阅读说明：本技术 基于改进模拟退火算法的电池soc估算方法及系统 (Battery SOC estimation method and system based on improved simulated annealing algorithm ) 是由 刘国平 王静哲 张小兵 刘鹏 王媛媛 于世超 孔江涛 孙海宁 赵锋 袁玉宝 陈贺 于 2019-09-11 设计创作，主要内容包括：本发明提供了一种基于改进模拟退火算法的电池SOC估算方法,通过设置并行计算的改进模拟退火实时算法和改进模拟退火修正算法,来计算电池当前SOC。同时本发明还提供了基于改进模拟退火算法的电池SOC估算系统,包括判断模块、六大获取模块、两大计算模块和结果输出模块,以实现上述基于改进模拟退火算法的电池SOC估算方法。本发明一方面通过获取大量的电池历史实验数据构建成电池历史数据库,并将其电池外部特性参数进行整合,保证了估算精度；另一方面通过设置改进模拟退火实时算法和改进模拟退火修正算法两部分,以提高电池SOC计算的实时性,并提高计算精度。(The invention provides a battery SOC estimation method based on an improved simulated annealing algorithm, which is used for calculating the current SOC of a battery by setting an improved simulated annealing real-time algorithm and an improved simulated annealing correction algorithm which are calculated in parallel. Meanwhile, the invention also provides a battery SOC estimation system based on the improved simulated annealing algorithm, which comprises a judgment module, six acquisition modules, two calculation modules and a result output module, so as to realize the battery SOC estimation method based on the improved simulated annealing algorithm. On one hand, the invention constructs a battery history database by acquiring a large amount of battery history experimental data and integrates the external characteristic parameters of the battery, thereby ensuring the estimation precision; on the other hand, the real-time performance of the SOC calculation of the battery is improved and the calculation precision is improved by arranging an improved simulated annealing real-time algorithm and an improved simulated annealing correction algorithm.)

1. A battery SOC estimation method based on an improved simulated annealing algorithm is characterized in that: comprises the following steps

S1: judging whether the battery is in a working state; if yes, go to S3; if not, go to S2;

s2: acquiring data of a battery history database; acquiring data of a battery cell in the battery; obtaining a current OCV-SOC curve of the battery, a weight used by the current SOC and the current SOC according to an improved simulated annealing correction algorithm;

s3: acquiring data of a battery history database; acquiring data of a battery cell in the battery; and obtaining the current OCV-SOC curve, the weight used by the current SOC and the current SOC of the battery according to the improved simulated annealing correction algorithm and the improved simulated annealing real-time algorithm.

2. The improved simulated annealing algorithm-based battery SOC estimation method according to claim 2, characterized in that: comprises the following steps

S1: judging whether the battery is in a working state; if yes, go to S3; if not, go to S2;

s2: acquiring charging data and discharging data of a battery history database; acquiring historical charging data and discharging data of the battery; obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the discharging data of the battery and an improved simulated annealing correction algorithm;

acquiring the open-circuit voltage of a battery cell in the battery and the ambient temperature of the battery cell in the battery; obtaining the current SOC of the battery according to the open-circuit voltage of a battery cell in the battery and the current OCV-SOC curve of the battery;

s3: acquiring charging data and discharging data of the battery history database; acquiring historical charging data and discharging data of the battery; obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the discharging data of the battery and an improved simulated annealing correction algorithm;

acquiring loop currents at two ends in the battery, voltages at two ends of an electric core in the battery, currents at two ends of the electric core in the battery and the ambient temperature of the battery; acquiring the current SOC weight of the battery; acquiring the last SOC of the battery; calculating to obtain the current SOC of the battery according to the loop current at the two ends of the battery, the voltage at the two ends of the battery core of the battery, the current at the two ends of the battery core of the battery, the ambient temperature of the battery, the current SOC weight of the battery and an improved simulated annealing real-time algorithm;

the improved simulated annealing real-time algorithm has the calculation formula as follows:

where SOC (t) is the current battery capacity, σ_iCorrection coefficient, sigma, for influence of charge-discharge rate on result_TCorrection factor, Q, for the effect of charging and discharging temperature on the results_cIs the battery capacity, i_kIs the charging and discharging current between the current SOC and the last SOC, I (t) is the current of the batterySOC (t-1) is the last SOC value of the battery, and △ t is the time difference between the current calculation and the last calculation.

