CN111537885A - Multi-time scale short circuit resistance estimation method for series battery pack - Google Patents

Multi-time scale short circuit resistance estimation method for series battery pack Download PDF

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CN111537885A
CN111537885A CN202010329043.XA CN202010329043A CN111537885A CN 111537885 A CN111537885 A CN 111537885A CN 202010329043 A CN202010329043 A CN 202010329043A CN 111537885 A CN111537885 A CN 111537885A
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short
circuit resistance
estimation
short circuit
time
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CN111537885B (en
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徐俊
林皓
孙铮
张政
梅雪松
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Xian Jiaotong University
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Xian Jiaotong 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]
    • G01R31/367—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
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/08—Measuring resistance by measuring both voltage and current
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • 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
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults

Abstract

The invention discloses a multi-time scale short circuit resistance estimation method for a series battery pack, which firstly provides a multi-time scale short circuit resistance estimation concept, comprises a short time scale estimation part and a long time scale estimation part, and is respectively used for obtaining a hard short circuit resistance value and a soft short circuit resistance value and simultaneously realizing quantitative diagnosis of two types of short circuit faults of hard short circuit and soft short circuit; the method specifically comprises the following steps: measuring and obtaining the measured values of the voltage and the current of the battery; short-time scale short-circuit resistance estimation is carried out, hard short-circuit resistance is estimated, and when the scale conversion condition is met, conversion is carried out to long-time scale estimation; estimating the short circuit resistance in a long time scale, and estimating the soft short circuit resistance; the real-time diagnosis capability of the battery management system is further improved. According to the method, only the monomers with the highest voltage and the lowest voltage in the series battery pack are selected for analysis, so that the calculation complexity is greatly reduced, the method is easy to apply practically, and the method has important significance for estimating the short circuit resistance of the battery pack and diagnosing faults.

Description

Multi-time scale short circuit resistance estimation method for series battery pack
Technical Field
The invention belongs to the technical field of battery fault diagnosis and estimation, and relates to a multi-time scale short circuit resistance estimation method for a series battery pack.
Background
A Battery Management System (BMS), which is considered to be one of the most important components in electric vehicles and mobile robots, functions to secure a Battery in a proper working state and to extend its service life. The first priority of battery application should be safety, and the battery State needs to be monitored or estimated to ensure safety, and corresponding estimation of the State of Charge (SOC) and the State of health (SOC) of the battery, battery equalization, inconsistency and the like are widely researched. With the development of electric vehicles and mobile robots, high energy density batteries are required as supports to extend driving range. As the energy density continues to increase, the battery system correspondingly carries a higher risk of failure.
In the study of battery pack faults, short-circuit faults are one of the current research hotspots. The short-circuit fault of the battery pack is caused by a plurality of reasons, the diaphragm is damaged by dust and the like in the production process of the battery to cause short circuit, and the battery pack can cause short circuit by over-charge, over-discharge, low-temperature charge and the like. Because the battery pack of the electric automobile has the problems of vibration, wire aging, collision and the like in the actual working condition, the occurrence probability of short-circuit faults is also increased.
Current short circuit fault studies can be largely divided into two categories, hard short circuit faults and soft short circuit faults. When a hard short circuit fault occurs, the short circuit resistance can reach the milliohm level, the short circuit current is large, the danger is high, and therefore the detection is required to be rapidly realized. The main method is to realize diagnosis based on an electrochemical thermal coupling model or an equivalent circuit model, wherein the electrochemical thermal coupling model is based on a battery and a short circuit mechanism, but is not suitable for online fault diagnosis due to model complexity and uncertainty of parameter selection; the equivalent circuit model has good rapidity and accuracy for diagnosing the hard short circuit fault, but a proper method needs to be selected to realize online application when the battery pack is diagnosed. Soft short circuit fault resistance is much larger than hard short circuit, about tens to 100 ohms, and short circuit current is relatively small, making direct estimation of the short circuit resistance difficult. The method is mainly based on a battery pack (difference) model and a state estimation method, obtains SOC difference or electric quantity difference between a normal monomer and a fault monomer, realizes resistance value estimation according to short-circuit resistance dissipation effect, is suitable for online estimation, and has estimation accuracy depending on SOC estimation accuracy.
