CN116401497A - SOH estimation method for feature fusion - Google Patents
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Abstract
The invention discloses a SOH estimation method for feature fusion, which comprises the following steps: step S1: estimating the SOH_Capacity state based on a Capacity method, and judging a Capacity triggering state; s2, when the capacity triggering state is not triggered, calculating SOH of the full temperature interval through real-time on-line cycle times; step S3: when the capacity triggering state triggering is met, outputting according to a capacity calculation result, inputting a value calculated by the state of the number of circulation times at the last moment as a posterior state variable in a Kalman equation, and updating and predicting the SOH through a Kalman filtering algorithm. According to the invention, the algorithm deployment is realized by combining the SOH estimation method with the maximum available capacity and the cycle times and combining the Kalman filtering algorithm, so that the defects of limited use conditions, poor time variability and poor precision of the existing SOH estimation method are overcome.
Description
Technical Field
The invention relates to the technical field of feature fusion algorithms, in particular to a SOH estimation method for feature fusion.
Background
Battery safety and status have been the focus of user attention, and also the core of terminal business forces, methods for evaluating battery SOH (state of health of battery) include: SOH is estimated at maximum available capacity, at cycle number, at battery impedance (or impedance spectrum); the SOH estimation can be represented by the characteristics of impedance, pressure drop change, temperature change and the like, but a full life cycle calibration experiment is required to be carried out in a laboratory to obtain the change rule of the characterization parameters, so that an empirical model is generated.
SOH has the characteristics of instant decay, strong time variation, nonlinearity and the like, so that the SOH is difficult to accurately estimate by single external characteristic expression.
In the most similar technique to the present invention, the SOH is estimated by using a capacity method from the definition, for example, texas instruments (Ti) calculates the maximum available capacity Qmax (where soh=qmax/Cn, cn is the rated capacity) by using a state synchronism that needs to maintain the depth of discharge, which is defined to be updated between 10 ℃ and 40 ℃, mainly because the capacity change is small in this temperature interval, the capacity change is large when the low temperature is changed, the degree of dispersion of SOH estimation is large, but in the high latitude area, the low temperature is normal. It is necessary to estimate SOH over the entire temperature interval, so that there is a clear shortboard for the use of this technique, and improvements to the prior art are urgently needed based on the current situation.
Disclosure of Invention
The invention aims to provide a SOH estimation method for feature fusion, which aims to solve the problems in the background technology.
The invention provides a SOH estimation method for feature fusion, which comprises the following steps:
step S1: estimating the SOH_Capacity state based on a Capacity method, and judging a Capacity triggering state;
the conditions for calculating the trigger state are:
(1) the temperature at the time t0 and the time tn is larger than a certain preset value;
(2) the accumulated discharge capacity at the time t0 and the time tn is larger than a certain preset value of rated capacity;
and if one of the two conditions is not met, the triggering state is not triggered.
S2, when the capacity triggering state is not triggered, calculating SOH of the full temperature interval through real-time on-line cycle times;
the recursion is:
Step S3: when the capacity triggering state triggering is met, outputting according to a capacity calculation result, inputting a value calculated by the state of the number of circulation times at the last moment as a posterior state variable in a Kalman equation, and updating and predicting the SOH through a Kalman filtering algorithm.
The invention has the following beneficial effects:
according to the invention, the algorithm deployment is realized by combining a Kalman filtering algorithm with an SOH estimation method with the fusion of the maximum available capacity and the circulation times, the defects of limited use conditions, poor time variability and poor precision of the existing SOH estimation method are overcome, and the prediction and correction functions are realized; the SOH real-time updating calculation of the full temperature interval is realized, and the SOH estimation precision is higher.
Drawings
FIG. 1 is a schematic diagram of the junction cell capacity versus temperature relationship of the present invention;
FIG. 2 is a graph showing the change of the open circuit voltage OCV curve of the battery of the present invention under different conditions;
FIG. 3 is a flow chart of the present invention when the capacity trigger state is determined not to trigger;
FIG. 4 is a flow chart of the present invention when determining a capacity trigger state trigger;
FIG. 5 is a flowchart illustrating the overall steps of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the present invention without making any inventive effort fall within the scope of the present invention.
