CN114355211A - Lithium ion power battery residual capacity estimation method - Google Patents

Lithium ion power battery residual capacity estimation method Download PDF

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Publication number
CN114355211A
CN114355211A CN202111499062.8A CN202111499062A CN114355211A CN 114355211 A CN114355211 A CN 114355211A CN 202111499062 A CN202111499062 A CN 202111499062A CN 114355211 A CN114355211 A CN 114355211A
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soc
battery
ampere
kalman filtering
estimating
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杨一鹏
林德荣
龚学锐
匡曙龙
邱长青
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Wuhan Institute of Marine Electric Propulsion China Shipbuilding Industry Corp No 712 Institute CSIC
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Wuhan Institute of Marine Electric Propulsion China Shipbuilding Industry Corp No 712 Institute CSIC
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator

Abstract

The invention discloses a method for estimating the remaining capacity of a lithium ion power battery, which comprises the steps of firstly analyzing and processing real-time sampled data and historical data, and calculating the initial value of the current SOC according to a corresponding curve of open-circuit voltage and SOC; and finally, if the error exceeds the tolerance, the SOC result is based on the ampere-hour integration algorithm, and if the result does not exceed the error tolerance, the result is based on the Kalman filtering calculation result. The method can ensure reliable and stable calculation of the residual capacity of the battery under the complex working conditions of the electric automobile, the electric ship and the hybrid ship, and improve the estimation precision of the residual capacity of the battery in the whole life cycle of the system.

Description

Lithium ion power battery residual capacity estimation method
Technical Field
The invention belongs to the technical field of high-precision estimation of the residual capacity of a lithium ion power battery, and particularly relates to a method for estimating the residual capacity of the lithium ion power battery.
Background
As one of core algorithms of the battery management system, the accurate SOC is a prerequisite condition for accurately representing the endurance mileage of an electric automobile, an electric ship, an underwater vehicle and the like, and is also a guarantee for normal and effective work of other functions of the battery management system.
The traditional method for estimating the residual capacity of the power battery mainly comprises an ampere-hour integration method and an open-circuit voltage method, but the ampere-hour integration method is greatly influenced by the accuracy of a current sensor, and errors are continuously accumulated along with the accumulation of time. Aiming at the problem that the estimation accuracy of the SOC of the power battery of the pure electric vehicle is low under the condition of large changes of the environmental temperature and the discharge current, the EKF is considered as an estimation algorithm of the SOC of the power battery of the electric vehicle with high accuracy. However, the essence of the EKF algorithm lies in the linearized recursion of the nonlinear function, and in order to ensure consistent convergence, the algorithm is mostly suitable for the situation that the system has general nonlinearity and no strong coupling.
Due to the fact that the driving states of the automobile and the ship are complex and unfixed, and meanwhile, due to the nonlinear relation between the battery model parameters and the battery capacity, a single residual electric quantity estimation method is not enough to match all working conditions of the battery, and comprehensive estimation accuracy is low.
