and the established delta SOC nonlinear index calculation model is established by using a nonlinear index algorithm based on SOC_OCV experimental data of different battery materials.
The beneficial effects of doing so are: by adopting a nonlinear index algorithm to establish a delta SOC nonlinear index calculation model, the simplicity and universality of delta SOC calculation application can be improved.
Further, in the above method, when the battery material is a lithium iron phosphate battery, the Δsoc nonlinear index calculation model is: a x e (B x monomer highest voltage) -a x e (B x monomer lowest voltage), wherein a and B are model coefficients obtained by fitting.
Further, in the method, whether the ratio of the low-voltage core to the delta SOC value is in an ascending trend within the corresponding set time is judged by using a difference function and a median filtering algorithm.
Further, in the above method, the number of times that each single cell in the battery has a low voltage state is obtained based on statistics of effective data, where the effective data is: and when the battery is in a driving state and under a stable current, the voltage data of each single battery cell are reported in real time by the battery.
The beneficial effects of doing so are: the selection of the low-voltage battery core and the calculation of the low-voltage battery core occupation ratio are performed based on the statistical result of the effective data, so that the calculation of the low-voltage battery core occupation ratio is more accurate, and the accuracy of the follow-up fault identification result is further ensured.
Further, in the method, the low-voltage battery core duty ratio trigger threshold and the Δsoc trigger threshold may each be set up in multiple groups according to factors affecting the battery self-discharge rate, where the factors affecting the battery self-discharge rate include a product type, a temperature, and an operation condition.
The beneficial effects of doing so are: the trigger threshold value of the duty ratio of the low-voltage battery core and the delta SOC trigger threshold value are established in groups, so that the objectivity of the trigger threshold value setting can be improved, and the fault identification accuracy is further improved.
Further, in the above method, the duty cycle trigger threshold and Δsoc trigger threshold of the battery cell are updated periodically.
The beneficial effects of doing so are: the trigger threshold is updated regularly, so that the fault identification accuracy can be further improved.
Drawings
FIG. 1 is a graph of the trend of change of key indicators of a self-discharging faulty vehicle battery in an embodiment of the method of the present invention;
FIG. 2 is a flow chart of a method for early warning of a battery self-discharge fault in an embodiment of the method of the present invention;
FIG. 3 is a graph showing the trend of ΔSOC values in an embodiment of the method of the present invention;
fig. 4 is a graph showing the trend of the ratio of the piezoelectric core to the ratio in the embodiment of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Method embodiment:
from the self-discharge failure mechanism, the self-discharge behavior of the battery is caused by the factors of capacity loss of spontaneous chemical side reaction inside the battery, micro short-circuit loss inside the battery core, diaphragm defect and the like, the self-discharge behavior is shown in the same or a plurality of battery cores, the delta SOC difference is large, and the problems of low-voltage alarm, SOC jump, vehicle anchoring and the like are easily caused in the running process of the vehicle.
Based on the single voltage data of the historical self-discharge fault vehicle battery, the key factors capable of identifying the self-discharge fault of the battery are researched by using a coaxial multi-index time sequence change trend method to obtain a key index change trend graph shown in fig. 1, and research discovers that: before the self-discharge fault occurs, the battery shows the phenomenon that the ratio of the battery core to the delta SOC value is increased sharply in a short period, and the delta SOC value change of the battery has certain hysteresis. The low-voltage battery cell is a single battery cell with the highest frequency of occurrence of a low-voltage state in a set time, the low-voltage battery cell occupation ratio is a ratio of the frequency of occurrence of the low-voltage state of the low-voltage battery cell to the total frequency of occurrence of the low-voltage state of all single battery cells in the battery, and the delta SOC value of the battery is determined according to the highest voltage and the lowest voltage of the single battery.
Therefore, based on the above research results, the present embodiment uses two indexes, i.e., the ratio of the low-voltage battery core and the Δsoc value of the battery (abbreviated as Δsoc value), as key indexes capable of identifying the self-discharge fault of the battery, and proposes a battery self-discharge fault early warning method based on the variation trend of the ratio of the low-voltage battery core and the Δsoc value from two dimensions of the ratio of the low-voltage battery core and the Δsoc value, so as to identify the self-discharge fault of the battery in advance.
