CN110635187A - Lithium battery charging method considering aging - Google Patents

Lithium battery charging method considering aging Download PDF

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CN110635187A
CN110635187A CN201910820320.4A CN201910820320A CN110635187A CN 110635187 A CN110635187 A CN 110635187A CN 201910820320 A CN201910820320 A CN 201910820320A CN 110635187 A CN110635187 A CN 110635187A
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battery
charging
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CN110635187B (en
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马乾
孙金磊
刘瑞航
唐传雨
王天如
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Nanjing Tech 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • 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
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H01M10/446—Initial charging measures
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The invention discloses a lithium battery charging method considering aging, which comprises two parts of battery aging state identification and charging curve optimization; identifying an ICA curve to obtain a battery aging state, and obtaining a battery parameter of the current aging state of the battery by using a full-life cycle model of the battery with the same model; then calculating the charging time and the temperature rise of the battery by establishing a battery model; calculating the charging time and the temperature variation; and finally, optimizing the charging curve of the battery in the current aging state by taking the shortest total charging time and the smallest charging temperature change of the battery as targets, so as to achieve the purpose of reducing the temperature rise of the battery as much as possible on the premise of shortest charging time. The invention is suitable for battery monomers and group application of electric automobiles, energy storage systems, electric tools and the like.

Description

Lithium battery charging method considering aging
Technical Field
The invention relates to the technical field of battery aging state identification and charging curve optimization, in particular to a lithium battery charging method considering aging.
Background
Due to the restriction of factors such as charging and discharging of the lithium battery, calendar aging and the like, the aging condition is inevitable. In the case of a lithium battery, the deterioration of the inside of the battery directly leads to a decrease in the chargeable and dischargeable capacity of the battery, and is accompanied by an increase in the internal resistance of the battery. The reduction in battery capacity directly results in a reduction in the amount of energy that the battery can provide; the increase of the internal resistance of the battery directly leads to the increase of the heat generation of the battery, and further aggravates the aging of the battery. In extreme cases, even safety problems can arise due to excessive heat generation. And thus more problems occur in the charging process of the aged battery.
In order to ensure the safety of the aged battery in the charging process and fully improve the performance of the aged battery, the charging current needs to be optimized according to the aging condition of the battery so as to improve the charging safety and reliability of the battery. The search shows that most of the existing documents optimize the battery charging method only in a single aging state, or only research on the aging of the battery, and do not consider the optimization of the two methods in combination.
Disclosure of Invention
The invention aims to provide a lithium battery charging method considering aging, which solves the problems that the charging temperature rise is too fast caused by the increase of internal resistance after the battery is aged, the battery aging is accelerated and safety accidents are caused by the over-temperature in the charging process, and the like.
The technical scheme for realizing the purpose of the invention is as follows: a method of charging a lithium battery with aging taken into account, comprising the steps of:
step 1, performing a characteristic test on a battery by using a capacity increment method to obtain an ICA curve of the current aging state of the battery;
step 2, analyzing the current aging state of the battery by using a battery full-life cycle model to obtain the internal resistance, the open-circuit voltage and the maximum chargeable capacity of the battery;
step 3, calculating the charging time and the temperature rise by combining a battery charging time and charging temperature rise formula;
and 4, optimizing a charging curve by using an optimization algorithm combining the particle swarm and the fuzzy control algorithm and combining a charging limiting factor and aiming at short charging time and small battery temperature rise in the charging process to determine the current of each constant current charging stage.
Compared with the prior art, the invention has the beneficial effects that: (1) the invention provides a charging method for a lithium battery considering aging in the using process, which can ensure that the lithium battery can still be charged quickly and safely after aging, and the method adjusts battery parameters according to the identified aging condition, adjusts charging current according to temperature change, reduces heat generation so as to reduce the temperature of the battery, is simple and practical and has universal applicability; (2) the lithium battery charging method considering aging can ensure that the maximum temperature of a monomer does not exceed 60 ℃ at a higher charging speed; battery aging caused by overhigh battery temperature is reduced, and the risk of charge thermal runaway is avoided.
Drawings
FIG. 1 is a graph of capacity incremental test current versus voltage.
