CN111413621A - Lithium battery aging rate estimation method based on iterative learning - Google Patents

Lithium battery aging rate estimation method based on iterative learning Download PDF

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CN111413621A
CN111413621A CN202010260956.0A CN202010260956A CN111413621A CN 111413621 A CN111413621 A CN 111413621A CN 202010260956 A CN202010260956 A CN 202010260956A CN 111413621 A CN111413621 A CN 111413621A
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aging
battery
lithium battery
iterative learning
iteration
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魏善碧
丁宝苍
王辉阳
余笑
吴睿
王昱
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Chongqing 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
    • 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
    • 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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Abstract

The invention relates to a lithium battery aging rate estimation method based on iterative learning, and belongs to the technical field of battery management. The method comprises the following steps: s1: modeling the aging behavior of the lithium battery based on the Brownian motion; s2: decomposing each parameter and simulating a nonlinear process of motion; s3: constructing a state space equation of the aging behavior of the battery; s4: and carrying out online estimation on the capacity and the service life of the lithium battery according to the historical data and the current data. The method has the advantages of no dependence on a specific model, high precision and high reliability. The invention solves the problem that the accurate modeling of the lithium battery is difficult mathematically due to the strong coupling and nonlinearity of the capacity and the service life attenuation of the current lithium battery.

