CN113759269B - Method and system for monitoring health state of battery of electric vehicle - Google Patents
Method and system for monitoring health state of battery of electric vehicle Download PDFInfo
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- CN113759269B CN113759269B CN202111323904.4A CN202111323904A CN113759269B CN 113759269 B CN113759269 B CN 113759269B CN 202111323904 A CN202111323904 A CN 202111323904A CN 113759269 B CN113759269 B CN 113759269B
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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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]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention provides a method and a system for monitoring the health state of an electric vehicle battery, which select a plurality of single electric vehicle batteries in the same batch in a brand new state, set charging and discharging cycle conditions, respectively carry out intermittent charging and discharging cycles, and extract the charging and discharging cycles at fixed intervals in the previous k times of charging and discharging cycles to extract the capacity increment curve, the discharging time, the charging voltage, the instantaneous capacity and the discharging current of each battery in the charging process which are correspondingly recorded; calculating corresponding arithmetic mean values of the parameters of the single batteries of the recorded corresponding times of charge-discharge cycles and the solved peak value neighborhood area; then, combining the stable discharge time of the current discharge cycle battery and the accumulated discharge time of each single electric vehicle battery, and constructing a model based on eight parameters and the predicted capacity of each single electric vehicle battery; and calculating the relation between the predicted capacity and the rated capacity of the single electric automobile battery to be tested at present.
Description
Technical Field
The invention relates to the technical field of new energy automobile equipment, in particular to a method and a system for monitoring the health state of an electric automobile battery.
Background
The new energy automobile is an automobile which adopts unconventional automobile fuel as a power source and integrates the automobile power control and driving technology. The new energy automobile mainly comprises a pure electric automobile, a fuel cell automobile or a hybrid electric automobile and the like. For a pure electric vehicle, a lithium ion battery is a core component of a power battery of the pure electric vehicle, and is a core device for determining a vehicle power system and improving mileage anxiety.
The aging problem is inevitably caused by long-term use of the battery, the aging inside the battery belongs to non-visual chemical change, and the health state of the battery is degraded to a certain state and then needs to be replaced, recombined, recycled in a gradient manner or disassembled for harmless recovery; however, the relationship between the parameters such as open-circuit voltage, discharge current, or internal resistance measured directly from the battery and the aging State Of the battery or the State Of Health (SOH) Of the battery is not intuitive, which makes it difficult to accurately estimate the State Of Health Of the battery under different working conditions, and makes the data Of the BMS Of the electric vehicle unstable and unreliable.
In summary, it is necessary to provide a method for estimating the predicted capacity of the battery of the electric vehicle, which is suitable for different operating conditions of the electric vehicle, and is convenient for determining the health status of the battery.
Disclosure of Invention
In view of the above, the invention provides a monitoring method and a monitoring system for calculating the conversion from the current specific working condition to the electric vehicle battery under the specific working condition according to the battery aging test and the battery prediction capacity model established in the factory.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides a method for monitoring the health status of a battery of an electric vehicle, comprising the following steps,
s1: selecting a plurality of single electric automobile batteries in a brand new state in the same batch, setting charging and discharging cycle conditions, respectively performing charging and discharging cycles, and setting a charging and discharging cycle number threshold until each single electric automobile battery reaches the charging and discharging cycle number threshold; periodically recording discharge time, charge voltage, instantaneous capacity and discharge current corresponding to charge-discharge circulation of each single electric automobile battery and a battery capacity increment curve taking the charge voltage as a horizontal axis and the instantaneous capacity as a vertical axis;
s2: in the previous k times of charge-discharge circulation processes, extracting the discharge time, charge voltage, instantaneous capacity, discharge current and battery capacity increment curves of each monomer electric vehicle battery in the charge process, which are correspondingly recorded every 20 times of charge-discharge circulation; wherein k is less than or equal to 500;
s3: the instantaneous capacity and the charging voltage corresponding to the first peak value of each battery capacity incremental curve of each single electric vehicle battery extracted at intervals in the step S2, the instantaneous capacity and the charging voltage corresponding to the second peak value, and the calculated first peak value neighborhood area formed by the surrounding of each first peak value neighborhood curve and the horizontal axis and the calculated second peak value neighborhood area formed by the surrounding of each second peak value neighborhood curve and the horizontal axis; respectively recording the arithmetic mean values of the corresponding measured values and calculated values of the single electric automobile batteries in the same batch in the same charging and discharging cycle times;
s4: taking the arithmetic mean value of each measured value and each calculated value obtained in the step S3, the stable discharge time of the corresponding discharge cycle battery and the accumulated discharge time of each single electric vehicle battery as the primary variables of the characteristic parameters;
s5: constructing a model based on eight original variables and the predicted capacity of the single electric vehicle battery;
s6: and calculating the relation between the predicted capacity and the rated capacity of the single electric vehicle battery to be tested at present according to the model in the step S5, and judging the health state of the single electric vehicle battery to be tested at present.