FIG. 2 is a graph showing the calculation results of ICA curves.
Fig. 3 is an identified equivalent charging internal resistance diagram.
Fig. 4 is a graph of the identified open circuit voltages.
Fig. 5 is a flowchart of a lithium battery charging method considering aging.
Fig. 6 is a graph of the optimized charging current.
Fig. 7 is a comparison graph of the charging effect after optimization.
Detailed Description
A method of charging a lithium battery considering aging, comprising the steps of:
the method comprises the following steps: performing a characteristic test on the battery by using an additive Capacity Analysis (ICA) method to obtain an ICA curve of the current aging state of the battery;
step two: analyzing the current aging state of the battery by using a battery full-life cycle model to obtain corresponding characteristic parameters of battery internal resistance, open-circuit voltage, maximum chargeable capacity and the like;
step three: calculating the charging time and the temperature rise by combining a battery charging time and charging temperature rise formula;
step four: and optimizing a charging curve by utilizing an optimization strategy combining particle swarm and a fuzzy control algorithm and combining a charging limiting factor and aiming at short charging time and small battery temperature rise in the charging process so as to determine the current of each constant current charging stage. And optimizing the aging battery monomer charging method.
The charge limiting factors include maximum charge current, upper and lower limits of charge cutoff voltage, and battery temperature.
Before the charging optimization is carried out, firstly, a characteristic test is carried out on the battery to be charged, and a capacity Increment (ICA) curve in the current aging state is obtained. Then, according to the aging model of the battery life cycle, the current aging state is identified, and battery characteristic parameters such as battery internal resistance, open-circuit voltage and maximum chargeable capacity are obtained. And thirdly, establishing a battery temperature estimation model and a charging time model, and calculating the time and the maximum temperature rise in the charging process. And finally, optimizing the charging current by adopting a particle swarm-based fuzzy control algorithm with the aims of short charging time and small charging temperature rise.
The charging optimization of the lithium batteries in different aging stages is realized by analyzing the change rule of the battery parameters in an aging state, combining a battery temperature estimation model and optimizing the charging current in different SOC intervals.
Further, the first step may obtain an ICA curve of the battery in the current aging state according to the following steps:
step 1, discharging a battery monomer at constant current of 0.5C multiplying power until the lower limit cut-off voltage of the battery;
step 2, standing for 2 hours;
step 3, charging at 1/20C multiplying power constant current until the upper limit cut-off voltage of the battery;
and 4, calculating the capacity change dQ/dV along with the voltage according to the corresponding relation between the charging capacity and the charging voltage in the step 3 to obtain a capacity increment and battery voltage relation curve, namely an ICA curve of the current aging state of the battery, wherein FIG. 2 is a calculated ICA curve result graph.
Further, the second step may obtain the characteristic parameter of the battery in the current aging state according to the following steps:
and (3) calculating two parameters of a peak value B, ICA curve of the ICA curve and an area A surrounded by an x axis by using the ICA curve of the current aging state of the battery obtained in the step one, and identifying the current aging state of the battery by combining a full life cycle aging model established by the batteries of the same batch and the same model. The life cycle aging model is as follows:
Figure BDA0002187370210000031
wherein B is the ICA curve peak value; SOH is the identified state of aging; crateThe charge and discharge rate of the battery is set; g is a gas constant, and 8.314Jmol is taken-1K-1；TaIs ambient temperature; a is the area enclosed by the ICA curve and the x-axis.
R＝f1(SOH)
OCV＝f2(SOH)
Q＝f3(SOH)
Wherein f is1The relation between the internal resistance R of the battery and the SOH of the aging state is obtained according to the aging test of the batteries in the same type and batch; f. of2The relationship between OCV and the SOH of the aging state is obtained according to the aging test of the batteries in the same type and batch; f. of3The relation between Q and the SOH of the aging state is obtained according to the aging test of the batteries in the same model and the same batch; and obtaining a two-dimensional graph with the SOC as an x axis and the battery internal resistance R and the open-circuit voltage OCV as a y axis according to the identification result, and the maximum chargeable capacity Q in the current aging state. Fig. 3 is an identified equivalent charging internal resistance diagram. Fig. 4 is a graph of the identified open circuit voltages.