Although the diagnosis effect of the method is good overall, the existing method cannot realize the common estimation of the resistance of the hard short circuit and the soft short circuit, and cannot realize effective positioning and diagnosis when two short circuits occur at different positions of the battery pack simultaneously. Therefore, how to estimate the hard short circuit resistance and the soft short circuit resistance simultaneously is a great necessity for troubleshooting of the battery pack in practical use, and is a key problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In order to overcome the drawbacks of the prior art, an object of the present invention is to provide a method for estimating a short-circuit resistance of a series battery, so as to achieve simultaneous estimation of a hard short-circuit resistance and a soft short-circuit resistance in the battery, reduce the complexity of calculation, and facilitate practical application.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
the invention discloses a multi-time scale short circuit resistance estimation method of a series battery pack, which is characterized by comprising the following steps of:
1) collecting working condition data of the series battery pack;
2) initializing state quantities at time k-1
Figure BDA0002464282620000021
And short-circuit current
Figure BDA0002464282620000022
3) Identifying the highest voltage monomer and the lowest voltage monomer in the series battery pack, and respectively recording the voltage values as max (V)k) And min (V)k) Carrying out short circuit resistance estimation aiming at the monomer with the highest voltage and the monomer with the lowest voltage;
updating state quantity of measurement k moment
Figure BDA0002464282620000023
And system output
Figure BDA0002464282620000024
And calculating the estimated value of the short-circuit current
Figure BDA0002464282620000025
When short circuit current estimation value
Figure BDA0002464282620000026
Greater than a preset hard short circuit current value ISCsetThen, the fault is determined to be a hard short circuit fault, and a hard short circuit resistance estimate is obtained
Figure BDA0002464282620000027
Completing short circuit resistance estimation of a short time scale;
otherwise, judging that the fault is not detected, and continuously updating the state quantity x at the moment of measuring kkAnd when the measuring time k meets the time scale conversion condition k ═ l · TmacroThen, the short-circuit resistance estimation mode is converted into long-time scale short-circuit resistance estimation, namely the SOC difference value dSOC of the highest voltage monomer and the lowest voltage monomer at the moment l is updatedl(ii) a When the measurement time k is larger than the long time scale time interval size TlongtermThen, calculate TlongtermSOC difference value delta dSOC in time periodlAnd obtaining a soft short circuit resistance estimate RSCAnd finishing the estimation of the short circuit resistance in a long time scale.
Preferably, in step 1), the operating condition data of the battery pack includes measured values of voltage and current of each battery cell.
Preferably, in step 3), when the measurement time k is not satisfiedTime scale conversion condition k ═ l · TmacroThen, the SOC difference value dSOC of the highest voltage cell and the lowest voltage cell at the time k is judgedkDifference value dSOC from preset SOCsetThe size relationship of (1):
① dSOCk＞dSOCsetThen, the short-circuit resistance estimation mode is converted into the short-circuit resistance estimation of a long time scale, namely, the T calculation is executedlongtermSOC difference value delta dSOC in time periodlAnd calculating the soft short circuit resistance estimated value R according to the soft short circuit resistance estimated value RSCCompleting short circuit resistance estimation in a long time scale;
② dSOCkNot greater than dSOCsetAnd updating the measurement time k to k +1, and re-executing the step 3), namely, selecting the highest-voltage monomer and the lowest-voltage monomer again, and continuing to circulate until the estimation system process is finished.
Preferably, in step 3), when the measurement time k is not greater than the long-time-scale time interval size TlongtermAnd then, updating the measurement time l to l +1, and shifting to the updated measurement time k to k +1, and re-executing the step 3), namely, selecting the highest-voltage and lowest-voltage monomers again, and continuing to circulate until the process of the estimation system is finished.
Preferably, in step 3), after the estimation of the short-circuit resistance in the long time scale is completed, the measurement time l ═ l +1 is continuously updated, the measurement time k ═ k +1 is updated, and step 3) is executed again, that is, the highest-voltage and lowest-voltage cells are selected again, and the loop is continued until the estimation system process is ended.
Preferably, in step 3), the preset hard short-circuit current ISCsetThe preset SOC difference value dSOC is a preset quantity in advance according to the actual condition of the hard short circuitsetIs a quantity preset in advance according to the actual condition of the soft short circuit.
