CN113466711A - SOC algorithm suitable for HEV - Google Patents

SOC algorithm suitable for HEV Download PDF

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CN113466711A
CN113466711A CN202110756815.2A CN202110756815A CN113466711A CN 113466711 A CN113466711 A CN 113466711A CN 202110756815 A CN202110756815 A CN 202110756815A CN 113466711 A CN113466711 A CN 113466711A
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value
voltage
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state
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陈作勰
邵磊
黄光美
何星
莫勇
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Shenzhen Tianbangda New Energy Technology Co ltd
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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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • 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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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

The invention relates to the technical field of HEV (electric vehicle-electronic) and particularly relates to an SOC (state of charge) algorithm suitable for HEV (electric vehicle-electronic).A first-order circuit model, namely an equivalent battery model, is established around a monomer in a battery pack, and the State (SOC) is optimally estimated by using EKF (extended Kalman filter algorithm). The method can well reduce the error accumulation caused by an ampere-hour integral method, reduce the dependence on an open-circuit correction method, and further improve the accuracy and robustness of the SOC estimation autonomous correction so as to be well suitable for complex driving working conditions.

Description

SOC algorithm suitable for HEV
Technical Field
The invention relates to the technical field of HEVs, in particular to an SOC algorithm suitable for HEVs.
Background
The lithium battery is used as a core component of a new energy automobile, the representation of the battery characteristics of the lithium battery is related to the safe and efficient operation of the automobile, a secondary power supply system-BMS (Battery management System) of the HEV is an important component for assisting the HEV to smoothly and safely start and stop driving, wherein the battery residual capacity-SOC (state of charge, which refers to the ratio of the residual capacity of a storage battery to the capacity of the storage battery in a fully charged state) is one of the most important state parameters of the BMS, and the BMS is one of basic factors for ensuring that the battery can be normally charged and discharged, prolonging the service life and reasonably providing power and is not negligible.
Most of methods for estimating SOC used by HEV and even EV in the market are mainly an ampere-hour integration method and open-circuit voltage correction, and because the ampere-hour integration method has error accumulation and the open-circuit voltage correction depends on harsh environmental conditions during power-on, the performance and effect of the scheme are not satisfactory in engineering implementation;
based on the scheme, the invention provides a closed-loop correction algorithm, namely an equivalent battery model, namely a first-order circuit model, is established around the monomer in the battery pack, and the EKF (extended Kalman filter algorithm) is used for carrying out State (SOC) optimal estimation; by using the closed-loop algorithm, the error accumulation caused by the ampere-hour integral method can be well reduced, the dependence on an open-circuit correction method is reduced, and the accuracy and the robustness of the SOC estimation autonomous correction are further improved, so that the SOC estimation autonomous correction method is well adapted to the complex driving working condition, and the reliability and the safety of the BMS are further improved.
Disclosure of Invention
The invention aims to provide an SOC algorithm suitable for an HEV (hybrid electric vehicle), and aims to solve the problems that the accuracy and the robustness of SOC estimation autonomous correction are low due to the fact that an ampere-hour integration method has error accumulation and open-circuit voltage correction depends on harsh environmental conditions during power-on.
In order to achieve the purpose, the invention provides the following technical scheme: an SOC algorithm for an HEV, comprising the steps of:
the method comprises the following steps: checking whether the lithium battery is fully kept stand;
step two: when the lithium battery is in a static state, an OCV (open control voltage) table look-up method is used, and SOC errors are calibrated according to OCV _ SOC curve data of the lithium battery;
step three: calculating the soc value by the formula soc to (t) ═ S0C (t-1) +1 × t/Q;
step four: by the formula DCR ═ Table1(SOC to (t), Temp); rp — Table2(SOC (t), Temp); table3(SOC to (t), Temp); 0CV (Table 4) (SOC "(t), Temp) respectively calculates the values of DCR, Rp, tP and 0CV, where DCR is a direct current resistance value, Rp is a polarization resistance value, and 0CV is an open circuit voltage, which means the potential difference between the two electrodes when the battery is not discharged and open circuit;
step five: establishing an equivalent battery model-a first-order circuit model around the monomer inside the battery pack;
step six: performing State (SOC) optimal estimation by using EKF (extended Kalman Filter algorithm);
step seven: and finally, correcting the polarization voltage Up, the SOC and the internal resistance voltage drop Ur in real time by using the EKF (extended Kalman filter) by taking the voltage drop of the direct current internal resistance in the first-order circuit model as a state parameter.
Preferably, after a stationary steady-state voltage V0 is obtained in the second step, table lookup is performed to calculate an SOC value, and a calibration SOC value is obtained and recorded as SOC (med); an error analysis and prediction is made.
Preferably, error analysis and prediction: v0+ SampleErr, calculating the SOC value by table lookup to obtain SOC (max); for V0-SampleErr, calculating an SOC value by looking up a table to obtain SOC (min); wherein SampleErr is a sampling error.
