CN109802190B - Multi-target charging method for battery pack - Google Patents

Multi-target charging method for battery pack Download PDF

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CN109802190B
CN109802190B CN201910098578.8A CN201910098578A CN109802190B CN 109802190 B CN109802190 B CN 109802190B CN 201910098578 A CN201910098578 A CN 201910098578A CN 109802190 B CN109802190 B CN 109802190B
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孙金磊
马乾
刘瑞航
唐传雨
王天如
刘钊
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Nanjing University of Science and Technology
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Abstract

The invention discloses a multi-target charging method for a battery pack, which comprises a battery charging model, a parameter acquisition part and a battery multi-target optimization charging method; extracting battery characteristic parameters under different multiplying powers and different SOC states, and interpolating to obtain a battery characteristic parameter set; then establishing a battery temperature estimation model to estimate the maximum temperature difference in the battery charging process; and finally, optimizing the current in the charged electric quantity of each 5% SOC by taking the shortest total charging time and the smallest charging temperature change difference of the battery as targets, thereby achieving the purpose of reducing the charging temperature change 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

Multi-target charging method for battery pack
Technical Field
The invention relates to an optimized charging method with dual targets of battery temperature estimation and charging time, in particular to a multi-target charging method for a battery pack.
Background
Because of the restriction of factors such as production and manufacturing process, calendar aging, charging and discharging current magnitude, use environment temperature and the like, characteristic differences inevitably occur among the power battery monomers. The characteristic difference of the single batteries causes the electric quantity imbalance of the single batteries after long-term use.
For the series battery, the imbalance of the single electric quantity directly causes the available capacity of the battery to be reduced, and has an influence on the charge-discharge power characteristics of the battery. In addition, the battery pack formed by serially connecting the monomers with unbalanced electric quantity can cause the inconsistency of heat generation and temperature in the cyclic charge and discharge process, and the aging difference of the battery is aggravated. In extreme cases even thermal runaway safety issues may arise. Since the unbalance in the amount of charge of the battery is often found at the time of regular maintenance and is equalized under the condition that a certain threshold is exceeded, the series battery pack is easily charged in the state of unbalance in the amount of charge.
In order to ensure the safe use of the battery pack, it is necessary to estimate the battery temperature and adjust the charging current during the charging process of the battery pack to improve the charging safety and reliability of the battery. In the prior art, the problems of charging efficiency, time and temperature rise of a single battery are mostly concerned, balancing is only concerned about a balancing circuit structure and a control method, and a series battery pack charging method considering temperature is rarely mentioned.
Disclosure of Invention
The invention aims to provide a multi-target charging method for a battery pack, which solves the problem of temperature-considered optimal charging of a series battery pack formed by monomers with unbalanced electric quantity when the battery pack does not have an equalization condition, and avoids the problems of inconsistent battery aging degree caused by monomer over-temperature or non-uniform temperature in the charging process.
The technical scheme for realizing the purpose of the invention is as follows: a method of charging an unbalanced battery pack taking into account temperature, comprising the steps of:
step 1, carrying out HPPC test with 5% SOC as an interval in a 5% -90% SOC range under different multiplying factors, and obtaining ohmic internal resistance, polarization capacitance and open-circuit voltage parameters required by a battery model;
step 2, estimating the SOC of the battery by using an ampere-hour integration method to obtain a relation curve of each parameter in the battery model with the charging rate and the SOC;
step 3, estimating the battery temperature in real time by combining a battery temperature estimation model;
and 4, optimizing the charging process by utilizing a multi-objective optimization control strategy and combining with the charging limiting factor and taking short charging time and small temperature difference in the charging process as targets, so as to determine the charging current in each 5% SOC charging interval.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention provides a charging method of a series battery pack considering temperature under the condition of unbalanced electric quantity, which can ensure that the battery pack can still be safely charged under the condition of unbalanced electric quantity, and the method adjusts charging current according to temperature change, reduces heat generation so as to reduce the temperature of a battery, is simple and practical and has universal applicability;
(2) the charging method for the battery pack with unbalanced electric quantity can ensure that the maximum temperature of the monomer is not more than 60 ℃ and the maximum temperature difference in the battery pack is lower than 5 ℃; the risk of inconsistent aging of the battery caused by overhigh charging temperature is reduced, and the risk of charge thermal runaway is reduced.
Drawings
FIG. 1 is a charging HPPC test current plot.
Fig. 2 is a flow chart of unbalanced battery pack charging in consideration of temperature.
Fig. 3 is a diagram of results of simulation and experiment of a battery temperature estimation model.
