CN110579711A - Battery residual capacity parameter pre-estimation device - Google Patents

Battery residual capacity parameter pre-estimation device Download PDF

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CN110579711A
CN110579711A CN201910966068.8A CN201910966068A CN110579711A CN 110579711 A CN110579711 A CN 110579711A CN 201910966068 A CN201910966068 A CN 201910966068A CN 110579711 A CN110579711 A CN 110579711A
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battery
module
soc
collection module
records
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杨文伟
许霞
李宏
孙杰
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Shanghai Industrial Utechnology Research Institute
Situ Shanghai Technology Co ltd
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Situ Shanghai 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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract

The invention relates to a device for estimating the residual electric quantity parameters of a battery, which comprises a data collection module and an estimation module, wherein the data collection module is connected with the estimation module and comprises an open-circuit voltage collection module, a current collection module and/or a workload collection module; open-circuit voltage collecting module for recording consumption open-circuit voltage Ut(ii) a The current collecting module records the consumption current It(ii) a The workload collection module records the workload; calculating amount SOC of estimation moduletSOC of usagetEquivalent open circuit voltage UtCorresponding, usage SOCtWith said dose current ItCorresponding; calculating subsequent consumption SOC by pre-estimation moduletInner predicted open circuit voltage utAnd estimateCurrent itAnd/or a predicted workload. The method estimates the residual capacity parameter by using the battery parameter in single operation, inputs all historical parameters such as the battery use times and the like into a long-short term memory model LSTM, and accurately estimates the SOC by using a time recurrent neural network of the LSTM, thereby having the advantages of time saving, labor saving and accurate estimation.

Description

battery residual capacity parameter pre-estimation device
Technical Field
The invention relates to a device for predicting residual electric quantity parameters of a battery in a total broadband, in particular to a device for predicting residual electric quantity suitable for various energy storage batteries.
Background
When the price of crude oil rises and global environmental problems lead the rapid development of novel battery energy storage systems. Lead-acid batteries, nickel-metal hydride batteries, nickel-cadmium batteries, lithium ion batteries and the like are the most commonly used batteries in the industry at present. The battery has the advantages of high working battery voltage, little pollution, low self-discharge rate and high power density. The battery is widely applied to pure electric vehicles or hybrid electric vehicles due to its portability.
estimating SOC (state OF charge) is a basic requirement for using a battery, and SOC is a very important parameter OF a control strategy, so accurate estimation OF SOC can protect the battery, prevent overdischarge, improve battery life, and enable applications to make a reasonable control strategy to save energy. However, batteries are chemical energy storage sources, no means to directly obtain such chemical energy values, and battery models are limited and there is uncertainty in the parameters, making accurate estimation of SOC very complex and difficult to implement. The existing SOC estimation technology mainly comprises (i) direct measurement: the method uses physical battery characteristics such as the voltage and impedance of the battery. (ii) Book-keeping estimation: the method uses the discharge current as an input and integrates the discharge current over time to calculate the SOC. (iii) The self-adaptive system comprises the following steps: the adaptive system is self-designed, can automatically adjust SOC according to different discharge conditions, and various SOC estimation adaptive systems exist in the prior art. (iv) The mixing method comprises the following steps: the hybrid model integrates the three SOC estimation methods, and the advantages are obtained respectively.
However, since the chemical energy characteristics of various energy storage batteries are not consistent, for example, the SOC and the OPEN CIRCUIT VOLTAGE (OCV) of a lithium battery lead-acid battery are approximately in a linear relationship, but the SOC and the OCV of a lithium battery are not in a linear relationship, the above various estimation methods need to be adjusted from time to time, and are time-consuming and labor-consuming, and the existing SOC estimation technology does not consider the influence of the number of times of battery usage and other factors on SOC estimation, which results in structural defects of the estimation methods.