3. The improved simulated annealing algorithm-based battery SOC estimation method according to claim 2, characterized in that: after S3 also include

S4: checking the SOC; the formula of the SOC verification is as follows:

in the formula of U_tIs a voltage;

if the SOC in the S4 is subtracted from the SOC in the S3 to obtain the current SOC of the battery; if the SOC in S4 is subtracted from the SOC in S3 to be more than 0.0001, the SOC is calculated again until the SOC in S3 is less than or equal to 0.0001 in S4, and the current SOC of the battery is obtained.

4. The improved simulated annealing algorithm-based battery SOC estimation method according to any of claims 1-3, characterized in that: s3 includes the following steps:

s31: acquiring the number of historical charging data and discharging data of the battery; if the number of the historical charging data and the discharging data of the battery is smaller than the preset threshold value of the number of the historical charging data and the discharging data, executing S32; if the number of the historical charging data and the discharging data is not less than the preset threshold value of the number of the historical charging data and the discharging data, executing S33;

s32: computing

Wherein tempmbl (i) is the ith temporary target volume, i and j of QZC are the ith battery in the memory and the jth charging/discharging in the same battery respectively, LSC (j) is the jth historical charging/discharging of the current battery, and LSC (j) is LSC (j-50) and so on when j is greater than 50, k belongs to (R | [1:1:50 ]);

if tempmbl (i)_kWhen the value is equal to 0, thenObtaining an OCV-SOC curve and a weight used by the current SOC of the battery;

if tempmbl (i)_kNot equal to 0, then S33 is executed;

s33: calculating an optimal solution to obtain an OCV-SOC curve and a weight used by the current SOC of the battery;

wherein t is the number of times of charging and discharging the battery,

5. The improved simulated annealing algorithm-based battery SOC estimation method according to claim 4, wherein: the preset historical charging data and discharging data number threshold is 50.

6. A battery SOC estimation system based on an improved simulated annealing algorithm is characterized in that: the battery SOC estimation system based on the improved simulated annealing algorithm comprises:

the first judgment module is used for judging whether the battery is in a working state;

the first obtaining module is connected to the first judging module and used for obtaining the open-circuit voltage of the battery cell in the battery, the voltage at two ends of the battery cell in the battery and the current at two ends of the battery cell in the battery;

the second acquisition module is connected to the first judgment module and used for acquiring the environmental temperature of the battery cell in the battery;

the third obtaining module is connected with the first calculating module and used for obtaining the current OCV-SOC curve of the battery and the weight used by the current SOC of the battery;

the fourth acquisition module is connected with the first judgment module and used for acquiring the charging data and the discharging data of the battery history database;

the fifth acquisition module is connected to the first judgment module and used for acquiring historical charging data and discharging data of the battery;

the sixth acquisition module is connected to the first judgment module and used for acquiring loop currents at two ends of the battery;

the seventh acquisition module is connected to the first judgment module and used for acquiring the last SOC of the battery;

the first calculation module is connected with the fourth acquisition module and the fifth acquisition module and used for calculating and obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the historical discharging data of the battery and an improved simulated annealing correction algorithm;

the second calculation module is connected to the third acquisition module, the first acquisition module, the second acquisition module, the sixth acquisition module and the seventh acquisition module, and is used for calculating and obtaining the current SOC of the battery according to the loop current at two ends of the battery, the voltage at two ends of the battery core in the battery, the current at two ends of the battery core in the battery, the ambient temperature of the battery, the current SOC weight of the battery, the last SOC of the battery and an improved simulated annealing real-time algorithm;

the result output module is connected with the third acquisition module, the first acquisition module, the second acquisition module and the second calculation module and is used for outputting the current SOC of the battery;

the improved simulated annealing real-time algorithm has the calculation formula as follows:

where SOC (t) is the current battery capacity, SOC (t)₀) For the current battery capacity, σ_iCorrection coefficient, sigma, for influence of charge-discharge rate on result_TCorrection factor, Q, for the effect of charging and discharging temperature on the results_cIs the battery capacity, i_kThe current is the charging and discharging current between the current SOC and the last SOC, I (t) is the current battery current, SOC (t-1) is the last SOC value of the battery, and △ t is the time difference between the current calculation and the last calculation.

