CN118033451A - Sparse data battery health state assessment method - Google Patents
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Abstract
The invention discloses a sparse data battery state of health assessment method, which relates to the technical field of new energy battery application. The technical scheme provided by the invention can solve the problem that SOH cannot be estimated by sparse data in the prior art, can avoid constraint on the condition of standing time before charging, and effectively improves the application value of the sparse data.
Description
Technical Field
The invention relates to the technical field of new energy battery application, in particular to a sparse data battery health state assessment method.
Background
The state of health (SOH) value of the battery refers to the ratio of the fully charged available capacity of the battery in the current state to the capacity of the new battery, which is a key indicator for measuring the capacity aging of the battery, and relates to whether the battery can continue to be serviced and to a retired disposal scheme. The battery cloud data is utilized to diagnose the battery health state, so that the battery health state information can be conveniently and quickly obtained, and the battery cloud data plays an important role in battery health state detection, quality assurance maintenance, echelon utilization evaluation and the like.
In the prior art, the battery health degree estimation method is mainly divided into two types, one type depends on battery discharge data, and the other type depends on charging data. In the method relying on discharge data, SOH estimation is realized mainly according to open-circuit voltage and ampere-hour integral synchronous processing, and in the method relying on charge data, SOH estimation is developed mainly by utilizing a capacity differential curve or a voltage change curve after charge. Although the accuracy of the two methods is higher, the requirements on the data acquisition frequency and the standing time before charging are higher.
However, the uploading frequency of the cloud data of most of the current enterprise batteries is generally low (for example, one 15 seconds and 110 seconds), and the application requirement of ampere-hour integration cannot be met, so that the application value of the sparse data is greatly reduced. And the vehicle can be charged immediately after driving, which is unfavorable for acquiring accurate static open-circuit voltage before charging, so that accurate SOC before charging is difficult to estimate, and sparse data is difficult to accurately estimate SOH.
Disclosure of Invention
The invention provides a sparse data battery health state evaluation method, which mainly aims to solve the problems that the existing battery health degree evaluation method has higher requirements on sparse data acquisition frequency and higher requirements on standing time before charging.
The invention adopts the following technical scheme:
A sparse data battery state of health assessment method comprises the following steps:
Step S1, searching the minimum state of charge value SOC min of the battery system in a set time from the vehicle history storage data, recording the time as t 2, searching the last charge ending time from t 2 to t 1, searching the charge ending time from t 2 to t 3;
Step S2, collecting the current I and the time t in the time period from t 2 to t 3, and obtaining the SOC 0 in the time period by applying an ampere-hour integration algorithm:
Wherein: q n is the battery rated capacity;
Step S3, collecting current I and voltage V in a time period from t 1 to t 2, building a battery model, calculating open-circuit voltage U oc corresponding to each voltage V by using a parameter identification algorithm, taking the last value as OCV 1 in the time period, and converting OCV 1 into SOC 1 according to an open-circuit voltage-state-of-charge relationship established in advance;
Step S4, adding the SOC 0 obtained in step S2 and the SOC 1 obtained in step S3 to obtain the available capacity retention rate SOH.
Further, the method also comprises the following steps: and S5, replacing the value targets of the voltage V, repeating the steps S3 and S4 to respectively obtain the available capacity retention rate SOH corresponding to the different value targets, and evaluating the consistency of the single capacities of the battery system.
Further, the value targets of the voltage V comprise a highest monomer voltage value V max and a lowest monomer voltage value V min; when the voltage V in the step S3 is the highest single voltage V max, the SOH obtained in the step S4 is the optimal cell available capacity retention rate SOH max; when the voltage V in step S3 is the lowest cell voltage value V min, the SOH obtained in step S4 is the battery system available capacity retention rate SOH sys.
Further, in step S5, the battery system cell capacity consistency is calculated from the optimal cell available capacity retention rate SOH max and the battery system available capacity retention rate SOH sys.
Further, the voltage V includes the voltage V i of the different unit cells, and when the voltage V in the step S3 is the voltage V i of a certain unit cell, the SOH obtained in the step S4 is the available capacity retention rate SOH i of the cell.
Further, in step S5, the cell capacity consistency of the battery system is calculated according to the different cell available capacity retention rates SOH i.
Further, the consistency of the single capacity of the battery system is calculated according to the absolute value or the dispersion of the difference value of the available capacity holding rate SOH corresponding to different voltage value targets.
Further, in step S1, it is determined whether the power is full at time t 3, if yes, a subsequent step is performed, otherwise, the minimum state of charge value SOC min outside the time period from t 1 to t 3 is searched again, and t 1、t2 and t 3 are redefined according to step S1.
Further, in step S3, the parameter identification algorithm may be based on a battery equivalent circuit model, a fractional order model or an electrochemical model, and may be matched with any one of a least squares identification algorithm, a kalman filter algorithm, an H infinity algorithm, and an intelligent machine learning optimization algorithm.