On the basis of the above technical solution, preferably, the set charging and discharging cycle condition is that an ampere-hour value corresponding to a rated capacity of a battery of the single electric vehicle is instructed to be 1C; keeping the ambient temperature at 25 ℃, charging by adopting 1/3C constant current in the charging process, and continuing to charge for 15-20 min by using the upper limit cut-off voltage when the upper limit cut-off voltage is reached; standing for 40min after charging, discharging when the temperature and the open-circuit voltage of the battery are constant, wherein the discharge current is 1/2C or 1C, and stopping discharging when the discharge voltage of the single electric vehicle battery reaches the lower limit cut-off voltage; repeating the charging and discharging process, wherein the threshold value of the number of charging and discharging cycles is 650 +/-50 times; and recording the capacity increment curve, the discharge time, the charge voltage, the instantaneous capacity and the discharge current of each battery in each charge-discharge cycle.
Preferably, the area of the first peak neighborhood region is defined as a first lower boundary on a vertical axis where-0.02V of the charging voltage corresponding to the first peak is located, a first upper boundary on a vertical axis where +0.03V of the charging voltage corresponding to the first peak is located, and an area surrounded by a horizontal axis where the charging voltage is located, the first lower boundary, the first upper boundary and the battery capacity increment curve is defined as the area of the first peak neighborhood region; the area of the second peak neighborhood region is a region area surrounded by a horizontal axis of the charging voltage, a second lower boundary, a second upper boundary and a battery capacity increment curve, wherein the vertical axis of the charging voltage-0.02V corresponding to the second peak is a second lower boundary, the vertical axis of the charging voltage +0.03V corresponding to the second peak is a second upper boundary, and the horizontal axis of the charging voltage, the second lower boundary, the second upper boundary and the battery capacity increment curve are taken as the area of the second peak neighborhood region.
Preferably, the building of the model based on the eight primary variables and the predicted capacity of the single electric vehicle battery is to firstly judge the correlation between any two primary variables, and the correlation judgment is to calculate any two independent variables by adopting the following formula:
(ii) a Wherein
Is a correlation coefficient;
is a mathematical expectation;
and
any two different original variables in the eight original variables are referred to; the value range of the correlation coefficient is [ -1, 1](ii) a A correlation coefficient of 0 indicates that the two original variables are uncorrelated; respectively obtaining the correlation of every two of the 8 original variables, reserving the original variables with the correlation, and sequencing the original variables according to the instantaneous capacity or the charge voltage correlation corresponding to the second peak value; if a certain original variable has no correlation with the other 7 original variables, rejecting the original variable; taking the predicted capacity of the single electric vehicle battery as a dependent variable, and constructing a BP neural network model: the node number of the input layer of the neural network is the number of terms of the original variables reserved after the correlation judgment of the original variables; the number of nodes of the output layer of the neural network is 1, and the number of nodes of the hidden layer is enabled to be M = (the number of nodes of the input layer of the neural network + the number of nodes of the output layer of the neural network)1/2+ constant a, constant a having a value in the range of 0, 10](ii) a Hiding layer with sigmoid function
As a function of the transfer function,
an input that is a node of an input layer of a neural network; purelin function is selected for neural network output layer
In order to be an output function of the output,
an output for each node of a hidden layer of the neural network; the output expression of the neural network hidden layer is
Wherein
Are nodes of the input layer of the neural network,
，
the number of nodes of the input layer is,
；
in order to hide the nodes of the layer,
；
as an input layer
Individual node and hidden layer
The weight of each node;
as an input layer
An input of each node;
as a hidden layer
A threshold for each node; the predicted capacity of the single electric vehicle battery output by the BP neural network is
Wherein
Indicating a hidden layer
The weight between each node and the output layer;
a threshold value that is an output layer node; taking 50% of the characteristic parameters extracted in the previous k charge-discharge cycles in the step S2 as training data, taking the other 50% as verification data, and revising the weight by adopting a steepest descent method until the error is less than 1% or the preset iteration number is reached to obtain the final weight
And
。
preferably, when the current discharge cycle battery stable discharge time or the accumulated discharge time of each battery cell of the electric vehicle is input as the node of the input layer of the neural network, the current ambient temperature or the accumulated discharge time needs to be converted:
order to
Wherein
The discharge time of the single electric automobile battery at the current discharge environment temperature,
is the discharge time converted into the discharge environment temperature of 25 ℃, and the current temperature
Value range of (0, 50)]C, centigrade degree;
as for the accumulated discharge time, it is,
(ii) a Wherein
Indicating the equivalent cumulative discharge time at the discharge current of 1C used in the current discharge process,
a discharge time representing an actual discharge current of not more than 1C during discharge;
represents the actual discharge time with a discharge current of not less than 1C during the discharge,
the current discharge current value is not less than 1C, and the two parts of the formula jointly form the accumulated equivalent discharge time in the discharge process and are accumulated in the accumulated discharge time of the current and subsequent charge-discharge cycles.