7. The improved simulated annealing algorithm-based battery SOC estimation system of claim 6, wherein: the current SOC used weight of the battery comprises: k₀、K₁、K₂、K₃、K₄And Q_c。

8. The improved simulated annealing algorithm-based battery SOC estimation system of claim 7, wherein: the third calculation module is connected to the second calculation module and used for SOC verification; the formula of the SOC verification is as follows:

in the formula of U_tIs a voltage, K₀、K₁、K₂、K₃、K₄Is a check parameter;

if the current SOC of the battery obtained by subtracting the current SOC of the battery obtained by the third calculation module from the current SOC of the battery obtained by the second calculation module is less than or equal to 0.0001, the current SOC of the battery obtained by the second calculation module is sent to a result output module; if the current SOC of the battery obtained by subtracting the third calculation module from the current SOC of the battery obtained by the second calculation module is more than 0.0001, the SOC is calculated again until the current SOC of the battery obtained by subtracting the third calculation module from the current SOC of the battery obtained by the second calculation module is less than or equal to 0.0001, and the current SOC of the battery obtained by the second calculation module is sent to a result output module.

9. The improved simulated annealing algorithm-based battery SOC estimation system according to any of claims 6-8, characterized in that: also comprises

The eighth acquisition module is connected to the first judgment module and used for acquiring the number of the historical charging data and the discharging data of the battery;

the second judgment module is connected to the eighth acquisition module and used for judging whether the battery is a new battery; if the number of the historical charging data and the discharging data of the battery is smaller than the preset threshold value of the number of the historical charging data and the discharging data, executing S32; if the number of the historical charging data and the discharging data is not less than the preset threshold value of the number of the historical charging data and the discharging data, executing S33;

a fourth calculating module connected to the second judging module for calculating

Wherein tempmbl (i) is the ith temporary target volume, i and j of QZC are the ith battery in the memory and the jth charging/discharging in the same battery respectively, LSC (j) is the jth historical charging/discharging of the current battery, and LSC (j) is LSC (j-50) and so on when j is greater than 50, k belongs to (R | [1:1:50 ]);

if tempmbl (i)_kWhen the value is equal to 0, then

if tempmbl (i)_kNot equal to 0, then S33 is executed;

the sixth calculation module is connected to the fifth calculation module and used for calculating an optimal solution; obtaining an OCV-SOC curve and a weight used by the current SOC of the battery;

wherein t is the number of battery charge and discharge,

10. The improved simulated annealing algorithm-based battery SOC estimation system of claim 9, wherein: the preset historical charging data and discharging data number threshold is 50.

Technical Field

The invention relates to the technical field of power battery management, in particular to a battery SOC estimation method based on an improved simulated annealing algorithm; meanwhile, the invention also relates to a battery SOC estimation method and system based on the improved simulated annealing algorithm.

Background

With the rapid deterioration of global environment and the increasing shortage of energy, electric vehicles are increasingly receiving attention and being favored. The power battery is an energy source of the electric automobile, plays a decisive role in the overall performance of the whole automobile, and is a core part of the electric automobile. In order to ensure good performance, improve safety and prolong service life of the battery, effective management of the battery is required, provided that the state of charge (SOC) of the battery must be accurately and reliably known. The SOC is the percentage of the capacity which can be released in the power battery according to the specified discharge condition to the available capacity, is an important parameter for representing the energy surplus of the power battery pack, is a key indication of the driving range of the electric vehicle, and is an important basis for the whole vehicle control system to make an optimal energy management strategy. Therefore, how to accurately estimate the SOC of the power battery is a key technology of the electric vehicle.

However, SOC is an internal characteristic of the battery, and cannot be directly measured, and can only be predicted from some directly measured external characteristic parameters such as voltage, current, temperature, internal resistance, and capacitance. In addition, the SOC of the power battery is related to a battery material system, a production process, an application environment and other factors, and presents nonlinear characteristics, so that great difficulty is brought to the estimation of the SOC. Therefore, although there are many kinds of methods for estimating the SOC of the battery, the currently applied SOC estimation methods have some drawbacks and cannot fully meet the actual use requirements of the battery.