Disclosure of Invention
The invention provides a method for estimating the parameter of the remaining capacity of a battery AND an estimating device thereof, which inputs all historical parameters such as the using times of the battery AND the like into a long-SHORT TERM memory model LSTM (Long AND SHORT TERM memory), AND accurately estimates the SOC by utilizing a time recursion neural network of the LSTM, thereby having the advantages of time saving, labor saving AND accurate estimation.
The invention provides a method for estimating the residual electric quantity parameter of a battery, which comprises the following steps: step 1000: collecting the battery parameters in the operation; step 2000: estimating the residual electric quantity parameter of the operation according to the battery parameter; wherein the battery parameter comprises the usage SOC in the operationtInternal volume open circuit voltage UtDosage current ItAnd/or workload; the residual capacity parameter comprises the subsequent consumption SOC of the operationtInner predicted open circuit voltage utEstimated current itAnd/or a predicted workload.
Preferably, step 1000 further comprises collecting battery history parameters, wherein the battery history parameters comprise: the charging frequency F is counted once after the battery is exhausted and is charged again; the full charge SOC corresponds to the charging times F; the battery use time delta T corresponding to the charging times F is the time from the battery exhaustion, the battery operation to the full SOC exhaustion after the battery is charged again to the full SOC; open circuit voltage U and/or current I within Δ T when the battery is in use; further comprising the step 100: collecting battery history parameters; step 200: inputting the battery history parameters to the LSTM; step 300: the LSTM output pre-estimated SOCi+1(ii) a Said dosage SOCtFor the estimated SOCi+1Less than 50%.
preferably, step 2000Further comprising, according to said dosage, an open circuit voltage UtMatching the open circuit voltage U to be close.
Preferably, step 2000 further comprises opening circuit voltage I according to the dosagetMatching the currents I closely.
Preferably, step 2000 further comprises the step of SOC according to the usage amounttMatching the close full SOC.
The method of claim 2, wherein the usage SOC istFor the estimated SOCi+120% of the total.
preferably, the workload is vehicle mileage; the estimated workload is estimated vehicle mileage.
preferably, the remaining power parameter further includes a subsequent usage SOCtThe estimated time of the inner part is related to the velocity V.
Preferably, the remaining power parameter further includes a subsequent usage SOCtThe relationship of the predicted time and the torque TQ in the inner part.
Preferably, the remaining power parameter further includes a subsequent usage SOCtestimated time and estimated open-circuit voltage utThe relationship (2) of (c).
Preferably, the remaining power parameter further includes a subsequent usage SOCtEstimated time and estimated current itThe relationship (2) of (c).
The device for pre-estimating the parameters of the residual electric quantity of the battery comprises a data collection module and a pre-estimation module, wherein the data collection module is connected with the pre-estimation module; the open-circuit voltage collecting module records the consumption open-circuit voltage Ut(ii) a The current collecting module records the consumption current It(ii) a The workload collection module records the workload; the pre-estimation module calculates the usage SOCtSaid amount of use SOCtEquivalent open circuit voltage UtCorrespondingly, the amount SOCtWith said dose current ItCorresponding; the estimation module calculates the subsequent use amount SOCtinner predicted open circuit voltage utestimated current itAnd/or a predicted workload.
Preferably, the system further comprises an LSTM module, the data collection module is connected with the LSTM, and the LSTM module is connected with the estimation module; the data collection module further comprises a historical data collection module, the historical data collection module comprising: the charging frequency F collecting module is used for recording the charging frequency F of the battery, wherein the charging frequency F is counted once after the battery is exhausted and recharged; the full-charge SOC collection module is used for recording a full-charge SOC, and the charging times F correspond to the full-charge SOC; the battery use time delta T collecting module is used for recording battery use time delta T, and the battery use time delta T is the time from the battery exhaustion to the battery operation to the full SOC after the battery is charged again to reach the full SOC; the open-circuit voltage collection module also records the open-circuit voltage U in the delta T when the battery is used; the current collection module also records the current I in the delta T when the battery is used; the LSTM module calculates the estimated SOC of the operation before the operationi+1(ii) a Said dosage SOCtFor the estimated SOCi+1less than 50%.
