CN112162198A - Battery health diagnosis system and method suitable for hybrid vehicle - Google Patents
Battery health diagnosis system and method suitable for hybrid vehicle Download PDFInfo
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- CN112162198A CN112162198A CN202011052017.3A CN202011052017A CN112162198A CN 112162198 A CN112162198 A CN 112162198A CN 202011052017 A CN202011052017 A CN 202011052017A CN 112162198 A CN112162198 A CN 112162198A
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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
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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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- Y—GENERAL 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
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
A health diagnosis system suitable for hybrid vehicles comprises a time period taking module, a battery parameter estimation module and a health diagnosis module, and a diagnosis method of the health diagnosis system comprises the following steps: the time period taking module extracts a corresponding time period according to initial temperature and SOC conditions, extracts current working condition data in the time period, introduces the current working condition data into the battery model, calculates a root mean square error between output voltage and real voltage of the battery model, and enters a second step when the error meets the conditions; extracting corresponding battery model parameters under the section of working condition by a battery parameter estimation module, and outputting a target voltage value V (i) and a uoc (i) by combining input of a target working condition; and thirdly, the health diagnosis module carries out multi-dimensional health parameter estimation through the target voltage values V (i) and uoc (i) output in the second step. The method provided by the invention can evaluate the battery health state from multiple dimensions, has more comprehensive health evaluation and high prediction precision, and is particularly suitable for online diagnosis of the battery health of the hybrid vehicle.
Description
Technical Field
The invention relates to the technical field of new energy battery application, in particular to a battery health diagnosis system and method suitable for a hybrid vehicle.
Background
The hybrid vehicle is powered by a power battery system and a fuel system together, and the power battery achieves the purposes of saving oil and optimizing comprehensive performance through the coordinated application of discharge and recharging and the fuel system. The power battery system on the hybrid vehicle generally has small load capacity, the battery has the capacity of bearing high-rate current, and the operation SOC interval is narrow. After the battery is used for a long period of time after being loaded on a vehicle, the battery is aged, the internal resistance is increased, and the bearable current shows a descending trend, namely the output power is reduced. The power performance of the battery system has a great influence on the dynamic performance and the fuel saving rate of the vehicle, and even part of the vehicle cannot be normally used due to the reduction of the power performance of the battery system.
At present, the battery rejection standard of the hybrid vehicle still follows the method for judging the capacity attenuation degree of the pure electric vehicle, and the battery health diagnosis parameter is single and the prediction precision is low. However, the hybrid vehicle needs to pay more attention to the power performance due to the use characteristics of the hybrid vehicle, and the power and capacity change degrees do not have completely consistent correlation. Therefore, the battery health diagnosis system and method suitable for the hybrid vehicle are provided from the multi-dimensional direction.
Disclosure of Invention
The invention provides a battery health diagnosis system and method suitable for a hybrid vehicle, and aims to overcome the defects of single parameter, low prediction precision and the like of hybrid vehicle health diagnosis in the prior art.
The invention adopts the following technical scheme:
a battery health diagnostic system adapted for use in a hybrid vehicle, comprising: the time period acquisition module is used for extracting current working condition data in a certain time period and importing the current working condition data into the battery model so as to calculate the root mean square error between the output voltage of the battery model and the real voltage; the battery parameter estimation module is used for combining the extracted qualified battery model parameters with the input target working condition to obtain a target voltage response value; and the health diagnosis module is used for estimating the multidimensional health parameters of the battery according to the target voltage value.
Further, the battery model is U (k) = f (U (k-1) … U (k-N), I (k-1) … I (k-N)), where U represents terminal voltage and I represents current.
Further, the battery model is a battery equivalent circuit model, an electrochemical model or a fractional order model.
Further, the multidimensional health parameters of the battery comprise capacity unbalance degree, polarization sum, internal resistance inconsistency degree, internal resistance increase rate, power state and voltage overrun.
