CN111983464A - Lithium battery lithium separation testing method based on pure electric vehicle - Google Patents

Lithium battery lithium separation testing method based on pure electric vehicle Download PDF

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CN111983464A
CN111983464A CN202010707369.1A CN202010707369A CN111983464A CN 111983464 A CN111983464 A CN 111983464A CN 202010707369 A CN202010707369 A CN 202010707369A CN 111983464 A CN111983464 A CN 111983464A
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lithium
time point
sampling period
charging current
lithium battery
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CN111983464B (en
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甘林灵
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Wuhan Institute Of Numerical Simulation Technology Co ltd
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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
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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

Abstract

The invention discloses a lithium battery lithium separation testing method based on a pure electric vehicle, which comprises the following steps: s1, collecting the temperature of the test environment, charging the battery with constant current to cut-off voltage, and then charging the battery with constant voltage by using the cut-off voltage; s2, setting a sampling period, collecting charging current data and lithium battery temperature data in the constant-voltage charging process, and then determining lithium analysis test parameters of a collection time point in the sampling period; wherein the lithium analysis test parameters comprise: the charging current based on the normalization adjustment, the charging current change rate based on the weight adjustment and the lithium battery temperature change rate; and S3, judging whether the lithium battery analyzes lithium according to the lithium analysis test parameters according to the sampling period. According to the invention, the lithium analysis test parameters of the collection time points in the sampling period are determined, and whether the lithium battery analyzes lithium is judged according to the lithium analysis test parameters, so that the problem that whether the lithium analysis judgment of the existing lithium battery is not comprehensive and accurate is effectively solved.

Description

Lithium battery lithium separation testing method based on pure electric vehicle
Technical Field
The invention relates to the technical field of lithium ion battery lithium analysis testing methods, in particular to a lithium ion battery lithium analysis testing method based on a pure electric vehicle.
Background
When the lithium ion battery is charged, lithium ions are extracted from the positive electrode and are inserted into the negative electrode; but when some abnormal conditions: when abnormality occurs such as the space for lithium insertion into the negative electrode is insufficient, the resistance for lithium ion insertion into the negative electrode is too large, and lithium ions are too rapidly extracted from the positive electrode but cannot be equally inserted into the negative electrode, the lithium ions which cannot be inserted into the negative electrode can only obtain electrons on the surface of the negative electrode, so that a silvery white metallic lithium simple substance, which is also called "lithium precipitation", is formed.
At present, the reason for lithium ion battery lithium separation mainly comprises: lithium precipitation caused by insufficient negative electrode allowance; lithium is separated out by a charger; lithium deposition caused by lithium intercalation path abnormality; lithium precipitation caused by main material abnormality; fixed position lithium precipitation caused by special reasons; the patent with the patent number of 201910169096.7, the application publication date of 2019.06.25 and the name of 'a detection method for lithium ion battery lithium separation' provides a detection method for lithium ion battery lithium separation, and the patent directly judges whether the lithium ion battery to be detected seriously separates lithium through a discharge curve, thereby quickly judging whether the battery is abnormal or not, the battery does not need to be disassembled, resources and cost are saved, steps are simple and the detection efficiency is high; however, this patent does not consider more influencing factors, and therefore has a problem that the determination is not comprehensive and accurate enough.
Disclosure of Invention
In view of the above, the invention aims to provide a lithium analysis test method for a lithium battery based on a pure electric vehicle, which effectively solves the problem that whether the existing lithium battery is subjected to lithium analysis judgment is not comprehensive and accurate by determining lithium analysis test parameters of acquisition time points in a sampling period and judging whether the lithium battery is subjected to lithium analysis according to the lithium analysis test parameters.
The invention adopts the technical scheme that a lithium battery lithium analysis testing method based on a pure electric vehicle comprises the following steps:
s1, collecting the temperature of the test environment, charging the battery with constant current to cut-off voltage, and then charging the battery with constant voltage by using the cut-off voltage;
s2, setting a sampling period, collecting charging current data and lithium battery temperature data in the constant-voltage charging process, and then determining lithium analysis test parameters of a collection time point in the sampling period;
wherein the lithium analysis test parameters comprise: the charging current based on the normalization adjustment, the charging current change rate based on the weight adjustment and the lithium battery temperature change rate;
and S3, judging whether the lithium battery analyzes lithium according to the lithium analysis test parameters according to the sampling period set in the S2.