The current integration method is that some influence factors are added for correction on the basis of Ah integration, and measurement errors are accumulated and enlarged continuously after long-term use; the discharge test method is the most reliable SOC estimation method, and has the principle that continuous discharge is carried out by adopting constant current, the product of the discharge current and time is the residual capacity, but the product cannot be used for calculating the power battery in a working state; the open-circuit voltage method is the same as the discharge test method, and is not suitable for the SOC estimation of the battery in operation; the ampere-hour integration method does not solve the relation between the electric quantity and the battery state from the inside of the battery, only records the energy entering and exiting the battery from the outside, and inevitably causes the measurement of the electric quantity to lose the precision due to the change of the battery state; the calculation period of the neural network method is long, the requirements of real-time online and high-precision SOC estimation are difficult to meet, meanwhile, the SOC value of the same group of batteries is estimated after long-term use, and the accuracy is greatly reduced; the most common Kalman filtering method for estimating the SOC accuracy greatly depends on the accuracy of a battery model, the working characteristic of the SOC is a highly nonlinear power battery, the Kalman filtering method is subjected to linearization processing, errors are avoided, if the model is not accurately established, the estimation result is not reliable, and meanwhile, the method relates to a very complex algorithm, the calculated amount is extremely large, the required calculation period is long, and the requirement on the hardware performance is strict.

According to the lithium ion battery SOC prediction method based on big data and a bp neural network provided by CN 110045292A, external characteristic parameters of a battery are collected to establish a data set of battery SOC big data; establishing a training set and a test set; constructing a multi-bp neural network prediction model; respectively putting the data sets into bp neural network prediction models with different parameters to obtain measurement precision; and analyzing the measurement precision obtained by the bp neural network prediction model with different parameters to obtain a prediction result.

CN 110068772A relates to a lithium ion battery state of charge estimation method based on an improved fractional order model, which belongs to the technical field of batteries and comprises the following steps: s1: selecting a power battery to be tested, collecting and sorting technical data of the power battery, establishing an improved fractional order battery model of the power battery, and determining model parameters required by estimating the state of charge of the power battery; s2: carrying out a charge-discharge experiment and an Electrochemical Impedance Spectroscopy (EIS) experiment with the current multiplying power of C/20 on a battery to be tested at 25 ℃, further establishing an experiment database of charge-discharge open-circuit voltage and battery model parameters, simulating various real vehicle working conditions, and establishing a working condition test experiment database; s3: performing parameter identification on EIS data to obtain battery model parameters, and obtaining a mapping relation between OCV and SOC through data fitting; s4: and (3) combining an improved fractional order battery model of the power battery with an FEKF algorithm to estimate the SOC state of the battery.

CN 110082684A discloses a lithium battery SOC estimation method based on weighted multi-innovation extended Kalman filtering, including: a second-order Thevenin equivalent circuit model is used as an equivalent circuit model of the lithium battery; establishing an electrical characteristic expression of a second-order Thevenin equivalent circuit model through kirchhoff's law; establishing a state space equation of a second-order Thevenin equivalent circuit model according to an ampere-hour integration method and an electrical characteristic expression, and discretizing the state space equation; identifying parameters of a second-order Thevenin equivalent circuit model through a current pulse experiment, and establishing a functional relation between OCV and SOC through a constant-current charge-discharge experiment; calculating the importance of the corresponding innovation at different moments in the multi-innovation extended Kalman filter based on the idea of calculating the weight of the particles in the particle filtering algorithm; and calculating the weight of each innovation corresponding to each importance according to each importance, and reasonably distributing the weight of each innovation in the multi-innovation extended Kalman.

Although the SOC estimation methods for the battery are various, all the methods have certain defects and are difficult to meet the requirements of real-time online and high-precision estimation of the SOC. In view of this, how to improve the real-time performance of the SOC calculation of the battery and improve the calculation accuracy becomes a problem to be solved by those skilled in the art.