Fig. 4 is a schematic diagram of Pareto fronts determined by considering various factors and an ideal solution obtained by a TOPSIS algorithm.
Detailed Description
The invention discloses a multi-target charging method for a battery pack, which comprises the following steps:
step 1, carrying out HPPC test with 5% SOC as an interval in a 5% -90% SOC range under different multiplying factors, and obtaining ohmic internal resistance, polarization capacitance and open-circuit voltage parameters required by a battery model;
step 2, estimating the SOC of the battery by using an ampere-hour integration method, and respectively obtaining a relation curve of each parameter in the battery model with the charging rate and the SOC; each parameter in the battery model comprises the ohmic internal resistance, the polarization capacitance and the open-circuit voltage parameter;
step 3, estimating the battery temperature in real time by combining a battery temperature estimation model;
and 4, optimizing the charging process by utilizing a multi-objective optimization control strategy and combining charging limiting factors (maximum charging current, voltage upper and lower limits and battery temperature) with the aim of short charging time and small temperature difference in the charging process, thereby determining the charging current in each 5% SOC charging interval and realizing the optimization of the charging process of the battery pack formed by the series connection of unbalanced monomers.
Before the charging optimization, firstly, HPPC tests are carried out on the battery under different multiplying powers, and battery data under different current stresses are obtained. And then, carrying out parameter identification on the obtained experimental data in different multiplying power and SOC interval ranges to obtain discrete model parameters, and carrying out interpolation processing on the parameters. And thirdly, establishing a battery temperature estimation model, and obtaining a temperature curve in the battery charging process under different charging currents and the maximum temperature difference in the process. And finally, optimizing the charging current in the charging process by taking short charging time and small charging temperature difference as optimization targets.
Further, step 1 specifically comprises:
firstly, carrying out primary circulating charge and discharge on a battery monomer, after the battery monomer is fully charged with constant current and constant voltage, carrying out constant current discharge to reach a lower limit cut-off voltage, and measuring the battery capacity Q; standing for more than 1 hour, carrying out charging HPPC test, and identifying charging internal resistance and an OCV-SOC curve by adopting a least square method;
step 1-1, charging 5% of the battery capacity with 1C multiplying power, and standing for 2 hours; recording the battery terminal voltage as the open circuit voltage OCV at that point;
step 1-2, discharging at a specific multiplying power for 10s, standing for 40s, and charging at 0.75 times of the same specific multiplying power for 10 s;
step 1-3, returning to step 1-1, and performing a pulse power test of a 10%, 15%, 20% … … 90% SOC state in a circulating manner, wherein the pulse power test is finished when the cell voltage exceeds the upper limit cut-off voltage of charging at any time in the process of step 1-1 or step 1-2; FIG. 1 is a HPPC test current curve at 1C charge-discharge rate.
And 1-4, repeating the steps 1-3 under different multiplying powers to obtain HPPC test results under different multiplying powers.
Further, step 2 uses the HPPC test results of different magnifications obtained in step 1, calculates an SOC value by an ampere-hour integration method, respectively identifies and obtains a three-dimensional graph with SOC as an x-axis, charge magnifications as y-axes, and Ro, Rp, Cp and OCV as z-axes, and uses a linear interpolation method to obtain a corresponding interpolated three-dimensional graph with 5% SOC per interval and 1A current per interval. The method specifically comprises the following steps:
estimating the SOC of each single battery according to the following formula:
Figure BDA0001965093440000031
wherein the subscript k represents k monomers, and n and 0 represent an arbitrary time and an initial time in the kth monomer charging stage, respectively; SOCk,0The SOC at the initial charging time of k cells is shown, I is the charging current, and Q is the battery capacity. SOCk,0The OCV that can be left standing for 2 hours or more is obtained by looking up the SOC-OCV corresponding curve.
And obtaining information of the open-circuit voltage OCV, the ohmic internal resistance Ro, the polarization internal resistance Rp and the polarization capacitance Cp in the SOC state of 5% per interval by utilizing the HPPC test data. Obtaining a three-dimensional graph corresponding to SOC as an x-axis (from SOC 5% to 90%), and Ro, Rp, Cp and OCV with multiplying power as a y-axis, and obtaining a corresponding interpolated three-dimensional graph of SOC at 5% intervals and current at 1A intervals by using a linear interpolation method, thereby facilitating later data reading. FIG. 2 is a graph of SOC variation curves of various parameters obtained from HPPC testing at different charging rates.