Disclosure of Invention
The invention aims to solve the problems that a single residual electric quantity estimation method is not enough to match all working conditions of a battery and the comprehensive estimation precision is low due to the fact that a driving state is complex and unfixed, and meanwhile, the nonlinear relation between battery model parameters and battery capacity causes that the residual electric quantity estimation method is improved, and the algorithm for estimating the residual electric quantity of the battery by combining the ampere-hour integration method and Kalman filtering is provided.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for estimating the residual capacity of a lithium ion power battery comprises the following steps
Step one, starting sampling after a battery management system is powered on for the first time: collecting the current battery temperature Tt of the battery, and taking the current voltage Ut as an initial open-circuit voltage OCV;
step two, processing the real-time sampling data and the historical storage data: analyzing and counting the collected information such as the current voltage Ut, the current battery temperature Tt, the current It, the SOC value stored before starting, the battery system standing time and the like;
thirdly, based on the initial open-circuit voltage OCV and the current battery temperature Tt, combining a historical SOC-OCV curve to obtain an initial value SOC of the battery residual capacity SOC corresponding to the initial open-circuit voltage OCV for the first electrification of the algorithm0And providing an SOC-OCV curve for Kalman filtering;
step four, establishing a battery equivalent circuit model, and estimating parameters of the battery equivalent circuit model based on an improved least square method:
selecting a second-order circuit model with 2 RC networks connected in series, taking current It as input excitation, U as output end voltage, OCV as open-circuit voltage value, and making y equal to OCV-U as output response according to the electrical relation of the battery equivalent circuit model
Figure BDA0003402044410000021
In the formula, R0Is the ohmic internal resistance, R, of the battery1、C1As a parameter of the first RC network, R2、C2Is a parameter of the second RC network; the following equation set can be obtained through solution:
Figure BDA0003402044410000031
in the formula, the coefficient α on the right side of the equation set1、α2、β0、β1、β2Has been solved by a modified recursive least square method, T is a sampling period, and is also knownQuantity, the left coefficient R can be obtained by solving the system of equations0、R1、C1、R2、C2；
Fifthly, estimating the SOC value of the current system by using an extended Kalman filtering algorithm:
the Kalman filter equation is as follows
xk＝Axk-1+Buk-1+wk-1
zk＝Hxk+vk-1
In the formula, xkIs a state variable of the system, zkIs an observation variable of the system, and the process excitation noise and the observation noise of the system are respectively used as random signals wkAnd vkDenotes ukRepresenting a control function or system stimulus; this formula is called the random state difference equation;
combining the second-order equivalent circuit model of the battery in the step four, the following relational formula exists in each state parameter under the action of current excitation:
Figure BDA0003402044410000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003402044410000033
a voltage that is an ohmic internal resistance;
Figure BDA0003402044410000034
is the voltage of the first RC network;
Figure BDA0003402044410000035
is the voltage of the second RC network; ccapRepresents the capacity of the battery and has the unit of ampere-second;
the state equation and the observation equation are as follows:
xk＝Axk-1+Bik-1+wk-1
zk＝Hxk+vk-1
Figure BDA0003402044410000041
Figure BDA0003402044410000042
Figure BDA0003402044410000043
H＝[-1 -1 -1 h]
Figure BDA0003402044410000044
in the formula (I), the compound is shown in the specification,
Figure BDA0003402044410000045
UEMFis a function of open circuit voltage with respect to SOC, and h is open circuit voltage with respect to UEMFA derivative function with respect to SOC;
step six, taking the SOC estimated value calculated in the first 10 periods of the extended Kalman filtering algorithm as the initial value SOC of the ampere-hour integration method0Estimating the current SOC value by using an ampere-hour integration algorithm based on optimized compensation;
the ampere-hour integral algorithm calculation formula based on the optimized compensation is as follows:
Figure BDA0003402044410000046
in the formula, omega (tau) is a temperature correction coefficient, gamma is a function of the cycle number of the battery, eta is a charge-discharge multiplying factor, beta is a battery aging and self-discharge correction coefficient, and the time t, i (t) is substituted into the formula to obtain the estimated value SOC of the SOC at the current momentt；
Seventhly, judging a Kalman filtering algorithm result SOCkWhether the tolerance of SOC calculation error is exceeded or not Δ SOC: SOC by ampere-hour integration methodtEstimation resultAs a reference, the SOC based on the extended Kalman filter algorithm is judgedkWhether the estimated value exceeds the range of the system error tolerance
|SOCk-SOCt|≤ΔSOC；
Step eight, if the result exceeds the SOC error tolerance Delta SOC, skipping to step nine; if the result is within the error tolerance range, jumping to the step ten;
step nine, based on the improved ampere-hour integration method result, calculating the current period SOC estimated value SOC ═ SOCtJumping to the eleventh step;
step ten, mainly using the result of Kalman filtering algorithm to calculate the current period SOC estimation value SOC ═ SOCkJumping to the eleventh step;
step eleven, taking the calculated SOC estimated value SOC 'as the SOC estimated value SOC of the current period, namely SOC', and updating the real-time sampling data of the current period;
and step twelve, finishing the algorithm task of jointly estimating the residual electric quantity of the battery based on the ampere-hour integral and the Kalman filtering by the system.