Preferably, a fitting verification link is further included between step S5 and step S6; the fitting verification step is that according to the parameters of the previous k times of charge-discharge cycle process extracted at intervals in the step S2, extracting the instantaneous capacity and the charge voltage corresponding to 50% of a second peak value, and fitting a change curve of the instantaneous capacity corresponding to the second peak value; fitting functions in MATLAB using polynomial curves
In the function, p is a charging voltage value corresponding to a second peak value of the battery capacity increment curve, q is an instantaneous capacity value corresponding to the second peak value of the battery capacity increment curve, and n is the order of the polynomial;as the number of charge-discharge cycles increases, p is monotonically increasing; n is less than or equal to 3; randomly selecting a group of instantaneous capacity and charging voltage data corresponding to a second peak value obtained by measurement from charging voltage data in the rest 50% of charging and discharging cycle process parameters which do not participate in the construction of the fitting curve, substituting the instantaneous capacity and the charging voltage data into the polynomial for trial calculation, solving the instantaneous capacity corresponding to the second peak value, according to the difference between the instantaneous capacity calculated by the polynomial and the actually measured instantaneous capacity, the weight of the first three original variables with the highest instantaneous capacity or charging voltage correlation corresponding to the second peak value is increased or decreased, all the value taking situations of the weight are traversed and all substituted into the neural network model, and randomly selecting verification data to calculate, reserving the weights of the first three original variables with the minimum predicted capacity error or the highest charging voltage correlation before weight adjustment, and updating the weights in the neural network model.
Further preferably, the adjusting or decreasing of the weight of the first three original variables with the highest correlation with the instantaneous capacity or the charging voltage corresponding to the second peak value is to simultaneously increase or decrease one or more of the three weights with the highest correlation, and the adjusting or decreasing amplitude is 0.2% -0.5% of the current weight.
Preferably, the calculating of the relationship between the predicted capacity and the rated capacity of the single electric vehicle battery to be tested at present is to charge and discharge the single electric vehicle battery to be tested at present, and obtain the instantaneous capacity and the charging voltage corresponding to the first peak value, the instantaneous capacity and the charging voltage corresponding to the second peak value, the area in the neighborhood of the first peak value, the area in the neighborhood of the second peak value, the stable discharging time of the current discharging cycle battery and the accumulated discharging time of the single electric vehicle battery to be tested; substituting into the BP neural network model constructed in the step S5 to estimate the predicted capacity
The rated capacity of the single electric vehicle battery is
(ii) a According to
And calculating, wherein when the calculation result is less than 80%, the single electric vehicle battery to be detected is not applicable to the electric vehicle, and if the calculation result is not less than 80%, the single electric vehicle battery to be detected meets the use requirement.
On the other hand, the invention also provides a system for monitoring the health state of the battery of the electric automobile, which comprises a data acquisition unit, a modeling unit and a judgment output unit;
the data acquisition unit is used for collecting the single electric automobile batteries to be tested and charging and discharging the single electric automobile batteries in the same factory batch to obtain the instantaneous capacity and charging voltage corresponding to the first peak value and the instantaneous capacity and charging voltage corresponding to the second peak value of the capacity increment curve of each single electric automobile battery; calculating a first peak value neighborhood area formed by encircling the first peak value neighborhood curves and the transverse axis, a second peak value neighborhood area formed by encircling the second peak value neighborhood curves and the transverse axis, the stable discharge time of the current discharge cycle battery and the accumulated discharge time of each single electric vehicle battery; the data acquisition unit sends the acquired data to the modeling unit;
the modeling unit is used for constructing a predicted capacity model of the single electric vehicle battery according to the obtained eight parameters of the single electric vehicle battery in the same new factory batch as primary variables and the electric vehicle battery health state monitoring method;
and the judgment output unit substitutes the original variable value of the single electric vehicle battery to be detected, which is acquired by the input data acquisition unit, into the predicted capacity model of the single electric vehicle battery, which is constructed by the modeling unit, and judges the health state of the single electric vehicle battery to be detected according to the predicted capacity.
Compared with the prior art, the method and the system for monitoring the health state of the battery of the electric automobile have the following beneficial effects:
(1) according to the method, a charge-discharge cycle aging test is carried out on single electric automobile batteries in the same batch and the same specification under a test condition, characteristic values of battery capacity increment curves of the single electric automobile batteries in previous k charge-discharge cycles are measured at equal intervals, peak neighborhood areas are calculated, a predicted capacity model of the single electric automobile batteries is constructed by integrating stable discharge time and accumulated discharge time under the test condition, and the health states of the single electric automobile batteries to be tested are estimated through the predicted capacity model;
(2) during the cyclic charge-discharge aging test, the discharge depth of the single electric automobile battery is basically similar to that of the battery under the actual working condition, such as 15%/20% -100%;
(3) when the predicted capacity model is modeled, priority evaluation and screening are carried out on 8 types of parameters, the rest parameters are used as original variables, the predicted capacity model is constructed, a fitting curve is constructed, the weights of the first three original variables with the highest instantaneous capacity or charging voltage correlation are adjusted according to the calculation result and the actual measurement result, and the predicted capacity model is finely adjusted;