The battery self-discharge fault early warning method of the embodiment is shown in fig. 2, and the method comprises the following steps:
(1) Acquiring voltage data of each single battery cell reported by a battery in real time;
(2) Screening data in a driving state and under a stable current from voltage data of each single battery cell reported by a battery in real time, counting the times of occurrence of a low-voltage state of each single battery cell in the battery in a third set time (namely set time T3, for example, the current day is taken as set time T3) based on the effective data, taking the battery cell with the largest times of occurrence of the low-voltage state as a low-voltage battery cell, and calculating the ratio of the times of occurrence of the low-voltage state of the low-voltage battery cell to the total times of occurrence of the low-voltage state of all single battery cells in the battery in the set time T3 as a low-voltage battery cell occupation ratio; when the voltage of the single battery cell is smaller than the set voltage value, the single battery cell is in a low-voltage state.
In this embodiment, the stable current refers to the current greater than 5A, so after the voltage data of each single battery cell reported in real time by the battery is obtained, the voltage data are screened, the voltage data meeting the driving state and the current greater than 5A are taken as effective data, and the selection of the low-voltage battery cell and the calculation of the low-voltage battery cell occupation ratio are performed based on the statistical result of the effective data, so that the accuracy of the calculation result of the low-voltage battery cell occupation ratio can be improved, and the accuracy of the subsequent fault identification is further ensured; as another embodiment, the number of times that each single battery cell in the battery has a low voltage state in the third set time may also be obtained by directly counting the voltage data of each single battery cell reported in real time by the battery.
(3) Combining the highest voltage of a monomer and the lowest voltage of the monomer in the battery, and calculating to obtain a delta SOC value of the battery according to an established delta SOC nonlinear index calculation model;
In the embodiment, the SOC_OCV experimental data of different battery materials are combined, and the nonlinear index algorithm is utilized to establish a delta SOC nonlinear index calculation model of the different battery materials, so that the simplicity and universality of delta SOC calculation application can be improved; taking a lithium iron phosphate battery as an example, a nonlinear index calculation model of the delta SOC of the lithium iron phosphate battery is as follows: a x e (B x monomer highest voltage) -a x e (B x monomer lowest voltage), wherein a and B are model coefficients obtained based on experimental data using least squares fitting.
As other embodiments, it is also possible to build a Δsoc nonlinear calculation model of different battery materials using other existing nonlinear fitting algorithms based on soc_ocv experimental data of the different battery materials, and then calculate a Δsoc value based on the built Δsoc nonlinear calculation model.
(4) When the delta SOC value of the battery is more than or equal to the delta SOC trigger threshold, if the delta SOC value is in an ascending trend within a set time T1 before reaching the delta SOC trigger threshold, judging that the battery has self-discharge fault; or when the low-voltage core occupation ratio is more than or equal to the low-voltage core occupation ratio trigger threshold, if the low-voltage core occupation ratio is in an ascending trend within a set time T2 before the low-voltage core occupation ratio trigger threshold is reached, judging that the battery has self-discharge faults.
That is, in the battery self-discharge fault early warning method of the embodiment, whether the Δsoc value and the low-voltage core occupation ratio of the battery reach the corresponding trigger thresholds is judged first, and whether the Δsoc value and the low-voltage core occupation ratio of the battery are in an ascending trend within the corresponding set time is continuously judged under the condition that the Δsoc value and the low-voltage core occupation ratio of the battery reach the corresponding trigger thresholds, and the battery self-discharge fault is judged only when the Δsoc value and the low-voltage core occupation ratio of the battery reach the corresponding trigger thresholds and in the ascending trend within the corresponding set time, wherein the reaching of the corresponding trigger thresholds is the premise of carrying out the ascending trend judgment.
For convenience of description, the "delta SOC value is in an ascending trend within a set time T1 before reaching the delta SOC trigger threshold" of the battery is taken as a first judgment condition, and the "low-voltage core occupation ratio is in an ascending trend within a second set time before reaching the low-voltage core occupation ratio trigger threshold" is taken as a second judgment condition, so that the battery self-discharge fault early warning method of the embodiment judges that the battery has self-discharge fault as long as one of the first judgment condition and the second judgment condition is satisfied.
In this embodiment, the battery self-discharge fault is also pre-warned in a grading manner, and the self-discharge fault pre-warning grades are divided into 3 grades of serious faults, important faults and general faults, and the severity is as follows: severe faults > critical faults > general faults. When the judging condition is met, judging that the self-discharge fault of the battery is a serious fault; and when the judging condition II is met, judging whether the delta SOC value is larger than or equal to the delta SOC trigger threshold, if so, judging that the self-discharge fault of the battery is an important fault, otherwise, judging that the self-discharge fault of the battery is a general fault.