Preferably, the charging frequency F collecting module is a counting circuit, and the counting circuit records the charging frequency F.
Preferably, the battery time Δ T collecting module is a timing circuit, and the timing circuit records the battery time Δ T.
Preferably, the workload collection module is a load operation recording module.
Preferably, the load operation recording module collects non-operation parameters, where the non-operation parameters refer to changes of battery-related parameters when the load does not operate.
Preferably, the load operation recording module further comprises a torque TQ collection module and/or the rate V collection module.
Preferably, the battery age Δ T collection module records battery age Δ T' when the torque TQ collection module records activity or the rate vtoller module records activity, and the load operation recording module records inactivity; during the battery elapsed time Δ T ', the torque TQ collection module records the torque change Δ TQ ', and/or the rate Vcollection module records the rate change Δ V '.
Preferably, the load operation recording module records the mileage and the time of driving.
The invention provides a method for estimating the parameter of the remaining capacity of a battery, which estimates the parameter of the remaining capacity of the battery by using the parameter of the battery in single operation, inputs all historical parameters such as the using times of the battery and the like into a long-short TERM memory model LSTM (Long ANDSHORT TERM MEMORY), and accurately estimates the SOC by using a time recursive neural network of the LSTM, thereby having the advantages of time saving, labor saving and accurate estimation. The battery pack can be applied to the field of portable battery use, such as the fields of new energy automobiles, Internet of vehicles, Internet of things and the like.
Drawings
FIG. 1 is a schematic diagram of a method for estimating SOC;
FIG. 2 is a schematic diagram of an estimated SOC estimation apparatus;
FIG. 3 is a flow chart of a method of estimating SOC;
FIG. 4 is a flow chart of a method for estimating remaining battery power parameters according to the present invention;
FIG. 5 shows the operation timing T of the present invention0Front and rear quantitative SOCtAnd estimating the parameter of the residual electric quantity of the battery.
Detailed Description
the following describes in detail a specific embodiment of the method for estimating remaining battery capacity according to the present invention with reference to the accompanying drawings.
In the drawings, the dimensional ratios of layers and regions are not actual ratios for the convenience of description. When a layer (or film) is referred to as being "on" another layer or substrate, it can be directly on the other layer or substrate, or intervening layers may also be present. In addition, when a layer is referred to as being "under" another layer, it can be directly under, and one or more intervening layers may also be present. In addition, when a layer is referred to as being between two layers, it can be the only layer between the two layers, or one or more intervening layers may also be present. Like reference numerals refer to like elements throughout. In addition, when two components are referred to as being "connected," they include physical connections, including, but not limited to, electrical connections, contact connections, and wireless signal connections, unless the specification expressly dictates otherwise.
The portable battery is widely applied to the fields of new energy automobiles, internet of vehicles, internet of things and the like, and in use, a battery use strategy based on full-charge SOC is particularly important, the full-charge SOC refers to the electric quantity of a battery which is exhausted and charged again to reach a full-charge state once, and how to estimate the next full-charge SOC (estimated SOC)i+1) The electric quantity becomes an important basis for making a battery use strategy.
At the same time, the SOC is estimatedi+1In a single operation, due to the influence of the workload and related factors in the process of consuming all the electric quantity, for example, in a journey of a day, the battery driving range (workload) of the new energy automobile is different from the related factors such as the weather condition of the day and the road condition of the driving range, and the battery parameters are different. Meanwhile, the battery works in an occasional scene and a normal scene, so that how to estimate the residual capacity parameter by using the high-correlation battery parameter of the operation at the time becomes an important basis of the battery operation strategy in a single operation.
The invention provides a method for estimating a parameter of the residual electric quantity of a battery, as shown in fig. 4, comprising the following steps:
Step 1000: collecting the battery parameters in the operation;
Step 2000: estimating the residual electric quantity parameter of the operation according to the battery parameter;
The battery parameters comprise the use amount SOC in the operationtInternal volume open circuit voltage UtDosage current ItAnd/or workload; the residual capacity parameter comprises the subsequent consumption SOC of the operationtInner predicted open circuit voltage utEstimated current itAnd/or a predicted workload.
as shown in fig. 5, according to the time point T in this operation0Front dose SOCtEstimating the time T by the internal battery parameter0Subsequent dose SOCtThe above-mentioned residual capacity parameterNumber, where the battery is depleted at Te.