(4) during actual measurement, the battery to be measured needs to be converted to the test condition under different working conditions and then calculated, so that the estimation accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a method and system for monitoring the health status of an electric vehicle battery according to the present invention;
fig. 2 is a system structure diagram of a method and a system for monitoring the state of health of a battery of an electric vehicle according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
On one hand, as shown in fig. 1, the invention provides a method for monitoring the state of health of a battery of an electric vehicle, which specifically comprises the following steps,
s1: selecting a plurality of single electric automobile batteries in a brand new state in the same batch, setting charging and discharging cycle conditions, respectively performing charging and discharging cycles, and setting a charging and discharging cycle number threshold until each single electric automobile battery reaches the charging and discharging cycle number threshold; periodically recording discharge time, charge voltage, instantaneous capacity and discharge current corresponding to charge-discharge circulation of each single electric automobile battery and a battery capacity increment curve taking the charge voltage as a horizontal axis and the instantaneous capacity as a vertical axis;
in this embodiment, setting the charge and discharge cycle conditions means: setting the ampere-hour value corresponding to the rated capacity of the battery of the single electric automobile as 1C; keeping the ambient temperature at 25 ℃, charging by adopting 1/3C constant current in the charging process, continuing to charge for 15-20 min by using the upper limit cut-off voltage when the upper limit cut-off voltage is reached, and recommending to execute according to the upper limit 20 min; standing for 40min after charging, discharging when the temperature and the open-circuit voltage of the battery are constant, wherein the discharge current is 1/2C or 1C, and stopping discharging when the discharge voltage of the single electric vehicle battery reaches the lower limit cut-off voltage; repeating the charging and discharging process, wherein the threshold value of the number of charging and discharging cycles is 650 +/-50 times; and recording the capacity increment curve, the discharge time, the charge voltage, the instantaneous capacity and the discharge current of each battery in each charge-discharge cycle. In the different types of lithium ion batteries, the upper cut-off voltage during charging differs from the lower cut-off voltage during discharging. Generally, the number of charge-discharge cycles of the lithium ion battery is not less than 800; however, after the number of charging and discharging cycles exceeds 500, the battery capacity can change obviously, so the threshold value of the number of the charging and discharging cycles selected in the scheme is 600-700, and the part with overlarge deviation is abandoned. The battery capacity increment curve is also called as an IC curve, in the charging process of the lithium ion battery, the battery capacity increment curve usually has two or three peaks, but after a plurality of charging and discharging cycles, the third peak disappears, the peak and the position of the first peak and the second peak shift rightwards relative to the initial position, and the peak relative to the initial value of the first peak and the second peak also decreases. And the subsequent step is to record the characteristic parameters of the battery capacity increment curve based on the characteristics.
S2: in the previous k times of charge-discharge circulation processes, extracting the discharge time, charge voltage, instantaneous capacity, discharge current and battery capacity increment curves of each monomer electric vehicle battery in the charge process, which are correspondingly recorded every 20 times of charge-discharge circulation; wherein k is less than or equal to 500.
S3: the instantaneous capacity and the charging voltage corresponding to the first peak value of each battery capacity incremental curve of each single electric vehicle battery extracted at intervals in the step S2, the instantaneous capacity and the charging voltage corresponding to the second peak value, and the calculated first peak value neighborhood area formed by the surrounding of each first peak value neighborhood curve and the horizontal axis and the calculated second peak value neighborhood area formed by the surrounding of each second peak value neighborhood curve and the horizontal axis; respectively recording the arithmetic mean values of the corresponding measured values and calculated values of the single electric automobile batteries in the same batch in the same charging and discharging cycle number, and respectively generating a plurality of characteristic parameter arrays for each single electric automobile battery;
the first peak neighborhood area mentioned here is a first peak neighborhood area defined by a vertical axis on which-0.02V of the charging voltage corresponding to the first peak is located, a vertical axis on which +0.03V of the charging voltage corresponding to the first peak is located, and a horizontal axis on which the charging voltage is located, the first lower boundary, the first upper boundary, and a battery capacity increment curve enclose; the area of the second peak neighborhood region is a region area surrounded by a horizontal axis of the charging voltage, a second lower boundary, a second upper boundary and a battery capacity increment curve, wherein the vertical axis of the charging voltage-0.02V corresponding to the second peak is a second lower boundary, the vertical axis of the charging voltage +0.03V corresponding to the second peak is a second upper boundary, and the horizontal axis of the charging voltage, the second lower boundary, the second upper boundary and the battery capacity increment curve are taken as the area of the second peak neighborhood region. As the positions of the first peak and the second peak are changed, the areas in the neighborhood of the corresponding first peak and the second peak are also changed, the trend of the area change is associated with the trend of the peak change, and the charging voltage corresponding to the abscissa of the battery capacity increment curve at the corresponding peak is also changed correspondingly.
S4: taking the arithmetic mean value of each measured value and each calculated value obtained in the step S3, the stable discharge time of the corresponding discharge cycle battery and the accumulated discharge time of each single electric vehicle battery as the primary variables of the characteristic parameters; for the convenience of subsequent calculation, the above parameters that may be inherently related in this step are respectively subjected to dimensionless normalization, and the dimensionless normalization may be performed by means of mean normalization or dispersion normalization, which belongs to a conventional technical means for data preprocessing in the field and is not described herein again. After the processing of step S4, the number of parameters in each group of feature parameter arrays reaches 8, and 8 parameters are used as 8 original variables.