Preferably, Δ SOC (SOC) ((max) — SOC (min)) is calculated, resulting in a calibration weight value K; making a calibration of SOC according to the formula SOC (new) ═ SOC (old) · 1 · K) · 100% + SOC (med) · K × 100%; wherein, SOC (new) is the SOC value after system calibration, SOC (old) is the current SOC value of the system, i.e. the SOC value to be calibrated, and according to the voltage value, the SOC value is calculated by looking up the table, i.e. the SOC value is obtained through the OCV-SOC curve.
Preferably, if the lithium battery is not in a standing state in the second step, reading the NVM data, and calculating according to the formula in the third step after reading.
Preferably according to the formula
Figure BDA0003147478100000031
It can be seen that the state variables include the polarization voltage Up, the SOC and the internal resistance voltage drop Ur, and Ur is calculated by a basic iterative formula, and the rough Ur is calculated by using the current change before and after the period and the internal resistance Ro.
Preferably, the difference between the model output voltage UL and the collected voltage is input into the EKF, and finally the EKF is used for correcting the polarization voltage Up, the SOC and the internal resistance voltage drop Ur in real time.
Compared with the prior art, the invention has the beneficial effects that: the SOC algorithm suitable for the HEV further improves the robustness, accuracy and stability of SOC estimation so as to adapt to complex and changeable working conditions in real vehicle running, and further improves the reliability and safety of the BMS;
1. and calculating the SOC based on an OCV correction method and ampere-hour integral, and additionally adding a closed-loop control algorithm: the real-time correction is realized by using an improved state equation and EKF, which comprises the following specific steps:
in the traditional first-order circuit model, the state variables only comprise SOC and polarization voltage Up, and because the internal resistance voltage drop Ur is also considered in the first-order circuit model, the first-order circuit model is corrected, the voltage drop of the direct current internal resistance in the first-order circuit model is also used as a state parameter, and the state parameter is obtained according to a formula
Figure BDA0003147478100000032
It can be seen that the state variables include polarization voltage Up, SOC and internal resistance voltage drop Ur, the Ur has basic iterative formula calculation, rough Ur is calculated by using current change and internal resistance Ro before and after a period, difference between model output voltage UL and collected voltage is input into EKF, and finally the polarization voltage Up, SOC and internal resistance voltage drop Ur are corrected in real time by using EKF.
The invention is based on the state correction extended Kalman filtering algorithm, and carries out the optimal estimation to the state quantities such as SOC, and the like, and comprises the following steps:
(1) based on an improved state equation, namely, the voltage drop of the direct current internal resistance in the first-order circuit model is also used as a state parameter, the scheme can avoid identifying key parameters in the first-order circuit model to a certain extent, reduce the complexity of an algorithm and a strategy, and basically meet the precision requirement of the HEV on the SOC;
(2) in the step (1), a plurality of fuzzy logic processing can be carried out on the state parameters according to certain experience and knowledge, and the stability and the accuracy of the EKF and the observation equation are ensured to a certain extent;
(3) the improved state equation can further improve the adaptability of the algorithm to each state of the life cycle of the battery, get rid of the dependence of prior parameters and further improve the adaptability.
In conclusion, by using the optimized and improved state equation, the robustness, stability, adaptability, accuracy, reliability and other performances of the optimal estimation of the state parameters are further improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
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 apparent that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and furthermore, the terms "first", "second", "third", "upper, lower, left, right", and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Meanwhile, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "connected" and "connected" should be interpreted broadly, for example, as being fixedly connected, detachably connected, or integrally connected; the connection can be mechanical connection or electrical connection; the embodiments of the present invention can be directly connected or indirectly connected through an intermediary, and all other embodiments obtained by those skilled in the art without any creative efforts belong to the protection scope of the present invention.
The structure of the SOC algorithm suitable for the HEV provided by the invention is shown as the figure, and comprises the following steps:
the method comprises the following steps: checking whether the lithium battery is sufficiently static (obtaining OCV requires standing for a long time after charging and discharging);
step two: when the lithium battery is in a standing state, according to OCV _ SOC curve data of the lithium battery, after a standing steady-state voltage V0 is obtained, a SOC value is calculated by table lookup to obtain a calibration SOC value, and the calibration SOC value is recorded as SOC (med); performing an error analysis and prediction: for V0+ SampleErr, calculating an SOC value by table lookup to obtain SOC (max); for V0-SampleErr, calculating an SOC value by looking up a table to obtain SOC (min); wherein SampleErr is a sampling error; calculating Δ SOC (SOC) ((max) -SOC (min)) to obtain a calibration weight value K; making a calibration of SOC according to the formula SOC (new) ═ SOC (old) · 1 · K) · 100% + SOC (med) · K × 100%; wherein, SOC (new) is the SOC value after system calibration, SOC (old) is the current SOC value of the system, i.e. the SOC value to be calibrated, and according to the voltage value, the SOC value is calculated by looking up the table, i.e. the SOC value is obtained through the OCV-SOC curve; if the lithium battery is not in a standing state, reading NVM data, and calculating according to the formula in the third step after reading;