Preferably, in step 3), the state quantity at the moment k is measured by updating based on the short-circuit battery model
Figure BDA0002464282620000031
And system output
Figure BDA0002464282620000032
And calculating the estimated value of the short-circuit current
Figure BDA0002464282620000033
The method comprises the following estimation processes:
updating state estimation time:
Figure BDA0002464282620000034
wherein A is a transfer matrix, E is an identity matrix, B is an input matrix, T is a sampling time,
Figure BDA0002464282620000035
is the state of charge, i.e. SOC, u, of the batteryk-1Inputting the quantity for the system;
output estimated time update:
Figure BDA0002464282620000041
wherein C is an output matrix, D is a direct transfer matrix,
Figure BDA0002464282620000042
an estimate of the system output;
short-circuit current estimation:
Figure BDA0002464282620000043
wherein, Ki1To design the gain parameter, ykMeasuring system output
Preferably, in step 3), TlongtermThe value of (A) is determined by experimental tests.
Preferably, in step 3), the battery capacity CnConsidered constant over a selected time interval.
Preferably, the batteries used in the series battery pack include lithium batteries, nickel cadmium batteries, nickel hydrogen batteries, or lead acid batteries.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a multi-time scale short circuit resistance estimation method of a series battery pack, which firstly provides a multi-time scale short circuit resistance estimation concept according to actual application requirements, carries out innovative design on an algorithm, respectively obtains a hard short circuit resistance value and a soft short circuit resistance value by combining and switching a short time scale estimation part and a long time scale estimation part, and simultaneously realizes quick hard short circuit resistance estimation of a short time scale and accurate soft short circuit resistance estimation of a long time scale. The method provided by the invention can realize reliable quantitative diagnosis of the short-circuit fault in multiple time scales, and improves the real-time diagnosis capability of the battery management system.
The method simplifies algorithm design, obtains voltage and current measurement values of the single batteries by collecting working condition data of the battery pack, selects the highest voltage single and the lowest voltage single in the series battery pack to carry out estimation analysis, avoids estimation calculation on the single batteries of all the series battery packs in the estimation analysis, simplifies short circuit resistance estimation flow, greatly reduces calculation complexity, and is easy to apply practically.
The algorithm of the invention has reasonable design, and in the long-time scale estimation part, the SOC difference value result obtained by the short-time scale estimation part is directly used for calculation, thereby avoiding the problem that the SOC difference value is obtained by independently carrying out soft short circuit fault of the long-time scale estimation part in the prior art through the modes of state estimation and the like.
Drawings
FIG. 1 is a flow chart of short circuit resistance estimation according to the present invention;
FIG. 2 is a diagram of a short-circuit cell model;
FIG. 3 shows the short-circuit resistance R in this embodimentSCShort-time scale short-circuit resistance estimation result when being 0.1 omega;
FIG. 4 is the long-term battery operation test current data in this example;
FIG. 5 shows the results of the long-term battery operation test in this example; wherein, (a) is a voltage experiment result, and (b) is an SOC experiment result;
FIG. 6 is a long-time scale short-circuit resistance estimation result in the case of short-circuit resistance in the present embodiment;
FIG. 7 shows the multi-time scale short circuit resistance estimation results for soft and hard shorts in this embodiment.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in detail below with reference to the figures and examples.