Further, in the third step, the charging time and the charging temperature rise can be calculated according to the following steps:
the invention is based on a five-stage constant current charging method, and each stage of charging is carried out until the upper limit cut-off voltage of the battery is reached.
Equation of terminal voltage of lithium battery
Ut＝OCV+IR
Wherein U istThe battery terminal voltage is obtained, the OCV is the battery open-circuit voltage, the I is the charging current, and the R is the battery internal resistance obtained in the step 2;
each stage is charged with constant current to the upper limit cut-off voltage, and the charging current and the SOC value of the switching point of each stage are as follows
SOCk＝f(Ik)
Total charging time of
Figure BDA0002187370210000041
Wherein t is1For phase 1 charging time, tkFor the k-th stage charging time, Q is the maximum chargeable capacity, IkFor charging current in k-th stage, SOCkThe k-th stage end-of-charge SOC point.
A temperature rise of the k stage of
Figure BDA0002187370210000042
Where m is the battery mass, C is the battery thermal capacity, T is the battery surface temperature, E is the battery open circuit voltage, h is the heat transfer coefficient, S is the battery surface area, T is the battery surface areaaIs ambient temperature;
total temperature rise of
Figure BDA0002187370210000043
Wherein T is1For the charging temperature rise of stage 1, TkCharge temperature rise for the k stage.
Further, the charging current in each SOC interval can be optimized in step four to realize the optimization of the charging process of the aged battery according to the following steps:
step 1, determining fitness function and optimization condition of fuzzy control algorithm
F＝w1Cct+w2Ctm。
In the objective function CctTime required for charging process, CtmFor the temperature rise of the battery during charging, w1As a function of the charging time, w2Is the weight coefficient of the charging temperature rise function;
Cct＝g1(I,U,SOC)
Ctm＝g2(m,I,C,S,Ta)
in the formula, U represents a charging voltage; SOC represents the battery state of charge.
The constraint conditions are embodied in the following four aspects:
1) the charging time and the temperature are balanced: when the temperature of the battery is lower than a first threshold value, charging by adopting current larger than a set threshold value; when the temperature of the battery is higher than a second threshold value, reducing the current charging current;
2) charging voltage and current constraints: the voltage and current in the charging process of each battery are kept within the maximum upper and lower limits allowed by the battery;
3) and (3) state of charge constraint: the SOC should be maintained within a set range during charging of the battery.
4) And (3) battery temperature restraint: the temperature of the battery itself during charging should not be higher than the maximum temperature allowed.
Step 2, inputting the charging time and the charging temperature rise obtained by calculation in the step three as input quantities into a fuzzy controller, and fuzzifying the input quantities by adopting a triangular membership function;
and 3, according to a Mandani fuzzy inference method, combining the set membership rule and a gravity center method to perform fuzzy resolving on the output value of the fuzzy controller to obtain a fitness function value.
And 4, continuously optimizing the charging current by a particle swarm algorithm until the following convergence conditions are met simultaneously:
(a)|Fmax,i-Fmax,i-1|＜m
Fmax,ithe maximum value of the fitness function after the ith iteration is obtained, and m is a constant;
(b) and after the ith iteration, the standard deviation of the fitness function value F is smaller than n, and n is a constant.
And 5, after the convergence condition in the step 4 is met, the obtained charging current is the optimal charging current.
The present invention will be specifically described below by taking a certain ternary lithium battery as an example.
Examples
A plurality of battery monomers are selected for parallel tests, firstly, standard cyclic charge and discharge are carried out according to a manual provided by a manufacturer, after the battery monomers are fully charged with constant current and constant voltage, the battery monomers are subjected to constant current discharge to the lower limit cut-off voltage of 2.75V at the multiplying factor of 0.5C (1.3A), and then the battery monomers are subjected to constant current charge to the upper limit cut-off voltage of 4.2V at the multiplying factor of 1/20C (0.13A), and the ICA test is completed, as shown in figure 1, the specific process is as.