S5: constructing a model based on eight original variables and the predicted capacity of the single electric vehicle battery;
s5-1: firstly, judging the correlation of any two original variables, wherein the correlation judgment is to calculate any two independent variables by adopting the following formula:
(ii) a Wherein
Is a correlation coefficient;
is a mathematical expectation;
and
any two different original variables in the eight original variables are referred to; the value range of the correlation coefficient is [ -1, 1](ii) a A correlation coefficient of 0 indicates that the two original variables are uncorrelated, and a closer 1 indicates a strong positive correlation, and a closer 1 is to the-1 tableShowing strong negative correlation; respectively obtaining the correlation of every two of the 8 original variables, reserving the original variables with the correlation, and sequencing the original variables according to the instantaneous capacity or the charge voltage correlation corresponding to the second peak value; if a certain original variable has no correlation with the other 7 original variables, rejecting the original variable;
s5-2: taking the predicted capacity of the single electric vehicle battery as a dependent variable, and constructing a BP neural network model: the node number of the input layer of the neural network is the number of terms of the original variables reserved after the correlation judgment of the original variables; the number of nodes of the output layer of the neural network is 1, and the number of nodes of the hidden layer is enabled to be M = (the number of nodes of the input layer of the neural network + the number of nodes of the output layer of the neural network)1/2+ constant a, constant a having a value in the range of 0, 10](ii) a Hiding layer with sigmoid function
As a function of the transfer function,
an input that is a node of an input layer of a neural network; purelin function is selected for neural network output layer
In order to be an output function of the output,
an output for each node of a hidden layer of the neural network; the output expression of the neural network hidden layer is
Wherein
Are nodes of the input layer of the neural network,
，
the number of nodes of the input layer is,
；
in order to hide the nodes of the layer,
；
as an input layer
Individual node and hidden layer
The weight of each node;
as an input layer
An input of each node;
as a hidden layer
A threshold for each node; the predicted capacity of the single electric vehicle battery output by the BP neural network is
Wherein
Indicating a hidden layer
The weight between each node and the output layer;
a threshold value that is an output layer node;
s5-3: taking 50% of the characteristic parameter arrays extracted at intervals in the previous k charge-discharge cycles in the step S3 as training data, taking the other 50% of the characteristic parameter arrays as verification data, and revising the weight by adopting a steepest descent method until the error is less than 1% or the preset iteration number is reached to obtain the final weight
And
。
it should be noted that, because the working environments of the single electric vehicle batteries to be tested are variable, when the stable discharge time of the original discharge cycle battery and the accumulated discharge time of each single electric vehicle battery are introduced in step S4, it is necessary to unify different discharge temperatures and discharge currents, so as to improve the measurement accuracy. When the current stable discharge time of the discharge cycle battery or the accumulated discharge time of each single electric vehicle battery is input as a node of an input layer of the neural network, the current environment temperature or the accumulated discharge time needs to be converted:
order to
Wherein
The discharge time of the single electric automobile battery at the current discharge environment temperature,
is the discharge time converted into the discharge environment temperature of 25 ℃, and the current temperature
Value range of (0, 50)]And C, comprehensively considering the discharge temperature of the lithium battery.
As for the accumulated discharge time, it is,
(ii) a Wherein
Indicating the equivalent cumulative discharge time at the discharge current of 1C used in the current discharge process,
a discharge time representing an actual discharge current of not more than 1C during discharge;
represents the actual discharge time with a discharge current of not less than 1C during the discharge,
the current discharge current value is not less than 1C, and the two parts of the formula jointly form the accumulated equivalent discharge time in the discharge process and are accumulated in the accumulated discharge time of the current and subsequent charge-discharge cycles. After conversion, dimensionless normalization is performed.
S6: calculating the relation between the predicted capacity and the rated capacity of the single electric vehicle battery to be tested at present according to the model in the step S5, and judging the health state of the single electric vehicle battery to be tested at present;
specifically, performing one-time charge and discharge cycle on the single electric vehicle battery to be tested according to the method in the step S1, and acquiring the instantaneous capacity and the charging voltage corresponding to the first peak value, the instantaneous capacity and the charging voltage corresponding to the second peak value, the first peak value neighborhood area, the second peak value neighborhood area, the stable discharge time of the current discharge cycle battery and the accumulated discharge time of the single electric vehicle battery to be tested; substituting into the BP neural network model constructed in the step S5 to estimate the predicted capacity
The rated capacity of the single electric vehicle battery is
(ii) a According to
And calculating, wherein when the calculation result is less than 80%, the battery of the single electric vehicle to be detected is not suitable for the requirement of the electric vehicle, and if the calculation result is not less than 80%, the battery of the single electric vehicle to be detected meets the use requirement, and the battery of the single electric vehicle to be detected needs to be singly replaced and combined with other similar batteries in a grading gradient manner for utilization or disassembly and the like.