The invention also provides a battery health diagnosis method suitable for the hybrid vehicle, which comprises the battery health diagnosis system and comprises the following specific steps:
the time period taking module extracts a corresponding time period according to initial temperature and SOC conditions, extracts current working condition data in the time period, introduces the current working condition data into the battery model, calculates the root mean square error between the output voltage and the real voltage of the battery model, enters the step two when the error meets the conditions, and otherwise reselects working conditions of other time periods;
extracting corresponding battery model parameters under the section of working condition by a battery parameter estimation module, and outputting a target voltage value V (i) and a uoc (i) by combining input of a target working condition;
and thirdly, the health diagnosis module carries out multidimensional health parameter estimation through the target voltage value V (i) and the uoc (i) output in the second step, wherein the multidimensional health parameter estimation comprises capacity unbalance accounting, polarization sum accounting, internal resistance inconsistency accounting, internal resistance increase rate accounting, power accounting and voltage overrun accounting.
Further, the criterion for the error meeting the condition in the first step is that e _ RMS is smaller than the threshold a.
Further, the threshold value a varies from 0.01 to 0.05 depending on the accuracy of the voltage sensor.
Further, in the third step, the power is calculated according to the formula (1): SOP = Vi _ avg × Imax — (1); the accounting for internal resistance r (i) according to equation (2): r (i) = [ V (i) — uoc (i) ]/Imax- (2), and R is calculated, and then the internal resistance increase rate SOR is calculated from the obtained R: and SOR = R/R0, and R0 is the internal resistance value of the battery cell obtained by the offline test of the new battery.
Further, when T _ avg is within the target temperature range, R is calculated using the formula R = mean [ R (i) ] - (3); and when the T _ avg is not in the target temperature range, fitting the obtained R (i) and the corresponding T (i) to obtain a formula R = h (T) -4, and calculating the R at the corresponding temperature according to the target temperature value after fitting.
Further, in the third step, the capacity imbalance SOD = f (uoc _1(i)) -f (uoc _2(i)) - (5) is checked according to the formula (5), wherein uoc _1 and uoc _2 are uoc (i) corresponding to different cell voltages or the highest and lowest cell voltages of the system as inputs, and f is a relation function of SOC-OCV; and (2) checking the polarization sum eta = abs (V (m) -uoc (m)) and + abs (V (n) -uoc (n) -6) according to a formula (6), wherein m and n respectively represent the positions of positive and negative corresponding current in the design target working condition, and the voltage overrun checking means comparing the obtained V (i) with the allowable working upper limit voltage and lower limit voltage of the battery and checking whether the V (i) exceeds a threshold value.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
according to the method, the problem of model precision deterioration caused by working conditions is controlled by taking the root mean square error e _ RMS of a time period module, the suitable working conditions of the suitable time period are selected to obtain high-precision battery model parameters, then the extracted battery model parameters are combined with the introduced target working conditions through a battery parameter estimation module to obtain target voltage response values, and finally the health diagnosis module carries out multi-dimensional health parameter estimation by using the target voltage values to obtain the health degree of a vehicle battery system. The battery health diagnosis system and method have the advantages that the obtained battery model has high parameter precision, the battery health state is evaluated from multiple dimensions, the health evaluation is more comprehensive, the prediction precision is high, the battery health diagnosis system and method are particularly suitable for online diagnosis of the battery health of the hybrid vehicle, and the problems of single parameter, low prediction precision and the like of the existing hybrid vehicle health diagnosis are solved.
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FIG. 1 is a block diagram of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings. Numerous details are set forth below in order to provide a thorough understanding of the present invention, but it will be apparent to those skilled in the art that the present invention may be practiced without these details. Well-known components, methods and processes are not described in detail below.
The invention provides a health diagnosis system suitable for a hybrid vehicle, which comprises a time period taking module, a battery parameter estimation module and a health diagnosis module, wherein the time period taking module is used for extracting current working condition data in a certain time period and importing the current working condition data into a battery model so as to calculate the root mean square error between the output voltage of the battery model and the real voltage, and the battery parameter estimation module is used for estimating the current working condition data of the battery model and the real voltage of the battery model. And the battery parameter estimation module is used for combining the extracted qualified battery model parameters with the input target working condition to obtain a target voltage response value. And the health diagnosis module is used for estimating the multidimensional health parameters of the battery by using the target voltage value.