Preferably, in S2, the charging current based on the normalization adjustment is obtained by normalizing the charging current data during the constant voltage charging, and the charging current based on the normalization adjustment is:
Figure BDA0002592875890000021
wherein, Ii' normalization-adjustment-based charging current for the ith test time point in the sampling period, IiIs the charging current at the ith test time point in the sampling period, Ii_minIs the minimum test charging current, I, at the ith test time point in the sampling periodi_maxThe maximum test charging current at the ith test time point in the sampling period.
Preferably, in S2, the charging current based on the normalization adjustment is normalized by the charging current data in the constant voltage charging process, so as to obtain a charging current based on the normalization adjustment;
Figure BDA0002592875890000031
wherein, f (T)I) As a function of the ambient temperature influence, f (T)I)＝8×10-5(TI)2-0.0023TI+0.11；
Wherein, Ii' normalization-adjustment-based charging Current, T, for the ith test time Point within a sampling periodIIs ambient temperature, TLisFor the desired lithium battery temperature, T, in the sampling periodLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii_minThe minimum test temperature T of the lithium battery at the ith test time point in the sampling periodLii_maxIs the maximum test temperature, I, of the lithium battery at the ith test time point in the sampling periodiIs the charging current at the ith test time point in the sampling period, Ii_minIs the first in the sampling periodMinimum test charging current, I, for I test time pointsi_maxτ is the normalized empirical coefficient for the maximum test charge current at the ith test time point in the sampling period.
Preferably, tau is in the range of 0.89-0.95.
Preferably, τ is 0.93.
Preferably, in S2, the weight-adjustment-based charging current change rate is obtained by weight adjustment of charging current data during constant-voltage charging with respect to a charging current change rate, and the weight-adjustment-based charging current change rate is:
Figure BDA0002592875890000032
wherein the content of the first and second substances,
Figure BDA0002592875890000033
wherein, WIiEndowing weight with experience of charging current change rate of ith test time point in sampling period, and satisfying conditions
Figure BDA0002592875890000034
EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' is the charging current change rate based on weight adjustment at the ith test time point in the sampling period, n is the sampling number in the sampling period, delta t is the time interval between the ith test time point and the (I-1) th test time point, IiIs the charging current at the ith test time point in the sampling period, Ii-1Is the charging current at the i-1 test time point in the sampling period.
Preferably, let N be 3N, in which case WIiThe conditions are satisfied as follows:
WI1＝WI2＝…＝WIN＝1.3；
WIN+1＝WIN+2＝…＝WI2N＝1；
WI2N+1＝WI2N+2＝…＝WI3N＝0.7。
preferably, in S3, the determining whether the lithium battery is analyzed according to the lithium analysis test parameter includes:
and judging whether the lithium battery analyzes lithium or not based on a BP neural network model according to the lithium analysis test parameters.
Preferably, the determining whether the lithium battery is analyzed according to the lithium analysis test parameter specifically includes the following steps:
step 1, determining an input layer vector x ═ x of a three-layer BP neural network according to a sampling period1,x2,…xi,…,xn}; wherein x isiIs the lithium analysis test parameter value of the ith test time point in the sampling period, which comprises the following steps: x is the number ofi＝(Ii′,Ei,Ei′,ETLii)，Ii' normalization-adjustment-based charging current for the ith test time point within a sampling period, EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' weight-adjustment-based charging current change rate for the ith test time point within a sampling period, ETLiiThe temperature change rate of the lithium battery at the ith test time point in the sampling period is obtained;
said ETLiiObtained by the following formula:
Figure BDA0002592875890000041
wherein, TLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii-1The temperature of the lithium battery at the ith-1 test time point in the sampling period is shown, and delta t is the time interval between the ith test time point and the ith-1 test time point;
step 2, mapping the input layer vector to a middle layer, wherein the middle layer vector y is { y ═ y1,y2,…,ym}; m is the number of intermediate layer nodes;
and 3, obtaining an output layer vector o ═ o1,o2,…oi,…,on}; wherein o isiFor the lithium battery lithium analysis state parameter value of the ith test time point,
Figure BDA0002592875890000051
when o isiWhen the test time point is 0, the lithium battery does not separate lithium at the ith test time point in the sampling period, and when the test time point is oiWhen the sampling period is 1, the lithium battery begins to extract lithium at the ith test time point in the sampling period.