Step 1, discharging a battery monomer at constant current of 0.5C multiplying power until the lower limit cut-off voltage of the battery;
step 2, standing for 2 hours;
step 3, charging at 1/20C multiplying power constant current until the upper limit cut-off voltage of the battery;
and 4, calculating the capacity-to-voltage variation dQ/dV according to the corresponding relation between the charging capacity and the charging voltage to obtain a relation curve between the capacity increment and the battery voltage, as shown in FIG. 2.
The aging state is identified by using the current aging state ICA curve of the battery and through a full life cycle aging model established by the same batch of batteries with the same model, a two-dimensional graph taking SOC as an x axis and characteristic parameters such as battery internal resistance R, open-circuit voltage OCV and the like as a y axis and the maximum chargeable capacity Q in the current aging state are respectively obtained through identification, and the identification results are respectively shown in fig. 3 and 4.
The charging method is optimized using the flow shown in fig. 5. Firstly, charging current is randomly given according to parameters such as battery internal resistance, open-circuit voltage and chargeable capacity obtained through identification and considering constraint conditions.
And respectively calculating the charging time and the charging temperature rise through a charging time and charging temperature rise formula. And inputting the result obtained by calculation into a fuzzy controller, and calculating to obtain an adaptive function value.
And judging a convergence condition, and if the convergence condition is met, taking the current optimization result as the optimal charging current. Otherwise, the charging current is iteratively updated through a particle swarm algorithm, and the charging time and the charging temperature rise are recalculated.
The obtained optimal charging curve is shown in fig. 6, and the optimization effect is shown in fig. 7, for example, in fig. 7, the initial charging temperature of the battery is 25 ℃, and as the charging is carried out, it can be seen that the temperature rise of the battery is obviously reduced by the adopted optimization charging method.
When 90% of the nominal capacity is charged, 1423 seconds are needed in the traditional constant-current constant-voltage charging method, and 1205 seconds are needed in the optimized charging method, so that the charging time is reduced.

Claims (7)

1. A method of charging a lithium battery with aging taken into account, comprising the steps of:
step 1, performing a characteristic test on a battery by using a capacity increment method to obtain an ICA curve of the current aging state of the battery;
step 2, analyzing the current aging state of the battery by using a battery full-life cycle model to obtain the internal resistance, the open-circuit voltage and the maximum chargeable capacity of the battery;
step 3, calculating the charging time and the temperature rise by combining a battery charging time and charging temperature rise formula;
and 4, optimizing a charging curve by using an optimization algorithm combining the particle swarm and the fuzzy control algorithm and combining a charging limiting factor and aiming at short charging time and small battery temperature rise in the charging process to determine the current of each constant current charging stage.
2. The method of charging a lithium battery considering aging as set forth in claim 1, wherein the charge limiting factors include a maximum charge current, upper and lower limits of a charge cut-off voltage, and a battery temperature.
3. The method for charging a lithium battery considering aging as claimed in claim 1, wherein the step 1 is specifically:
step 1-1, discharging a battery monomer at constant current of 0.5C multiplying power until the lower limit cut-off voltage of the battery;
step 1-2, standing for 2 hours;
step 1-3, charging at 1/20C multiplying power constant current until the upper limit cut-off voltage of the battery;
and 1-4, calculating the capacity variation dQ/dV along with the voltage according to the corresponding relation between the charging capacity and the charging voltage in the step 1-3 to obtain a relation curve of the capacity increment and the battery voltage, namely an ICA curve of the current aging state of the battery.
4. The method for charging a lithium battery considering aging as claimed in claim 1, wherein the step 2 is specifically:
calculating two parameters of a peak value B, ICA curve of the ICA curve and an area A surrounded by an x axis by using the ICA curve of the current aging state of the battery obtained in the step 1, and identifying the current aging state of the battery by combining a full life cycle model established by batteries of the same batch and the same model; the full life cycle model is as follows:
where B is the ICA curve peak, SOH is the identified aging state, CrateG is a gas constant, T is a battery charge-discharge rateaThe ambient temperature is shown, and A is the area enclosed by the ICA curve and the x axis;
wherein f is1The relation between the internal resistance R of the battery and the SOH of the aging state is obtained according to the aging test of the batteries in the same type and batch; f. of2The relationship between the open-circuit voltage OCV and the aging state SOH of the battery is obtained according to the aging test of the batteries in the same type and batch; f. of3The relation between the maximum chargeable capacity Q and the aging state SOH is obtained according to the aging test of batteries of the same type and the same batch; and obtaining a two-dimensional graph with the SOC as an x axis and the battery internal resistance R and the open-circuit voltage OCV as a y axis according to the identification result, and the maximum chargeable capacity Q in the current aging state.