Referring to the flowchart shown in fig. 1, the method for estimating the short circuit resistance of the series battery pack in multiple time scales provided by the invention comprises the following steps:
firstly, measuring and obtaining the measured values of the voltage and the current of a battery;
and secondly, estimating short-time-scale short-circuit resistance, comprising the following steps of:
① initialize parameters such as state quantities at time k-1
Figure BDA0002464282620000061
Short circuit current
Figure BDA0002464282620000062
② selecting the highest voltage and the lowest voltage in the series battery and recording the voltage as max (V)k) And min (V)k) Estimating the selected monomer;
③ state estimation time update:
Figure BDA0002464282620000063
wherein A is a transfer matrix, E is an identity matrix, B is an input matrix, T is a sampling time,
Figure BDA0002464282620000064
is the state of charge, i.e. SOC, u, of the batteryk-1Inputting the quantity for the system;
④ output estimated time update:
Figure BDA0002464282620000065
where C is the output matrix, D is the direct transfer matrix,
Figure BDA0002464282620000066
an estimate of the system output;
⑤ short-circuit current estimation:
Figure BDA0002464282620000067
wherein Ki1In order to design the gain parameters for the gain control,
Figure BDA0002464282620000068
as an estimate of the short-circuit current, ykOutput measurements for the system;
⑥ presetting a hard short-circuit current judgment if
Figure BDA0002464282620000069
Then the fault is judged to be a hard short circuit fault, and the hard short circuit resistance is calculated:
Figure BDA00024642826200000610
otherwise, failure is not detectedBarrier, go to step ⑦;
⑦ state estimation measurement update:
Figure BDA00024642826200000611
wherein Kp、Ki2To design the gain parameter, xkIs the system state;
⑧ judging if k is l.TmacroThen the process transitions to the long timescale estimation, third step, otherwise the short timescale estimation continues and step ⑨ is performed, where TmacroIs a long time scale time unit, and l is a long time scale moment;
⑨ update the SOC Difference dSOCk＝max(SOCk)-min(SOCk) (ii) a Where max (SOC)k) The voltage of the highest cell is k time SOC, min (SOC)k) The SOC is the time of the lowest voltage monomer k;
⑩ judging if dSOC difference is presetk＞dSOCsetThen the process transitions to long timescale estimation and proceeds to step ③ in the third step to update the SOC delta value, otherwise, the process proceeds to step
Figure BDA00024642826200000612
Figure BDA0002464282620000071
Updating the time 1, namely k is k +1, returning to the step ②, and continuing to loop until the system process is finished;
and thirdly, estimating the short circuit resistance in a long time scale, comprising the following steps:
① update the difference in SOC at time l, dSOCl＝max(SOCl)-min(SOCl) (ii) a Where max (SOC)l) The voltage of the highest cell is the time of SOC, min (SOC)l) The SOC is the time of the lowest voltage monomer I;
② judging time if k > TlongtermIf not, executing the step ⑤;
③ calculating TlongtermSOC variation value in time period: Δ dSOCl＝dSOCl-dSOCl-m(ii) a Wherein T islongtermThe time interval is a long time scale, and m is the size of a selected calculation window;
④ soft short resistance estimation:
Figure BDA0002464282620000072
wherein R isSCIs a soft short-circuit resistance, VokMeasuring voltage value, C, for short-circuited cellsnTaking the battery capacity as the capacity, and executing step ⑤;
⑤ time update 2 ═ l +1, and transfer to the second step
Figure BDA0002464282620000073
Time update 1: and k is k +1, and the circulation is continued.
The preset hard short-circuit current ISCsetA preset amount in advance according to the actual condition of the hard short circuit;
the preset SOC difference value dSOCsetThe quantity is preset in advance according to the actual condition of the soft short circuit;
the battery is a lithium battery, a nickel-cadmium battery, a nickel-hydrogen battery or a lead-acid battery and the like.
In the second step, the short-circuit resistance estimation is implemented based on the short-circuit battery model, as shown in fig. 2. It comprises an open-circuit voltage source VocAnd ohmic internal resistance R of the battery1Component Rint model part, and equivalent short-circuit resistance RSCAnd a load RL. Short-circuit current is denoted as ISCMeasuring terminal voltage as V0；
Referring to FIG. 2, the short-circuit current ISCAnd measuring the current ILThere is a relationship: i isL＝I+ISCThen the system state space expression is as follows,
Figure BDA0002464282620000074
wherein A is a transfer matrix, B is an input matrix, C is an output matrix, D is a direct transfer matrix, u is a system input,
Figure BDA0002464282620000075
and y is the system output measurement.