Preferably, the number m of the intermediate layer nodes satisfies:
Figure BDA0002592875890000052
the invention has the beneficial effects that:
the invention collects the charging current data and the lithium battery temperature data in the constant voltage charging process, determines the lithium analysis test parameters of the collection time point in the sampling period, judges whether the lithium battery analyzes the lithium according to the lithium analysis test parameters, namely obtains the lithium analysis test parameters of the collection time point in the sampling period according to the lithium analysis test parameters and establishes a lithium analysis test model of the lithium battery for the pure electric vehicle based on a BP neural network model, tests and prejudges the lithium analysis condition of the lithium battery for the pure electric vehicle through the BP neural network model, so that a tester can rapidly judge whether the lithium analysis condition exists in the battery charging and the time point of the lithium analysis starting under the condition of not disassembling the battery, effectively solves the problem that the lithium analysis judgment of the existing lithium battery is not comprehensive and accurate, and can protect the lithium battery for the electric vehicle for more reasonable use, the service life is prolonged.
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Fig. 1 is a flow chart of a lithium analysis test method based on a lithium battery for a pure electric vehicle provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A lithium analysis test method based on a lithium battery for a pure electric vehicle is shown in figure 1 and comprises the following steps:
s1, determining and collecting the test environment temperature, carrying out constant current charging on the battery to cut-off voltage, and then carrying out constant voltage charging by using the cut-off voltage;
s2, determining and setting a sampling period, acquiring charging current data and lithium battery temperature data in the constant-voltage charging process, and then determining lithium analysis test parameters of an acquisition time point in the sampling period;
wherein the lithium analysis test parameters comprise: the charging current based on the normalization adjustment, the charging current change rate based on the weight adjustment and the lithium battery temperature change rate;
and S3, judging whether the lithium battery analyzes lithium according to the lithium analysis test parameters according to the sampling period set in the S2.
In a specific implementation, in S2, the charging current based on the normalization adjustment is obtained by performing the normalization adjustment on the charging current data during the constant voltage charging, and the charging current based on the normalization adjustment is:
Figure BDA0002592875890000061
wherein, Ii' normalization-adjustment-based charging current for the ith test time point in the sampling period, IiIs the charging current at the ith test time point in the sampling period, Ii_minIs the minimum test charging current, I, at the ith test time point in the sampling periodi_maxThe maximum test charging current at the ith test time point in the sampling period.
In a specific implementation, the charging current based on the normalization adjustment may be obtained by normalizing the charging current based on the normalization adjustment according to the charging current data during the constant voltage charging in S2 to obtain the charging current based on the normalization adjustment;
Figure BDA0002592875890000071
wherein, f (T)I) As a function of the ambient temperature influence, f (T)I)＝8×10-5(TI)2-0.0023TI+0.11；
Wherein, Ii' normalization-adjustment-based charging Current, T, for the ith test time Point within a sampling periodIIs ambient temperature, TLisFor the desired lithium battery temperature, T, in the sampling periodLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii_minThe minimum test temperature T of the lithium battery at the ith test time point in the sampling periodLii_maxIs the maximum test temperature, I, of the lithium battery at the ith test time point in the sampling periodiIs the charging current at the ith test time point in the sampling period, Ii_minIs the minimum test charging current, I, at the ith test time point in the sampling periodi_maxThe maximum test charging current at the ith test time point in the sampling period is tau, the tau is a normalized empirical coefficient, and the value range of the tau is 0.89-0.95; a preferred value of τ is 0.93.
