CN115329277A - SOH prediction method for movement-withdrawing nickel-metal hydride battery - Google Patents

SOH prediction method for movement-withdrawing nickel-metal hydride battery Download PDF

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CN115329277A
CN115329277A CN202210950484.0A CN202210950484A CN115329277A CN 115329277 A CN115329277 A CN 115329277A CN 202210950484 A CN202210950484 A CN 202210950484A CN 115329277 A CN115329277 A CN 115329277A
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任文举
谢新宇
郑太雄
朱意霖
刘劲松
易源
黄溢
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Chongqing University of Post and Telecommunications
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Abstract

The invention relates to a method for predicting SOH of a movement-withdrawing nickel-metal hydride battery, belonging to the technical field of energy storage. Firstly, collecting data indexes related to battery capacity decline in a battery charging state, ensuring data validity through data cleaning, constructing characteristic values by taking battery internal resistance and battery maximum internal pressure as health factors, and forming a sample set by the health factors and battery capacitance together; optimizing a LightGBM algorithm through an improved adaptive loss function, and obtaining an optimal parameter combination by applying a Hyperopt hyper-parameter framework; loading the sample set into the optimized algorithm model for training; and (4) predicting the SOH by adopting the trained algorithm training model. By adopting the method, the SOH prediction efficiency and precision of the returned batteries can be improved, and the returned batteries can be efficiently and accurately classified by utilizing the echelon.

Description

SOH prediction method for movement-withdrawing nickel-metal hydride battery
Technical Field
The invention belongs to the technical field of energy storage, and relates to a method for predicting SOH of a movement force withdrawing nickel-metal hydride battery.
Background
The SOH prediction is a key step for the gradient utilization of the returned battery, the applicable scene of the battery is determined according to the current SOH of the returned battery, and the SOH prediction accuracy determines the rationality of the battery in screening and regrouping. With the coming of the retired period of electric vehicle power batteries in China, the number of retired batteries of electric vehicles is huge, and the traditional SOH test method is time-consuming, labor-consuming and material-consuming, so that a method for quickly predicting the SOH of large-scale retired batteries is necessary to be invented, and the development of the field of gradient utilization of the retired batteries is promoted.
At present, parameters such as voltage and equal pressure drop time are used as health factors, methods such as SVM algorithm, genetic algorithm and LSTM neural network can be well applied, and although the algorithms can realize SOH prediction, in the face of a large number of retired batteries, the algorithms cannot accurately and quickly give prediction results. Therefore, for the rapid and accurate prediction of the returned battery, it is critical to efficiently obtain the characteristic value strongly related to the SOH, and it is also necessary to select a suitable prediction algorithm model to ensure the prediction accuracy and the operation speed.
Disclosure of Invention
In view of this, the invention aims to provide an SOH prediction method for a movement-canceling nickel-metal hydride battery, which solves the problems of complex prediction process and insufficient prediction precision of the traditional SOH prediction method, overcomes the problems of large training set, large calculation amount, long calculation time and high requirement on computer hardware in general machine learning, improves the adaptability of the algorithm, and avoids overfitting of a training model.
In order to achieve the purpose, the invention provides the following technical scheme:
a SOH prediction method for a motion force removing nickel-metal hydride battery comprises the following steps:
s1: collecting battery capacity, battery internal resistance of 20-80% of electric quantity state and maximum internal pressure data of the power nickel-hydrogen battery in a charging state;
s2: cleaning the acquired data by filling missing values, deleting abnormal values and carrying out normalization processing;
s3: constructing a health factor by using the internal resistance of the battery and the maximum internal pressure of the battery together, and establishing a mapping relation by using the health factor as a characteristic value and the battery capacity matched with the health factor to form a sample library;
s4: adopting an improved LightGBM as a health state SOH prediction subject algorithm; the original loss function is improved through the self-adaptive loss function, and the influence of data outliers is reduced;
s5: constructing a model parameter space, a LightGBM model factory and a score acquirer by using a hyper-parameter Hyperopt frame, and realizing efficient model optimization parameter adjustment;
s6: establishing a model frame by the steps S5 and S4, and importing the sample library processed in the step S2 into a model for training;
s7: and importing the maximum internal pressure and internal resistance data of the battery in the charging state of the current returned battery into the model to quickly predict the battery capacity.