The specific values of the first, second and third set times and the number of divided fault early warning grades can be set according to practical situations, for example, the first and second set times can be shorter time before reaching the corresponding trigger threshold.
In the embodiment, whether the ratio of the piezoelectric core to the delta SOC value is in an ascending trend within the corresponding set time is judged by using a difference function and a median filtering algorithm; and determining a trigger threshold value of the duty ratio of the low voltage battery core and a trigger threshold value of the delta SOC according to the self-discharge case car failure mode and the experimental result.
As other embodiments, determining whether the ratio of the piezoelectric core and the Δsoc value are in an upward trend within a corresponding set time may also be implemented by using other filtering algorithms; in addition, multiple groups of trigger thresholds with the duty ratio of the battery core are established according to factors (such as product types, temperatures and operation conditions) influencing the self-discharge rate of the battery, or multiple groups of trigger thresholds with the duty ratio of the battery core and the trigger thresholds with the duty ratio of the battery core are established according to actual conditions, and the trigger thresholds are updated periodically (for example, the trigger thresholds are updated periodically by using the obtained latest data), so that the objectivity of the setting of the trigger thresholds is improved, and further, the fault recognition accuracy and the fault recognition quantity are improved, for example: the trigger threshold corresponding to each product type can be established, or the trigger threshold corresponding to each temperature can be established, or the trigger threshold under each operation condition can be established, or the trigger threshold can be established by combining a plurality of influence factors simultaneously, for example, the trigger threshold can be established by combining two influence factors of the product type and the temperature simultaneously or combining three influence factors of the product type, the temperature and the operation condition simultaneously. When in actual use, the corresponding trigger threshold is selected according to the actual situation.
An application example of the battery self-discharge fault early warning method in this embodiment is shown in fig. 3 and fig. 4, where fig. 3 and fig. 4 are key index change trend graphs of identified vehicles (hereinafter referred to as problem vehicles) with a battery self-discharge fault, and 8 vehicles (hereinafter referred to as problem vehicles) with a battery self-discharge fault identified based on a Δsoc value and a Δsoc value change trend are identified, 5 problem vehicles identified based on a low-voltage core occupation ratio and a low-voltage core occupation ratio change trend are identified, and 8 vehicles with a problem are identified together, wherein 5 vehicles with a serious fault are identified, and 3 vehicles with a general fault are identified. Through data verification, the battery self-discharge fault early warning method can identify faults more than 10 days in advance, and the accuracy of the fault identification result is more than 90%.
In summary, the embodiment provides a battery self-discharge fault early warning method based on a battery key index change trend from two dimensions of a battery core occupation ratio and a delta SOC value, and the method has the following advantages:
(1) The low-voltage battery core occupation ratio and the delta SOC value are used as key indexes for identifying the self-discharge faults of the battery, a difference function and a filtering algorithm are adopted to evaluate the variation trend of the key indexes in a short period on the basis of the initial fault judgment by utilizing the low-voltage battery core occupation ratio and the delta SOC value, the faults are further identified on the basis of the variation trend of the low-voltage battery core occupation ratio and the delta SOC value in the short period before reaching corresponding trigger threshold values, the accuracy of fault identification is improved, and the problem that the accuracy of the self-discharge fault identification of the battery is insufficient by utilizing the self-discharge rate measuring and calculating value in the prior art is solved; the fault recognition based on the key index change trend ensures that the fault recognition result has certain advance, the fault problem can be found more than 10 days in advance, and the coverage of the fault recognition is improved on the premise of ensuring the recognition accuracy;
(2) The nonlinear index algorithm is adopted to fit the SOC_OCV curve, a nonlinear index calculation model of delta SOC is established, and the simplicity and universality of delta SOC calculation application are improved;
(3) Performing fault identification from the 2 dimensions of the ratio of the piezoelectric core to the delta SOC value, wherein the identification results of the 2 dimensions have the mutual supplementing effect, and the accuracy and coverage of model identification are further improved;
(4) And the fault early warning grades are classified, so that the subsequent application management is facilitated.