step three: calculating the soc value by the formula soc to (t) ═ S0C (t-1) +1 × t/Q;
step four: by the formula DCR ═ Table1(SOC to (t), Temp); rp — Table2(SOC (t), Temp); table3(SOC to (t), Temp); 0CV (Table 4) (SOC "(t), Temp) respectively calculates the values of DCR, Rp, tP and 0CV, where DCR is a direct current resistance value, Rp is a polarization resistance value, and 0CV is an open circuit voltage, which means the potential difference between the two electrodes when the battery is not discharged and open circuit;
step five: an equivalent battery model-a first-order circuit model (the battery equivalent circuit model refers to a model for representing the electrical characteristics of the internal circuit of the battery by using solid electronic elements such as a voltage source (UOCV), a resistor, a capacitor and the like, so-called 'equivalence' does not have the same effect but refers to different representation modes of the same circuit, in the research of lithium batteries, the equivalent circuit model can clearly express the external characteristics of the battery and gives consideration to the influence factors such as the voltage, the current, the temperature and the like of the battery, the mathematical expression mode is clear and clear, the most extensive battery model is adopted at present, the first-order circuit is a linear circuit containing a dynamic element after the circuit is simplified (such as the series-parallel connection of the resistor, the series-parallel connection of the capacitor and the series-parallel connection of the inductor into an element), and the equation of the first-order linear ordinary differential equation is a linear equation, referred to as a first order circuit);
step six: performing State (SOC) optimal estimation by using EKF (extended Kalman Filter algorithm) (the extended Kalman Filter algorithm is developed on the basis of a standard Kalman Filter algorithm, and the basic idea is that a Taylor expansion algorithm is applied to expand a nonlinear system near a filter value, and high-order terms above the second order are all saved, so that the original system becomes a linear system, then the idea of the standard Kalman Filter algorithm is utilized to filter a system linearization model, and the optimal estimation refers to the estimation that data resolved by the KF algorithm is infinitely close to a true value, and the posterior probability estimation is infinitely close to the true value by mathematical expression);
step seven: calculating the SOC based on an OCV correction method and an ampere-hour integral, (OCV reflects the stable electromotive force of a battery in a certain SOC state, corresponds to SOC one to one and can be used as the basis of SOC static calibration, when the battery is fully placed, the terminal voltage of the battery is considered to be equal to OCV, the current SOC of the battery can be obtained by checking a curve by utilizing a previously obtained SOC-OCV curve, but the time consumed for carrying out a complete experiment is too long because the battery is fully placed each time, the ampere-hour integral method is a common SOC estimation method, and if the charging and discharging initial state is recorded as SOC0, the SOC of the current state is as follows:
Figure BDA0003147478100000061
wherein, CNThe rated capacity of the battery; i is the battery current; eta is the SOC calculation error caused by inaccurate current measurement in the application of the charging and discharging efficiency ampere-hour integral method, long-term accumulation and larger error) and a new closed-loop control algorithm is added: the real-time correction is realized by using an improved state equation and EKF, which comprises the following specific steps:
in the traditional first-order circuit model, the state variables only comprise SOC and polarization voltage Up, and because the internal resistance voltage drop Ur is also considered in the first-order circuit model, the first-order circuit model is corrected, the voltage drop of the direct current internal resistance in the first-order circuit model is also used as a state parameter, and the state parameter is obtained according to a formula
Figure BDA0003147478100000071
It can be seen that the state variables include polarization voltage Up, SOC and internal resistance voltage drop Ur, the Ur is calculated by a basic iterative formula, the rough Ur is calculated by using current change before and after a period and internal resistance Ro, the difference between model output voltage UL and collected voltage is input into EKF, and finally the polarization voltage Up, SOC and internal resistance voltage drop Ur are corrected in real time by using EKF.
By using the closed-loop algorithm, the robustness, accuracy and stability of SOC estimation are further improved, so that the method is suitable for complex and changeable working conditions in real vehicle running, and the reliability and safety of the BMS are further improved.
The invention is based on the state correction extended Kalman filtering algorithm, and carries out the optimal estimation to the state quantities such as SOC, and the like, and comprises the following steps:
(1) based on an improved state equation, namely, the voltage drop of the direct current internal resistance in the first-order circuit model is also used as a state parameter, the scheme can avoid identifying key parameters in the first-order circuit model to a certain extent, reduce the complexity of an algorithm and a strategy, and basically meet the precision requirement of the HEV on the SOC;
(2) in the step (1), a plurality of fuzzy logic processing can be carried out on the state parameters according to certain experience and knowledge, and the stability and the accuracy of the EKF and the observation equation are ensured to a certain extent;
(3) the improved state equation can further improve the adaptability of the algorithm to each state of the life cycle of the battery, get rid of the dependence of prior parameters and further improve the adaptability.
In conclusion, by using the optimized and improved state equation, the robustness, stability, adaptability, accuracy, reliability and other performances of the optimal estimation of the state parameters are further improved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (7)