The multidimensional health parameters of the battery comprise capacity unbalance degree, polarization sum, internal resistance inconsistency degree, internal resistance increase rate, power state and voltage overrun. The "power state" can be used for evaluating the charge and discharge transient power of the battery system, the "internal resistance increase rate" can be used for evaluating the increase rate of the internal resistance of the battery cell in the battery system compared with that in a new battery state, the "polarization sum estimation" can be used for evaluating the sum of charge polarization and discharge polarization of the battery cell under certain virtual working conditions, and is used for evaluating a polarization redundancy space, the "internal resistance inconsistency degree" can be used for evaluating the difference of the internal resistance and the difference of the power between the battery cells, and is used for finding an abnormal battery cell, the "unbalance degree" can be used for evaluating the SOC difference between the battery cells of the battery system, and is used for finding the abnormal battery cell and evaluating the power space which can be improved by balance maintenance, and the "voltage overrun accounting" can be used for evaluating whether the.
Referring to fig. 1, the diagnostic method of the health diagnostic system specifically includes the following steps:
step one, a time period module selects proper temperature and SOC ranges as a data starting point (t 1) and a data ending point (t 2), real current working conditions in a time period of t1-t2 of a vehicle are extracted, a battery model is led in, a root mean square error e _ RMS between output voltage of the battery model and the real voltage is calculated, when the error meets conditions, namely the root mean square error e _ RMS is smaller than a threshold value a, the step two is carried out, and otherwise, working conditions in other time periods are selected again. The threshold value a varies between 0.01 and 0.05 depending on the accuracy of the voltage sensor.
The battery model is U (k) = f (U (k-1) … U (k-N), I (k-1) … I (k-N)), wherein U represents terminal voltage and I represents current. The response voltage value of the model is obtained by converting the current value and the voltage value of N steps before the step through a certain formula, wherein U represents the terminal voltage, and I represents the current. The battery model may also be a battery equivalent circuit model, an electrochemical model, a fractional order model.
Extracting corresponding battery model parameters under the section of working condition by a battery parameter estimation module, and outputting a target voltage value V (i) and a uoc (i) by combining input of a target working condition;
and step three, the health diagnosis module carries out multidimensional health parameter estimation through the target voltage values V (i) and uoc (i) output in the step two, wherein the multidimensional health parameter estimation comprises capacity unbalance accounting, polarization sum accounting, internal resistance inconsistency accounting, internal resistance increase rate accounting, power accounting and voltage overrun accounting.
In the third step, the power is calculated according to the formula (1): SOP = Vi _ avg × Imax — (1); the accounting for internal resistance r (i) according to equation (2): r (i) = [ V (i) — uoc (i) ]/Imax- (2), and R is calculated, and then the internal resistance increase rate SOR is calculated from the obtained R: and SOR = R/R0, and R0 is the internal resistance value of the battery cell obtained by the offline test of the new battery.
When T _ avg is within the target temperature range, calculate R using the formula R = mean [ R (i) ] - (3); and when the T _ avg is not in the target temperature range, fitting the obtained R (i) and the corresponding T (i) to obtain a formula R = h (T) -4, and calculating the R at the corresponding temperature according to the target temperature value after fitting. Calculating the internal resistance increase rate SOR according to the obtained R: SOR = R/R0- (5). Wherein, T _ avg is the average value of the temperatures corresponding to the data, and R0 is the internal resistance value of the battery monomer obtained by the offline test of the new battery;
the fitting formula (4) of r (i) and t (i) may be a known linear, exponential, gaussian, or the like formula, or a relational expression derived by an algorithm such as particle filtering; the fitting parameters may be changed from R (i) and T (i) to ln (R (i)), 1/T (i), and so on.
In the third step, calculating the capacity unbalanced SOD = f (uoc _1(i)) -f (uoc _2(i)) - (5) according to formula (5), wherein uoc _1 and uoc _2 are uoc (i) corresponding to different cell voltages or the highest and lowest cell voltages of the system as inputs, and f is a relation function of SOC-OCV; and (2) checking the polarization sum eta = abs (V (m) -uoc (m)) and + abs (V (n) -uoc (n) -6) according to a formula (6), wherein m and n respectively represent the positions of positive and negative corresponding current in the design target working condition, and the voltage overrun checking means comparing the obtained V (i) with the allowable working upper limit voltage and lower limit voltage of the battery and checking whether the V (i) exceeds a threshold value.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (10)
1. A battery health diagnostic system adapted for use in a hybrid vehicle, comprising:
the time period acquisition module is used for extracting current working condition data in a certain time period and importing the current working condition data into the battery model so as to calculate the root mean square error between the output voltage of the battery model and the real voltage;
the battery parameter estimation module is used for combining the extracted qualified battery model parameters with the input target working condition to obtain a target voltage response value;
and the health diagnosis module is used for estimating the multidimensional health parameters of the battery according to the target voltage value.
