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CN113406510A - Lithium ion battery state-of-charge online estimator with measurement data anomaly detection function and method - Google Patents

Lithium ion battery state-of-charge online estimator with measurement data anomaly detection function and method Download PDF

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CN113406510A
CN113406510A CN202110683457.7A CN202110683457A CN113406510A CN 113406510 A CN113406510 A CN 113406510A CN 202110683457 A CN202110683457 A CN 202110683457A CN 113406510 A CN113406510 A CN 113406510A
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data
lithium
abnormal
state
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林琼斌
谢臻杰
柴琴琴
蔡逢煌
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Fuzhou University
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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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables

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Abstract

本发明提出一种带量测数据异常检测的锂离子电池荷电状态在线估计器及方法，其包括信息采集单元、控制单元、数据异常检测单元、信息处理单元和显示单元。通过信息采集单元采集锂电池端电压、锂电池温度和流过锂电池的电流信息，控制单元控制采集单元与信息处理单元的传输过程以及监测状态估计器的是否处于正常环境，数据异常检测单元检测采集到的数据是否在正常范围内，并且在数据异常时按照一定规则进行数据纠正，信息处理单元则根据接收来自异常检测单元的数据，根据改进的粒子滤波算法对锂电池的状态进行估计，并将估计的结果实时存储与显示。

Figure 202110683457

The present invention provides an on-line estimator and method for lithium-ion battery state of charge with abnormal detection of measurement data, which includes an information acquisition unit, a control unit, a data abnormality detection unit, an information processing unit and a display unit. The information acquisition unit collects the information of the terminal voltage of the lithium battery, the temperature of the lithium battery and the current flowing through the lithium battery. The control unit controls the transmission process between the acquisition unit and the information processing unit and monitors whether the state estimator is in a normal environment. The abnormal data detection unit detects and collects Whether the received data is within the normal range, and the data is corrected according to certain rules when the data is abnormal, the information processing unit estimates the state of the lithium battery according to the data received from the abnormality detection unit and the improved particle filter algorithm, and calculates the status of the lithium battery. The estimated results are stored and displayed in real time.