In this embodiment, step 1000 further includes collecting battery history parameters,
The battery history parameters comprise charging times F, which are counted once when the battery is exhausted and recharged;
The battery history parameters also comprise a full charge SOC corresponding to the charging times F;
The battery history parameters also comprise a battery use time delta T corresponding to the charging times F, which is the time from the battery exhaustion to the battery operation to the full SOC after the battery is charged again to reach the full SOC;
The battery history parameters also comprise an open-circuit voltage U and/or a current I within the time delta T of the battery;
Step 1000 further comprises:
step 100: collecting battery history parameters;
Step 200: inputting the battery history parameters to the LSTM;
Step 300: the LSTM output pre-estimated SOCi+1；
wherein, the dosage SOCtFor the estimated SOCi+1Less than 50%.
adaptive systems include Back Propagation (BP) neural networks, Radial Basis Function (RBF) neural networks, fuzzy logic methods, support vector machines, fuzzy neural networks, and Kalman, which may be automatically tuned in a constantly changing system. Since batteries are affected by many chemical factors and have non-linear SOC, adaptive systems provide a good solution for SOC estimation. The integrated long-SHORT term memory model LSTM (LONG AND SHORT TERMMEMORY) neural network serving as the self-adaptive system has good nonlinear mapping, self-organizing AND self-learning capabilities AND time recursion, can determine the relation AND the problem of each parameter in SOC estimation when being applied to complex SOC estimation, wherein the relation between an input AND a target is nonlinear, AND the SOC is predicted by using each parameter of a battery based on the LSTM neural network. Therefore, the LSTM time recursive neural network is utilized, and the method has the advantages of accurately estimating the SOC, saving time and labor and accurately estimating.
The battery history parameters comprise charging times F, the number of times F is counted once after the battery is exhausted and recharged, as is known, energy storage chemical substances in the battery can change in physical properties along with the fact that the charging times and the service time of the battery are normal, and the SOC estimation technology does not have structural defects of estimation results due to the fact that an estimation method for the charging times is not considered.
The battery history parameters also comprise a full charge SOC corresponding to the charging times F;
the battery history parameters also comprise a battery use time delta T corresponding to the charging times F, which is the time from the battery exhaustion to the battery operation to the full SOC after the battery is charged again to reach the full SOC;
the battery history parameters further include an open circuit voltage U and/or a current I within a time Δ T of the battery.
In this embodiment, as shown in fig. 1, there are g neurons in the LSTM, which are respectively associated with the above battery history parameters, the input layer is the above battery history parameters, and the output layer is the estimated SOCi+1。
In the present embodiment, the estimated SOC is obtained by the following equation (1),
The number of charging times Fn is equal to n, f (n) is the weight of SOCn, g is the number of neurons in LSTM, and K is the relationship between the weight W of each neuron in LSTM and SOCn that best meets the estimation factor of open circuit voltage U.
In this embodiment, the estimated SOC can also be obtained from the following equation (2),
Wherein the number of charging times FnN, f (n) is the weight of SOCn, g is the number of neurons in LSTM，
K is the relationship between the weight W of each neuron in LSTM and SOCn which best meets the estimation factor current I.