In a specific implementation, in S2, the weight-adjustment-based charging current change rate is obtained by weight adjustment of a charging current change rate according to charging current data during constant-voltage charging, where the weight-adjustment-based charging current change rate is:
Figure BDA0002592875890000072
wherein the content of the first and second substances,
Figure BDA0002592875890000073
wherein, WIiEndowing weight with experience of charging current change rate of ith test time point in sampling period, and satisfying conditions
Figure BDA0002592875890000074
EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' is the charging current change rate based on weight adjustment at the ith test time point in the sampling period, n is the sampling number in the sampling period, delta t is the time interval between the ith test time point and the (I-1) th test time point, IiIs the charging current at the ith test time point in the sampling period, Ii-1Is the charging current at the i-1 test time point in the sampling period.
In specific implementation, the temperature change rate of the lithium battery may be:
Figure BDA0002592875890000081
wherein E isTLiiIs the temperature change rate, T, of the lithium battery at the ith test time point in the sampling periodLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii-1The temperature of the lithium battery at the i-1 test time point in the sampling period is shown, and delta t is the time interval between the i test time point and the i-1 test time point.
Preferably, N is 3N, and in this case, W isIiThe conditions are satisfied as follows:
WI1＝WI2＝…＝WIN＝1.3；
WIN+1＝WIN+2＝…＝WI2N＝1；
WI2N+1＝WI2N+2＝…＝WI3N＝0.7。
in a specific implementation, in S3, determining whether to analyze lithium in the lithium battery according to the lithium analysis test parameter includes:
and judging whether the lithium battery analyzes lithium or not based on a BP neural network model according to the lithium analysis test parameters.
Specifically, the judging whether the lithium battery is subjected to lithium analysis according to the lithium analysis test parameters specifically comprises the following steps:
step 1, establishing a BP neural network model:
the BP network system structure adopted by the invention is composed of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are provided by a data preprocessing module. The second layer is an intermediate layer, and the intermediate layer comprises m nodes and is determined in a self-adaptive mode through a training process of the network. The third layer is an output layer, which comprises n nodes and is determined by the response actually required to be output by the system.
The mathematical model of the network is:
inputting a layer vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a layer vector: o ═ O1,o2,...,on)T
Determining an input layer vector x ═ { x ] of the three-layer BP neural network according to a sampling period1,x2,…xi,…,xn}; wherein x isiIs the lithium analysis test parameter value of the ith test time point in the sampling period, which comprises the following steps: x is the number ofi＝(Ii′,Ei,Ei′,ETLii)，Ii' normalization-adjustment-based charging current for the ith test time point within a sampling period, EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' weight-adjustment-based charging current change rate for the ith test time point within a sampling period, ETLiiThe temperature change rate of the lithium battery at the ith test time point in the sampling period is obtained;
said ETLiiObtained by the following formula:
Figure BDA0002592875890000091
wherein, TLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii-1The temperature of the lithium battery at the ith-1 test time point in the sampling period is shown, and delta t is the time interval between the ith test time point and the ith-1 test time point;
step 2, mapping the input layer vector to a middle layer, wherein the middle layer vector y is { y ═ y1,y2,…,ym}; m is the number of intermediate layer nodes;
preferably, the number m of the intermediate layer nodes satisfies:
Figure BDA0002592875890000092
and 3, obtaining an output layer vector o ═ o1,o2,…oi,…,on}; wherein o isiFor the lithium battery lithium analysis state parameter value of the ith test time point,
Figure BDA0002592875890000093
when o isiWhen the test time point is 0, the lithium battery does not separate lithium at the ith test time point in the sampling period, and when the test time point is oiWhen the sampling period is 1, the lithium battery begins to extract lithium at the ith test time point in the sampling period.
The embodiment further includes training the BP neural network, that is, training the BP neural network, and the specific process is as follows:
after the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; in this embodiment, the charging time is 1800s, n is 30, and Δ t is 60s, and the output samples during the training of each subnet are shown in table 1.