Figure 202110683457

Description

Lithium ion battery state-of-charge online estimator with measurement data anomaly detection function and method
Technical Field
The invention belongs to the technical field of lithium battery state estimation, and particularly relates to an online estimator and method for a lithium battery state of charge with measurement data anomaly detection.
Background
The lithium battery state of charge estimation method can be roughly divided into a traditional algorithm and an intelligent algorithm, wherein the traditional algorithm comprises an open-circuit voltage method, an internal resistance method, an ampere-hour integral method and the like, and the lithium battery state of charge estimation method has the advantages of being simple in calculation and large in error, difficult to measure, not suitable for real-time estimation and the like. The traditional algorithms have more limitations, so the current research focus is on intelligent algorithms, including neural network algorithms, kalman filtering, particle filtering, and the like. The neural network algorithm needs a large amount of data support, and the accuracy of the Kalman filtering in battery estimation is influenced by the applicable condition of the Kalman filtering.
Disclosure of Invention
In view of the above, in order to make up for the blank and the deficiency of the prior art, the present invention aims to provide an online estimator and method for a state of charge of a lithium ion battery with measurement data anomaly detection, so as to reduce the problems of increased calculation amount and reduced estimation accuracy caused by measurement data anomaly.
The system comprises an information acquisition unit, a control unit, a data abnormity detection unit, an information processing unit and a display unit. The method comprises the steps that the information acquisition unit acquires the terminal voltage of the lithium battery, the temperature of the lithium battery and the current information flowing through the lithium battery, the control unit controls the transmission process of the acquisition unit and the information processing unit and monitors whether the state estimator is in a normal environment or not, the data abnormity detection unit detects whether acquired data are in a normal range or not, data correction is carried out according to a certain rule when the data are abnormal, the information processing unit estimates the state of the lithium battery according to an improved particle filter algorithm and stores and displays the estimated result in real time according to the received data from the abnormity detection unit. The state estimator identifies equivalent circuit parameters by using a recursive least square algorithm with a rectangular window, estimates the state of the lithium battery by combining a genetic algorithm and particle filtering, solves the problem of particle depletion of the particle filtering, improves estimation precision, and ensures that the abnormal data does not increase the calculated amount of the particle filtering and ensures the calculation precision by using an abnormal data detection unit.
The invention specifically adopts the following technical scheme:
the on-line estimator of the charge state of the lithium ion battery with the measurement data anomaly detection function is characterized by comprising an information acquisition unit, an anomaly data detection unit, an information processing unit, a control unit and a display unit;
the information acquisition unit is used for acquiring the voltage, the current and the temperature of the lithium battery or the lithium battery pack;
the abnormal data detection unit is used for receiving the data of the information acquisition unit and sending the processed data to the information processing unit;
the control unit is used for controlling the data transmission between the information acquisition unit and the information processing unit and monitoring the work of the estimator;
the display unit is connected with the information processing unit.
Further, the control unit controls the transmission process of the information acquisition unit and the information processing unit and monitors whether the state online estimator is in a normal environment;
the abnormal data detection unit detects whether the acquired data is in a normal range or not, and corrects the data when the data is abnormal;
and the information processing unit receives the data from the abnormality detection unit, estimates the state of the lithium battery according to an improved particle filter algorithm, and stores and displays the estimation result in real time.
Further, the information processing unit identifies equivalent circuit parameters by using a recursive least square algorithm with a rectangular window, and estimates the state of the lithium battery by combining a genetic algorithm and particle filtering.
Further, the abnormal data detection unit multiplies the current measured at the moment by the internal resistance range of the lithium battery obtained in advance to obtain a limit range, if the voltage measured at the moment and the voltage at the last moment exceed the limit range, the abnormal data detection unit judges that the data is abnormal, discards the voltage data at the moment and takes the data at the last moment as the value at the moment.
Further, the improved particle filtering algorithm comprises the following processes: after generating the initial particles, the following loop is performed until the prediction is finished: updating the particle position, updating the particle weight, calculating an estimated value, and judging whether to resample: if so, performing genetic algorithm resampling and predicting the next moment; if not, directly predicting the next moment, judging whether to finish the prediction, and if not, returning to the step of updating the particle position;
the genetic algorithm resampling includes intersection and variation of particles.
The online estimation method for the state of charge of the lithium ion battery with the measurement data anomaly detection is characterized by comprising the following steps of:
step S1: obtaining the voltage, the current and the temperature value of the lithium battery during working by using a sensor;
step S2: carrying out data anomaly detection processing on the acquired data;
step S3: performing online identification on the equivalent circuit parameters of the lithium battery by using a recursive least square algorithm;
step S4: and establishing a state equation of the equivalent circuit model, and estimating the state of the battery by using an improved particle filter algorithm.
Further, in step S2, the specific method of the data anomaly detection processing is as follows: multiplying the current data at the current moment by the internal resistance range estimated in advance, subtracting the voltage at the current moment from the voltage at the previous moment, and comparing the two to obtain a judgment condition: if the latter data is smaller than the former, the data is valid; otherwise, the voltage data at the current moment is discarded and is filled by the voltage data at the previous moment.
Further, in step S3, the lithium battery equivalent circuit is a second-order Thevenin equivalent circuit.
Further, in step S4, the specific steps of the improved particle filtering algorithm are as follows:
step S41: generating initial particles N according to the prior probability, wherein the initial weight of all the particles is 1/N;
step S42: updating the particle position and the weight value, and calculating an estimated value at the moment;
step S43: judging whether resampling is needed, if not, returning to the step S42 for estimating the next moment; and if resampling is needed, carrying out the operations of crossing and mutation of the particles to obtain a new particle set.
Further, in step S43, a specific method of determining whether resampling is required is to determine based on the particle variance at that time.