An observer is established by a short-circuit battery model, and an estimated value of the short-circuit current can be obtained by the special property of the observer
Figure BDA0002464282620000081
Then obtaining the short-circuit resistance by ohm's law, the short-circuit current estimation expression is as follows,
Figure BDA0002464282620000082
wherein A is a transfer matrix, B is an input matrix, C is an output matrix, D is a direct transfer matrix, T is a sampling time, E is an identity matrix,
Figure BDA0002464282620000083
is the state of charge of the battery or SOC,
Figure BDA0002464282620000084
is the system output quantity, xk-1Is the system state, ykA system output measurement value; kp、Ki1、Ki2For designing gain parameters, i.e. quantities of state
Figure BDA0002464282620000085
And short circuit current estimation
Figure BDA0002464282620000086
Converging to the actual value, the SOC and the short-circuit current of the battery monomer can be realized
Figure BDA00024642826200000815
Hard short circuit resistance
Figure BDA0002464282620000087
(ii) an estimate of (d);
therefore, the process for short-time-scale short-circuit resistance estimation can be summarized as follows:
(1) initializing parameters, e.g. state quantities, at time k-1
Figure BDA0002464282620000088
Short circuit current
Figure BDA0002464282620000089
(2) Updating state estimation time:
Figure BDA00024642826200000810
(3) output estimated time update:
Figure BDA00024642826200000811
(4) short-circuit current estimation:
Figure BDA00024642826200000812
(5) hard short circuit resistance estimation:
Figure BDA00024642826200000813
(6) state estimation measurement update:
Figure BDA00024642826200000814
the implementation is concretely realized in each step of the second step;
in a third step ②, TlongtermThe value of (a) is determined by experimental tests;
in a third step ④, battery capacity CnConsidered constant over a selected time interval;
the battery is a lithium battery, a nickel-cadmium battery, a nickel-hydrogen battery or a lead-acid battery and the like.
Example (b):
the battery adopted in the embodiment is a ternary lithium soft package battery, the nominal capacity is 10Ah, the upper limit voltage is 4.2V, the lower limit voltage is 2.75V, and 4 monomers are connected in series to form a battery pack. In order to simulate short-circuit fault in the battery pack, a resistor R is connected in parallel with the corresponding battery monomerSCDisclosure of the inventionOver-comparison RSCActual value and RSCEstimate values to validate the method.
In the invention, when the short-circuit current of the hard short-circuit fault is about 2C (C is a discharge multiplying factor unit), the resistance value of the corresponding short-circuit resistor is less than 0.2 omega. Setting a short-circuit current threshold ISCsetIs 10A, the SOC difference threshold value dSOCsetIs 4%, RSCRespectively selected to be 0.1 omega, 40 omega, 50 omega and 100 omega.
An experimental simulation was performed for a hard short circuit fault, with a 0.1 Ω resistor connected in parallel with cell 2 at 1000 s. The results of the short-time scale short-circuit resistance estimation are shown in fig. 3. As can be seen from fig. 3, when t <1000s, no resistance is connected and the estimated value of the short circuit resistance is infinite; when t >1000s, the short circuit resistance estimate converges rapidly to the reference short circuit resistance value within 3s, verifying rapid estimation on a short time scale.
To verify the long time scale estimation, a long time battery run test was performed with current data as shown in fig. 4. In the examples, the simulated Driving conditions were selected as Urban road conditions (UDDS, Urban dynameter Driving Schedule). As can be seen from fig. 4, the battery pack performs a plurality of UDDS operating conditions for cyclic discharge, and then performs constant current charging, and the process is repeated continuously to simulate the actual operating conditions.
In order to simulate a soft short circuit fault, a plurality of short circuit resistors are respectively connected in parallel with the monomers in the battery pack, wherein the monomers 1 are not connected in parallel with resistors, the monomers 2 are connected in parallel with 100 Ω, the monomers 3 are connected in parallel with 50 Ω, the monomers 4 are connected in parallel with 40 Ω, and the voltage and SOC test results are shown in FIG. 5. As can be seen from fig. 5, the voltage difference and the SOC difference between the cells are gradually amplified over time, and the amplification of the SOC difference is more significant than the former.
The long-time scale short-circuit resistance estimation result is shown in FIG. 6, which is a time unit T of the long-time scalemacroSet to 500s, long timescale time interval size Tlongterm20000 s. As can be seen from fig. 6, the most severe short-circuit fault in the battery pack was detected from cell 4, which was connected in parallel with a 40 Ω short-circuit resistor, from TlongtermStarting from the moment, the estimated resistance value can realize good tracking on the reference resistance value, the standard deviation of the resistance value estimation is only 1.5 omega, and the maximum resistance value estimation is realizedThe error is 7.2 omega.
