CN114325444A - Battery life model parameter fitting method - Google Patents
Battery life model parameter fitting method Download PDFInfo
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- CN114325444A CN114325444A CN202111342012.9A CN202111342012A CN114325444A CN 114325444 A CN114325444 A CN 114325444A CN 202111342012 A CN202111342012 A CN 202111342012A CN 114325444 A CN114325444 A CN 114325444A
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Abstract
The invention discloses a battery life model parameter fitting method, which comprises the following steps: s1: counting data of battery storage time and battery accumulated charge throughput of the endurance test vehicle; s2: according to the battery storage time data and a fitting formula QfadeCaleUsing heuristic algorithm to proceed parameter A to be identified1、A2、A3Fitting; s3: from the accumulated charge throughput data of the battery and a fitting formula QfadecycUsing heuristic algorithm to proceed parameter B to be identified1、B2、B3Fitting. The method of the invention directly uses the durability test data of the whole vehicle to fit the service life model parameters, can well solve the error caused by the fact that the traditional method uses the equivalent whole vehicle working condition fitting parameters under the fixed constant temperature working condition, and simultaneously can directly fit the battery service life model parameters by using the durability test data of the whole vehicle, thereby saving the million-level battery service life test cost.
Description
Technical Field
The invention relates to the field of battery life prediction, in particular to a battery life model parameter fitting method.
Background
The traditional battery life model is mainly used for fitting battery life model parameters based on cycle and calendar life test data under different constant temperatures, but various different working conditions in the using process of the whole vehicle cannot be really equivalent by using the method, so the fitting effect of the traditional method is generally good, but the effect of the traditional method applied to the whole vehicle is very general.
For example, a method for predicting the lifetime of an aging model of a lithium ion battery, which is disclosed in chinese patent literature and has publication number CN110320474A, comprises the following steps: (1) testing the capacity retention rate of the lithium battery in a 50% SOC state after storage and placement at different temperatures and the value of the direct current internal resistance of the battery changing along with the storage time; (2) storing the batteries in different voltage states for the same time at the same temperature, and carrying out alternating current impedance test and direct current internal resistance test; (3) analyzing the calendar life capacity attenuation, the correlation between the alternating current internal resistance and direct current internal resistance change and the temperature and voltage, and describing the change process of the experimental aging data along with the time through a fitting function; (4) and analyzing the model according to the fitted data, fitting the actually measured actual effect data, and effectively predicting the future calendar life and internal resistance parameters of the battery. Although the testing method is simple, does not need high-end equipment and complex operation, and greatly shortens the testing period, the method mainly fits the battery service life model parameters based on cycle and calendar service life testing data under different constant temperatures, and does not solve the problem that the method can not really equivalent various working conditions in the using process of the whole vehicle.
Disclosure of Invention
The invention provides a battery life model parameter fitting method, aiming at overcoming the problems that the prior art can not really achieve various different working conditions in the using process of a whole vehicle, so that the generated fitting effect is good, but the effect of the method applied to the whole vehicle is very general.
In order to achieve the purpose, the invention adopts the following technical scheme:
a battery life model parameter fitting method comprises the following steps: s1: counting data of battery storage time and battery accumulated charge throughput of the endurance test vehicle; s2: according to the battery storage time data and a fitting formula QfadeCaleUsing heuristic algorithm to proceed parameter A to be identified1、A2、A3Fitting; s3: according to the batteryAccumulated charge throughput data and fitting equation QfadecycUsing heuristic algorithm to proceed parameter B to be identified1、B2、B3Fitting. The method of the invention directly uses the durability test data of the whole vehicle to fit the service life model parameters, can well solve the error caused by the fact that the traditional method uses the equivalent whole vehicle working condition fitting parameters under the fixed constant temperature working condition, and simultaneously can directly fit the battery service life model parameters by using the durability test data of the whole vehicle, thereby saving the million-level battery service life test cost.
As a preferable aspect of the present invention, the battery storage time data in S1 is battery storage time data of the endurance test vehicle at different temperatures. The method uses the durability test data of the whole vehicle and fits the service life model parameters, can well solve the error caused by the fact that the traditional method uses the fixed constant-temperature working condition to be equivalent to the whole vehicle working condition fitting parameters, and solves the problem that the fitting effect is good but the effect is very general when the method is applied to the whole vehicle.
As a preferable aspect of the present invention, the battery accumulated charge throughput data in S1 is battery accumulated charge throughput data of the endurance test vehicle at different temperatures. The method uses the durability test data of the whole vehicle and fits the service life model parameters, can well solve the error caused by the fact that the traditional method uses the fixed constant-temperature working condition to be equivalent to the whole vehicle working condition fitting parameters, and solves the problem that the fitting effect is good but the effect is very general when the method is applied to the whole vehicle.