Disclosure of Invention

In view of the above, the present invention is directed to a battery SOC estimation method based on an improved simulated annealing algorithm, so as to improve the real-time performance of the battery SOC calculation and improve the calculation accuracy.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a battery SOC estimation method based on an improved simulated annealing algorithm comprises the following steps:

s1: judging whether the battery is in a working state; if yes, go to S3; if not, go to S2;

s2: acquiring data of a battery history database; acquiring data of a battery cell in the battery; obtaining a current OCV-SOC curve of the battery, a weight used by the current SOC and the current SOC according to an improved simulated annealing correction algorithm;

s3: acquiring data of a battery history database; acquiring data of a battery cell in the battery; and obtaining the current OCV-SOC curve, the weight used by the current SOC and the current SOC of the battery according to the improved simulated annealing correction algorithm and the improved simulated annealing real-time algorithm.

Further, the battery SOC estimation method based on the improved simulated annealing algorithm comprises the following steps:

s1: judging whether the battery is in a working state; if yes, go to S3; if not, go to S2;

s2: acquiring charging data and discharging data of a battery history database; acquiring historical charging data and discharging data of the battery; obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the discharging data of the battery and an improved simulated annealing correction algorithm;

acquiring the open-circuit voltage of a battery cell in the battery and the ambient temperature of the battery cell in the battery; obtaining the current SOC of the battery according to the open-circuit voltage of a battery cell in the battery and the current OCV-SOC curve of the battery;

s3: acquiring charging data and discharging data of the battery history database; acquiring historical charging data and discharging data of the battery; obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the discharging data of the battery and an improved simulated annealing correction algorithm;

acquiring loop currents at two ends in the battery, voltages at two ends of an electric core in the battery, currents at two ends of the electric core in the battery and the ambient temperature of the battery; acquiring the current SOC weight of the battery; acquiring the last SOC of the battery; calculating to obtain the current SOC of the battery according to the loop current at the two ends of the battery, the voltage at the two ends of the battery core of the battery, the current at the two ends of the battery core of the battery, the ambient temperature of the battery, the current SOC weight of the battery and an improved simulated annealing real-time algorithm;

the improved simulated annealing real-time algorithm has the calculation formula as follows:

where SOC (t) is the current battery capacity, σ_iCorrection coefficient, sigma, for influence of charge-discharge rate on result_TCorrection factor, Q, for the effect of charging and discharging temperature on the results_cIs the battery capacity, i_kThe current is the charging and discharging current between the current SOC and the last SOC, I (t) is the current of the battery, SOC (t-1) is the SOC value of the battery last time, and △ t is the time difference between the current calculation and the last calculation.

It should be noted that, if there is no last SOC, the following formula is used for calculation:

in the formula, SOC (t)₀) The SOC of the battery at the initial time is generally 100%.

Further, S3 is followed by

S4: checking the SOC; the formula of the SOC verification is as follows:

in the formula of U_tIs a voltage;

if the SOC in the S4 is subtracted from the SOC in the S3 to obtain the current SOC of the battery; if the SOC in S4 is subtracted from the SOC in S3 to be more than 0.0001, the SOC is calculated again until the SOC in S3 is less than or equal to 0.0001 in S4, and the current SOC of the battery is obtained.

Further, S3 includes the following steps:

s31: acquiring the number of historical charging data and discharging data of the battery; if the number of the historical charging data and the discharging data of the battery is smaller than the preset threshold value of the number of the historical charging data and the discharging data, executing S32; if the number of the historical charging data and the discharging data is not less than the preset threshold value of the number of the historical charging data and the discharging data, executing S33;

s32: computing

Wherein tempmbl (i) is the ith temporary target volume, i and j of QZC are the ith battery in the memory and the jth charging/discharging in the same battery respectively, LSC (j) is the jth historical charging/discharging of the current battery, and LSC (j) is LSC (j-50) and so on when j is greater than 50, k belongs to (R | [1:1:50 ]);

if tempmbl (i)_kWhen the value is equal to 0, then

Obtaining an OCV-SOC curve and a weight used by the current SOC of the battery;

if tempmbl (i)_kNot equal to 0, then S33 is executed;

s33: calculating an optimal solution to obtain an OCV-SOC curve and a weight used by the current SOC of the battery;

wherein t is the number of times of charging and discharging the battery,

k is the boltzmann constant in physics.

