CN110515001A - A kind of two stages battery performance prediction technique based on charge and discharge - Google Patents
A kind of two stages battery performance prediction technique based on charge and discharge Download PDFInfo
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- CN110515001A CN110515001A CN201910844890.7A CN201910844890A CN110515001A CN 110515001 A CN110515001 A CN 110515001A CN 201910844890 A CN201910844890 A CN 201910844890A CN 110515001 A CN110515001 A CN 110515001A
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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
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
Abstract
The invention discloses the present invention of battery performance detection field to provide the following technical solutions a kind of two stages battery performance prediction technique based on charge and discharge, comprising the following specific steps S1: according to battery charge and discharge process primary condition x, initial recommendation electric current A1 is provided by model, whether expected blanking voltage meets threshold values, if satisfaction is directly used in practical battery core test, if being unsatisfactory for executing step S2；S2: actual test is carried out, it obtains recommending virtual voltage V1 of the electric current A1 after charge and discharge, it is provided by model and recommends electric current A2, expected blanking voltage meets threshold values, it is tested for practical battery core, design two stage battery performance prediction technique, by accurately predicting, substitute a large amount of manual testing's process, it is fitted by modeling, and the method for Automatic-searching optimal value, this method are not limited to use in the prediction between electric current, voltage, it can also be used for the Relationship Prediction in battery detecting between any two correlated variables, the entire model algorithm speed of service is very fast.
Description
Technical field
The present invention relates to battery performance detection technique field, specially a kind of two stages battery performance based on charge and discharge is pre-
Survey method.
Background technique
Primary completely electric vehicle battery core detection generally requires to carry out more wheel charge and discharge electrical measurements in the case where controlling various environmental conditions
Examination.When battery carries out charge and discharge with specified constant current value, a blanking voltage can be obtained after continuing for some time.In order to
Stablize blanking voltage in some national normal value, it usually needs to specify initial current value by artificial experience；Or when not
When with initial current progress charge-discharge test under environment, is given, need to estimate blanking voltage.By to varying environment
Under, the Current Voltage value of different battery core modeled, actual manual testing's number can be significantly reduced, promote battery performance
The efficiency of assessment and detection.
The power battery performance flexibility of existing open source information such as Publication No. CN109604186A assesses method for separating,
It how does not all define clearly for the relationship between some key parameter variables such as battery current and voltage in battery detecting
Modeling method how the initial value of another parameter is set and in the case where given target component.
Based on this, the present invention devises a kind of two stages battery performance prediction technique based on charge and discharge, above-mentioned to solve
The problem of mentioning.
Summary of the invention
The two stages battery performance prediction technique based on charge and discharge that the purpose of the present invention is to provide a kind of, it is above-mentioned to solve
The problem of being proposed in background technique.
To achieve the above object, the invention provides the following technical scheme: a kind of two stages battery performance based on charge and discharge
Prediction technique, comprising the following specific steps
S1: according to battery charge and discharge process primary condition x, initial recommendation electric current A1 is provided by model, it is contemplated that cut-off
Whether voltage meets threshold values, practical battery core test is directly used in if meeting, if being unsatisfactory for executing step S2；
S2: carrying out actual test, obtains recommending virtual voltage V1 of the electric current A1 after charge and discharge, is provided and pushed away by model
Recommend electric current A2, it is contemplated that blanking voltage meet threshold values, for practical battery core test.
Preferably, the primary condition x includes remaining capacity, temperature, initial internal resistance, initial voltage, initial weight, charge and discharge
Electric duration, battery capacity, charging current and blanking voltage parameter
Preferably, specific step is as follows for the training method of the model:
S100: data are extracted: from history charge and discharge data, choosing the data record for reaching complete charge and discharge duration；
S200: data supplement: for primary prolonged charge-discharge test, the voltage that discharge process will record each second becomes
Change；
S300: above-mentioned data and battery information are associated, and clean some apparent exceptions, and data set is instructed
Practice the division of collection and test set；
S400: regression forecasting is carried out using regression algorithm, two models, two regression models of generation are respectively trained are as follows:
V1=f1 (x, A1) and A1=g1 (x, V1),
Wherein, f1 is from current forecasting voltage, and g1 is from voltage prediction electric current；
S500: data set makes the difference: all charge and discharge electrographic recording for the same battery battery core carry out positive and negative two ratios two-by-two
It is right, compare the difference DeltaA, the difference DeltaV of two blanking voltages, the hundred of electric current or voltage change of two predetermined currents
Divide than %, re-starts data division；
S600: two models, two regression models of generation are respectively trained are as follows:
V2=f2 (x, A1, V1, A2, DeltaA, %) and A2=g2 (x, V1, A1, V2, DeltaV, %),
Wherein, f2 is from last round of electric current, voltage and to work as front-wheel current forecasting voltage, and g2 is from last round of voltage, electric current
With work as front-wheel voltage prediction electric current.