As a preferred embodiment of the present invention, the fitting formula Qfade in S2CaleThe method specifically comprises the following steps:
wherein A is1、A2、A3As a parameter to be identified, T1、T2…TnDifferent ambient temperatures, t, at which the vehicle is subjected to endurance testing1、t2…tnThe storage time of the battery corresponding to the endurance test vehicle under different environmental temperatures is t, and the sum of the storage time of the battery corresponding to the endurance test vehicle under different temperatures isI.e. t ═ t1+t2+…+tn. The existing method is more inclined to academic research, the complexity of engineering application environment is not considered, a specific constant temperature experiment environment is equivalent to a complex temperature environment of the whole vehicle, the effect is poor, the whole vehicle data is directly used for fitting the service life of the battery, and the precision is higher.
As a preferred embodiment of the present invention, the fitting formula Qfade in S3cycThe method specifically comprises the following steps:
wherein, B1、B2、B3As a parameter to be identified, T1、T2…TnFor endurance testing the different ambient temperatures, Ah, to which the vehicle is subjected1、Ah2…AhnThe accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different temperatures are obtained, Ah is the sum of the accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different environmental temperatures, namely Ah-Ah1+Ah2+…+Ahn. The existing method is more inclined to academic research, the complexity of engineering application environment is not considered, a specific constant temperature experiment environment is equivalent to a complex temperature environment of the whole vehicle, the effect is poor, the whole vehicle data is directly used for fitting the service life of the battery, and the precision is higher.
As a preferred embodiment of the present invention, the heuristic algorithm in S2 and S3 is specifically a PS0 algorithm. The PS0 algorithm is one of heuristic algorithms, namely, a particle swarm optimization algorithm, and the heuristic algorithm further includes a simulated annealing algorithm (SA), a Genetic Algorithm (GA), an ant colony Algorithm (ACO), an Artificial Neural Network (ANN), and the like.
Therefore, the invention has the following beneficial effects: according to the method, the endurance test data of the whole vehicle are directly used for fitting the service life model parameters, so that errors caused by the fact that the traditional method uses the fixed constant-temperature working condition to be equivalent to the working condition fitting parameters of the whole vehicle can be well solved, meanwhile, the endurance test data of the whole vehicle can be directly used for fitting the service life model parameters of the battery, and million-level battery service life test cost is saved; the method uses the durability test data of the whole vehicle and fits the service life model parameters, can well solve the error caused by the fact that the traditional method uses the fixed constant-temperature working condition to be equivalent to the whole vehicle working condition fitting parameters, and solves the problem that the fitting effect is good but the effect is very general when the method is applied to the whole vehicle; the invention directly uses the data of the whole vehicle to fit the service life of the battery, and has higher precision.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a graph of the cumulative Ah amount at different temperatures and the corresponding equivalent cycle times for the statistics of the present invention;
fig. 3 is a diagram of the effect of battery capacity fade parameter fit resulting from battery storage time in accordance with the present invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
The battery capacity fading mainly comprises two parts, namely storage time and circulation, wherein the existing common fitting formula of the battery capacity fading caused by the storage time is as follows:
wherein A is1、A2、A3For the parameter to be identified, T represents the ambient temperature, and T represents the storage time.
The current common cycle-induced battery capacity fade is commonly fitted with the following formula:
wherein, B1、B2、B3For the parameter to be identified, T represents the ambient temperature, and Ah represents the accumulated charge throughput.
Battery capacity health status: SOH-1-QfadeCale-Qfadecyc。
The existing method is more inclined to academic research, does not consider the complexity of engineering application environment,the specific constant temperature experiment environment is equivalent to the complex temperature environment of the whole vehicle, the effect is poor, and the parameter A to be fitted in the prior art1、A2、A3、B1、B2、B3The method is characterized in that the single variable method is used for respectively testing the influence of different temperatures on the battery capacity health state, the method causes the problem of model error caused by larger difference between data used by fitting parameters and finished automobile data, and the method for fitting the battery life model parameters is provided aiming at the problem, the method directly uses the finished automobile data to fit the battery life, and the accuracy is higher, as shown in figure 1, the method comprises the following steps: s1: counting data of battery storage time and battery accumulated charge throughput of the endurance test vehicle; s2: according to the battery storage time data and a fitting formula QfadeCaleUsing heuristic algorithm to proceed parameter A to be identified1、A2、A3Fitting; s3: from the accumulated charge throughput data of the battery and a fitting formula QfadecycUsing heuristic algorithm to proceed parameter B to be identified1、B2、B3Fitting.