S8: calculating the SOH of the battery to obtain the SOH of the battery, wherein the calculation formula is as follows:
Figure BDA0003788928370000021
wherein: c p To predict battery capacity, C 0 The rated capacity of the battery;
optionally, in S1, the battery capacity, the battery internal resistance and the maximum internal pressure data of the power nickel-hydrogen battery in the charging state are collected, wherein the battery internal resistance collects data of the battery in a 20% -80% soc state, so as to ensure the accuracy of the internal resistance; maximum internal pressure data acquisition of 80% -100% data of SOC state, ensuring authenticity of internal pressure.
Optionally, in S2, the specific data normalization processing method includes: the mean variance normalization method is a method for mapping data into data distribution with mean 0 and variance 1.
The calculation formula is as follows:
Figure BDA0003788928370000022
wherein, f i In order to be a characteristic of the input,
Figure BDA0003788928370000023
is the normalized input features. All data are normalized, so that the complexity of a data structure can be greatly reduced, and the method plays a crucial role in reducing the calculation time.
Optionally, in S3, a health factor is constructed by using the dynamic internal resistance and the maximum internal pressure together as a characteristic value of the prediction model; the dynamic internal resistance data is simple and efficient to obtain; the characteristic of stable internal pressure of the nickel-metal hydride battery has strong correlation with the service life of the battery, and the accuracy of predicting the SOH of the battery is improved;
when the nickel-hydrogen battery is charged, the reactions of the positive electrode and the negative electrode are as follows:
and (3) positive electrode: ni (OH) 2 +OH - →NiOOH+H 2 O+e -
Negative electrode: h 2 O+e - →OH - +1/2H 2
The hydrogen storage alloy of the cathode of the nickel-metal hydride battery has a corresponding relation with the internal pressure, the hydrogen storage alloy of the cathode of the battery SOH has a corresponding relation with the hydrogen storage alloy of the cathode of the battery, the internal pressure of the battery has a strong correlation with the SOH of the battery, and the internal pressure of the battery is used as a health factor to predict the residual service life of the battery.
Optionally, in S3, an improved LightGBM is used as a main algorithm for target detection, and an adaptive loss function is used to improve an original loss function, so as to reduce the influence of outliers on prediction accuracy;
the loss function is calculated as:
Figure BDA0003788928370000031
different superparameters α correspond to the loss functions:
Figure BDA0003788928370000032
when the hyper-parameter alpha takes different values, the hyper-parameter alpha is expressed as a proper loss function according to the data characteristics, and the influence of the discrete group on the prediction precision is reduced.
Optionally, in S4, a hyper parameter optimization framework, a training mode based on a multi-thread parallel histogram, and a GOSS processing mode are adopted, data are preprocessed, a LightGBM model factory and a score acquirer are created, each parameter model and the score acquirer are produced in the factory to evaluate each model, and finally, the optimal model parameters are screened out, so as to realize efficient optimization parameter adjustment of the improved LightGBM model;
the method is based on a multi-thread parallel histogram training mode: and converting continuous floating point numbers of the characteristic values into K discrete values, and finally constructing a histogram with the width of K, wherein K is the calculation times.
GOSS processing mode: reserving samples with large gradients in the data processing process, presetting a threshold value, and randomly removing samples with small gradients, wherein the sampling rate of the large gradients in the data is a, and the sampling rate of the small gradients in the data is b; data split points are reduced, and learning efficiency and prediction accuracy are improved by setting iteration termination conditions to train the learner.