Further, the specific SOC operation process in the fifth step is as follows:
firstly, obtaining an estimated value of a state variable at the moment k according to a state matrix equation as follows:
xk *＝Axk-1+Bik-1+wk-1
the star indicates that the data is an estimated value obtained according to a state equation, and accordingly, a covariance matrix after state deduction is obtained:
Figure BDA0003402044410000051
second, solve Kalman gain Kk
Figure BDA0003402044410000052
Wherein R iskIs an observation noiseAn acoustic covariance matrix.
Thirdly, correcting the estimation value of the state vector and a corresponding covariance matrix according to Kalman gain:
xk＝xk *+Kk(zk-Hxk *)＝xk *+Kk(uk-Hxk *)
Figure BDA0003402044410000053
where I is the identity matrix. In addition, the working voltage u at two ends of the battery is adoptedkAs an observed variable, therefore, there is zk＝uk；
After the third step is executed, the time k is increased by 1, then the step is circulated back to the first step for continuous calculation, so that the estimation of the residual capacity value of the battery based on the battery model and the extended Kalman filter is realized, and the SOC estimation value SOC at the current moment is obtainedk。
In the method for estimating the remaining capacity of the lithium ion power battery, the SOC-OCV curve for Kalman filtering in the third step is a functional relation between the open-circuit voltage OCV and the SOC, which is obtained by standing the battery for a fixed time at different temperatures and different SOCs.
According to the method for estimating the remaining capacity of the lithium ion power battery, the battery remaining capacity is estimated through a Kalman filtering algorithm in the fifth step, 10 periods of Kalman filtering are used as input values of an ampere-hour integration method, accumulated errors of the ampere-hour integration method can be effectively optimized, and the overall SOC estimation precision is improved.
In the sixth step, an improved ampere-hour integral algorithm is adopted, and the temperature, the cycle number of the battery, the charge-discharge multiplying power, the battery aging and the self-discharge condition are used as optimization parameters of the ampere-hour integral method.
The method for estimating the residual electric quantity of the lithium ion power battery adopts an ampere-hour integral algorithm, aims at the problems that the discharge multiplying power of the battery is suddenly changed under the power utilization conditions of an electric automobile, an electric ship, a hybrid electric automobile and the like under complex working conditions, the charging and discharging working conditions are frequently switched, and Kalman filtering divergence is caused, utilizes the comparison of errors between the ampere-hour integral within 10s and the Kalman filtering algorithm, and utilizes the ampere-hour integral to inhibit the Kalman filtering divergence if the difference is greater than the error tolerance, so that the estimation precision of the residual capacity of a local battery is improved.
The invention has the beneficial effects that:
according to the method, the ampere-hour integral and Kalman filtering combined estimation method is adopted, aiming at the estimation of the battery residual capacity under the complex working condition, the ampere-hour integral method and the Kalman filtering algorithm combined estimation can inhibit the divergence of Kalman filtering under the condition of eliminating the accumulated error of the ampere-hour integral method, and the estimation precision of the battery residual capacity can reach more than 97%.
The method can ensure reliable and stable calculation of the residual capacity of the battery under the complex working conditions of the electric automobile, the electric ship and the hybrid ship, and improve the estimation precision of the residual capacity of the battery in the whole life cycle of the system.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
The invention relates to a high-precision high-stability battery residual capacity estimation method, and provides an improved ampere-hour integral method and Kalman filtering combined estimation battery residual capacity algorithm to solve the problems that the running states of electric automobiles, electric ships and hybrid ships are complex and unfixed, and simultaneously, the nonlinear relation between battery model parameters and battery capacity causes that a single residual capacity estimation method is not enough to match all working conditions of a battery, and the comprehensive estimation precision is low.
Aiming at the lithium ion power battery equivalent model, the estimation SOC algorithm of the Kalman filtering algorithm is optimized by adopting an improved ampere-hour integral method, and the condition of estimation error divergence is restrained by adopting the optimized filtering algorithm, so that the aim of improving the estimation precision of the residual electric quantity of the battery is fulfilled.
Firstly, analyzing and processing real-time sampled data and historical data, and calculating a current SOC initial value according to a corresponding curve of open-circuit voltage and SOC; and then, estimating the SOC by using a Kalman filtering algorithm and an ampere-hour integration algorithm respectively. Then, taking an ampere-hour integration method as a reference, and judging whether the Kalman filtering result exceeds the tolerance of the SOC calculation error; and finally, if the error exceeds the tolerance, the SOC result is based on an ampere-hour integration algorithm, and if the result does not exceed the error tolerance, the result is based on a Kalman filtering calculation result.