1. An SOC algorithm for an HEV, comprising: the method comprises the following steps:
the method comprises the following steps: checking whether the lithium battery is fully kept stand;
step two: when the lithium battery is in a static state, an OCV (open control voltage) table look-up method is used, and SOC errors are calibrated according to OCV _ SOC curve data of the lithium battery;
step three: calculating the soc value by the formula soc to (t) ═ S0C (t-1) +1 × t/Q;
step four: by the formula DCR ═ Table1(SOC to (t), Temp); rp — Table2(SOC (t), Temp); table3(SOC to (t), Temp); 0CV (Table 4) (SOC "(t), Temp) respectively calculates the values of DCR, Rp, tP and 0CV, where DCR is a direct current resistance value, Rp is a polarization resistance value, and 0CV is an open circuit voltage, which means the potential difference between the two electrodes when the battery is not discharged and open circuit;
step five: establishing an equivalent battery model-a first-order circuit model around the monomer inside the battery pack;
step six: performing State (SOC) optimal estimation by using EKF (extended Kalman Filter algorithm);
step seven: and finally, correcting the polarization voltage Up, the SOC and the internal resistance voltage drop Ur in real time by using the EKF (extended Kalman filter) by taking the voltage drop of the direct current internal resistance in the first-order circuit model as a state parameter.
2. The SOC algorithm for HEVs as claimed in claim 1, wherein: after a static steady-state voltage V0 is obtained in the second step, the SOC value is calculated by table lookup to obtain a calibration SOC value which is recorded as SOC (med); an error analysis and prediction is made.
3. The SOC algorithm for HEVs as claimed in claim 2, wherein: error analysis and prediction: for V0+ SampleErr, calculating an SOC value by table lookup to obtain SOC (max); for V0-SampleErr, calculating an SOC value by looking up a table to obtain SOC (min); wherein SampleErr is a sampling error.
4. The SOC algorithm for HEVs as claimed in claim 3, wherein: calculating Δ SOC (SOC) ((max) -SOC (min)) to obtain a calibration weight value K; making a calibration of SOC according to the formula SOC (new) ═ SOC (old) · 1 · K) · 100% + SOC (med) · K × 100%; wherein, SOC (new) is the SOC value after system calibration, SOC (old) is the current SOC value of the system, i.e. the SOC value to be calibrated, and according to the voltage value, the SOC value is calculated by looking up the table, i.e. the SOC value is obtained through the OCV-SOC curve.
5. The SOC algorithm for HEVs as claimed in claim 1, wherein: and in the second step, reading the NVM data if the lithium battery is not in a standing state, and calculating according to the formula in the third step after reading.
6. The SOC algorithm for HEVs as claimed in claim 1, wherein: according to the formula
Figure FDA0003147478090000021
It can be seen that the state variables include the polarization voltage Up, the SOC and the internal resistance voltage drop Ur, and Ur is calculated by a basic iterative formula, and the rough Ur is calculated by using the current change before and after the period and the internal resistance Ro.
7. The SOC algorithm for an HEV according to claim 6, wherein: and inputting the difference between the model output voltage UL and the collected voltage into the EKF, and finally correcting the polarization voltage Up, the SOC and the internal resistance voltage drop Ur in real time by using the EKF.
CN202110756815.2A 2021-07-05 2021-07-05 SOC algorithm suitable for HEV Pending CN113466711A (en)

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