Referring to fig. 1, firstly, SOC (state of charge) and SOH (state of health) of a battery are two very important states in a battery management system, which are coupled to each other and affect each other, so that SOH with high accuracy is required to determine the maximum available capacity of the battery when estimating the SOC, accuracy of the maximum available capacity is guaranteed, accurate parameter characteristics are required to be obtained in different states of SOC when estimating the SOH, and when the battery capacity is attenuated to 80% of the new battery capacity, impedance becomes large, voltage drop becomes large, discharging capability becomes weak, heating rate becomes fast, internal lithium crystal branches accumulate to a certain extent, and there is a risk of causing thermal runaway, so that accurate SOH estimation can effectively control the thermal runaway risk;
referring to fig. 5, according to the risk analysis, the present invention provides a feature fusion SOH estimation method, which is used for solving the disadvantages of limited use conditions, poor time variability and poor accuracy of the existing SOH estimation method, and specifically includes the following steps:
step S1: estimating the SOH_Capacity state based on a Capacity method, and judging a Capacity triggering state;
referring to fig. 2, as an alternative embodiment of the present invention, the aging of the battery may be accompanied by the degradation of the maximum available capacity, and in the discharging process, when the same capacity is discharged, the open circuit voltage OCV of the new and old batteries may show different states, the maximum available capacity of the new battery at different temperatures may be obtained by calibration in a laboratory, the temperature change at time t0 and the temperature change at time tn may not affect the capacity change, and the SOC may be obtained by querying the OCV-SOC table in the rest state; in order to obtain an accurate SOC in a stationary state, it is necessary to clearly obtain an OCV, the SOC and the OCV have a relatively clear correspondence, the battery aging has a small influence on the relationship, the polarization characteristics of different types of batteries are inconsistent, the time constant corresponding to the polarization voltage is difficult to obtain accurately, and when the variation of the voltage approaches 0 in a period of time under a lighter load and no load, the stationary state is considered to be reached, and the terminal voltage is approximately equal to the open circuit voltage OCV.
In an embodiment, the trigger state is calculated by two parameters:
(1) the temperatures at the times t0 and tn are larger than a certain preset value, and 15 ℃ is adopted as the preset temperature value in the embodiment;
(2) the accumulated discharge capacity at time t0 and tn is larger than a certain preset value of rated capacity, and 37% of rated capacity is adopted as the preset value in the embodiment;
and if one of the two conditions is not met, the triggering state is not triggered.
In this example, the maximum available capacity is carried out on the basis of 25 DEG CCn max The method comprises the steps of (1) judging a trigger state at 25 ℃ through estimation of the Capacity health state SOH_capacity;
the calculation process is as follows:
according to the formula:
；
wherein ,
the remaining capacity at time t 0; />
The remaining capacity at time tn; />
The current accumulation capacity from t0 to tn;
the method can obtain:
wherein ：
representing a maximum available capacity; SOH_Capacity represents the Capacity health status; i represents current, SOC represents current state of charge, +.>
Representing the SOC value equivalent to 25℃at time t 0; />
Representing the SOC value equivalent to 25℃at time tn;SOC T SOC values at T℃are indicated, which are obtained by means of an OCV look-up table;Cn T representing the maximum available capacity value at T ℃;Cn 25℃ representing the maximum available capacity value at 25 ℃;
in the embodiment, OCV is obtained when the time t0 reaches the stationary state, and this time is obtained by soc=f (OCV)SOC T . Then the equivalent formula is used for obtaining
。
In the embodiment, the OCV is obtained when the tn time reaches the stationary state, and the time is obtained by soc=f (OCV)SOC T . Then the equivalent formula is used for obtaining
。
In an embodiment, the maximum capacity estimation formula is calculatedCn max 。
In an embodiment, SOH_Capacity is calculated by SOH definition formulas.
Therefore, in this embodiment, the temperature time 25 ℃ is greater than the preset temperature value 15 ℃, if the calculated soh_capacity value is greater than the preset value of 37% of the rated Capacity, the trigger state is judged to be triggered, and if one of the two conditions is not satisfied, the trigger state is not triggered.
Referring to fig. 3, step S2 is to calculate SOH of a full temperature interval, which refers to the entire ambient temperature interval, by real-time on-line cycle times when the capacity trigger state is not triggered.
In an embodiment, when the cycle number is used to characterize the state of health of a battery, the following is a calibration table for the cycle number when a battery is fully charged to 80% capacity at different temperatures:
it can be written as a recurrence:
Referring to fig. 4, step S3: when the capacity triggering state triggering is met, outputting according to a capacity calculation result, inputting a value calculated by the state of the circulation times at the last moment as a posterior state variable in a Kalman equation, and updating and predicting SOH through a Kalman filtering algorithm;
calculating capacity result SOH as required based on the calculation formula in step S2 k Then, according to a Kalman standard equation, carrying out prediction calculation, wherein the calculation process is as follows:
wherein 
Calculating as an initial input state of the cycle number state calculation at the next moment; wherein the method comprises the steps of
；/>
Is the result of the calculation of step S2.
The superscript "+" in the Kalman filter standard equation represents the posterior state, "-" represents the prior state, and the superscript "+_" represents the prediction, where Q represents the number of cycles calculated noise covariance, R represents the capacity calculated noise covariance, and R < Q.
In an embodiment, the whole calculation process can be regarded as event filtering, the step S2 process is a real-time calculation process, and if and only if the calculation state of the step S1 is satisfied, the kalman filtering process of the step S3 process is started, and the SOH calculated in the step S2 is corrected by using soh_capacity. In the process of step S3, the calculation result of step S2 is firstly used as the input value of the prior state prediction
Then, the prior state prediction calculation is carried out to obtain +.>
Finally updating the prior state covariance ++>
Both a priori state prediction and a priori state covariance process are prediction processes, and then the predicted value is modified.