In particular, as a preferred embodiment of the present invention, step S5 may tend to be a local optimal solution rather than a global optimal solution, and therefore, a fitting verification link is further introduced between step S5 and step S6. In the fitting verification step, according to the parameters of the previous k times of charge-discharge cycle process extracted at intervals in the step S2, extracting 50% of the instantaneous capacity and charging voltage values corresponding to the second peak value, and fitting a variation curve of the instantaneous capacity corresponding to the second peak value; fitting functions in MATLAB using polynomial curves
In the function, p is a charging voltage value corresponding to a second peak value of the battery capacity increment curve, q is an instantaneous capacity value corresponding to the second peak value of the battery capacity increment curve, and n is the order of the polynomial; as the number of charge-discharge cycles increases, p is monotonically increasing; n is less than or equal to 3; randomly selecting a group of instantaneous capacity and charging voltage data corresponding to a second peak value obtained by measurement from charging voltage data in the rest 50% of charging and discharging circulation process parameters which do not participate in the construction of a fitting curve, substituting the instantaneous capacity and the charging voltage data into the polynomial for trial calculation, solving the instantaneous capacity corresponding to the second peak value, increasing or decreasing the weight of the instantaneous capacity corresponding to the second peak value or the first three original variables with the highest charging voltage correlation according to the difference between the instantaneous capacity calculated by the polynomial and the actually measured instantaneous capacity, traversing all the values of the weight and substituting the weights into a neural network model, and randomly constructing the fitting curveAnd selecting verification data to calculate, reserving weights of the first three original variables with the minimum predicted capacity error or the highest charging voltage correlation before weight adjustment, and updating the weights in the neural network model.
The adjusting or reducing of the weight of the first three original variables with the highest correlation with the instantaneous capacity or the charging voltage corresponding to the second peak value is to adjust or reduce one or more of the three original variables with the highest correlation at the same time, and the adjusting or reducing amplitude is 0.2% -0.5% of the current weight. When it comes to adjusting the weights of the first three original variables with the highest correlation to the instantaneous capacity, the following truth-value combination situation may occur: [0, 0, +1], [0, +1, 0], [0, +1, +1], [ +1, 0, 0], [ +1, 0, +1], [ +1, +1, 0], [ +1, +1, +1], [0, 0, -1], [0, -1, 0], [0, -1, -1], [ -1, 0, 0], [ -1, 0, -1], [ -1, -1, 0], [ -1, -1, -1], [0, -1, +1], [0, +1, -1], [ +1, 0, -1], [ +1, -1, +1], [ -1, 0, +1], [ +1, -1], +1], [ -1, +1, -1] and [ -1, +1, 0 ]. In the above true value, 0 indicates that the original variable weight is unchanged, +1 indicates that the original variable weight is increased by 1 amplitude, and-1 indicates that the original variable weight is decreased by 1 amplitude; note that the above true values have different meanings from the correlation degrees. The above true value corresponds to all possible conditions of one to three original variables increasing or decreasing, all 24 conditions are substituted into the neural network model during the process of the process, and the solution is respectively carried out.
On the other hand, as shown in fig. 2, the invention also provides a system for monitoring the health status of the battery of the electric vehicle, which comprises a data acquisition unit, a modeling unit and a judgment output unit;
the data acquisition unit is used for collecting the single electric automobile batteries to be tested and charging and discharging the single electric automobile batteries in the same factory batch to obtain the instantaneous capacity and charging voltage corresponding to the first peak value and the instantaneous capacity and charging voltage corresponding to the second peak value of the capacity increment curve of each single electric automobile battery; calculating a first peak value neighborhood area formed by encircling the first peak value neighborhood curves and the transverse axis, a second peak value neighborhood area formed by encircling the second peak value neighborhood curves and the transverse axis, the stable discharge time of the current discharge cycle battery and the accumulated discharge time of each single electric vehicle battery; the data acquisition unit sends the acquired data to the modeling unit;
the modeling unit is used for constructing a predicted capacity model of the single electric vehicle battery according to the obtained eight parameters of the single electric vehicle battery in the same new factory batch as primary variables and the electric vehicle battery health state monitoring method;
and the judgment output unit substitutes the original variable value of the single electric vehicle battery to be detected, which is acquired by the input data acquisition unit, into the predicted capacity model of the single electric vehicle battery, which is constructed by the modeling unit, and judges the health state of the single electric vehicle battery to be detected according to the predicted capacity.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (2)
1. A method for monitoring the health state of a battery of an electric vehicle is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1: selecting a plurality of single electric automobile batteries in a brand new state in the same batch, setting charging and discharging cycle conditions, respectively performing charging and discharging cycles, and setting a charging and discharging cycle number threshold until each single electric automobile battery reaches the charging and discharging cycle number threshold; periodically recording discharge time, charge voltage, instantaneous capacity and discharge current corresponding to charge-discharge circulation of each single electric automobile battery and a battery capacity increment curve taking the charge voltage as a horizontal axis and the instantaneous capacity as a vertical axis;
s2: in the previous k times of charge-discharge circulation processes, extracting the discharge time, charge voltage, instantaneous capacity, discharge current and battery capacity increment curves of each monomer electric vehicle battery in the charge process, which are correspondingly recorded every 20 times of charge-discharge circulation; wherein k is less than or equal to 500;