The battery self-discharge fault early warning method is suitable for various battery material types such as a ternary battery and a lithium iron phosphate battery of a new energy vehicle, is not influenced by pure electric products, battery types and operation conditions, and can be widely applied to all new energy commercial vehicles and passenger vehicles in the market.
Claims (8)
1. A battery self-discharge fault early warning method is characterized by comprising two judging conditions, and the battery self-discharge fault is judged as long as one of the two judging conditions is met; wherein, the first judgment condition is: under the condition that the delta SOC value of the battery is more than or equal to the delta SOC trigger threshold, the delta SOC value of the battery is in an ascending trend in a first set time before reaching the delta SOC trigger threshold; the judgment condition II is as follows: under the condition that the low-voltage core occupation ratio is more than or equal to the low-voltage core occupation ratio trigger threshold, the low-voltage core occupation ratio is in an ascending trend in a second set time before reaching the low-voltage core occupation ratio trigger threshold;
determining a delta SOC value of the battery according to the highest voltage of the single body and the lowest voltage of the single body in the battery;
the low-voltage core ratio is obtained through the following steps: calculating the times of occurrence of low-voltage states of each single battery cell in the battery in a third set time based on the voltage data of each single battery cell reported by the battery in real time, taking the battery cell with the largest times of occurrence of the low-voltage states as the low-voltage battery cell, and calculating the ratio of the times of occurrence of the low-voltage states of the low-voltage battery cell to the total times of occurrence of the low-voltage states of all single battery cells in the battery in the third set time as the ratio of the low-voltage battery cells; when the voltage of the single battery cell is smaller than the set voltage value, the single battery cell is in a low-voltage state.
2. The battery self-discharge fault pre-warning method according to claim 1, further comprising the step of performing a hierarchical pre-warning of the battery self-discharge fault, the hierarchical pre-warning comprising: when the judging condition is met, judging that the self-discharge fault of the battery is a serious fault; and when the judging condition II is met, judging whether the delta SOC value is larger than or equal to the delta SOC trigger threshold, if so, judging that the self-discharge fault of the battery is an important fault, otherwise, judging that the self-discharge fault of the battery is a general fault.
3. The battery self-discharge fault early warning method according to claim 1 or 2, characterized in that the delta SOC value of the battery is calculated according to an established delta SOC nonlinear index calculation model based on soc_ocv experimental data of different battery materials by using a nonlinear index algorithm by combining the highest voltage of a single body and the lowest voltage of the single body in the battery.
4. The battery self-discharge fault early warning method according to claim 3, wherein when the battery material is a lithium iron phosphate battery, the Δsoc nonlinear index calculation model is: a x e (B x monomer highest voltage) -a x e (B x monomer lowest voltage), wherein a and B are model coefficients obtained by fitting.
5. The battery self-discharge fault early warning method according to claim 1 or 2, characterized in that whether the ratio of the battery core to the delta SOC value is in an ascending trend within a corresponding set time is judged by using a difference function and a median filtering algorithm.
6. The battery self-discharge fault early warning method according to claim 1 or 2, wherein the number of times that each single cell in the battery has a low-voltage state is obtained based on statistics of effective data, wherein the effective data is: and when the battery is in a driving state and under a stable current, the voltage data of each single battery cell are reported in real time by the battery.
7. The battery self-discharge fault early warning method according to claim 1 or 2, wherein the battery core duty ratio trigger threshold and the Δsoc trigger threshold can each establish a plurality of groups according to factors affecting the battery self-discharge rate, and the factors affecting the battery self-discharge rate include a product type, a temperature, and an operation condition.
8. The battery self-discharge fault warning method according to claim 7, wherein the battery core duty cycle trigger threshold and the Δsoc trigger threshold are updated periodically.
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| DE102017009448A1 (en) * | 2017-10-11 | 2018-04-19 | Daimler Ag | Method for determining a self-discharge of a battery with at least one battery cell |
| CN108717167A (en) * | 2018-06-04 | 2018-10-30 | 合肥工业大学 | Batteries of electric automobile self discharge fault judgment method based on equivalent short-circuit internal resistance model |
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| DE102017009448A1 (en) * | 2017-10-11 | 2018-04-19 | Daimler Ag | Method for determining a self-discharge of a battery with at least one battery cell |
| CN108717167A (en) * | 2018-06-04 | 2018-10-30 | 合肥工业大学 | Batteries of electric automobile self discharge fault judgment method based on equivalent short-circuit internal resistance model |
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