In the embodiment, the Kalman gain (which can be understood as a correction coefficient) is calculated first, and then the posterior state prediction is performed to obtain the SOH output result at the current time after correction
The correction process can be understood as: if the SOH_Capacity calculation is fully trusted, the Kalman gain is 1 and the correction should be +.>
Posterior state prediction value +.>
The method comprises the steps of carrying out a first treatment on the surface of the If the SOH_Capacity calculation result is not trusted, the Kalman gain is 0, the correction amount is 0, and the posterior state prediction value is +.>
. Of course, the Kalman gain is a process of representing dynamic changes according to the current state and the historical covariance; and finally updating the posterior state covariance.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (4)
1. A method for SOH estimation for feature fusion, the method comprising:
step S1: estimating the SOH_Capacity state based on a Capacity method, and judging a Capacity triggering state;
in step S1, the condition for determining the capacity trigger state includes:
the temperature at the time t0 and the time tn is larger than a certain preset value;
the accumulated discharge capacity at the time t0 and the time tn is larger than a certain preset value of rated capacity;
and when the conditions of the triggering state are all met, the triggering state is triggering; if any one or all of the conditions of the triggering state are not met, the triggering state is not triggered;
in step S1, the calculation formula for determining the capacity trigger state is:
obtaining parameters for judging the capacity trigger state according to a calculation formula of the capacity trigger state comprises the following steps:
the said
The calculation method of (1) comprises the following steps: OCV is obtained by reaching a stationary state at time t0, and +.sup.0 at time t0 is obtained by SOC=f (OCV)>
Finally, obtain ∈K through the equivalent formula>
；
The said
The calculation method of (1) comprises the following steps: OCV is obtained by reaching a stationary state at time tn, and +.sup.f at time tn is obtained by SOC=f (OCV)>
Finally, obtain ∈K through the equivalent formula>
；
Step S2: when the capacity triggering state is not triggered, calculating SOH of the full temperature interval through real-time on-line cycle times;
step S3: when the capacity triggering state triggering is met, outputting according to a capacity calculation result, inputting a value calculated by the state of the number of circulation times at the last moment as a posterior state variable in a Kalman equation, and updating and predicting the SOH through a Kalman filtering algorithm.
2. The SOH estimation method of feature fusion according to claim 1, characterized in that: the temperature change at time t0 or time tn does not affect the change in capacity; and, in addition, the method comprises the steps of,
in the stationary state, the terminal voltage at time t0 or time tn is equal to the open circuit voltage OCV.
3. The SOH estimation method of feature fusion according to claim 1, characterized in that: in step S2, a recurrence is calculated as
4. The SOH estimation method of feature fusion according to claim 1, characterized in that: in step S3, a prediction calculation is performed according to the kalman standard equation, and the calculation process is as follows:
wherein the superscript "+" in the Kalman filtering standard equation represents the posterior state, "-" represents the prior state, the superscript "+" "represents the prediction, where Q represents the number of cycles calculated noise covariance, R represents the capacity calculated noise covariance, and R < Q.
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|---|---|---|---|---|
| CN111581904A (en) * | 2020-04-17 | 2020-08-25 | 西安理工大学 | Lithium battery SOC and SOH collaborative estimation method considering influence of cycle number |
| KR20200102891A (en) * | 2019-02-22 | 2020-09-01 | 주식회사 엘지화학 | Battery management system, battery management method and battery pack |
| CN112540313A (en) * | 2019-09-20 | 2021-03-23 | 比亚迪股份有限公司 | Method for correcting available capacity of battery, vehicle and medium |
| CN114184962A (en) * | 2021-10-19 | 2022-03-15 | 北京理工大学 | Multi-algorithm fusion lithium ion battery SOC and SOH joint estimation method |
| CN114355211A (en) * | 2021-12-09 | 2022-04-15 | 武汉船用电力推进装置研究所（中国船舶重工集团公司第七一二研究所） | Lithium ion power battery residual capacity estimation method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200102891A (en) * | 2019-02-22 | 2020-09-01 | 주식회사 엘지화학 | Battery management system, battery management method and battery pack |
| CN112540313A (en) * | 2019-09-20 | 2021-03-23 | 比亚迪股份有限公司 | Method for correcting available capacity of battery, vehicle and medium |
| CN111581904A (en) * | 2020-04-17 | 2020-08-25 | 西安理工大学 | Lithium battery SOC and SOH collaborative estimation method considering influence of cycle number |
| CN114184962A (en) * | 2021-10-19 | 2022-03-15 | 北京理工大学 | Multi-algorithm fusion lithium ion battery SOC and SOH joint estimation method |
| CN114355211A (en) * | 2021-12-09 | 2022-04-15 | 武汉船用电力推进装置研究所（中国船舶重工集团公司第七一二研究所） | Lithium ion power battery residual capacity estimation method |
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