Preferably, in the step S200, further include the electric discharge duration occurred in the step s 100 in discharge process,
Add to data set.
Preferably, in the step 400, regression algorithm RF, GBDT, LR or deep neural network regression algorithm are therein
It is a kind of.
Preferably, increase further screening and confirmation in the recommendation electric current A1, the specific method is as follows:
S401: when specified target cutoff voltage, initial current a1 is calculated using model g1；
S402: input a1 provides a predicted voltage v1 using f1 model；
S403: check whether v1 meets threshold value difference and repeated authentication number reaches the upper limit, if being, enters step
S405；Otherwise, S404 is entered step；
S404: mode one: input a1, v1 obtain a new electric current a2 using g2 model；A1 is inputted, a2 uses f2 mould
Type predicts a voltage；
Mode two: using f1 model, but using a1 as initial value, with certain learning rate such as 0.2, front and back electric current it is absolute
Difference is scanned for as variable quantity, enters step S403 after increasing the number of iterations；
S405: being that the difference with predicted voltage and target voltage is key, electric current and prediction by the arrangement of above-mentioned prediction result
Voltage is to the dictionary for value；
S406: list is unfolded in electric current and voltage, finds out longest Current Voltage using dynamic programming method and meets together
Step increases or reduced sequence；
S407: to sequence above, predicted voltage and the smallest key of target voltage difference are picked out, corresponding electric current is used
Recommend in first time.
Compared with prior art, the beneficial effects of the present invention are: by designing two stage battery performance prediction technique, In
Accuracy rate reaches about 85% when predicting for the first time；When predicting for second, accuracy rate is more than 95%；By accurately predicting, replace
It for a large amount of manual testing's process, is fitted by modeling, and the method for Automatic-searching optimal value, this method are not limited to use in electricity
Prediction between stream, voltage, it can also be used to which the Relationship Prediction in battery detecting between any two correlated variables, entire model are calculated
The method speed of service is very fast.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, will be described below to embodiment required
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is schematic structural view of the invention；
Fig. 2 is model training method flow chart of the present invention；
Fig. 3 is the method flow diagram that the present invention further screens and confirms on the basis of a1.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts all other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, the present invention provides a kind of technical solution: a kind of two stages battery performance prediction side based on charge and discharge
Method, comprising the following specific steps
S1: the first stage: according to battery charge and discharge process primary condition x, initial recommendation electric current A1 is provided by model, in advance
Whether the blanking voltage of phase meets threshold values, practical battery core test is directly used in if meeting, if being unsatisfactory for executing step S2；
S2: the first stage: carrying out actual test, obtains recommending virtual voltage V1 of the electric current A1 after charge and discharge, pass through
Model provide recommend electric current A2, it is contemplated that blanking voltage meet threshold values, for practical battery core test.
Wherein, the primary condition x includes remaining capacity, temperature, initial internal resistance, initial voltage, initial weight, charge and discharge
Duration, battery capacity, charging current and blanking voltage parameter.