TABLE 1 output samples during training of subnets
Figure BDA0002592875890000101
Figure BDA0002592875890000111
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
Figure BDA0002592875890000121
In the formula (I), the compound is shown in the specification,
Figure BDA0002592875890000122
for the weighted sum of the j unit information of the l layer at the nth calculation,
Figure BDA0002592875890000123
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
Figure BDA0002592875890000124
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Figure BDA0002592875890000125
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
Figure BDA0002592875890000126
And is
Figure BDA0002592875890000127
If neuron j belongs to the first hidden layer (l ═ 1), then there are
Figure BDA00025928758900001211
If neuron j belongs to the output layer (L ═ L), then there are
Figure BDA00025928758900001212
And ej(n)＝xj(n)-oj(n)；
(b) And (3) calculating the error reversely:
for output unit
Figure BDA0002592875890000129
Pair hidden unit
Figure BDA00025928758900001210
(c) Correcting the weight value:
Figure BDA0002592875890000131
η is the learning rate.
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of the differential of the error to the weight, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
The lithium analysis test is carried out on the nickel-manganese-cobalt/graphite ternary lithium battery with the rated capacity of 2Ah under different test conditions, the lithium battery which is detected is discharged with the discharge rate of 1C until the lithium battery is emptied, then the lithium battery is disassembled, whether the lithium battery has the lithium analysis phenomenon is determined, and therefore the detection result of the embodiment is determined to be consistent with the fact, and the test result is shown in table 2.
TABLE 2 lithium cell test results
Figure BDA0002592875890000132
Figure BDA0002592875890000141
The invention discloses a lithium analysis test method for a lithium battery for a pure electric vehicle, which is used for testing the lithium battery for the electric vehicle to be tested and evaluated, and judging whether lithium analysis exists in the battery charging process or not according to lithium analysis test parameters by collecting lithium analysis test parameters including charging current based on normalized adjustment, charging current change rate based on weight adjustment and lithium battery temperature change rate, so that a tester can quickly judge whether the lithium analysis exists in the battery charging process or not under the condition that the battery is not required to be disassembled, the lithium battery for the electric vehicle can be protected, the lithium battery can be used more reasonably, and the service life is prolonged.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and it can be fully applied to various fields suitable for the present invention; also, any changes or substitutions that may be easily made by one skilled in the art within the technical scope of the present disclosure should be covered by the protection scope of the present disclosure. The scope of the invention should, therefore, be determined with reference to the appended claims, which are not to be limited to the specific details and specific examples, but rather are to be accorded the full scope of the generic concept defined herein.

Claims (10)

1. A lithium battery lithium separation test method based on a pure electric vehicle is characterized by comprising the following steps:
s1, collecting the temperature of the test environment, charging the battery with constant current to cut-off voltage, and then charging the battery with constant voltage by using the cut-off voltage;
s2, setting a sampling period, collecting charging current data and lithium battery temperature data in the constant-voltage charging process, and then determining lithium analysis test parameters of a collection time point in the sampling period;
wherein the lithium analysis test parameters comprise: the charging current based on the normalization adjustment, the charging current change rate based on the weight adjustment and the lithium battery temperature change rate;
and S3, judging whether the lithium battery analyzes lithium according to the lithium analysis test parameters according to the sampling period set in the S2.
2. The lithium battery lithium analysis test method based on the pure electric vehicle as claimed in claim 1, wherein in step S2, the charging current based on the normalized adjustment is obtained by performing the normalized adjustment on the charging current data in the constant voltage charging process, and the charging current based on the normalized adjustment is:
Figure FDA0002592875880000011
wherein, Ii' normalization-adjustment-based charging current for the ith test time point in the sampling period, IiIs the charging current at the ith test time point in the sampling period, Ii_minIs the minimum test charging current, I, at the ith test time point in the sampling periodi_maxThe maximum test charging current at the ith test time point in the sampling period.
3. The lithium battery lithium analysis test method based on the pure electric vehicle as claimed in claim 1, wherein in S2, the charging current based on the normalized adjustment is normalized according to the charging current data during the constant voltage charging process, so as to obtain the charging current based on the normalized adjustment;
Figure FDA0002592875880000021
wherein, f (T)I) As a function of the ambient temperature influence, f (T)I)＝8×10-5(TI)2-0.0023TI+0.11；
Wherein, Ii' normalization-adjustment-based charging Current, T, for the ith test time Point within a sampling periodIIs ambient temperature, TLisFor the desired lithium battery temperature, T, in the sampling periodLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii_minThe minimum test temperature T of the lithium battery at the ith test time point in the sampling periodLii_maxIs the maximum test temperature, I, of the lithium battery at the ith test time point in the sampling periodiIs the charging current at the ith test time point in the sampling period, Ii_minIs the minimum test charging current, I, at the ith test time point in the sampling periodi_maxτ is the normalized empirical coefficient for the maximum test charge current at the ith test time point in the sampling period.
