CN111965548A - Battery system sensor fault diagnosis method based on state estimation method - Google Patents

Battery system sensor fault diagnosis method based on state estimation method Download PDF

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CN111965548A
CN111965548A CN202011036233.9A CN202011036233A CN111965548A CN 111965548 A CN111965548 A CN 111965548A CN 202011036233 A CN202011036233 A CN 202011036233A CN 111965548 A CN111965548 A CN 111965548A
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battery
soc
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CN111965548B (en
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于全庆
万长江
金毅
王大方
杨博文
郝志伟
张毕
董浩崧
李宪营
俄立新
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Weihai Tianda Automobile Technology Co Ltd
Harbin Institute of Technology Weihai
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Harbin Institute of Technology Weihai
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378Arrangements 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
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries

Abstract

The invention provides a battery system sensor fault diagnosis method based on a state estimation method. The method comprises the following steps: firstly, a battery model is constructed, then, a method based on the model is adopted to carry out real-time estimation on the state of charge of the battery, then, the ratio of the electric quantity change to the state of charge change in a certain time period is adopted to determine the capacity of the battery, a capacity reference value and five groups of capacity estimated values are respectively obtained by changing the initial time corresponding to the starting point and the end point of the used time period, then, the capacity reference value and the estimated values are respectively subjected to difference to obtain five groups of capacity residual errors, finally, the five groups of capacity residual errors are respectively compared with a threshold value, and when the absolute value of one group or a plurality of groups of residual errors reaches or exceeds.