2. The battery health diagnostic system for a hybrid vehicle as set forth in claim 1, wherein: the battery model is U (k) = f (U (k-1) … U (k-N), I (k-1) … I (k-N)), wherein U represents terminal voltage and I represents current.
3. The battery health diagnostic system for a hybrid vehicle as set forth in claim 1, wherein: the battery model is a battery equivalent circuit model, an electrochemical model or a fractional order model.
4. The battery health diagnostic system for a hybrid vehicle as set forth in claim 1, wherein: the multi-dimensional health parameters of the battery comprise capacity unbalance degree, polarization sum, internal resistance inconsistency degree, internal resistance increase rate, power state and voltage overrun.
5. A battery health diagnostic method for a hybrid vehicle, comprising the battery health diagnostic system of claim 1, comprising the steps of:
the time period taking module extracts a corresponding time period according to initial temperature and SOC conditions, extracts current working condition data in the time period, introduces the current working condition data into the battery model, calculates the root mean square error between the output voltage and the real voltage of the battery model, enters the step two when the error meets the conditions, and otherwise reselects working conditions of other time periods;
extracting corresponding battery model parameters under the section of working condition by a battery parameter estimation module, and outputting a target voltage value V (i) and a uoc (i) by combining input of a target working condition;
and thirdly, the health diagnosis module carries out multidimensional health parameter estimation through the target voltage value V (i) and the uoc (i) output in the second step, wherein the multidimensional health parameter estimation comprises capacity unbalance accounting, polarization sum accounting, internal resistance inconsistency accounting, internal resistance increase rate accounting, power accounting and voltage overrun accounting.
6. The battery health diagnosis method for a hybrid vehicle according to claim 5, wherein: the judgment basis of the error meeting condition in the first step is that e _ RMS is smaller than the threshold value a.
7. The battery health diagnosis method for a hybrid vehicle according to claim 6, wherein: the threshold value a varies between 0.01 and 0.05 according to the accuracy of the voltage sensor.
8. The battery health diagnosis method for a hybrid vehicle according to claim 5, wherein: in the third step, the power is calculated according to the formula (1): SOP = Vi _ avg × Imax — (1); the accounting for internal resistance r (i) according to equation (2): r (i) = [ V (i) — uoc (i) ]/Imax- (2), and R is calculated, and then the internal resistance increase rate SOR is calculated from the obtained R: and SOR = R/R0, and R0 is the internal resistance value of the battery cell obtained by the offline test of the new battery.
9. The battery health diagnosis method for a hybrid vehicle according to claim 8, wherein: when T _ avg is within the target temperature range, calculate R using the formula R = mean [ R (i) ] - (3); and when the T _ avg is not in the target temperature range, fitting the obtained R (i) and the corresponding T (i) to obtain a formula R = h (T) -4, and calculating the R at the corresponding temperature according to the target temperature value after fitting.
10. The battery health diagnosis method for a hybrid vehicle according to claim 5, wherein: in the third step, calculating the capacity unbalanced SOD = f (uoc _1(i)) -f (uoc _2(i)) - (5) according to a formula (5), wherein uoc _1 and uoc _2 are uoc (i) corresponding to the highest and lowest cell voltages of different cell voltages or systems as input, and f is a relation function of SOC-OCV; and (2) checking the polarization sum eta = abs (V (m) -uoc (m)) and + abs (V (n) -uoc (n) -6) according to a formula (6), wherein m and n respectively represent the positions of positive and negative corresponding current in the design target working condition, and the voltage overrun checking means comparing the obtained V (i) with the allowable working upper limit voltage and lower limit voltage of the battery and checking whether the V (i) exceeds a threshold value.
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| CN114114053A (en) * | 2021-12-10 | 2022-03-01 | 厦门金龙联合汽车工业有限公司 | Method for measuring life state of hybrid vehicle battery |
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