Compared with the prior art, the method and the device solve the problems of low precision and large calculation amount caused by measurement data loss during online estimation of the state of charge of the lithium ion battery. The method comprises the steps that the information acquisition unit acquires the terminal voltage of the lithium battery, the temperature of the lithium battery and the current information flowing through the lithium battery, the control unit controls the transmission process of the acquisition unit and the information processing unit and monitors whether the state estimator is in a normal environment or not, the data abnormity detection unit detects whether the acquired data is in a normal range or not, data correction is carried out according to a certain rule when the data is abnormal, the information processing unit estimates the state of the lithium battery according to an improved particle filtering algorithm according to the data received from the abnormity detection unit, and the estimated result is stored and displayed in real time. The state estimator identifies equivalent circuit parameters by using a recursive least square algorithm with a rectangular window, estimates the state of the lithium battery by combining a genetic algorithm and particle filtering, solves the problem of particle depletion of the particle filtering, improves estimation precision, and ensures that the abnormal data does not increase the calculated amount of the particle filtering and ensures the calculation precision by using an abnormal data detection unit.
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The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present invention;
fig. 2 is a schematic overall flow chart of an online estimation method for the state of charge of a lithium ion battery with abnormal measurement data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a process of identifying parameters of an equivalent circuit model of a lithium battery according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating exception handling for metrology data according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of an improved particle filter algorithm according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a second-order Thevenin equivalent circuit according to an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
referring to fig. 1, the present embodiment provides a design scheme of an online state-of-charge estimator for a lithium ion battery with measurement data anomaly detection, including: the system comprises an information acquisition unit, an abnormal data detection unit, an information processing unit, a control unit and a display unit; the information acquisition unit acquires the voltage, the current and the temperature of the lithium battery or the lithium battery pack; the abnormal data detection unit receives the data of the information acquisition unit, and the processed data is sent to the information processing unit; the control unit controls the data transmission between the information acquisition unit and the information processing unit and monitors the work of the estimator; the display unit is connected with the information processing unit. As shown in fig. 1, the information acquisition unit acquires and transmits information such as voltage, current, temperature and the like of the lithium battery to the abnormal data detection unit, the abnormal data detection unit judges and processes the data, the information processing unit performs state estimation according to the estimation method, and finally, required information is transmitted to the upper computer and the display unit.
The invention provides an online estimation method for the state of charge of a lithium ion battery of a measurement data anomaly detection device, the flow is shown in figure 2, and the online estimation method is characterized in that: the method comprises the following steps:
step S1: the acquisition unit is a series of sensors and is used for acquiring the terminal voltage of the lithium battery, the current flowing through the lithium battery and the temperature information of the lithium battery.
Step S2: before the collected data are transmitted to the information processing unit for processing, the abnormal data detection unit judges whether the data are in the fluctuation range. The judgment flow chart is shown in fig. 4, namely: and multiplying the current data at the current moment by the internal resistance range estimated in advance, subtracting the voltage at the current moment from the voltage at the previous moment, and comparing the two to obtain a judgment condition. If the latter data is smaller than the former data, the data is valid, otherwise, the voltage data at the current moment is abandoned and filled by the voltage data at the previous moment.
Step S3: the parameters of the lithium battery equivalent circuit are identified on line by using a recursive least square algorithm, and the lithium battery equivalent circuit is a second-order Thevenin equivalent circuit as shown in fig. 6.
Referring to fig. 3, firstly, a frequency domain equation (1) of the circuit is established, and the discrete transformation is in the form of (2), so as to obtain an equation in the form of:
Figure BDA0003123920290000051
Figure BDA0003123920290000052
in the formula tau1、τ2Is the time constant of the RC element, i.e. tau1＝R1×C1，τ2＝R2×C2(ii) a Coefficient ak(k＝1，2)、bk(k ═ 1, 2, 3) is an unknown coefficient which is equivalent to the parameter R in the circuit0、R1、R2、C1、C2Has a certain mathematical relationship and can be calculated by the following process.
Bilinear inverse transformation formula
Figure BDA0003123920290000053
Substituting the formula (2), comparing the obtained formula with the formula (1) to obtain the relationship between the battery parameters and the coefficients, and calculating according to the flow of FIG. 3 to obtain the battery parameters and coefficientsAnd the coefficient of each moment is used for obtaining the equivalent circuit model parameter according to the relational expression. In the formula: t is the sampling time.
Step S4: establishing a state equation of an equivalent circuit model, and estimating the state of the battery by using an improved particle filter algorithm:
it can first establish the discrete state equation of the system:
Figure BDA0003123920290000054
in the formula: t is sampling time; u shapep1、Up2Voltages for the RC link, i.e. corresponding to U in the circuit1、U2(ii) a k is a certain current moment, and k +1 is the current next moment; omegak、υkRespectively process noise and observation noise at the time k; h isSOC-OCVIs a function relation of open-circuit voltage and state of charge (SOC); s (k +1) is the battery state of charge SOC at time k + 1.
According to the improved particle filter algorithm flowchart, real-time estimation of the battery state of charge is performed, as shown in fig. 5, which includes the following steps:
step S41: generating initial particles N according to the prior probability, wherein the initial weight of all the particles is 1/N;
step S42: updating the particle position and the weight value, and calculating an estimated value at the moment;
step S43: and judging whether resampling is needed or not, and judging according to the particle variance at the moment. If resampling is not needed, returning to the step S42 to estimate the next moment; and if resampling is needed, carrying out the operations of crossing and mutation of the particles to obtain a new particle set.
The present invention is not limited to the above preferred embodiments, and any other various types of on-line estimation methods and apparatuses for lithium ion battery state of charge with measurement data anomaly detection can be obtained from the present invention, and all equivalent changes and modifications made according to the claimed scope of the present invention shall fall within the scope of the present invention.