Wherein, the maximum information gain point calculation formula is:
Figure BDA0003788928370000033
x i for the data samples of the training set, x ij Dividing training set data samples under the characteristic j; g i Representing the direction of the negative gradient of the loss function of the model data variable at each gradient iteration; o represents a training set of a certain fixed node, d represents a segmentation point under a segmentation characteristic j;
n O ＝∑I[x i ∈O]
n o representing the number of training set samples of a certain fixed node;
Figure BDA0003788928370000034
n j representing the number of samples with j-th characteristic upper value less than or equal to d;
Figure BDA0003788928370000041
n j representing the number of samples with the value larger than d on the jth characteristic;
hyperopt hyper-parameter optimization framework: by means of the set objective function and parameter space, a TPE (Tree-of-Parzen-Estimators, TPE) algorithm in a Hyperopt frame is adopted, some hyper-parameters are sampled randomly, then the sampled hyper-parameters are used for evaluating the objective function, the number S of models needing to be trained is set, and the hyper-parameters S' are adopted randomly to realize optimized parameter adjustment.
The invention has the beneficial effects that:
1. the invention aims to provide a rapid SOH prediction method for a motion force removing nickel-metal hydride battery, which solves the problems of complex prediction process and insufficient prediction precision of the traditional SOH prediction method and also solves the problems of large model parameter quantity and difficult calculation in a machine learning method. Meanwhile, the algorithm is improved, so that the self-adaptability of the algorithm avoids over-fitting of a training model.
2. The invention adopts internal resistance and maximum internal pressure to construct health factors in the charging process of the battery, wherein the internal resistance data is obtained more simply and the internal resistance value is more constant in the 20-80% SOC state of the battery, thereby improving the data acquisition efficiency; the maximum internal pressure and the battery SOH have strong correlation in one-to-one correspondence, and the SOH prediction accuracy is greatly improved.
3. The method adopts the improved LightGBM as a main body algorithm of prediction, introduces the loss function to improve the original loss function, and reduces the influence of outliers on the prediction precision. The hyper-parameters are expressed as appropriate loss functions according to the data characteristics, so that the influence of discrete groups on the prediction accuracy is reduced.
4. The method uses a Hyperopt hyper-parameter optimization framework, based on a training mode and a GOSS processing mode of a multi-thread parallel histogram, pre-processes data, creates a LightGBM model factory and a score acquirer, evaluates each model through each parameter model and the score acquirer produced by a factory, and finally screens out the optimal model parameters, so that automatic optimization parameter adjustment is realized, model precision is improved, and prediction efficiency is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For a better understanding of the objects, aspects and advantages of the present invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a histogram training mode;
FIG. 3 is a flow chart of GOSS processing;
FIG. 4 is a Hyperopt workflow diagram.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the SOH rapid prediction method for a ni-mh battery with a back motion force comprises the following specific steps:
(1) Collecting battery capacity, battery internal resistance (20-80% SOC state) and maximum internal pressure data of the power nickel-hydrogen battery in a charging state;
(2) Cleaning collected data, filling missing values by using an average value method, deleting observation abnormal values, and carrying out normalization processing by using a mean value variance method;
(3) Constructing a health factor by using the cleaned internal resistance of the battery and the maximum internal pressure data of the battery together, and establishing a mapping relation by using the health factor as a characteristic value and the battery capacity matched with the characteristic value to form a sample library;
(4) An improved LightGBM is adopted as an SOH prediction main algorithm, a loss function is introduced to improve an original loss function, and the influence of a data outlier is reduced;
(5) Constructing a model parameter space, a LightGBM model factory and a score acquirer by using a hyper-parameter Hyperopt frame, and realizing efficient model optimization parameter adjustment;
(6) Establishing a model frame through the steps (4) and (5), and importing the sample library processed in the step (2) into a model for training;
(7) And importing the maximum internal pressure and internal resistance data of the battery in the current retreating battery charging state into the model, and rapidly calculating the battery capacity.
(8) And calculating the SOH of the battery to obtain the real-time SOH of the battery.
Based on a multi-thread parallel histogram training mode: and converting continuous floating point numbers of the characteristic values into K discrete values, and finally constructing a histogram with the width of K, wherein K is the number of calculation times. The histogram training mode is shown in fig. 2.