This embodiment takes the capacity fading formula caused by circulation as an example: the cumulative Ah amount of the endurance test vehicle at different temperatures was first counted as shown in the following table:
| T(℃) | T1 | T2 | … | Tn |
| Ah | Ah1 | Ah2 | … | Ahn |
equivalent Qfade using the following formulacycBattery capacity fade equation:
wherein, B1、B2、B3As a parameter to be identified, T1、T2…TnDurability test vehicles were subjected to different environmental temperatures, in degrees Celsius, Ah1、Ah2…AhnThe accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different temperatures are obtained, Ah is the sum of the accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different environmental temperatures, namely Ah-Ah1+Ah2+…+Ahn。
According to an equivalent formula and statistical durability data of the whole vehicle, a heuristic algorithm is utilized, and a PSO algorithm is selected to fit the parameter B to be identified1、B2、B3。
Based on the battery pack simulation whole vehicle working condition life test data, the accumulated Ah amount and the corresponding equivalent cycle number at different temperatures are counted as shown in the following table and figure 2:
| T (℃) | 20 | 22 | 24 | 26 | 28 | 30 | 32 | 34 | 36 | 38 | 40 | 42 | 44 | 46 | 48 | 50 | 52 |
| Ah | 4266.0 9 | 17037. 6 | 53355. 8 | 45445. 4 | 40096 1 | 321175 8 | 1.2E+ 07 | 1.3E+ 07 | 1.4E+ 07 | 227191 3 | 239511 3 | 615195 8 | 680463 2 | 730057 7 | 718128 5 | 341047 6 | 115868 2 |
based on the above data and QfadecvcA formula, and B is obtained by utilizing PSO algorithm fitting in heuristic algorithm1＝948，B2＝5008，B3The effect of the fit is shown in fig. 3 as 0.6347.
The fitting mode of the battery capacity fading parameters caused by the storage time is the same as the method, and is not described in detail.
The embodiment only provides a method for equivalently fitting the battery life parameters, a common life model formula is used, and other similar life formulas are used.
According to the method, the service life test data of the whole vehicle is used for fitting the battery service life model parameters, the service life test workload is reduced, million-level battery service life test cost is saved, errors caused by the fact that the fixed constant-temperature working condition is used for equivalent whole vehicle working condition fitting parameters in the traditional method can be well solved, meanwhile, the service life model parameters of the battery can be directly fitted by the service life test data of the whole vehicle, and the actual operation parameters of a cloud client can be used for optimizing the service life model parameters in the later period.
The method uses the durability test data of the whole vehicle and fits the service life model parameters, can well solve the error caused by the fact that the traditional method uses the fixed constant-temperature working condition to be equivalent to the whole vehicle working condition fitting parameters, and solves the problem that the fitting effect is good but the effect is very general when the method is applied to the whole vehicle.
The battery life model parameters are fitted according to the actual working conditions of the whole vehicle, so that the battery life model parameters are more suitable for the actual use conditions, and the precision is higher.
The above description is only for the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be covered within the protection scope of the present invention.
Claims (6)
1. A battery life model parameter fitting method is characterized by comprising the following steps:
s1: counting data of battery storage time and battery accumulated charge throughput of the endurance test vehicle;
s2: according to the battery storage time data and a fitting formula QfadeCaleUsing heuristic algorithm to proceed parameter A to be identified1、A2、A3Fitting;
s3: from the accumulated charge throughput data of the battery and a fitting formula QfadecycUsing heuristic algorithm to proceed parameter B to be identified1、B2、B3Fitting.
2. The method as claimed in claim 1, wherein the battery storage time data in S1 is battery storage time data of endurance test vehicles at different temperatures.
3. The method of claim 1, wherein the battery cumulative charge throughput data in S1 is the battery cumulative charge throughput data of the endurance test vehicle at different temperatures.
4. A battery life model parameter fitting method according to claim 1 or 2Method, characterized by the fitting formula Qfade in S2CaleThe method specifically comprises the following steps:
wherein A is1、A2、A3As a parameter to be identified, T1、T2…TnDifferent ambient temperatures, t, at which the vehicle is subjected to endurance testing1、t2…tnThe storage time of the battery corresponding to the endurance test vehicle under different environmental temperatures is obtained, t is the sum of the storage time of the battery corresponding to the endurance test vehicle under different temperatures, namely t is t1+t2+…+tn。
5. The method as claimed in claim 1 or 3, wherein the fitting formula Qfade in S3cycThe method specifically comprises the following steps:
wherein, B1、B2、B3As a parameter to be identified, T1、T2…TnFor endurance testing the different ambient temperatures, Ah, to which the vehicle is subjected1、Ah2…AhnThe accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different temperatures are obtained, Ah is the sum of the accumulated charge throughputs of the batteries corresponding to the endurance test vehicles at different environmental temperatures, namely Ah-Ah1+Ah2+…+Ahn。
6. The method of claim 1, wherein the heuristic algorithm of S2 and S3 is PS 0.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115267546A (en) * | 2022-06-24 | 2022-11-01 | 重庆长安汽车股份有限公司 | Battery life model parameter fitting method |
| WO2024093269A1 (en) * | 2022-10-31 | 2024-05-10 | 比亚迪股份有限公司 | Battery state of health prediction method, electronic device, and readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115267546A (en) * | 2022-06-24 | 2022-11-01 | 重庆长安汽车股份有限公司 | Battery life model parameter fitting method |
| WO2024093269A1 (en) * | 2022-10-31 | 2024-05-10 | 比亚迪股份有限公司 | Battery state of health prediction method, electronic device, and readable storage medium |
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