Claims (10)

1.一种带量测数据异常检测的锂离子电池荷电状态在线估计器，其特征在于，包括信息采集单元、异常数据检测单元、信息处理单元、控制单元以及显示单元；1. an on-line estimator for lithium-ion battery state of charge with abnormal detection of measurement data, characterized in that it comprises an information acquisition unit, an abnormal data detection unit, an information processing unit, a control unit and a display unit; 所述信息采集单元用于采集锂电池或锂电池组的电压、电流和温度；The information collection unit is used to collect the voltage, current and temperature of the lithium battery or lithium battery pack; 所述异常数据检测单元用于接收信息采集单元的数据，经处理后的数据发送给信息处理单元；The abnormal data detection unit is used for receiving data from the information collection unit, and the processed data is sent to the information processing unit; 所述控制单元用于控制信息采集单元与信息处理单元数据传输，以及监测估计器工作；The control unit is used to control the data transmission between the information acquisition unit and the information processing unit, and to monitor the work of the estimator; 所述显示单元与信息处理单元连接。The display unit is connected with the information processing unit. 2.根据权利要求1所述的带量测数据异常检测的锂离子电池荷电状态在线估计器，其特征在于：2. the lithium ion battery state-of-charge online estimator with abnormal detection of measurement data according to claim 1, is characterized in that: 所述控制单元控制信息采集单元与信息处理单元的传输过程以及监测状态在线估计器是否处于正常环境；The control unit controls the transmission process between the information acquisition unit and the information processing unit and monitors whether the state online estimator is in a normal environment; 所述异常数据检测单元检测采集到的数据是否在正常范围内，并且在数据异常时进行数据纠正；The abnormal data detection unit detects whether the collected data is within the normal range, and performs data correction when the data is abnormal; 所述信息处理单元接收来自异常检测单元的数据，根据改进的粒子滤波算法对锂电池的状态进行估计，并将估计的结果实时存储与显示。The information processing unit receives the data from the abnormality detection unit, estimates the state of the lithium battery according to the improved particle filter algorithm, and stores and displays the estimated result in real time. 3.根据权利要求2所述的带量测数据异常检测的锂离子电池荷电状态在线估计器，其特征在于：所述信息处理单元使用带矩形窗的递推最小二乘算法辨识等效电路参数，将遗传算法与粒子滤波结合估计锂电池状态。3. The lithium-ion battery state-of-charge online estimator with abnormal detection of measurement data according to claim 2, wherein the information processing unit uses a recursive least squares algorithm with a rectangular window to identify the equivalent circuit parameters, combining genetic algorithm and particle filter to estimate the state of lithium battery. 4.根据权利要求2所述的带量测数据异常检测的锂离子电池荷电状态在线估计器，其特征在于：所述异常数据检测单元根据事先得到的锂电池内阻范围，与该时刻测量得到的电流相乘获得一个限制范围，如果该时刻测量得到的电压与上一时刻的电压超出该范围，则判断该数据为异常，将该时刻的电压数据丢弃，并将上一时刻的数据当作该时刻的值。4. The lithium-ion battery state-of-charge online estimator with abnormality detection of measurement data according to claim 2, characterized in that: the abnormal data detection unit is obtained by measuring the internal resistance range of the lithium battery obtained in advance according to the time Multiply the current to obtain a limit range, if the voltage measured at this moment and the voltage at the previous moment exceed this range, the data is judged to be abnormal, the voltage data at this moment is discarded, and the data at the previous moment is regarded as value at this moment. 5.根据权利要求2所述的带量测数据异常检测的锂离子电池荷电状态在线估计器，其特征在于：所述改进的粒子滤波算法包括以下过程：在生成初始粒子后执行以下循环，直至结束预测：更新粒子位置、更新粒子权值、计算估计值、判断是否重采样：如果为是则进行遗传算法重采样后预测下一时刻；如果为否则直接预测下一时刻、判断是否结束预测，如果为否，则回到更新粒子位置的步骤；5. The lithium-ion battery state-of-charge online estimator with abnormal detection of measurement data according to claim 2, wherein the improved particle filter algorithm comprises the following process: after generating the initial particle, the following loop is performed, Until the end of the prediction: update the particle position, update the particle weight, calculate the estimated value, and determine whether to resample: if so, perform genetic algorithm resampling to predict the next moment; if so, directly predict the next moment, and determine whether to end the prediction , if no, go back to the step of updating the particle position; 所述遗传算法重采样包括粒子的交叉和变异。