With regard to equations (1) and (2), as the number of battery charges increases, the number of charges F becomes the most dominant factor affecting the full SOC, and therefore the SOC is estimated by taking the fitting relationship SOCn between the number of charges F and the full SOC as the base budgeti+1(ii) a f (n) representing each full SOC (the battery power in the last full state) and the estimated SOCi+1The magnitude of the fitted weight relationship, in terms of existing battery maintenance and energy storage material replacement possibilities, in combination with data, loss of physical and chemical performance of the battery is an irreversible process, i.e., there is always f (f:)n)>f(n-1) such a relationship. The number of neurons in LSTM, g, K, is the most consistent estimated SOC selected by LSTM choosei+1The function relationship between the factor open-circuit voltage U or current I and the weight W of each neuron will jointly determine the estimated SOCi+1The size floats. Wherein the best fit estimation of the estimated SOC in equation (1)i+1The factor(s) is the open circuit voltage U, which best matches the estimated SOC in equation (2)i+1The factor current I.
in another embodiment, the estimated SOC is given by equation (3),
Where f (n) is the weight of SOCn, g is the number of neurons in LSTM, and K is the relationship between the weight W of each neuron in LSTM and SOCn that best meets the pre-estimated factor open circuit voltage U.
In another embodiment, the estimated SOC may also be derived from equation (4) below,
Where f (n) is the weight of SOCn, g is the number of neurons in LSTM, and K is the relationship between the weight W of each neuron in LSTM and SOCn that best meets the predictor current I.
For formulas (3) (4)) In other words, as the number of times the battery is charged increases, the battery age Δ T becomes the most significant factor affecting the full SOC, and therefore the fitting relationship SOCn between the battery age Δ T and the full SOC is used as the base budget estimate SOCi+1(ii) a f (n) representing each full SOC (the battery power in the last full state) and the estimated SOCi+1The magnitude of the fitted weight relationship, in terms of existing battery maintenance and energy storage material replacement possibilities, in combination with data, loss of physical and chemical performance of the battery is an irreversible process, i.e., there is always f (f:)n)>f(n-1) Such a relationship. The number of neurons in LSTM, g, K, is the most consistent estimated SOC selected by LSTM choosei+1The function relationship between the factor open-circuit voltage U or current I and the weight W paper of each neuron will jointly determine the estimated SOCi+1the size floats. Wherein the best fit estimation of the estimated SOC in equation (3)i+1The factor(s) is the open circuit voltage U, which best matches the estimated SOC in equation (4)i+1The factor current I.
Note that the SOC is estimatedi+1Can be estimated by one of the above equations (1), (2), (3) or (4) alone or by at least two of the equations (1), (2), (3), (4) together, without the inventors being limited thereto.
In this embodiment, step 100 includes calculating the full SOC from the battery time Δ T and the current I.
in other embodiments, particularly in the field of new energy vehicles, the battery age Δ T is associated with a load operation logging module.
in the field of new energy vehicles, the battery history parameters further include non-operation parameters, and the non-operation parameters refer to changes of battery related parameters when a load does not operate a function. Namely, the variation of the battery-related parameter in the abnormal leakage of the battery when the vehicle is not in operation.
Preferably, the non-operational parameter includes a battery elapsed time Δ T ', and a voltage change Δ U' or a current change Δ I 'within the battery elapsed time Δ T'.
Thus, the non-operation parameter outputs the non-operation loss Δ SOC ' from the battery elapsed time Δ T ' and the voltage change Δ U ' or the current change Δ I ' within the battery elapsed time Δ T '.
In this embodiment, step 2000 further includes opening the circuit voltage U according to the usage amounttAnd matching the similar open-circuit voltage U, namely selecting the open-circuit voltage similar to the open-circuit voltage in the operation in the battery historical parameters, wherein the similarity is one of consistent fitting and consistency of an open-circuit voltage depletion curve.
In another embodiment, step 2000 further comprises opening circuit voltage I according to said dosagetMatching the current I that is close, i.e. selecting a current in the battery history parameter that is close to that in this operation, is one of a consistent and consistent fit of the current depletion curve.
In other embodiments, step 2000 further comprises, according to the usage SOCtAnd matching the close full-charge SOC, namely selecting the SOC close to the operation in the battery historical parameters, wherein the close is one of fitting consistency and consistency of an SOC exhaustion curve.
In the field of new energy automobiles, the workload is the mileage of the vehicle; the estimated workload is estimated vehicle mileage.