Description

Battery system sensor fault diagnosis method based on state estimation method
Technical Field
The invention relates to the field of power battery systems, in particular to a battery system sensor fault diagnosis method based on a state estimation method.
Background
Sensor fault diagnosis is one of core functions of a Battery Management System (BMS), and has an important meaning for Battery system safety. The BMS performs modeling and battery state estimation based on collected data of the sensors, so diagnosis of sensor failure using a change in the state of the battery is one of typical approaches. The State of the battery includes a State of charge (SOC), a State of energy (SOE), and a State of Health (SOH), and the SOH is mainly measured by a decline in capacity. Therefore, the SOC, SOE and capacity C of the battery can be utilizednAnd generating corresponding residual errors for sensor fault diagnosis. In fact, the three change rates in the charge-discharge cycle process are different, the SOC and the SOE are continuously reduced along with the current value in a complete discharge process (i.e. the process of reducing the SOC from 100% to 0), and the capacity C isnRemains substantially unchanged during the complete discharge process, so that the capacity C is usednThe method of generating the residual error is more suitable for the diagnosis of sensor faults.
There are a lot of documents and patents on a battery capacity estimation method, and a method based on a ratio of a change in electric quantity to a change in SOC over a certain period of time as a capacity value is one of typical methods. This method, while simple, is heavily dependent on the start and end of the selected time period. The current research adopts the method that the starting point data is fixed, and the terminal point data is continuously updated along with the increase of the sampling time, so that the electric quantity and the SOC variation in the later discharging period are gradually increased, and the problem of the increase of the failure diagnosis alarm-missing rate is caused. Therefore, how to obtain a battery system sensor fault diagnosis method with higher precision based on a state estimation method is still the current technical difficulty.
Disclosure of Invention
The invention aims to provide a battery system sensor fault diagnosis method based on a state estimation method. The method still obtains a capacity estimation value based on the ratio of the electric quantity change and the SOC change in a certain time period, obtains a plurality of groups of capacity estimation results by changing the starting point time and the ending point time of the selected time period, further obtains a plurality of groups of capacity residual errors, respectively compares the plurality of groups of capacity residual errors with a fault diagnosis threshold value to judge whether a sensor fault occurs, and can judge that the sensor has a fault when one or more residual error absolute values reach or exceed the threshold value.
A battery system sensor fault diagnosis method based on a state estimation method is characterized by comprising the following steps:
the method comprises the following steps: constructing an equivalent circuit model of the lithium ion power battery, and estimating the SOC of the battery in real time;
step two: based on a certain period of time (tth)1From one sampling instant to the t2Sampling time) and the ratio of the change in the amount of electricity to the change in the SOC, the discharge start time being the starting point t of the time period1Taking a sampling time corresponding to a certain SOC as a time period end point t2Following the discharge process of the battery, t1Is not changed but t2Updating along with the updating of the sampling time, and taking the calculated capacity estimation result as a capacity reference value; when t is1And t2Are updated along with the update of the sampling time, and then the starting point t of the time period is changed1And end point t2The five groups of capacity results obtained by sampling number are capacity estimated values;
further, capacity CnThe estimated expression is as follows:
Figure BDA0002705166180000011
in which the index k denotes the kth sampling instant t1And t2Is the sampling time interval [1k ]]And t is different from the other2>t1(ii) a I represents a battery current;
further, the capacity reference value determination process is as follows:
the fully charged battery (with an SOC of 100%) is fully discharged until the SOC is 0, n sampling times are elapsed, and λ sampling times are elapsed from the start of discharge to the discharge of 2% SOC, and the sampling time intervals between two adjacent sampling times are the same.
t1Taking and placing electricity at the starting moment: t is t1Let t be 1 when the sampling time k is λ2K, with a value λ, λ +1
Figure BDA0002705166180000021
Figure BDA0002705166180000022
To maintain capacity estimation accuracy and sensitivity to sensor failure, t1And t2The corresponding SOC variation at the initial sampling time of (1) is not less than 2%,
Figure BDA0002705166180000023
the value continuously approaches the true value as the sampling instant k increases.
Further, the capacity estimation value determination process is as follows:
t1corresponding to an initial sampling instant of 1, t2The corresponding initial sampling moments are respectively the 1.5 lambda, 2 lambda, 2.5 lambda, 3 lambda and 3.5 lambda sampling moments, t1And t2Updated with the sampling time update, the following 5 sets of capacity estimation values can be obtained
Figure BDA0002705166180000024
Figure BDA0002705166180000025
In the formula, m represents the number of intervals from the 1 st sampling timing as the sampling timing k increases. To maintain capacity estimation accuracy and sensitivity to sensor failure, t1And t2The corresponding SOC variation should be not less than 2% and not more than 10%.
Step three: respectively subtracting the capacity reference value and the five groups of capacity estimation values to obtain corresponding capacity residual errors;
Figure BDA0002705166180000026
in the formula, r1~r5Five sets of capacity residuals;
step four: and judging whether the sensor fails or not by comparing the capacity residual error with a failure threshold value.
The invention has the beneficial effects that:
(1) the SOC of the power battery is one of the necessary functions of the BMS, and the invention obtains the capacity residual error through simple processing on the SOC estimation, so the required calculated amount is small;
(2) when the SOC variation in the time period selected by the capacity calculation exceeds 10% and increases along with the increase of the sampling time, the ratio of the electric quantity variation to the SOC variation tends to a stable value, the sensitivity of the sensor fault value is gradually reduced, and the fault alarm missing rate is easily caused; further, studies have shown that the capacity estimation result error is large when the SOC variation amount is less than 2% in a selected period. In practice, however, due to uncertainty in the current regime, it is not guaranteed that the SOC variation for the selected time interval is always between 2% and 10%, but only that t is selected1And t2The number of samples in between is constant, if a fixed set of t is used1And t2Easily cause the later period t of discharge1And t2The SOC variation between the two is less than 2% or more than 10%, thereby causing the alarm leakage rate and the alarm error rate to increaseA big problem. The invention changes t1And t2Corresponding to the initial sampling time, calculating multiple groups of capacity residuals in parallel to enable at least one group of t in the discharging process1And t2The SOC variation between the two sensors is between 2% and 10%, and the false alarm rate during the fault diagnosis of the sensors can be effectively reduced.
Drawings
FIG. 1 is a schematic flow chart of the method provided by the present invention
FIG. 2 is a schematic diagram of the equivalent circuit model of Thevenin
FIG. 3 is a schematic diagram of current and power supply for an urban road circulating UDDS operating condition
Detailed Description
The following is a 3.6V LiFePO4The battery is used for elaborating the sensor fault diagnosis method provided by the invention in combination with the attached drawing. The invention provides a battery system sensor fault diagnosis method based on a state estimation method, which specifically comprises the following steps as shown in figure 1:
the method comprises the following steps: constructing a Thevenin equivalent circuit model shown in FIG. 2, wherein the model consists of three parts, namely a voltage source, ohmic internal resistance and an RC network. The discrete mathematical expression of the model is as follows:
Figure BDA0002705166180000031
where k is the sampling time, UtIs terminal voltage, UpFor polarization voltage, OCV is the open circuit voltage of the battery, whose value can be expressed as a sixth order polynomial of SOC: OCV (f) (soc), polynomial coefficients are determined by OCV experiments; i is the battery current, R0Is ohmic internal resistance, CpIs a polarization capacitance, RpIs the polarization resistance. R0、CpAnd RpCan be identified by a recursive least square method.
Loading an Urban road circulation (UDDS) working condition on a battery, recording current and voltage information, and introducing an SOC online estimation process by taking an extended Kalman filter algorithm as an example:
(1) establishing a linear discretization equation of the Thevenin power battery model:
Figure BDA0002705166180000032
in the formula, the state vector x ═ Up SOC]TI is the input vector U, U is the output vector ytW and v are the system noise and the measurement noise, respectively, with mean zero, and their covariance matrices are Q and R, respectively, and coefficient matrices A, B, C and D are:
Figure BDA0002705166180000033
Figure BDA0002705166180000034
Dk=OCVk-Up,k-R0uk-Ckxk
wherein τ ═ Rp×Cp,α12,...,α6The fitting coefficient of SOC in OCV (f) (SOC) can be obtained by an open circuit voltage experiment.
(2) And (3) filter initialization: setting an initial value of a state observer: initial value x of state0The value in the value range can be arbitrarily given, and the filtering algorithm can quickly converge to be close to the true value when the initial value is not accurate. Covariance matrix P0Q and R are determined according to empirical debugging. Here, we give:
x0=[0 80%]
Figure BDA0002705166180000041
Figure BDA0002705166180000042
R=0.04
in the formula, subscript 0 represents an initial value.
(3) For k 1,2, …, the following update is done (superscript-number indicates a priori estimate, superscript + number indicates a posteriori estimate, superscript ^ indicates estimate):
estimating the system state x:
Figure BDA0002705166180000043
estimating error covariance P:
Figure BDA0002705166180000044
updating an innovation matrix e:
ek=yk-Ckxk+Dk
updating a Kalman gain matrix K:
Figure BDA0002705166180000045
and (3) correcting the system state x:
Figure BDA0002705166180000046
error covariance P update:
Figure BDA0002705166180000047
the state vector of the battery at the moment k can be obtained through the steps
Figure BDA0002705166180000048
The SOC at the time k is obtainedkThe value is obtained.
Step two: the formula for obtaining the capacity according to the ratio of the electric quantity change to the SOC change in a certain time period is as follows:
Figure BDA0002705166180000049
in the formula, t1And t2Are two different sampling instants.
In the UDDS condition, 13185 sampling times are required for a fully charged battery (SOC of 100%) to fully discharge to a SOC of 0, and λ is 238 sampling times after the start of discharge to discharge 2% SOC.
The capacity reference value determination process is as follows:
when t is1T is the starting time of electrical discharge1=1,t2λ, λ + 1.., 13185, a capacity reference value can be calculated by the above capacity formula
Figure BDA00027051661800000410
The value is continuously approaching the true value as the sampling instant increases.
Figure BDA0002705166180000051
By varying t1And t2Corresponding to the sampling time, the following 5 groups of capacity estimation values are obtained
Figure BDA0002705166180000052
Figure BDA0002705166180000053
Step three: on the basis of obtaining the capacity reference value and the five groups of capacity estimation values, obtaining five groups of capacity residual errors by making differences:
Figure BDA0002705166180000054
step four: the fault diagnosis threshold J value is the rated capacity C of the batterya0.1 times of:
J=Ca×10%
and comparing the five groups of residual absolute values with a fault diagnosis threshold value respectively, and judging that the sensor has faults when one or more groups of residual absolute values reach or exceed the threshold value, otherwise, judging that the sensor does not have faults.
Figure BDA0002705166180000055
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (2)