In order to verify the multi-time scale short circuit resistance estimation method, soft short circuit and hard short circuit faults are established simultaneously in a scene, the estimation result is shown in fig. 7, a single body 4 is connected with a 40 omega short circuit resistance in parallel at 10000s, and a single body 2 is connected with a 0.1 omega short circuit resistance in parallel at 40000 s. As can be seen from fig. 7, since 20000s, the soft short-circuit resistance estimation value can be converged to the reference value quickly, the estimation standard deviation error is not more than 4%, and hard short-circuit fault detection and short-circuit resistance estimation can be realized within 3s triggered by a hard short circuit, thereby verifying the effectiveness of the multi-time scale short-circuit resistance estimation method.
From the practical safety perspective, the invention researches the battery short-circuit fault to realize diagnosis of different time scales, namely, the short-time scale estimation is used for quickly estimating the hard short-circuit resistance, and meanwhile, the long-time scale estimation is combined for accurately estimating the soft short-circuit resistance, thereby filling the blank of the current short-circuit fault research and improving the use safety of the series battery system.
The invention corresponds two types of resistance simultaneous estimation and two types of time scale estimation methods, and belongs to the innovative design of an algorithm; by switching between the two time scales, real-time estimation of the two resistances can be alternately realized, and when the method is applied to practice, quantitative diagnosis can be simultaneously carried out on hard short circuit and soft short circuit. In addition, the method selects the single body with the highest voltage and the single body with the lowest voltage through the innovative design of the algorithm, and only needs to estimate and calculate the two single bodies, so that two short-circuit resistance estimations of the fault battery can be realized, and the related calculation of all batteries in the battery pack is not needed, thereby effectively reducing the calculation complexity. In addition, the conventional soft short circuit method needs to obtain the SOC difference value by means of state estimation and the like, and the SOC difference value result obtained by the hard short circuit estimation part is directly used for calculation in the invention.
The invention discloses a multi-time scale short circuit resistance estimation method for a series battery pack, which comprises the following steps: measuring and obtaining the measured values of the voltage and the current of the battery; short-time scale short-circuit resistance estimation is carried out to quickly obtain a hard short-circuit resistance value, and when the scale conversion condition is met, conversion is carried out to long-time scale estimation; and estimating the short circuit resistance in a long time scale, and estimating the soft short circuit resistance. According to the invention, a multi-time scale short circuit resistance estimation concept is firstly provided according to the actual application requirements, the fast hard short circuit resistance estimation of a short time scale and the accurate soft short circuit resistance estimation of a long time scale are realized at the same time, the quantitative diagnosis of short circuit faults under different short circuit degrees is realized, only the monomers with the highest voltage and the lowest voltage in the series battery pack are selected for analysis in the process, the calculation complexity is greatly reduced, the practical application is easy, and the method has important significance for the estimation of the short circuit resistance of the battery pack and the fault diagnosis.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A multi-time scale short circuit resistance estimation method of a series battery pack is characterized by comprising the following steps:
1) collecting working condition data of the series battery pack;
2) initializing state quantities at time k-1
Figure FDA0002464282610000011
And short-circuit current
Figure FDA0002464282610000012
3) Identifying the highest voltage monomer and the lowest voltage monomer in the series battery pack, and respectively recording the voltage values as max (V)k) And min (V)k) Carrying out short circuit resistance estimation aiming at the monomer with the highest voltage and the monomer with the lowest voltage;
updating state quantity of measurement k moment
Figure FDA0002464282610000013
And system output
Figure FDA0002464282610000014
And calculate accordinglyShort circuit current estimation
Figure FDA0002464282610000015
When short circuit current estimation value
Figure FDA0002464282610000016
Greater than a preset hard short circuit current value ISCsetThen, the fault is determined to be a hard short circuit fault, and a hard short circuit resistance estimate is obtained
Figure FDA0002464282610000017
Completing short circuit resistance estimation of a short time scale;
otherwise, judging that the fault is not detected, and continuously updating the state quantity x at the moment of measuring kkAnd when the measuring time k meets the time scale conversion condition k ═ l · TmacroThen, the short-circuit resistance estimation mode is converted into long-time scale short-circuit resistance estimation, namely the SOC difference value dSOC of the highest voltage monomer and the lowest voltage monomer at the moment l is updatedl(ii) a When the measurement time k is larger than the long time scale time interval size TlongtermThen, calculate TlongtermSOC difference value delta dSOC in time periodlAnd obtaining a soft short circuit resistance estimate RSCAnd finishing the estimation of the short circuit resistance in a long time scale.