Further, the preset historical charging data and discharging data number threshold is 50.

Meanwhile, the invention also provides a battery SOC estimation system based on the improved simulated annealing algorithm, so as to realize the battery SOC estimation method based on the improved simulated annealing algorithm.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a battery SOC estimation system based on an improved simulated annealing algorithm, comprising:

the first judgment module is used for judging whether the battery is in a working state;

the first obtaining module is connected to the first judging module and used for obtaining the open-circuit voltage of the battery cell in the battery, the voltage at two ends of the battery cell in the battery and the current at two ends of the battery cell in the battery;

the second acquisition module is connected to the first judgment module and used for acquiring the environmental temperature of the battery cell in the battery;

the third obtaining module is connected with the first calculating module and used for obtaining the current OCV-SOC curve of the battery and the weight used by the current SOC of the battery;

the fourth acquisition module is connected with the first judgment module and used for acquiring the charging data and the discharging data of the battery history database;

the fifth acquisition module is connected to the first judgment module and used for acquiring historical charging data and discharging data of the battery;

the sixth acquisition module is connected to the first judgment module and used for acquiring loop currents at two ends of the battery;

the seventh acquisition module is connected to the first judgment module and used for acquiring the last SOC of the battery;

the first calculation module is connected with the fourth acquisition module and the fifth acquisition module and used for calculating and obtaining a current OCV-SOC curve of the battery and a weight used by the current SOC of the battery according to the charging data and the discharging data of the battery historical database, the historical charging data and the historical discharging data of the battery and an improved simulated annealing correction algorithm;

the second calculation module is connected to the third acquisition module, the first acquisition module, the second acquisition module, the sixth acquisition module and the seventh acquisition module, and is used for calculating and obtaining the current SOC of the battery according to the loop current at two ends of the battery, the voltage at two ends of the battery core in the battery, the current at two ends of the battery core in the battery, the ambient temperature of the battery, the current SOC weight of the battery, the last SOC of the battery and an improved simulated annealing real-time algorithm;

the result output module is connected with the third acquisition module, the first acquisition module, the second acquisition module and the second calculation module and is used for outputting the current SOC of the battery;

the improved simulated annealing real-time algorithm has the calculation formula as follows:

where SOC (t) is the current battery capacity, SOC (t)₀) For the current battery capacity, σ_iCorrection coefficient, sigma, for influence of charge-discharge rate on result_TCorrection factor, Q, for the effect of charging and discharging temperature on the results_cIs the battery capacity, i_kThe current charge and discharge current between the current SOC and the last SOC, and I (t) is the current battery current; sOC (t-1) is the last SOC value of the battery, and △ t is the time difference between the current calculation and the last calculation.

Further, the weight used by the current SOC of the battery includes: k₀、K₁、K₂、K₃、K₄And Q_c。

Further, the system also comprises a third calculation module which is connected with the second calculation module and used for SOC verification; the formula of the SOC verification is as follows:

in the formula of U_tIs a voltage, K₀、K₁、K₂、K₃、K₄Is a check parameter;

if the current SOC of the battery obtained by subtracting the current SOC of the battery obtained by the third calculation module from the current SOC of the battery obtained by the second calculation module is less than or equal to 0.0001, the current SOC of the battery obtained by the second calculation module is sent to a result output module; if the current SOC of the battery obtained by subtracting the third calculation module from the current SOC of the battery obtained by the second calculation module is more than 0.0001, the SOC is calculated again until the current SOC of the battery obtained by subtracting the third calculation module from the current SOC of the battery obtained by the second calculation module is less than or equal to 0.0001, and the current SOC of the battery obtained by the second calculation module is sent to a result output module.