Detailed description of the preferred embodiments
Referring to fig. 1, the method for estimating the remaining capacity of a lithium ion power battery disclosed by the invention comprises the following steps
Step one, starting sampling after a battery management system is powered on for the first time: and collecting the current battery temperature Tt of the battery, and taking the current voltage Ut as the initial open-circuit voltage OCV.
Step two, processing the real-time sampling data and the historical storage data: and analyzing and counting the collected information such as the current voltage Ut, the current battery temperature Tt, the current It, the SOC value stored before starting, the battery system standing time and the like.
Thirdly, based on the initial open-circuit voltage OCV and the current battery temperature Tt, combining a historical SOC-OCV curve to obtain an initial value SOC of the battery residual capacity SOC corresponding to the initial open-circuit voltage OCV for the first electrification of the algorithm0And providing an SOC-OCV curve for Kalman filtering.
Step four, establishing a battery equivalent circuit model, and estimating parameters of the battery equivalent circuit model based on an improved least square method:
selecting a second-order circuit model with 2 RC networks connected in series, taking current It as input excitation, U as output end voltage, OCV as open-circuit voltage value, and making y equal to OCV-U as output response according to the electrical relation of the battery equivalent circuit model
Figure BDA0003402044410000081
In the formula, R0Is the ohmic internal resistance, R, of the battery1、C1As a parameter of the first RC network, R2、C2Is a parameter of the second RC network; the following equation set can be obtained through solution:
Figure BDA0003402044410000082
in the formula, the coefficient α on the right side of the equation set1、α2、β0、β1、β2The left coefficient R can be obtained by solving the equation system after T is the sampling period and is also a known quantity which is solved by an improved recursive least square method0、R1、C1、R2、C2。
Fifthly, estimating the SOC value of the current system by using an extended Kalman filtering algorithm:
the Kalman filter equation is as follows
xk＝Axk-1+Buk-1+wk-1
zk＝Hxk+vk-1
In the formula, xkIs a state variable of the system, zkIs an observation variable of the system, and the process excitation noise and the observation noise of the system are respectively used as random signals wkAnd vkDenotes ukRepresenting a control function or system stimulus; this formula is called the random state difference equation;
combining the second-order battery model in the fourth step, under the current excitation action, the following relational formula exists for each state parameter:
Figure BDA0003402044410000091
in the formula (I), the compound is shown in the specification,
Figure BDA0003402044410000092
a voltage that is an ohmic internal resistance;
Figure BDA0003402044410000093
is the voltage of the first RC network;
Figure BDA0003402044410000094
is the voltage of the second RC network; ccapRepresents the capacity of the battery and has the unit of ampere-second;
the state equation and the observation equation are as follows:
xk＝Axk-1+Bik-1+wk-1
zk＝Hxk+vk-1
Figure BDA0003402044410000095
Figure BDA0003402044410000096
Figure BDA0003402044410000097
H＝[-1-1-1h]
Figure BDA0003402044410000098
in the formula (I), the compound is shown in the specification,
Figure BDA0003402044410000101
UEMFis a function of open circuit voltage with respect to SOC, and h is open circuit voltage with respect to UEMFDerivative function with respect to SOC.
The specific SOC operation process is as follows:
firstly, obtaining an estimated value of a state variable at the moment k according to a state matrix equation as follows:
xk *＝Axk-1+Bik-1+wk-1
the star here indicates that the data is estimated according to the state equation, and accordingly, the covariance matrix after the state extrapolation is obtained:
Figure BDA0003402044410000102
second, solve Kalman gain Kk
Figure BDA0003402044410000103
Wherein R iskIs the observed noise covariance matrix.