GOSS processing mode: in the data processing process, samples with large gradients (preset threshold values) are reserved, samples with small gradients (the sampling rate of the large gradients in the data is a, and the sampling rate of the small gradients in the data is b) are randomly removed, data split points are reduced, and the learning efficiency and the prediction accuracy are improved by setting an iteration termination condition to train a learning device. The process flow of GOSS is shown in fig. 3.
Wherein, the maximum information gain point calculation formula is:
Figure BDA0003788928370000061
x i for the data samples of the training set, x ij Dividing training set data samples under the characteristic j; g i Representing the direction of the negative gradient of the loss function of the model data variable at each gradient iteration; o represents a training set of a certain fixed node, d represents a segmentation point under a segmentation characteristic j;
n O ＝ΣI[x i ∈O]
n o representing the number of training set samples of a certain fixed node;
Figure BDA0003788928370000062
n j representing the number of samples with j-th characteristic upper value less than or equal to d;
Figure BDA0003788928370000063
n j representing the number of samples with the value larger than d on the jth characteristic;
hyper pt hyper parameter optimization framework: by means of the set objective function and parameter space, a TPE (Tree-of-Parzen-Estimators) algorithm in a Hyperopt frame is adopted, some hyper-parameters are sampled randomly, and then the sampled hyper-parameters are used for evaluating the objective function (the number S of models needing to be trained is set, and the hyper-parameters S' are adopted randomly), so that optimal parameter adjustment is achieved. The Hyperopt workflow is shown in FIG. 4.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (6)

1. A method for predicting the SOH of a withdrawing force nickel-metal hydride battery is characterized by comprising the following steps: the method comprises the following steps:
s1: collecting battery capacity, battery internal resistance of 20-80% of electric quantity state and maximum internal pressure data of the power nickel-hydrogen battery in a charging state;
s2: cleaning the acquired data by filling missing values, deleting abnormal values and carrying out normalization processing;
s3: constructing a health factor by using the internal resistance of the battery and the maximum internal pressure of the battery together, and establishing a mapping relation by using the health factor as a characteristic value and the battery capacity matched with the characteristic value to form a sample library;
s4: adopting an improved LightGBM as a health state SOH prediction subject algorithm; the original loss function is improved through the self-adaptive loss function, and the influence of data outliers is reduced;
s5: constructing a model parameter space, a LightGBM model factory and a score acquirer by using a hyper-parameter Hyperopt frame, and realizing efficient model optimization parameter adjustment;
s6: establishing a model frame by the steps S5 and S4, and importing the sample library processed in the step S2 into a model for training;
s7: and importing the maximum internal pressure and internal resistance data of the battery in the current quit-transported battery charging state into a model, and rapidly predicting the battery capacity.
S8: calculating the SOH of the battery to obtain the SOH of the battery, wherein the calculation formula is as follows:
Figure FDA0003788928360000011
wherein: c p To predict battery capacity, C 0 The rated capacity of the battery.
2. The method for predicting the SOH of the deactivatable nickel-metal hydride battery according to claim 1, wherein: in the step S1, acquiring battery capacity, battery internal resistance and maximum internal pressure data of the power nickel-hydrogen battery in a charging state, wherein the battery internal resistance acquires the data of the battery in a 20-80% SOC state, and the accuracy of the internal resistance is ensured; maximum internal pressure data acquisition of 80% -100% data of SOC state, ensuring authenticity of internal pressure.
3. The method for predicting the SOH of the deactivatable nickel-metal hydride battery according to claim 2, wherein: in the S2, a specific data normalization processing method comprises the following steps: the mean variance normalization method is a method for mapping data to data distribution with a mean value of 0 and a variance of 1.
The calculation formula is as follows:
Figure FDA0003788928360000012
wherein f is i As a characteristic of the input, the input is,
Figure FDA0003788928360000013
is the normalized input features. All data are normalized, so that the complexity of a data structure can be greatly reduced, and the method plays a crucial role in reducing the calculation time.