The system further comprises an eighth acquiring module connected to the first judging module and used for acquiring the number of the historical charging data and the discharging data of the battery;

the second judgment module is connected to the eighth acquisition module and used for judging whether the battery is a new battery; if the number of the historical charging data and the discharging data of the battery is smaller than the preset threshold value of the number of the historical charging data and the discharging data, executing S32; if the number of the historical charging data and the discharging data is not less than the preset threshold value of the number of the historical charging data and the discharging data, executing S33;

a fourth calculating module connected to the second judging module for calculating

Wherein tempmbl (i) is the ith temporary target volume, i and j of QZC are the ith battery in the memory and the jth charging/discharging in the same battery respectively, LSC (j) is the jth historical charging/discharging of the current battery, and LSC (j) is LSC (j-50) and so on when j is greater than 50, k belongs to (R | [1:1:50 ]);

if tempmbl (i)_kWhen the value is equal to 0, thenOutputting an OCV-SOC curve and a weight used by the current SOC of the battery;

if tempmbl (i)_kNot equal to 0, then S33 is executed;

the sixth calculation module is connected to the fifth calculation module and used for calculating an optimal solution; the obtained OCV-SOC curve was,

and the current SOC used weight of the battery;

wherein t is the number of battery charge and discharge,

k is the boltzmann constant in physics.

Further, the preset historical charging data and discharging data number threshold is 50.

Compared with the prior art, the invention has the following advantages:

on one hand, the invention constructs a battery history database by acquiring a large amount of battery history experimental data and integrates the external characteristic parameters of the battery, thereby ensuring the estimation precision; on the other hand, the real-time performance of the SOC calculation of the battery is improved and the calculation precision is improved by arranging two parts of an improved simulated annealing real-time algorithm and an improved simulated annealing correction algorithm which are calculated in parallel. The improved simulated annealing real-time algorithm is simple, convenient and quick to calculate, the calculation time is millisecond level, the battery electric quantity can be quickly tracked in real time, the battery SOC can be real-timely, and the condition that a user can see the residual electric quantity of the battery in real time can be met. The improved simulated annealing correction algorithm is based on big data analysis, a large amount of other battery charging and discharging experimental data in a battery historical database and historical charging and discharging records of a corresponding battery are needed, and finally data needed by a real-time algorithm are obtained; by updating the OCV-SOC curve and the current SOC weight of the battery, the method can avoid the accumulative error generated after the battery is charged and discharged for many times and caused by the characteristic change of the battery in real time. Secondly, by adopting the calculation scheme of the invention, the current SOC calculation error of the battery is not more than 0.2 percent, thereby realizing effective management of the battery and improving the customer experience; finally, the battery can be ensured to have good performance, the use safety of the battery is improved, the service life of the battery is prolonged, and the running cost of the electric automobile is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts. In the drawings:

FIG. 1 is a schematic flow chart of a battery SOC estimation method based on an improved simulated annealing algorithm in embodiment 1 of the present invention;

FIG. 2 is a block diagram of a battery SOC estimation system based on an improved simulated annealing algorithm in embodiment 1 of the present invention;

fig. 3 is an OCV-SOC curve of the "fresh battery" obtained in example 2 of the present invention.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.

As will be appreciated by one skilled in the art, embodiments of the present invention may implement a system, apparatus, device, method or computer program. Thus, the present invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

Moreover, any number of elements in the drawings are by way of example and not by way of limitation, and any naming is by way of distinction only and not by way of limitation.

In the invention, the battery pack of the electric vehicle is formed by combining a plurality of electric cores in series and parallel connection, so the current used in the invention is the loop current divided by the number m of the parallel electric cores, namely the current flowing through the electric cores; the battery voltage is obtained by connecting the battery cells in series, so the voltage is the voltage at two ends of the battery cells; the SOC is a ratio of a remaining capacity of the battery after being used for a certain period of time or left unused for a long time to a capacity of its full charge state, so that it is not necessary to distinguish between the SOC of the battery and the SOC of the battery cell, and the two are identical.

In the present invention, the OCV-SOC curve is a voltage-electric quantity curve.

It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.

The invention relates to a battery SOC estimation method based on an improved simulated annealing algorithm, which mainly comprises the following design ideas: the real-time performance of the SOC calculation of the battery is improved and the calculation precision is improved by arranging an improved simulated annealing real-time algorithm and an improved simulated annealing correction algorithm.

Through the arrangement of the overall design idea, the electric quantity of the battery and the SOC of the battery can be tracked rapidly in real time; meanwhile, the accumulated error generated after the battery is charged and discharged for many times and caused by the characteristic change of the battery in real time can be avoided, and the purposes that a user can see the residual electric quantity of the battery in real time and the user experience is improved are finally achieved.