Description

Lithium battery aging rate estimation method based on iterative learning
Technical Field
The invention belongs to the technical field of battery management, and relates to a lithium battery aging rate estimation method based on iterative learning.
Background
The lithium battery is used as a clean and efficient energy storage medium, has the advantages of high energy density, high rated voltage, light weight and the like, is widely applied to power energy storage systems of hydraulic power, firepower, wind power, solar power stations and the like, and is used in the fields of uninterrupted power supplies, electric vehicles and the like in post and telecommunications, and is paid more and more attention. However, there are some problems in the development of lithium batteries, and the typical problem is that the capacity and life of the lithium battery are deteriorated with the lapse of time. The aging rate of the lithium battery has important significance for controlling, maintaining and using the lithium battery. However, the aging rate cannot be measured directly, but only indirectly by way of estimation.
The main problems of the estimation process are as follows: the attenuation of the capacity and the service life of the lithium battery is characterized by strong coupling and nonlinearity; accurate modeling of lithium battery power systems is mathematically difficult. The model parameters of each specific battery are often greatly different, so that a method which has low requirements on the precision of the model is needed.
Disclosure of Invention
In view of this, the present invention provides a method for estimating an aging rate of a lithium battery based on iterative learning.
In order to achieve the purpose, the invention provides the following technical scheme:
a lithium battery aging rate estimation method based on iterative learning comprises the following steps:
s1: establishing a lithium battery aging behavior model;
s2: establishing a lithium battery aging state equation based on Brownian motion;
s3: and constructing a state space equation of the aging behavior of the battery, and estimating the residual life of the battery after continuous iteration.
Optionally, the S1 specifically includes:
the lithium battery aging behavior model is yt＝xt+vt＝λtt+σBBt+vtWherein y istThe representation containing measurement noise, xtTo determine the percent decay in battery capacity, vtPercent cell capacity fade, vt～N(0，Qv)，λtThe drift parameter represents the aging rate of the battery at time t, and represents the deterministic portion of the aging rate caused by aging stress, σBThe random part of the aging speed caused by aging stress fluctuation is expressed for the constant value of the scale parameter.
Optionally, the S2 specifically includes:
the lithium battery aging state equation based on the Brownian motion is expressed as follows:
Figure BDA0002439269140000021
Figure BDA0002439269140000022
wherein the content of the first and second substances,
Figure BDA0002439269140000023
representing the battery aging rate calculated from the i-th iteration estimate, ηi-1～N(0，Qn) Is a gaussian noise, and is a noise,
Figure BDA0002439269140000024
representing the estimation of the true measurement for the ith iteration,
Figure BDA0002439269140000025
nonlinear brownian motion is simulated, and the lithium battery aging state equation based on the brownian motion is transformed into:
Figure BDA0002439269140000026
optionally, the S3 specifically includes:
the state space equation for constructing the aging behavior of the battery is as follows:
Figure BDA0002439269140000027
wherein the content of the first and second substances,
Figure BDA0002439269140000028
zi＝[xi,λi]T，C＝[1,0]，wi＝[σBBi-1ηi-1]T，t∈[0,Ti]，Tithe method is characterized in that the aging rate is that the battery capacity is attenuated along with the aging of the battery, the charge-discharge period is shortened along with the aging of the battery, namely the charge-discharge period of the working of the lithium battery, and the iterative cycle in the iterative learning process
Figure BDA0002439269140000029
Aging rate after learning by the i-th iteration in relation to the decay rate of the battery capacity
Figure BDA00024392691400000210
The capacity of the battery at the moment is calculated, thereby estimating the charge-discharge period T of the batteryiI.e., the iteration period of the next cycle, and estimates the remaining life of the battery after successive iterations.
The invention has the beneficial effects that: the lithium battery aging rate estimation method based on iterative learning aims to overcome the defects of large battery aging and residual life estimation calculation amount and high economic cost in the prior art.
Compared with the traditional self-adaptive estimation and sliding mode estimation, the method has the advantages that the premise that Strict Positive Reality (SPR) conditions need to be met is met, and the method has higher precision and reliability in an actual operation system.
The invention has the advantage of no dependence on a specific and accurate mathematical model in an operation system in practical application.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a block diagram of iterative learning system operation control;
FIG. 2 is a flow chart of an estimation system implementation.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the iterative learning system work control block diagram includes three units, namely an iterative learning controller, a controlled object and a memory, and the flow of the implementation mode is shown in fig. 2.
A lithium battery aging rate estimation method based on iterative learning comprises the steps of establishing an aging model based on Brownian motion, expressing all parameters, constructing a state space equation expression and converting an aging modeling problem into online estimation according to historical data and current data.
Further, the aging behavior of the lithium battery can be modeled as follows: y ist＝xt+vt，ytThe representation contains measurement noise vtPercent cell capacity fade, vt～N(0，Qv)，xtTo an accurate percent capacity fade.
Further, xt+vt＝λtt+σBBt+vt. Wherein λ istThe drift parameter represents the aging rate of the battery at time t, representing the deterministic portion of the aging rate caused by aging stress. SigmaBIs a scale parameter, usually a deterministic constant value, representing the stochastic part of the aging rate caused by aging stress fluctuations.
Further, the brownian motion based aging modeling problem can be converted into an online estimation of lambda from historical aging data and current aging datat. Therefore, the aging state equation of the lithium battery based on brownian motion is expressed as:
Figure BDA0002439269140000041
Figure BDA0002439269140000042
wherein the content of the first and second substances,
Figure BDA0002439269140000043
representing the battery aging rate calculated from the i-th iteration estimate, ηi-1～N(0，Qn) Is gaussian noise.
Figure BDA0002439269140000044
Representing the estimation of the true measurement for the ith iteration.
Further, K (τ, θ) represents a nonlinear function of θ, simulating nonlinear brownian motion. In the present method, the first and second liquid crystal compositions are,
Figure BDA0002439269140000045
therefore, the above equation of the aging state of the lithium battery based on brownian motion can be expressed as follows again:
Figure BDA0002439269140000046
further, a state space equation of the aging behavior of the battery is constructed:
Figure BDA0002439269140000047
wherein z isi＝[xi,λi]T，C＝[1,0]，wi＝[σBBi-1ηi-1]T
Figure BDA0002439269140000048
In the model, T ∈ [0, Ti]Wherein T isiThe method is an iteration cycle in an iteration learning process, namely a charging and discharging cycle of the lithium battery. As the battery ages, the battery capacity decays and the charge-discharge cycle is shortened. Rate of aging
Figure BDA0002439269140000049
Is related to the decay rate of the battery capacity, and therefore the aging rate after the ith iteration learning
Figure BDA00024392691400000410
The capacity of the battery at the moment is calculated, thereby estimating the charge-discharge period T of the batteryiI.e. the iteration period of the next loop.
Further, after successive iterations, an estimate of the remaining life of the battery may be made.
The configuration and specific work of each parameter are as follows:
① sets the iteration parameter i to 1 and sets the expected track lambdad(t) and an initial control amount u1(t)＝0，t∈[0,Ti]. Wherein T isiThe iteration cycle of the ith time is the charge-discharge cycle of the lithium battery.
② setting input initial value xi(0) And outputting the initial value lambdai(0) And setting the initial condition to be the same as the expected condition, so that the system running track is the same in each iteration period.
③ controlled object adding control input ui(t), the system starts to operate repeatedly, sampling is carried out, and the system outputs lambdai(t) storing in a memory.
④ calculating the output error, ei(t)＝λd(t)-λi(t) calculating the input value of the next iteration through the iterative learning controllerui+1(t) and storing in memory. The learning law for the next iteration cycle consists of the learning law for the current cycle and the output error, which can be expressed as ui+1(t)＝ui(t)+U{ei(t), where U is a given mapping function, taking the linear function.
⑤ comparing the result with the set condition, stopping iteration if the condition is satisfied, and entering the next iteration cycle if the condition is not satisfied, i is i +1iThe battery capacity fade rate may be estimated.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (4)