Thirdly, correcting the estimation value of the state vector and a corresponding covariance matrix according to Kalman gain:
xk＝xk *+Kk(zk-Hxk *)＝xk *+Kk(uk-Hxk *)
Figure BDA0003402044410000104
where I is the identity matrix. In addition, the working voltage u at two ends of the battery is adoptedkAs an observed variable, therefore, there is zk＝uk。
After the third step is executed, the time k is increased by 1, then the step is circulated back to the first step for continuous calculation, so that the estimation of the residual capacity value of the battery based on the battery model and the extended Kalman filter is realized, and the SOC estimation value SOC at the current moment is obtainedk。
Step six, taking the SOC estimated value calculated in the first 10 periods of the extended Kalman filtering algorithm as the initial value SOC of the ampere-hour integration method0Estimating the current SOC value by using an ampere-hour integration algorithm based on optimized compensation;
the ampere-hour integral algorithm calculation formula based on the optimized compensation is as follows:
Figure BDA0003402044410000105
in the formula, omega (tau) is a temperature correction coefficient, gamma is a function of the cycle number of the battery, eta is a charge-discharge multiplying factor, beta is a battery aging and self-discharge correction coefficient, and the time t, i (t) is substituted into the formula to obtain the estimated value SOC of the SOC at the current momentt。
Seventhly, judging a Kalman filtering algorithm result SOCkWhether the tolerance of SOC calculation error is exceeded or not Δ SOC: SOC by ampere-hour integration methodtThe estimation result is used as a reference to judge the SOC based on the extended Kalman filtering algorithmkWhether the estimated value exceeds the range of the system error tolerance
|SOCk-SOCt|≤ΔSOC。
Step eight, if the result exceeds the SOC error tolerance Delta SOC, skipping to step nine; and if the result is within the error tolerance range, jumping to a step ten.
Step nine, based on the improved ampere-hour integration method result, calculating the current period SOC estimated value SOC ═ SOCtAnd jumping to the step eleven.
Step ten, mainly using the result of Kalman filtering algorithm to calculate the current period SOC estimation value SOC ═ SOCkAnd jumping to the step eleven.
Step eleven, taking the calculated SOC estimated value SOC 'as the SOC estimated value SOC of the current period, namely SOC', and updating the real-time sampling data of the current period.
And step twelve, finishing the algorithm task of jointly estimating the residual electric quantity of the battery based on the ampere-hour integral and the Kalman filtering by the system.
Detailed description of the invention
The third step of the algorithm for jointly estimating the residual electric quantity of the battery by the ampere-hour integration and the kalman filter in the first specific embodiment is further described, and the algorithm is characterized in that an SOC-OCV function is a functional relation between an open-circuit voltage OCV and an SOC, which is obtained by standing the battery for a fixed time at different temperatures and different SOCs.
Detailed description of the invention
The embodiment further illustrates the fifth step of the algorithm for jointly estimating the residual capacity of the battery by the ampere-hour integration and the kalman filtering in the first specific embodiment, and is characterized in that the kalman filtering algorithm is used for estimating the residual capacity of the battery, so that the accumulated error of the ampere-hour integration method can be effectively optimized, and the global SOC estimation precision is improved.
The Kalman filtering algorithm is used for estimating the residual capacity of the battery, 10 periods of Kalman filtering are used as input values of the ampere-hour integration method, the accumulated error of the ampere-hour integration method can be effectively optimized, and the global SOC estimation precision is improved.
Detailed description of the invention
The embodiment further describes the sixth step of the algorithm for jointly estimating the residual electric quantity of the battery by the ampere-hour integration and the kalman filtering in the first specific embodiment, and is characterized in that the improved ampere-hour integration algorithm is adopted, and the temperature, the cycle number of the battery, the charge-discharge rate, the battery aging and the self-discharge condition are used as the optimized parameters of the ampere-hour integration method, so that the estimation precision of the residual capacity of the battery can be effectively improved.
Detailed description of the preferred embodiment
The seventh step of the algorithm for jointly estimating the residual electric quantity of the battery by using the ampere-hour integral and the kalman filter in the first specific embodiment is further described, and the algorithm is characterized in that the ampere-hour integral algorithm is adopted, and the battery discharge multiplying power is suddenly changed and the charge and discharge working conditions are frequently switched to cause divergence of the kalman filter according to the electricity utilization conditions of the electric automobile, the electric ship, the hybrid electric automobile and the like under the complex working conditions. The ampere-hour integration method is involved in correction, so that Kalman filtering divergence can be effectively inhibited, and the estimation precision of the residual capacity of the local battery is improved.