2. The method for estimating the short-circuit resistance of the series battery pack based on the multiple time scales of claim 1, wherein in the step 1), the operating condition data of the battery pack comprises the measured values of the voltage and the current of each battery cell.
3. The method for estimating a multi-time scale short circuit resistance of a series battery pack according to claim 1, wherein in step 3), when the measurement time k does not satisfy the time scale conversion condition k-l-TmacroThen, the SOC difference value dSOC of the highest voltage cell and the lowest voltage cell at the time k is judgedkDifference value dSOC from preset SOCsetThe size relationship of (1):
① dSOCk＞dSOCsetThen, the short-circuit resistance estimation mode is converted into the short-circuit resistance estimation of a long time scale, namely, the T calculation is executedlongtermSOC difference value delta dSOC in time periodlAnd calculating the soft short circuit resistance estimated value R according to the soft short circuit resistance estimated value RSCCompleting short circuit resistance estimation in a long time scale;
② dSOCkNot greater than dSOCsetAnd updating the measurement time k to k +1, and re-executing the step 3), namely, selecting the highest-voltage monomer and the lowest-voltage monomer again, and continuing to circulate until the estimation system process is finished.
4. The method for estimating short circuit resistance in multiple time scales of a series battery pack according to claim 1, wherein in the step 3), when the measurement time k is not more than the long time scale time interval size TlongtermAnd then, updating the measurement time l to l +1, and shifting to the updated measurement time k to k +1, and re-executing the step 3), namely, selecting the highest-voltage and lowest-voltage monomers again, and continuing to circulate until the process of the estimation system is finished.
5. The method for estimating short-circuit resistance of series battery packs according to claim 1, wherein in step 3), after the estimation of short-circuit resistance of long time scale is completed, the measurement time l ═ l +1 is continuously updated, the measurement time k ═ k +1 is updated, step 3) is executed again, that is, the highest voltage cell and the lowest voltage cell are selected again, and the process is continued to circulate until the estimation system is finished.
6. The method for estimating short-circuit resistance of series battery pack according to claim 1, wherein in step 3), the preset hard short-circuit current ISCsetThe preset SOC difference value dSOC is a preset quantity in advance according to the actual condition of the hard short circuitsetIs a quantity preset in advance according to the actual condition of the soft short circuit.
7. The method for estimating short-circuit resistance of series battery pack according to claim 1, wherein in step 3), the short-circuit battery model is basedBy updating the state quantity at the moment of measurement k
Figure FDA0002464282610000021
And system output
Figure FDA0002464282610000022
And calculating the estimated value of the short-circuit current
Figure FDA0002464282610000023
The method comprises the following estimation processes:
updating state estimation time:
Figure FDA0002464282610000024
wherein A is a transfer matrix, E is an identity matrix, B is an input matrix, T is a sampling time,
Figure FDA0002464282610000025
is the state of charge, i.e. SOC, u, of the batteryk-1Inputting the quantity for the system;
output estimated time update:
Figure FDA0002464282610000026
wherein C is an output matrix, D is a direct transfer matrix,
Figure FDA0002464282610000027
an estimate of the system output;
short-circuit current estimation:
Figure FDA0002464282610000028
wherein, Ki1To design the gain parameter, ykIs a measure of system output.
8. The method for estimating the short circuit resistance of the series battery pack in multiple time scales according to claim 1, wherein in the step 3), T islongtermThe value of (A) is determined by experimental tests.
9. The method for estimating short-circuit resistance of series battery according to claim 1, wherein in step 3), the battery capacity CnConsidered constant over a selected time interval.
10. The method of claim 1, wherein the battery used in the series battery comprises a lithium battery, a nickel cadmium battery, a nickel metal hydride battery or a lead acid battery.
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