1. A lithium battery aging rate estimation method based on iterative learning is characterized in that: the method comprises the following steps:
s1: establishing a lithium battery aging behavior model;
s2: establishing a lithium battery aging state equation based on Brownian motion;
s3: and constructing a state space equation of the aging behavior of the battery, and estimating the residual life of the battery after continuous iteration.
2. The lithium battery aging rate estimation method based on iterative learning of claim 1, wherein: the S1 specifically includes:
the lithium battery aging behavior model is yt＝xt+vt＝λtt+σBBt+vtWherein y istThe representation containing measurement noise, xtTo determine the percent decay in battery capacity, vtPercent cell capacity fade, vt～N(0，Qv)，λtThe drift parameter represents the aging rate of the battery at time t, and represents the deterministic portion of the aging rate caused by aging stress, σBThe random part of the aging speed caused by aging stress fluctuation is expressed for the constant value of the scale parameter.
3. The lithium battery aging rate estimation method based on iterative learning of claim 1, wherein: the S2 specifically includes:
the lithium battery aging state equation based on the Brownian motion is expressed as follows:
Figure FDA0002439269130000011
Figure FDA0002439269130000012
wherein the content of the first and second substances,
Figure FDA0002439269130000013
representing the battery aging rate calculated from the i-th iteration estimate, ηi-1～N(0，Qn) Is a gaussian noise, and is a noise,
Figure FDA0002439269130000014
representing the estimation of the true measurement for the ith iteration,
Figure FDA0002439269130000015
nonlinear brownian motion is simulated, and the lithium battery aging state equation based on the brownian motion is transformed into:
Figure FDA0002439269130000016
4. the lithium battery aging rate estimation method based on iterative learning of claim 1, wherein: the S3 specifically includes:
the state space equation for constructing the aging behavior of the battery is as follows:
Figure FDA0002439269130000017
wherein the content of the first and second substances,
Figure FDA0002439269130000018
zi＝[xi,λi]T，C＝[1,0]，wi＝[σBBi-1ηi-1]T，t∈[0,Ti]，Tithe method is characterized in that the aging rate is that the battery capacity is attenuated along with the aging of the battery, the charge-discharge period is shortened along with the aging of the battery, namely the charge-discharge period of the working of the lithium battery, and the iterative cycle in the iterative learning process
Figure FDA0002439269130000021
Aging rate after learning by the i-th iteration in relation to the decay rate of the battery capacity
Figure FDA0002439269130000022
The capacity of the battery at the moment is calculated, thereby estimating the charge-discharge period T of the batteryiI.e., the iteration period of the next cycle, and estimates the remaining life of the battery after successive iterations.
CN202010260956.0A 2020-04-03 2020-04-03 Lithium battery aging rate estimation method based on iterative learning Pending CN111413621A (en)

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CN106353691A (en) * 2016-10-31 2017-01-25 首都师范大学 Method for degradation modeling and life prediction of lithium battery with self-healing effect
CN107918103A (en) * 2018-01-05 2018-04-17 广西大学 A kind of lithium ion battery residual life Forecasting Methodology based on grey particle filter
CN108829983A (en) * 2018-06-21 2018-11-16 四川大学 Equipment method for predicting residual useful life based on more hidden state fractional Brownian motions

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104850736A (en) * 2015-04-27 2015-08-19 大连理工大学 Service life prediction method of high-speed numerical control milling machine cutter on basis of state space model
CN106353691A (en) * 2016-10-31 2017-01-25 首都师范大学 Method for degradation modeling and life prediction of lithium battery with self-healing effect
CN107918103A (en) * 2018-01-05 2018-04-17 广西大学 A kind of lithium ion battery residual life Forecasting Methodology based on grey particle filter
CN108829983A (en) * 2018-06-21 2018-11-16 四川大学 Equipment method for predicting residual useful life based on more hidden state fractional Brownian motions

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Application publication date: 20200714