s3: the instantaneous capacity and the charging voltage corresponding to the first peak value of each battery capacity incremental curve of each single electric vehicle battery extracted at intervals in the step S2, the instantaneous capacity and the charging voltage corresponding to the second peak value, and the calculated first peak value neighborhood area formed by the surrounding of each first peak value neighborhood curve and the horizontal axis and the calculated second peak value neighborhood area formed by the surrounding of each second peak value neighborhood curve and the horizontal axis; respectively recording the arithmetic mean values of the corresponding measured values and calculated values of the single electric automobile batteries in the same batch in the same charging and discharging cycle times;
s4: taking the arithmetic mean value of each measured value and each calculated value obtained in the step S3, the stable discharge time of the corresponding charge-discharge cycle battery and the accumulated discharge time of each single electric vehicle battery as the primary variables of the characteristic parameters;
s5: constructing a model based on eight original variables and the predicted capacity of the single electric vehicle battery;
s6: calculating the relation between the predicted capacity and the rated capacity of the single electric vehicle battery to be tested at present according to the model in the step S5, and judging the health state of the single electric vehicle battery to be tested at present;
the set charging and discharging cycle condition is that an ampere-hour value corresponding to the rated capacity of the single electric vehicle battery is instructed to be 1C; keeping the ambient temperature at 25 ℃, charging by adopting 1/3C constant current in the charging process, and continuing to charge for 15-20 min by using the upper limit cut-off voltage when the upper limit cut-off voltage is reached; standing for 40min after charging, discharging when the temperature and the open-circuit voltage of the battery are constant, wherein the discharge current is 1/2C or 1C, and stopping discharging when the discharge voltage of the single electric vehicle battery reaches the lower limit cut-off voltage; repeating the charging and discharging process, wherein the threshold value of the number of charging and discharging cycles is 650 +/-50 times; recording the capacity increment curve, the discharge time, the charge voltage, the instantaneous capacity and the discharge current of each battery in each charge-discharge cycle;
the first peak value neighborhood area is a first lower boundary which is a longitudinal axis where-0.02V of the charging voltage corresponding to the first peak value is located, a longitudinal axis where +0.03V of the charging voltage corresponding to the first peak value is located is a first upper boundary, and an area surrounded by a horizontal axis where the charging voltage is located, the first lower boundary, the first upper boundary and a battery capacity increment curve is used as the first peak value neighborhood area; the second peak value neighborhood area is a second lower boundary which is a longitudinal axis where-0.02V of the charging voltage corresponding to the second peak value is located, a second upper boundary which is a longitudinal axis where +0.03V of the charging voltage corresponding to the second peak value is located, and an area surrounded by a horizontal axis where the charging voltage is located, the second lower boundary, the second upper boundary and the battery capacity increment curve is used as the second peak value neighborhood area;
the construction of the model based on the eight original variables and the predicted capacity of the single electric vehicle battery is that the correlation of any two original variables is firstly judged, and the correlation judgment is that any two independent variables are calculated by adopting the following formula:
(ii) a Wherein
Is a correlation coefficient;
is a mathematical expectation;
and
any two different original variables in the eight original variables are referred to; the value range of the correlation coefficient is [ -1, 1](ii) a A correlation coefficient of 0 indicates that the two original variables are uncorrelated; respectively obtaining the correlation of every two of the 8 original variables, reserving the original variables with the correlation, and sequencing the original variables according to the instantaneous capacity or the charge voltage correlation corresponding to the second peak value; if a certain original variable has no correlation with the other 7 original variables, rejecting the original variable; make the single body poweredThe predicted capacity of the electric vehicle battery is a dependent variable, and a BP neural network model is constructed: the node number of the input layer of the neural network is the number of terms of the original variables reserved after the correlation judgment of the original variables; the number of nodes of the output layer of the neural network is 1, and the number of nodes of the hidden layer is enabled to be M = (the number of nodes of the input layer of the neural network + the number of nodes of the output layer of the neural network)1/2+ constant a, constant a having a value in the range of 0, 10](ii) a Hiding layer with sigmoid function
As a function of the transfer function,
an input that is a node of an input layer of a neural network; purelin function is selected for neural network output layer
In order to be an output function of the output,
an output for each node of a hidden layer of the neural network; the output expression of the neural network hidden layer is
Wherein
Are nodes of the input layer of the neural network,
，
the number of nodes of the input layer is,
；
in order to hide the nodes of the layer,
；
as an input layer
Individual node and hidden layer
The weight of each node;
as an input layer
An input of each node;
as a hidden layer
A threshold for each node; the predicted capacity of the single electric vehicle battery output by the BP neural network is
Wherein
Indicating a hidden layer
The weight between each node and the output layer;
a threshold value that is an output layer node; taking 50% of the characteristic parameters extracted in the previous k charge-discharge cycles in the step S2 as training data, taking the other 50% as verification data, and revising the weight by adopting a steepest descent method until the error is less than 1% or the preset iteration number is reached to obtain the final weight
And
；
when the current stable discharge time of the discharge cycle battery or the accumulated discharge time of each single electric vehicle battery is input as a node of an input layer of the neural network, the current environment temperature or the accumulated discharge time needs to be converted:
order to
Wherein
The discharge time of the single electric automobile battery at the current discharge environment temperature,
is the discharge time converted into the discharge environment temperature of 25 ℃, and the current temperature
Value range of (0, 50)]C, centigrade degree;
as for the accumulated discharge time, it is,
(ii) a Wherein