As shown in Fig. 2, specific step is as follows for the training method of the model:
S100: data are extracted: from history charge and discharge data, choosing the data record for reaching complete charge and discharge duration；
S200: data supplement: for primary prolonged charge-discharge test, such as discharge time is 180s, discharge process
Will record each second voltage change, discharge process will record each second voltage change, further include in discharge process in step
The electric discharge duration occurred in rapid S100, such as the data of 30s, 60s intermediate state, add to data set；
S300: above-mentioned data and battery information are associated, and clean some apparent exceptions, and data set is instructed
Practice the division of collection and test set；
S400: regression forecasting is carried out using regression algorithm, wherein regression algorithm RF, GBDT, LR or deep neural network
Regression algorithm is one such, and two models, two regression models of generation are respectively trained are as follows:
V1=f1 (x, A1) and A1=g1 (x, V1),
Wherein, f1 is from current forecasting voltage, and g1 is from voltage prediction electric current；
S500: data set makes the difference: all charge and discharge electrographic recording for the same battery battery core, it may be possible to 1-N item, two-by-two into
Positive and negative two comparisons of row, compare the difference DeltaA of two predetermined currents, the difference DeltaV of two blanking voltages, electric current or
The percentage % of voltage change re-starts data division；
S600: two models, two regression models of generation are respectively trained are as follows:
V2=f2 (x, A1, V1, A2, DeltaA, %) and A2=g2 (x, V1, A1, V2, DeltaV, %),
Wherein, f2 is from last round of electric current, voltage and to work as front-wheel current forecasting voltage, and g2 is from last round of voltage, electric current
With work as front-wheel voltage prediction electric current.
As shown in figure 3, with reference to Fig. 1, recommendation for first stage current value a1 can use and obtain g1 algorithm in Fig. 2
Direct calculating current, but The present invention gives a kind of more preferably method, a1 is not used directly, but on the basis of a1, into one
The screening and confirmation of step, the specific method is as follows:
S401: when specified target cutoff voltage, initial current a1 is calculated using model g1；
S402: input a1 provides a predicted voltage v1 using f1 model；
S403: check whether v1 meets threshold value difference and repeated authentication number reaches the upper limit, if being, enters step
S405；Otherwise, S404 is entered step；
S404: mode one: input a1, v1 obtain a new electric current a2 using g2 model；A1 is inputted, a2 uses f2 mould
Type predicts a voltage；
Mode two: using f1 model, but using a1 as initial value, with certain learning rate such as 0.2, front and back electric current it is absolute
Difference is scanned for as variable quantity, enters step S403 after increasing the number of iterations；
S405: being that the difference with predicted voltage and target voltage is key, electric current and prediction by the arrangement of above-mentioned prediction result
Voltage is to the dictionary for value；
S406: list is unfolded in electric current and voltage, finds out longest Current Voltage using dynamic programming method and meets together
Step increases or reduced sequence；
S407: to sequence above, predicted voltage and the smallest key of target voltage difference are picked out, corresponding electric current is used
Recommend in first time.
Concrete principle is as described below:
According to the history charge and discharge data of lithium battery, the incidence relation between some important parameters is can establish and predicted,
As whether electric current, voltage have certain correlation under various circumstances.With model to correlation modeling, expected cut-off is given
Voltage predicts input of the suitable initial current as charge and discharge process.In view of in practical applications, the type and ring of battery
The variation of border factor is very big, and in order to promote generalization ability, the present invention devises two stage method: first stage, root
According to primary condition x, the recommendation of initial current A1 is provided using prediction model, the precision of most of situation first stage is just non-
Chang Gao is used directly for practical battery core test；If meeting the fluctuation range of blanking voltage, usually ± 0.05v, model fortune
Row is primary.For upper new battery types, inaccuracy may be recommended in the first stage, pass through actual charge and discharge electrical measurement at this time
After examination, true blanking voltage V1 can be obtained.It can be accurate to some characteristics for capturing new battery using first time test.Make
The prediction of the second primary current is carried out with model.
In the description of this specification, the description of reference term " one embodiment ", " example ", " specific example " etc. means
Particular features, structures, materials, or characteristics described in conjunction with this embodiment or example are contained at least one implementation of the invention
In example or example.In the present specification, schematic expression of the above terms may not refer to the same embodiment or example.
Moreover, particular features, structures, materials, or characteristics described can be in any one or more of the embodiments or examples to close
Suitable mode combines.
Present invention disclosed above preferred embodiment is only intended to help to illustrate the present invention.There is no detailed for preferred embodiment
All details are described, are not limited the invention to the specific embodiments described.Obviously, according to the content of this specification,
It can make many modifications and variations.These embodiments are chosen and specifically described to this specification, is in order to better explain the present invention
Principle and practical application, so that skilled artisan be enable to better understand and utilize the present invention.The present invention is only
It is limited by claims and its full scope and equivalent.