4. The lithium battery lithium separation test method based on the pure electric vehicle as claimed in claim 3, wherein τ is in a range of 0.89-0.95.
5. The lithium separation test method based on the lithium battery for the pure electric vehicle as claimed in claim 4, wherein τ is 0.93.
6. The lithium battery lithium analysis test method based on the pure electric vehicle as claimed in claim 1, wherein in S2, the weight-adjustment-based charging current change rate is obtained by weight adjustment of charging current data in a constant voltage charging process to a charging current change rate, and the weight-adjustment-based charging current change rate is:
Figure FDA0002592875880000031
wherein the content of the first and second substances,
Figure FDA0002592875880000032
wherein, WIiEndowing weight with experience of charging current change rate of ith test time point in sampling period, and satisfying conditions
Figure FDA0002592875880000033
EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' is the charging current change rate based on weight adjustment at the ith test time point in the sampling period, n is the sampling number in the sampling period, delta t is the time interval between the ith test time point and the (I-1) th test time point, IiIs the charging current at the ith test time point in the sampling period, Ii-1Is the charging current at the i-1 test time point in the sampling period.
7. The lithium separation test method based on the lithium battery for the pure electric vehicle as claimed in claim 6, wherein N is 3N, and W is the same as WIiThe conditions are satisfied as follows:
WI1＝WI2＝…＝WIN＝1.3；
WIN+1＝WIN+2＝…＝WI2N＝1；
WI2N+1＝WI2N+2＝…＝WI3N＝0.7。
8. the lithium battery lithium analysis testing method based on the pure electric vehicle as claimed in claim 1, wherein in S3, a determination is made as to whether the lithium battery analyzes lithium according to the lithium analysis testing parameters, specifically:
and judging whether the lithium battery analyzes lithium or not based on a BP neural network model according to the lithium analysis test parameters.
9. The lithium battery lithium analysis testing method based on the pure electric vehicle as claimed in claim 8, wherein the step of determining whether the lithium battery analyzes lithium according to the lithium analysis testing parameters specifically comprises the following steps:
step 1, determining an input layer vector x ═ x of a three-layer BP neural network according to a sampling period1,x2,…xi,…,xn}; wherein x isiIs the lithium analysis test parameter value of the ith test time point in the sampling period, which comprises the following steps: x is the number ofi＝(Ii′,Ei,Ei′,ETLii)，Ii' normalization-adjustment-based charging current for the ith test time point within a sampling period, EiIs the rate of change of charging current at the ith test time point within the sampling period, Ei' weight-adjustment-based charging current change rate for the ith test time point within a sampling period, ETLiiThe temperature change rate of the lithium battery at the ith test time point in the sampling period is obtained;
said ETLiiObtained by the following formula:
Figure FDA0002592875880000041
wherein, TLiiThe temperature T of the lithium battery at the ith test time point in the sampling periodLii-1The temperature of the lithium battery at the i-1 st test time point in the sampling periodΔ t is the time interval between the ith test time point and the (i-1) th test time point;
step 2, mapping the input layer vector to a middle layer, wherein the middle layer vector y is { y ═ y1,y2,…,ym}; m is the number of intermediate layer nodes;
and 3, obtaining an output layer vector o ═ o1,o2,…oi,…,on}; wherein o isiFor the lithium battery lithium analysis state parameter value of the ith test time point,
Figure FDA0002592875880000042
when o isiWhen the test time point is 0, the lithium battery does not separate lithium at the ith test time point in the sampling period, and when the test time point is oiWhen the sampling period is 1, the lithium battery begins to extract lithium at the ith test time point in the sampling period.
10. The lithium battery lithium analysis test method based on the pure electric vehicle as claimed in claim 9, wherein the number m of intermediate layer nodes satisfies:
Figure FDA0002592875880000043
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