Indicating the equivalent cumulative discharge time at the discharge current of 1C used in the current discharge process,
a discharge time representing an actual discharge current of not more than 1C during discharge;
represents the actual discharge time with a discharge current of not less than 1C during the discharge,
the current discharge current value is not less than 1C, the two parts of the formula jointly form the accumulated equivalent discharge time in the discharge process and are accumulated in the accumulated discharge time of the current and subsequent charge-discharge cycles;
a fitting verification link is further included between the step S5 and the step S6; the fitting verification step is that according to the parameters of the previous k times of charge-discharge cycle process extracted at intervals in the step S2, extracting the instantaneous capacity and the charge voltage corresponding to 50% of a second peak value, and fitting a change curve of the instantaneous capacity corresponding to the second peak value; fitting functions in MATLAB using polynomial curves
In the function, p is a charging voltage value corresponding to a second peak value of the battery capacity increment curve, q is an instantaneous capacity value corresponding to the second peak value of the battery capacity increment curve, and n is the order of the polynomial; as the number of charge-discharge cycles increases, p is monotonically increasing; n is less than or equal to 3; randomly selecting a group of instantaneous capacity and charging voltage data corresponding to a second peak value obtained by measurement from charging voltage data in the rest 50% of charging and discharging cycle process parameters which do not participate in the construction of a fitting curve, substituting the instantaneous capacity and the charging voltage data into the polynomial for trial calculation, solving the instantaneous capacity corresponding to the second peak value, and adjusting or increasing the instantaneous capacity and the actually measured instantaneous capacity according to the difference between the instantaneous capacity calculated by the polynomial and the actually measured instantaneous capacityAdjusting and reducing the weights of the first three original variables with the highest correlation with the instantaneous capacity or the charging voltage corresponding to the second peak value, traversing all the value taking situations of the weights and substituting the traversed values into the neural network model, randomly selecting verification data for calculation, reserving the weights of the first three original variables with the smallest error with the predicted capacity before weight adjustment or the highest correlation with the charging voltage, and updating the weights in the neural network model;
the adjustment or reduction of the weight of the first three original variables with the highest correlation with the instantaneous capacity or the charging voltage corresponding to the second peak value is realized by simultaneously adjusting one or more of the three original variables with the highest correlation with the current weight by 0.2% -0.5%; when the weight of the first three original variables with the highest correlation with the instantaneous capacity is adjusted, the following truth combination situation occurs: [0, 0, +1], [0, +1, 0], [0, +1, +1], [ +1, 0, 0], [ +1, 0, +1], [ +1, +1, 0], [ +1, +1, +1], [0, 0, -1], [0, -1, 0], [0, -1, -1], [ -1, 0, 0], [ -1, 0, -1], [ -1, -1, 0], [ -1, -1, -1], [0, -1, +1], [0, +1, -1], [ +1, 0, -1], [ +1, -1, +1], [ -1, 0, +1], [ +1, -1], +1], [ -1, +1, -1] and [ -1, +1, 0 ]; in the above true value, 0 indicates that the original variable weight is unchanged, +1 indicates that the original variable weight is increased by 1 amplitude, and-1 indicates that the original variable weight is decreased by 1 amplitude; the above truth value has a different meaning from the correlation; all the situations are completely substituted into the neural network model during the process, the solution is respectively carried out, and the weight values of the corresponding original variables are different when the charge and discharge cycle times are different;
the method comprises the steps of charging and discharging the single electric vehicle battery to be tested to obtain the instantaneous capacity and charging voltage corresponding to the first peak value, the instantaneous capacity and charging voltage corresponding to the second peak value, the neighborhood area of the first peak value, the neighborhood area of the second peak value, the stable discharging time of the current discharging circulating battery and the single electric vehicle to be testedThe accumulated discharge time of the electric vehicle battery; substituting into the BP neural network model constructed in the step S5 to estimate the predicted capacity
The rated capacity of the single electric vehicle battery is
(ii) a According to
And calculating, wherein when the calculation result is less than 80%, the single electric vehicle battery to be detected is not applicable to the electric vehicle, and if the calculation result is not less than 80%, the single electric vehicle battery to be detected meets the use requirement.
2. The utility model provides an electric automobile battery health status monitoring system which characterized in that: the device comprises a data acquisition unit, a modeling unit and a judgment output unit;
the data acquisition unit is used for collecting the single electric automobile batteries to be tested and charging and discharging the single electric automobile batteries in the same factory batch to obtain the instantaneous capacity and charging voltage corresponding to the first peak value and the instantaneous capacity and charging voltage corresponding to the second peak value of the capacity increment curve of each single electric automobile battery; calculating a first peak value neighborhood area formed by encircling the first peak value neighborhood curves and the transverse axis, a second peak value neighborhood area formed by encircling the second peak value neighborhood curves and the transverse axis, the stable discharge time of the current discharge cycle battery and the accumulated discharge time of each single electric vehicle battery;
the modeling unit is used for constructing a predicted capacity model of the single electric vehicle battery according to the eight primary variables of the single electric vehicle battery in the same new-factory batch and the health state monitoring method of the electric vehicle battery according to claim 1;
and the judgment output unit substitutes the original variable value of the single electric vehicle battery to be detected, which is acquired by the input data acquisition unit, into a predicted capacity model of the single electric vehicle battery, and judges the health state of the single electric vehicle battery to be detected according to the predicted capacity.
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