Preferably, the remaining power parameter further includes a subsequent usage SOCtThe estimated time of the inner part is related to the velocity V.
Preferably, the remaining power parameter further includes a subsequent usage SOCtThe relationship of the predicted time and the torque TQ in the inner part.
Preferably, the remaining power parameter further includes a subsequent usage SOCtEstimated time and estimated open-circuit voltage utThe relationship (2) of (c).
Preferably, the remaining power parameter further includes a subsequent usage SOCtestimated time and estimated current itthe relationship (2) of (c).
As shown in fig. 2, the present invention further provides a device for estimating remaining battery capacity parameters, which includes a data collection module and an estimation module 30. The data collection module is connected with the estimation module 30.
The data collection moduleThe blocks include an open circuit voltage collection module 14, a current collection module 14, and/or a workload collection module 15; the open-circuit voltage collecting module 14 records the consumption open-circuit voltage Ut(ii) a The current collection module 14 records the usage current It(ii) a The workload collection module 15 records the workload; the estimation module 30 calculates the usage SOCtSaid amount of use SOCtequivalent open circuit voltage UtCorrespondingly, the amount SOCtWith said dose current ItCorresponding; the estimation module 30 calculates the subsequent usage SOCtInner predicted open circuit voltage utEstimated current itAnd/or a predicted workload.
In this embodiment, the system further includes an LSTM module, the data collection module is connected to the LSTM module 20, and the LSTM module 20 is connected to the estimation module 30; the data collection module also includes a historical data collection module.
the historical data collection module comprises a charging frequency F collection module 11 for recording the charging frequency F of the battery, wherein the charging frequency F is counted once after the battery is exhausted and recharged;
The historical data collection module further comprises a full-charge SOC collection module for recording a full-charge SOC, and the charging times F correspond to the full-charge SOC;
The historical data collection module also comprises a battery use time delta T collection module for recording the battery use time delta T, wherein the battery use time delta T is the time from the battery exhaustion, the recharging to the full SOC and the battery operation to the full SOC exhaustion;
The historical data collection module also comprises an open-circuit voltage collection module which also records the open-circuit voltage U in the delta T when the battery is used;
The historical data collection module also comprises a current collection module which also records the current I in the delta T when the battery is used; the LSTM module calculates the estimated SOC of the operation before the operationi+1；
The data collection module comprises a charging frequency F collection module 11 for recording the charging frequency F of the battery, wherein the charging frequency F is counted once after the battery is exhausted and recharged, as is known, along with the normal charging frequency and the normal use time of the battery, the energy storage chemical substances in the battery can change in physical properties, and the SOC estimation technology does not consider an estimation method of the charging frequency to cause structural defects of an estimation result.
The historical data collection module further comprises a full charge SOC collection module 12 to record a full charge SOC, the number of charges F corresponding to the full charge SOC.
The historical data collection module further comprises a battery use time delta T collection module 13 for recording the battery use time delta T, wherein the battery use time delta T is the time from the battery exhaustion, the recharging to the full-charge SOC and the battery operation to the full-charge SOC exhaustion.
The historical data collection module also includes an open circuit voltage collection module 14 and/or a current collection module 14 to record the open circuit voltage U and/or current I over the time Δ T of the battery. The LSTM module 20 calculates the estimated SOC of the current operation before the current operationi+1(ii) a Said dosage SOCtFor the estimated SOCi+1Less than 50%.
in this embodiment, the charging frequency F collecting module 11 is a counting circuit, and the counting circuit records the charging frequency F.
In this embodiment, the battery age Δ T collecting module 13 is a timing circuit, and the timing circuit records the battery age Δ T.
in the present embodiment, the full SOC is calculated from the battery time Δ T and the current I.
in another embodiment, the full SOC is calculated from the time Δ T spent by the battery and the voltage U.
in other embodiments, especially in the new energy automobile field, the workload collection module 15 is a load operation recording module, such as a speedometer.
preferably, the workload collection module 15 collects non-operation parameters, which refer to the changes of the battery related parameters when the load is not in operation.