Further, in step S3, the fitting manner of the open-circuit voltage-state-of-charge relationship may be a formula fitting manner of a gaussian function, a polynomial function or a hyperbolic tangent function, or may be a fitting manner of a linear difference.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, based on the sparse data, the charge data and the discharge data of the battery system are acquired, the safe integral capacity SOC 0 of the charge process under the condition of acquiring the sparse data is acquired by utilizing the characteristic of small current fluctuation in the charge process, the open-circuit voltage is identified by utilizing the characteristic of large current fluctuation in the discharge process by adopting a battery model, and the corresponding real SOC 1 before charging is obtained by converting the open-circuit voltage-charge state relationship, so that the estimation of the SOH of the battery is realized, the condition constraint of the rest time before charging is avoided, the universality of an algorithm is improved, and the utilization value of the sparse data is improved.
2. The algorithm provided by the invention can estimate a plurality of available capacity retention rates SOH of the battery system only by changing different voltage values in the discharging process, thereby being beneficial to preliminary evaluation of the battery health state; and then, the battery system single body capacity consistency can be obtained after the plurality of available capacity retention rates SOH are further processed, so that the battery health state is comprehensively and accurately subjected to system evaluation.
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Fig. 1 is a control flow chart of the present invention.
Detailed Description
Specific embodiments of the present invention will be described below with reference to the accompanying drawings. Numerous details are set forth in the following description in order to provide a thorough understanding of the present invention, but it will be apparent to one skilled in the art that the present invention may be practiced without these details.
Referring to fig. 1, a sparse data battery state of health assessment method includes the steps of:
Step S1, searching the minimum state of charge value SOC min of the battery system within a set time from the vehicle history data, recording the time as t 2, searching the last charge end time from t 2, recording the last charge end time as t 1, searching the charge end time from t 2, recording the last charge end time as t 3.
The battery system mentioned in the step can be a new energy vehicle battery system or an energy storage system. Preferably, the set time is within the last month. In order to obtain more accurate data, after searching t 3, it should first be determined whether t 3 is full, if so, it is indicated that t 3 meets the requirements, a subsequent step may be performed, if not, the minimum state of charge value SOC min outside the time period from t 1 to t 3 is searched again, and t 1、t2 and t 3 are redefined according to step S1. Specifically, full charge refers to the state of charge value soc=100% of the battery system.
And S2, collecting the current I and the time t in the time period from t 2 to t 3, and obtaining the SOC 0 in the time period by using an ampere-hour integration algorithm. The data collected in the step are charging data, and the purpose of the data is to obtain the ampere-hour integral capacity SOC 0 of the charging process under the condition of sparse data by utilizing the characteristic of small current fluctuation in the charging process. The specific calculation formula is as follows:
wherein: q n is the battery rated capacity.
And S3, collecting current I and voltage V in a time period from t 1 to t 2, building a battery model, calculating open-circuit voltage U oc corresponding to each voltage V by using a parameter identification algorithm, taking the last value as OCV 1 in the time period, and converting OCV 1 into SOC 1 according to an open-circuit voltage-state-of-charge relationship established in advance. The data collected in the step are discharge data, and the purpose of the data is to identify the open-circuit voltage U oc by using a battery model according to the characteristic of large current fluctuation in the discharge process, and then obtain the corresponding real SOC 1 before charging through the conversion of the open-circuit voltage-charge state relation.
The parameter identification algorithm can be based on a battery equivalent circuit model, a fractional order model or an electrochemical model and is matched with any one algorithm of a least square identification algorithm, a Kalman filtering algorithm, an H infinity algorithm and an intelligent machine learning optimization algorithm. The fitting mode of the open-circuit voltage-charge state relation can be a formula fitting mode of a Gaussian function, a polynomial function or a hyperbolic tangent function, or a fitting mode of a linear difference value.
Based on the characteristics of the parameter identification algorithm, only about 600 data points can be acquired, a certain step size is ensured to approach a true value from an initial value, and the data in the time period from t 1 to t 2 are not required to be acquired and calculated.
In order to realize multi-dimensional evaluation of the battery state of health, the voltage V in this step may be the highest cell voltage value V max or the lowest cell voltage value V min corresponding to each current I 1 in the battery system in the time period from t 1 to t 2, or may be the voltage V i of a specific cell.
Step S4, adding the SOC 0 obtained in step S2 to the SOC 1 obtained in step S3, so as to obtain the available capacity retention SOH, namely:
SOH＝SOC0+SOC1
specifically, when the voltage V in the step S3 is the highest cell voltage value V max, the SOH obtained in the step S4 is the optimum cell available capacity retention rate SOH max; when the voltage V in the step S3 is the lowest cell voltage value V min, the SOH obtained in the step S4 is the battery system available capacity retention rate SOH sys; when the voltage V in step S3 is the voltage V i of a single cell, the SOH obtained in step S4 is the cell available capacity holding rate SOH i. Therefore, the algorithm provided by the invention can estimate a plurality of available capacity retention rates SOH of the battery system only by changing the value target of the voltage V, thereby being beneficial to preliminary evaluation of the battery health state.