1. A battery system sensor fault diagnosis method based on a state estimation method is characterized by comprising the following steps:
the method comprises the following steps: constructing an equivalent circuit model of the lithium ion power battery, and estimating the SOC of the battery in real time;
step two: based on a certain period of time (tth)1From one sampling instant to the t2Sampling time) and the ratio of the change in the amount of electricity to the change in the SOC, the discharge start time being the starting point t of the time period1Taking a sampling time corresponding to a certain SOC as a time period end point t2Following the discharge process of the battery, t1Is not changed but t2Updating along with the updating of the sampling time, and calculating a capacity reference value according to the updating; when t is1And t2Are updated along with the update of the sampling time, and then the starting point t of the time period is changed1And end point t2The sampling number among the five groups of capacity estimation values is obtained;
the method specifically comprises the following steps: capacity CnThe estimated expression is:
Figure FDA0002705166170000011
in which the index k denotes the kth sampling instant t1And t2Are two different sampling instants in a sampling instant interval, and t2>t1(ii) a I represents a battery current;
the capacity reference value determination process is as follows:
the fully charged battery is fully discharged to have an SOC of 0, n sampling instants (the time interval between two adjacent sampling instants is the same), λ sampling instants elapse from the start of discharge to the discharge of 2% SOC,
t1taking and placing electricity at the starting moment: t is t1Let t be 1 when the sampling time k is λ2K, with a value λ, λ +1
Figure FDA0002705166170000012
Figure FDA0002705166170000013
t1And t2The corresponding SOC variation at the initial sampling moment is not less than 2%;
the capacity estimation determination process is as follows:
t1corresponding to an initial sampling instant of 1, t2The corresponding initial sampling moments are respectively the 1.5 lambda, 2 lambda, 2.5 lambda, 3 lambda and 3.5 lambda sampling moments, t1And t2Updated with the sampling time update, the following 5 sets of capacity estimation values can be obtained
Figure FDA0002705166170000014
Figure FDA0002705166170000015
Where m represents the number of intervals from the 1 st sampling time as the sampling time k increases, t1And t2Corresponding SOC variation of not less than 2% andnot more than 10%;
step three: respectively subtracting the capacity reference value and the five groups of capacity estimation values to obtain corresponding capacity residual errors;
Figure FDA0002705166170000021
in the formula, r1~r5Five sets of capacity residuals;
step four: and judging whether the sensor fails or not by comparing the capacity residual error with a failure threshold value.
2. The state estimation method-based battery system sensor fault diagnosis method according to claim 1, wherein in the first step, the battery SOC is estimated online in real time using a kalman filter or a modified form of kalman filter algorithm.
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CN116540125B (en) * 2023-07-05 2023-10-03 中国华能集团清洁能源技术研究院有限公司 Diagnosis method and system for battery state-of-charge estimation fault

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