Claims (6)
1. a kind of two stages battery performance prediction technique based on charge and discharge, it is characterised in that: comprising the following specific steps
S1: according to battery charge and discharge process primary condition x, initial recommendation electric current A1 is provided by model, it is contemplated that blanking voltage
Whether meet threshold values, practical battery core test is directly used in if meeting, if being unsatisfactory for executing step S2；
S2: carrying out actual test, obtains recommending virtual voltage V1 of the electric current A1 after charge and discharge, provides recommendation electricity by model
Flow A2, it is contemplated that blanking voltage meet threshold values, for practical battery core test.
2. a kind of two stages battery performance prediction technique based on charge and discharge according to claim 1, it is characterised in that: institute
Stating primary condition x includes remaining capacity, temperature, initial internal resistance, initial voltage, initial weight, charge and discharge duration, battery capacity,
Charging current and blanking voltage parameter.
3. a kind of two stages battery performance prediction technique based on charge and discharge according to claim 1, it is characterised in that: institute
Stating the training method of model, specific step is as follows:
S100: data are extracted: from history charge and discharge data, choosing the data record for reaching complete charge and discharge duration；
S200: data supplement: for primary prolonged charge-discharge test, discharge process will record each second voltage change；
S300: above-mentioned data and battery information are associated, and clean some apparent exceptions, and data set is trained collection
With the division of test set；
S400: regression forecasting is carried out using regression algorithm, two models, two regression models of generation are respectively trained are as follows:
V1=f1 (x, A1) and A1=g1 (x, V1),
Wherein, f1 is from current forecasting voltage, and g1 is from voltage prediction electric current；
S500: data set makes the difference: all charge and discharge electrographic recording for the same battery battery core, carries out positive and negative two comparisons two-by-two,
Compare the difference DeltaA of two predetermined currents, the difference DeltaV of two blanking voltages, the percentage of electric current or voltage change
Than %, data division is re-started；
S600: two models, two regression models of generation are respectively trained are as follows:
V2=f2 (x, A1, V1, A2, DeltaA, %) and A2=g2 (x, V1, A1, V2, DeltaV, %),
Wherein, f2 is from last round of electric current, voltage and to work as front-wheel current forecasting voltage, and g2 is from last round of voltage, electric current and to work as
Front-wheel voltage prediction electric current.
4. a kind of two stages battery performance prediction technique based on charge and discharge according to claim 3, it is characterised in that: institute
It states in step S200, further includes that the electric discharge duration occurred in the step s 100 in discharge process is added to data set.
5. a kind of two stages battery performance prediction technique based on charge and discharge according to claim 3, it is characterised in that: institute
It states in step 400, regression algorithm RF, GBDT, LR or deep neural network regression algorithm are one such.
6. a kind of two stages battery performance prediction technique based on charge and discharge according to claim 3, it is characterised in that: In
The recommendation electric current A1 increases further screening and confirmation, and the specific method is as follows:
S401: when specified target cutoff voltage, initial current a1 is calculated using model g1；
S402: input a1 provides a predicted voltage v1 using f1 model；
S403: check whether v1 meets threshold value difference and repeated authentication number reaches the upper limit, if being, enters step S405；It is no
Then, S404 is entered step；
S404: mode one: input a1, v1 obtain a new electric current a2 using g2 model；A1 is inputted, a2 uses f2 model, in advance
Survey a voltage；
Mode two: f1 model is used, but using a1 as initial value, with certain learning rate such as 0.2, the absolute difference of front and back electric current
It is scanned for as variable quantity, enters step S403 after increasing the number of iterations；
S405: being that the difference with predicted voltage and target voltage is key, electric current and predicted voltage by the arrangement of above-mentioned prediction result
To the dictionary for value；
S406: list is unfolded in electric current and voltage, finds out longest Current Voltage using dynamic programming method and meets synchronous increase
Long or reduced sequence；
S407: to sequence above, picking out predicted voltage and the smallest key of target voltage difference, and corresponding electric current is for the
It is primary to recommend.
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