5. The method of claim 4, wherein the step 3 of calculating the charging time and the temperature rise of the charging is performed by:
based on a five-stage constant current charging method, each stage is charged to the upper limit cut-off voltage of the battery;
the equation of the terminal voltage of the lithium battery is as follows:
Ut＝OCV+IR (3)
wherein U istThe battery terminal voltage is obtained, the OCV is the battery open-circuit voltage, the I is the charging current, and the R is the battery internal resistance obtained in the step 2;
each stage is charged with constant current to the upper limit cut-off voltage, and the charging current and the SOC value of the switching point of each stage are obtained as follows
SOCk＝f(Ik) (4)
Total charging time of
Figure FDA0002187370200000022
Wherein t is1For phase 1 charging time, tkFor the k-th stage charging time, Q is the maximum chargeable capacity, IkFor charging current in k-th stage, SOCkA charging end SOC point of the kth stage is obtained;
a temperature rise of the k stage of
Where m is the battery mass, C is the battery thermal capacity, T is the battery surface temperature, E is the battery open circuit voltage, h is the heat transfer coefficient, S is the battery surface area, T is the battery surface areaaIs ambient temperature;
total temperature rise of
Figure FDA0002187370200000031
Wherein T is1For the charging temperature rise of stage 1, TkCharge temperature rise for the k stage.
6. The lithium battery charging method considering aging of claim 1, wherein the fitness function of the optimization algorithm of step 4 is the same as the fitness function of the fuzzy control algorithm;
the fitness function expression in the fuzzy control algorithm is
F＝w1Cct+w2Ctm。
CctTime required for charging process, CtmFor the temperature rise of the battery during charging, w1As a function of the charging time, w2Is the weight coefficient of the charging temperature rise function;
the specific steps of calculating the fitness function value F by using the fuzzy control algorithm are as follows:
step 1: inputting the charging time and the charging temperature rise obtained by calculating the formula (5) and the formula (7) into a fuzzy controller as input quantities;
step 2: fuzzifying the input quantity by adopting a triangular membership function;
and step 3: according to a Mandani fuzzy reasoning method, combining the set membership rule and a gravity center method to perform fuzzy resolving on the output value of the fuzzy controller to obtain a fitness function value;
Cct＝g1(I,U,SOC)
Ctm＝g2(m,I,C,S,Ta)
in the formula, U is a charging voltage; SOC is the state of charge of the battery; g1As a function of the mapping between the charging time and variables that affect its value; g2Is a mapping relation function between the charging temperature rise and variables influencing the value of the charging temperature rise;
the constraints of the optimization algorithm are as follows:
1) the charging time and the temperature are balanced: when the temperature of the battery is lower than a first threshold value, charging by adopting current larger than a set threshold value; when the temperature of the battery is higher than a second threshold value, reducing the current charging current;
2) charging voltage and current constraints: the voltage and current in the charging process of each battery are kept within the maximum upper and lower limits allowed by the battery;
3) and (3) state of charge constraint: the SOC is kept in a set range in the charging process of the battery;
4) and (3) battery temperature restraint: the temperature of the battery itself during charging should not be higher than the maximum temperature allowed.
7. The method for multiple target charging of batteries according to claim 6, wherein: in step 4, the optimization objectives of the optimization algorithm include two objectives of short charging time and small charging temperature rise, and a group of solutions which enable the fitness function value to be maximum under the current iteration number are found; and continuously optimizing the charging current by the particle swarm optimization until the following convergence conditions are met simultaneously, and finishing the optimization of the charging current:
(a)|Fmax,i-Fmax,i-1|＜m
Fmax,ithe maximum value of the fitness function after the ith iteration is obtained, and m is a constant;
(b) and after the ith iteration, the standard deviation of the fitness function value F is smaller than n, and n is a constant.
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