Preferably, the workload collection module 15 further comprises a torque TQ collection module and/or the rate V collection module. The battery elapsed time deltat collection module records battery elapsed time deltat' while the torque TQ collection module records activity or the rate vtwater collection module records activity, while the workload collection module 15 records inactivity; during the battery elapsed time Δ T ', the torque TQ collection module records the torque change Δ TQ ', and/or the rate Vcollection module records the rate change Δ V '.
In this embodiment, the workload collection module 15 records the miles traveled and the time traveled.
The invention provides a method and a device for estimating the residual capacity parameter of a battery, which estimate the residual capacity parameter by using the battery parameter in single operation, input all historical parameters such as the using times of the battery and the like into a long-SHORT TERM memory model LSTM (Long and SHORT TERM memory), and accurately estimate SOC by using a time recursive neural network of the LSTM, and have the advantages of time saving, labor saving and accurate estimation. The battery pack can be applied to the field of portable battery use, such as the fields of new energy automobiles, Internet of vehicles, Internet of things and the like.
the foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (9)

1. A device for pre-estimating the parameters of the residual electric quantity of a battery comprises a data collection module and a pre-estimation module, wherein the data collection module is connected with the pre-estimation module and is characterized in that the data collection module comprises an open-circuit voltage collection module, a current collection module and/or a workload collection module;
The open-circuit voltage collecting module records the consumption open-circuit voltage Ut；
The current collecting module records the consumption current It；
The workload collection module records the workload;
The pre-estimation module calculates the usage SOCtSaid amount of use SOCtEquivalent open circuit voltage UtCorrespondingly, the amount SOCtWith said dose current ItCorresponding;
The estimation module calculates the subsequent use amount SOCtinner predicted open circuit voltage utestimated current itAnd/or a predicted workload.
2. The estimation device according to claim 1, further comprising an LSTM module, wherein the data collection module is connected to the LSTM, and the LSTM module is connected to the estimation module;
The data collection module further comprises a historical data collection module, the historical data collection module comprising:
The charging frequency F collecting module is used for recording the charging frequency F of the battery, wherein the charging frequency F is counted once after the battery is exhausted and recharged;
The full-charge SOC collection module is used for recording a full-charge SOC, and the charging times F correspond to the full-charge SOC;
The battery use time delta T collecting module is used for recording battery use time delta T, and the battery use time delta T is the time from the battery exhaustion to the battery operation to the full SOC after the battery is charged again to reach the full SOC;
The open-circuit voltage collection module also records the open-circuit voltage U in the delta T when the battery is used;
The current collection module also records the current I in the delta T when the battery is used;
The LSTM module calculates the estimated SOC of the operation before the operationi+1；
Said dosage SOCtFor the estimated SOCi+1less than 50%.
3. The estimation device according to claim 2, wherein the charging number Fcollecting module is a counting circuit, and the counting circuit records the charging number F.
4. The estimation device according to claim 2, wherein the battery age Δ T collection module is a timing circuit that records the battery age Δ T.
5. The estimation device according to claim 1, wherein the workload collection module is a load operation record module.
6. the estimation device as claimed in claim 5, wherein the load operation recording module collects non-operation parameters, and the non-operation parameters refer to changes of battery related parameters when the load is not in operation.
7. The estimator as defined in claim 5, wherein the load operation recording module further comprises a torque TQ collection module and/or the velocity VQ collection module.
8. The forecasting apparatus as claimed in claim 7, wherein the battery age Δ T collecting module records a battery elapsed time Δ T' when the torque TQ collecting module records activity or the speed VQ collecting module records activity, and the load operation recording module records inactivity;
during the time taken for the battery to take deltat',
The torque TQ collection module records the torque variation Δ TQ', and/or
The velocity V collection module records the velocity change Δ V'.
9. The estimation device according to claim 5, wherein the load operation recording module records the mileage and the time of the trip.
CN201910966068.8A 2019-10-12 2019-10-12 Battery residual capacity parameter pre-estimation device Withdrawn CN110579711A (en)

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