Detailed description of the invention
The embodiment further describes an ampere-hour integral and Kalman filtering combined estimation battery residual capacity algorithm in the first specific embodiment, and the method is characterized in that an ampere-hour integral and Kalman filtering combined estimation method is adopted, and aiming at battery residual capacity estimation under complex working conditions, the ampere-hour integral method and Kalman filtering algorithm combined estimation can inhibit divergence of Kalman filtering under the condition of eliminating an accumulated error of the ampere-hour integral method, and the estimation precision of the battery residual capacity can reach below 3%.
The ampere-hour integral algorithm is adopted, aiming at the problems that the battery discharge multiplying power can suddenly change and the charging and discharging working conditions can be frequently switched to cause Kalman filtering divergence under the power consumption conditions of an electric automobile, an electric ship, a hybrid electric automobile and the like under complex working conditions, the error comparison between the ampere-hour integral within 10s and the Kalman filtering algorithm is utilized, if the difference is greater than the error tolerance, the ampere-hour integral is utilized to inhibit the Kalman filtering divergence, and the estimation precision of the residual capacity of a local battery is improved. By adopting the ampere-hour integral and Kalman filtering combined estimation method, aiming at the estimation of the residual capacity of the battery under a complex working condition, the ampere-hour integral method and Kalman filtering algorithm combined estimation can inhibit the divergence of Kalman filtering under the condition of eliminating the accumulated error of the ampere-hour integral method, and the estimation precision of the residual capacity of the battery can reach more than 97 percent.
The present invention is not limited to the above-mentioned preferred embodiments, and any person skilled in the art can derive other variants and modifications within the scope of the present invention, however, any variation in shape or structure is within the scope of protection of the present invention, and any technical solution similar or equivalent to the present application is within the scope of protection of the present invention.

Claims (6)

1. A method for estimating the residual capacity of a lithium ion power battery is characterized by comprising the following steps: comprises the following steps
Step one, collecting the current battery temperature Tt of a battery, and taking the current voltage Ut as an initial open-circuit voltage OCV;
sampling an SOC value stored before starting up;
thirdly, based on the initial open-circuit voltage OCV and the current battery temperature Tt, combining a historical SOC-OCV curve to obtain an initial value SOC of the battery residual capacity SOC corresponding to the initial open-circuit voltage OCV0Obtaining an SOC-OCV curve for Kalman filtering;
step four, establishing a battery equivalent circuit model, and estimating parameters of the battery equivalent circuit model based on an improved least square method:
selecting a second-order circuit model with 2 RC networks connected in series, taking current It as input excitation, U as output end voltage, OCV as open-circuit voltage value, and making y equal to OCV-U as output response according to the electrical relation of the battery equivalent circuit model
Figure FDA0003402044400000011
In the formula, R0Is the ohmic internal resistance, R, of the battery1、C1As a parameter of the first RC network, R2、C2Is a parameter of the second RC network; the following equation set can be obtained through solution:
Figure FDA0003402044400000012
in the formula, the coefficient α on the right side1、α2、β0、β1、β2Has been solved by an improved recursive least square method, T is the sampling period, and the left coefficient R can be obtained by solving the equation system0、R1、C1、R2、C2；
Fifthly, estimating the SOC value of the current system by using an extended Kalman filtering algorithm:
xk＝Axk-1+Buk-1+wk-1
zk＝Hxk+vk-1
in the formula, xkIs a state variable of the system, zkIs an observed variable of the system, wkAnd vkProcess excitation noise and observation noise, u, representing the systemkRepresenting a control function or system stimulus;
in combination with a battery second-order circuit model, the following relational formula exists for each state parameter under the action of current excitation:
Figure FDA0003402044400000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003402044400000022
a voltage that is an ohmic internal resistance;
Figure FDA0003402044400000023
is the voltage of the first RC network;
Figure FDA0003402044400000024
is the voltage of the second RC network; ccapIs representative of the capacity of the battery;
the state equation and the observation equation are as follows:
xk＝Axk-1+Bik-1+wk-1
zk＝Hxk+vk-1
Figure FDA0003402044400000025
Figure FDA0003402044400000026
Figure FDA0003402044400000027
H＝[-1 -1 -1 h]
Figure FDA0003402044400000028
in the formula (I), the compound is shown in the specification,
Figure FDA0003402044400000029
UEMFis a function of open circuit voltage with respect to SOC, and h is open circuit voltage with respect to UEMFA derivative function with respect to SOC;
step six, mixingThe SOC estimated value calculated in the first 10 periods of the extended Kalman filtering algorithm is used as the initial value SOC of the ampere-hour integration method0Estimating the current SOC value by using an ampere-hour integration algorithm based on optimized compensation;
the ampere-hour integral algorithm calculation formula based on the optimized compensation is as follows:
Figure FDA0003402044400000031
in the formula, omega (tau) is a temperature correction coefficient, gamma is a function of the cycle number of the battery, eta is a charge-discharge multiplying factor, beta is a battery aging and self-discharge correction coefficient, and time t, i (t) is substituted into a formula to obtain an estimated value SOC of the SOC at the current momentt；
Seventhly, judging a Kalman filtering algorithm result SOCkWhether the tolerance of SOC calculation error is exceeded or not Δ SOC: SOC by ampere-hour integration methodtThe estimation result is used as a reference to judge the SOC based on the extended Kalman filtering algorithmkWhether the estimated value exceeds the range of the system error tolerance
|SOCk-SOCt|≤ΔSOC；
Step eight, if the result exceeds the SOC error tolerance Delta SOC, skipping to step nine; if the result is within the error tolerance range, jumping to the step ten;
step nine, calculating the current period SOC estimated value SOC' to SOC by the improved ampere-hour integration method resulttJumping to the eleventh step;
step ten, calculating the current period SOC estimated value SOC' to SOC according to the result of the Kalman filtering algorithmkJumping to the eleventh step;
step eleven, taking the calculated SOC estimated value SOC 'as the SOC estimated value SOC of the current period, namely SOC', and updating the real-time sampling data of the current period;
and step twelve, finishing the task of the algorithm for estimating the residual battery power by the system.
2. The method for estimating the remaining capacity of the lithium-ion power battery according to claim 1, wherein the specific SOC calculation process in the fifth step is as follows:
firstly, obtaining an estimated value of a state variable at the moment k according to a state matrix equation as follows:
xk *＝Axk-1+Bik-1+wk-1
the star marks show that the data is estimated according to a state equation to obtain a covariance matrix after state recurrence is finished
Figure FDA0003402044400000041
Second, solve Kalman gain
Figure FDA0003402044400000042
In the formula, RkIs an observed noise covariance matrix;
thirdly, correcting the estimation value of the state vector and a corresponding covariance matrix according to Kalman gain:
xk＝xk *+Kk(zk-Hxk *)＝xk *+Kk(uk-Hxk *)
Figure FDA0003402044400000043
in which I is a unit matrix and the operating voltage u across the cell is usedkAs the observed variable zk＝uk；
Circulating the first step, continuing to calculate to obtain the SOC estimated value SOC at the current momentk。
3. The method for estimating the remaining capacity of the lithium-ion power battery according to claim 1 or 2, wherein the SOC-OCV curve for kalman filtering in the third step is a function relationship between the open-circuit voltage OCV and the SOC of the battery obtained by standing the battery for a fixed time at different temperatures and different SOCs.
4. The method for estimating the remaining capacity of the lithium-ion power battery according to claim 1 or 2, wherein 10 cycles of kalman filtering are used as input values of the ampere-hour integration method when the kalman filtering algorithm in the step five estimates the remaining capacity of the battery.
5. The method for estimating the remaining capacity of the lithium-ion power battery according to claim 1 or 2, wherein in the sixth step, an improved ampere-hour integration algorithm is adopted, and the temperature, the battery cycle number, the charge-discharge rate, the battery aging and the self-discharge condition are used as the optimization parameters of the ampere-hour integration method.
6. The method for estimating the remaining capacity of the lithium-ion power battery according to claim 1, wherein when an ampere-hour integration algorithm is adopted, the ampere-hour integration within 10s is used for comparing the error with a Kalman filtering algorithm, and if the difference is greater than the error tolerance, the ampere-hour integration is used for restraining Kalman filtering divergence.
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