And S5, replacing the value targets of the voltage V, repeating the steps S3 and S4 to respectively obtain the available capacity retention rate SOH corresponding to the different value targets, and evaluating the consistency of the single capacities of the battery system.
Specifically, the step calculates the consistency of the single capacity of the battery system according to the absolute value or the dispersion of the difference value of the available capacity holding rate SOH corresponding to different voltage value targets, so as to comprehensively carry out system evaluation on the state of health of the battery. Therefore, the algorithm provided by the invention can solve the problem that the SOH can not be estimated by the sparse data in the prior art, can improve the application value of the sparse data, and can obtain the SOH estimation and capacity consistency diagnosis results of the battery system.
When the voltage V has the highest cell voltage V max and the lowest cell voltage V min, the cell capacity consistency of the battery system can be calculated according to the optimal cell available capacity retention rate SOH max and the battery system available capacity retention rate SOH sys. When the voltage V is the voltage V i of the different single cells, the consistency of the single cell capacity of the battery system can be calculated according to the available capacity retention rate SOH i of each cell.
The foregoing is merely illustrative of specific embodiments of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modification of the present invention by using the design concept shall fall within the scope of the present invention.
Claims (10)
1. A sparse data battery state of health evaluation method is characterized in that: the method comprises the following steps:
Step S1, searching the minimum state of charge value SOC min of the battery system in a set time from vehicle history storage data, recording the time as t 2, searching the last charging end time from t 2 to t 1, and searching the charging beam time from t 2 to t 3;
Step S2, collecting the current I and the time t in the time period from t 2 to t 3, and obtaining the SOC 0 in the time period by applying an ampere-hour integration algorithm:
Wherein: q n is the battery rated capacity;
Step S3, collecting current I and voltage V in a time period from t 1 to t 2, building a battery model, calculating open-circuit voltage U oc corresponding to each voltage V by using a parameter identification algorithm, taking the last value as OCV 1 in the time period, and converting OCV 1 into SOC 1 according to an open-circuit voltage-state-of-charge relationship established in advance;
Step S4, adding the SOC 0 obtained in step S2 and the SOC 1 obtained in step S3 to obtain the available capacity retention rate SOH.
2. The sparse data battery state of health assessment method of claim 1, wherein: the method also comprises the following steps: and S5, replacing the value targets of the voltage V, repeating the steps S3 and S4 to respectively obtain the available capacity retention rate SOH corresponding to the different value targets, and evaluating the consistency of the single capacities of the battery system.
3. The sparse data battery state of health assessment method of claim 2, wherein: the value targets of the voltage V comprise a highest monomer voltage value V max and a lowest monomer voltage value V min; when the voltage V in the step S3 is the highest single voltage V max, the SOH obtained in the step S4 is the optimal cell available capacity retention rate SOH max; when the voltage V in step S3 is the lowest cell voltage value V min, the SOH obtained in step S4 is the battery system available capacity retention rate SOH sys.
4. A sparse data battery state of health assessment method as defined in claim 3, wherein: in step S5, the battery system cell capacity consistency is calculated according to the optimal cell available capacity retention rate SOH max and the battery system available capacity retention rate SOH sys.
5. The sparse data battery state of health assessment method of claim 2, wherein: the value target of the voltage V includes the voltages V i of different single cells, and when the voltage V in the step S3 is the voltage V i of a single cell, the SOH obtained in the step S4 is the available capacity retention rate SOH i of the cell.
6. The sparse data battery state of health assessment method of claim 4, wherein: in step S5, the consistency of the cell capacities of the battery system is calculated according to the different cell available capacity retention rates SOH i.
7. The sparse data battery state of health assessment method of claim 2, wherein: and calculating the consistency of the single capacity of the battery system according to the absolute value or the dispersion of the difference value of the available capacity holding rate SOH corresponding to different voltage value targets.
8. The sparse data battery state of health assessment method of claim 1, wherein: in step S1, it is determined whether the time t 3 is full, if yes, a subsequent step is performed, otherwise the minimum state of charge value SOC min outside the time period from t 1 to t 3 is searched again, and t 1、t2 and t 3 are redefined according to step S1.
9. The sparse data battery state of health assessment method of claim 1, wherein: in step S3, the parameter identification algorithm may be based on a battery equivalent circuit model, a fractional order model or an electrochemical model, and may be matched with any one of a least squares identification algorithm, a kalman filter algorithm, an H infinity algorithm, and an intelligent machine learning optimization algorithm.
10. The sparse data battery state of health assessment method of claim 1, wherein: in step S3, the fitting manner of the open-circuit voltage-state-of-charge relationship may be a formula fitting manner of a gaussian function, a polynomial function or a hyperbolic tangent function, or may be a fitting manner of a linear difference.
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