The genetic algorithm resampling includes crossover and mutation of particles. 6.一种带量测数据异常检测的锂离子电池荷电状态在线估计方法，其特征在于，包括以下步骤：6. A lithium-ion battery state-of-charge online estimation method with abnormal detection of measurement data, characterized in that, comprising the following steps: 步骤S1：使用传感器获得锂电池工作时的电压、电流以及温度值；Step S1: use the sensor to obtain the voltage, current and temperature values of the lithium battery when it is working; 步骤S2：将采集的数据经过数据异常检测处理；Step S2: subject the collected data to data anomaly detection processing; 步骤S3：使用递推最小二乘算法进行锂电池等效电路参数在线辨识；Step S3: using the recursive least squares algorithm to perform online identification of the equivalent circuit parameters of the lithium battery; 步骤S4：建立等效电路模型的状态方程，使用改进的粒子滤波算法进行电池的状态估计。Step S4: establishing the state equation of the equivalent circuit model, and using the improved particle filter algorithm to estimate the state of the battery. 7.根据权利要求6所述的带量测数据异常检测的锂离子电池荷电状态在线估计方法，其特征在于：在步骤S2中，数据异常检测处理的具体方法为：将当前时刻电流数据与事先估计好的内阻范围进行相乘，然后将当前时刻的电压与上一时刻电压相减，将上述两者作比较，得出判断条件：如果后者数据小于前者，则数据有效；否则，将当前时刻的电压数据舍弃，用上一时刻的电压数据填补。7. The lithium-ion battery state-of-charge online estimation method with abnormality detection of measurement data according to claim 6, characterized in that: in step S2, the concrete method of data abnormality detection processing is: the current data at the current moment is compared with Multiply the pre-estimated internal resistance range, then subtract the voltage at the current moment from the voltage at the previous moment, and compare the above two to obtain the judgment condition: if the latter data is smaller than the former, the data is valid; otherwise, Discard the voltage data at the current moment and fill it with the voltage data at the previous moment. 8.根据权利要求6所述的带量测数据异常检测的锂离子电池荷电状态在线估计方法，其特征在于：在步骤S3中，所述锂电池等效电路为二阶Thevenin等效电路。8 . The method for estimating the state of charge of lithium ion batteries online with abnormal detection of measurement data according to claim 6 , wherein in step S3 , the lithium battery equivalent circuit is a second-order Thevenin equivalent circuit. 9 . 9.根据权利要求6所述的带量测数据异常检测的锂离子电池荷电状态在线估计方法，其特征在于：在步骤S4中，所述改进的粒子滤波算法的具体步骤如下：9. The lithium-ion battery state-of-charge online estimation method with abnormal detection of measurement data according to claim 6, characterized in that: in step S4, the concrete steps of the improved particle filter algorithm are as follows: 步骤S41：由先验概率产生初始粒子N，所有粒子初始权值为1/N；Step S41: generating an initial particle N from a priori probability, and the initial weight of all particles is 1/N; 步骤S42：进行粒子位置与权值更新，计算该时刻估计值；Step S42: update the particle position and weight, and calculate the estimated value at this moment; 步骤S43：判断是否需要重采样，如果不需要重采样则返回步骤S42进行下一时刻的估计；如果需要重采样，则进行粒子的交叉、变异操作获得新的粒子集。Step S43: Determine whether resampling is required, and if resampling is not required, return to step S42 for estimation at the next moment; if resampling is required, perform particle crossover and mutation operations to obtain a new particle set. 10.根据权利要求9所述的带量测数据异常检测的锂离子电池荷电状态在线估计方法，其特征在于：在步骤S43中，判断是否需要重采样的具体方法为，以此时的粒子方差大小为依据进行判断。10. The lithium-ion battery state-of-charge online estimation method with abnormal detection of measurement data according to claim 9, characterized in that: in step S43, the specific method for judging whether resampling is necessary is to use the particle size at this time The size of the variance is based on the judgment.
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