4. The method for predicting the SOH of a depravation force nickel-metal hydride battery according to claim 3, wherein: in the S3, a health factor is constructed by using the dynamic internal resistance and the maximum internal pressure together to serve as a characteristic value of a prediction model; the dynamic internal resistance data is simple and efficient to obtain; the characteristic of stable internal pressure of the nickel-metal hydride battery has strong correlation with the service life of the battery, and the accuracy of predicting the SOH of the battery is improved;
when the nickel-hydrogen battery is charged, the reactions of the positive electrode and the negative electrode are as follows:
and (3) positive electrode: ni (OH) 2 +OH - →NiOOH+H 2 O+e -
Negative electrode: h 2 O+e - →OH - +1/2H 2
The hydrogen storage alloy of the cathode of the nickel-metal hydride battery has a corresponding relation with the internal pressure, the hydrogen storage alloy of the cathode of the battery SOH has a corresponding relation with the hydrogen storage alloy of the cathode of the battery, the internal pressure of the battery has a strong correlation with the SOH of the battery, and the internal pressure of the battery is used as a health factor to predict the residual service life of the battery.
5. The method of claim 4 for predicting the SOH of the deactivatable nickel-metal hydride battery, wherein: in the S3, the improved LightGBM is adopted as a main algorithm of target detection, and the original loss function is improved through a self-adaptive loss function, so that the influence of outliers on the prediction precision is reduced;
the loss function is calculated as:
Figure FDA0003788928360000021
different superparameters α correspond to the loss functions:
Figure FDA0003788928360000022
when the hyper-parameter alpha takes different values, the hyper-parameter alpha is expressed as a proper loss function according to the data characteristics, and the influence of the discrete group on the prediction precision is reduced.
6. The method of claim 5 for predicting the SOH of the deactivatable nickel-metal hydride battery, wherein: in the S4, a Hyperopt hyper-parameter optimization frame, a training mode based on a multi-thread parallel histogram and a GOSS processing mode are adopted, a LightGBM model factory and a score acquirer are created after data are preprocessed, each parameter model and each score acquirer are produced in the factory, each model is evaluated, the optimal model parameters are finally screened out, and efficient optimization parameter adjustment of the improved LightGBM model is achieved;
the method is based on a multi-thread parallel histogram training mode: and converting continuous floating point numbers of the characteristic values into K discrete values, and finally constructing a histogram with the width of K, wherein K is the calculation times.
GOSS processing mode: reserving samples with large gradients in the data processing process, presetting a threshold value, and randomly removing samples with small gradients, wherein the sampling rate of the large gradients in the data is a, and the sampling rate of the small gradients in the data is b; data split points are reduced, and learning efficiency and prediction accuracy are improved by setting an iteration termination condition to train the learner.
Wherein, the maximum information gain point calculation formula is:
Figure FDA0003788928360000031
x i for the data samples of the training set, x ij Dividing training set data samples under the characteristic j; g i Representing the direction of the negative gradient of the loss function of the model data variable at each gradient iteration; o represents a training set of a certain fixed node, d represents a segmentation point under a segmentation characteristic j;
n O ＝∑I[x i ∈O]
n o representing the number of training set samples of a certain fixed node;
Figure FDA0003788928360000032
n j representing the number of samples with j-th characteristic upper value less than or equal to d;
Figure FDA0003788928360000033
n j representing the number of samples with the value larger than d on the jth characteristic;
hyperopt hyper-parameter optimization framework: according to the set target function and parameter space, a TPE (Tree-of-Parzen-Estimators) algorithm in a Hyperopt frame is adopted, some hyper-parameters are sampled randomly, then the sampled hyper-parameters are used for evaluating the target function, the number S of models needing to be trained is set, and the hyper-parameters S' are adopted randomly to realize optimized parameter adjustment.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115514068A (en) * 2022-11-18 2022-12-23 杭州程单能源科技有限公司 Cell pressure difference optimization method for gradient utilization of lithium battery

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115514068A (en) * 2022-11-18 2022-12-23 杭州程单能源科技有限公司 Cell pressure difference optimization method for gradient utilization of lithium battery

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