Further, the specific steps of calculating the temperature of each monomer in the step 3 are as follows:
the thermal balance equation of the battery is
Figure BDA0001965093440000041
Where m is the cell mass, C is the cell heat capacity, TsAs the cell surface temperature, it is considered herein that the surface temperature of the individual cells is uniform, QgFor the heat-generating power of the battery, QdDissipating heat power for the battery;
Qg＝I2R (2)
wherein I is charging current, and R is equivalent charging internal resistance obtained in the step 1, and the value of the equivalent charging internal resistance is equal to the sum of ohmic internal resistance and polarization internal resistance;
Qd＝hA(Ts-Ta) (3)
where h is the heat transfer coefficient, A is the cell surface area, TaIs ambient temperature;
solving a linear differential equation according to the equations (1) - (3), and calculating a temperature iteration formula at the current moment according to the temperature at the previous moment;
Figure BDA0001965093440000042
wherein T issampleFor sample time, p denotes the p-th sample. Fig. 3 is a diagram of a battery temperature estimation model and experimental measured data.
Further, the objective function and constraint conditions of the multi-objective optimization method in step 4 are as follows:
the expression of the objective function is
minJw＝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,A,Ta)
in the formula, U represents a charging voltage; SOC represents the battery state of charge.
The constraint conditions are represented by the following three 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.
Further, in step 4, the optimization algorithm has more than one optimization target, including two contradictory targets of short charging time and small charging temperature rise, that is, when the charging current is large in each stage, the charging time is shortened, but the temperature rise is large.
In the multi-objective optimization problem in step 4, although the optimal solution for both targets cannot be obtained, a group of non-inferior optimal solutions that are compatible with both targets can be found. Pareto fronts were obtained, yielding the function value curves for both objectives, as shown in fig. 4.
The final optimization scheme determines that on the basis of obtaining a Pareto front edge, an ideal solution between the charging time and the charging temperature rise is found by using a TOPSIS algorithm, and the method comprises the following specific steps:
(1) respectively corresponding the temperature rise and the charging time in the charging process to a y axis and an x axis, so that a 2-dimensional space can be constructed, and each non-inferior optimal solution corresponds to a coordinate point in the 2-dimensional space according to the data;
(2) selecting an optimal value (an ideal solution corresponding to an optimal coordinate point) and a worst value (a negative ideal solution corresponding to a worst coordinate point) of the index from all non-inferior optimal solutions aiming at each index, and sequentially calculating the distances d from the coordinate points of the non-inferior optimal solutions to the optimal coordinate point and the worst coordinate point respectively*And d0。
(3) Reference value for structural evaluation
Figure BDA0001965093440000051
The larger the f value, the more excellent the evaluation result.
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
Examples
The present invention will be specifically described below by taking a lithium iron phosphate battery as an example.
Selecting a plurality of battery monomers, firstly carrying out one-time standard cyclic charge and discharge according to a manual provided by a manufacturer, after a constant current-constant voltage (CC-CV) is fully charged, carrying out constant current discharge to reach a lower limit cut-off voltage, and measuring the battery capacity Q; after standing for more than 1 hour, the charging HPPC test was carried out according to the method shown in FIG. 1. The parameter acquisition process comprises the following steps:
(1) after charging 5% of the battery capacity at 1C rate, the mixture was left standing for 2 hours. Recording the battery terminal voltage as the open circuit voltage OCV at that point;
(2) discharging at 1C rate for 10s, standing for 40s, and charging at 0.75C rate for 10 s;
(3) returning to the step (1), carrying out the pulse power test HPPC of the SOC state of 10%, 15% and 20% … … 90% circularly, and ending when the cell voltage exceeds the charging upper limit cut-off voltage at any time in the process of (1) or (2).
(4) And repeating the experimental process by using different charging and discharging multiplying factors of 0.3C,0.5C,2C,3C,4C and 5C to obtain HPPC test experimental data under different multiplying factors.
And (3) adopting a TOPSIS algorithm, aiming at two targets of short charging time and small temperature rise in the charging process, carrying out scheme optimization from the Pareto front edge, and thus determining the charging current in each 5% SOC charging interval.
Before the battery pack is charged, acquiring the SOC of each monomer by using the step two, setting the initial charging current to be 1C multiplying power when the maximum SOC is less than 20%, and charging at 0.3C multiplying power when the charging reaches the SOC which is more than or equal to 40%; when the maximum SOC is more than or equal to 80%, the initial charging current is 0.1C multiplying power.
And (3) in the charging process, estimating the temperature of each battery monomer in real time by using the method in the step (3), and when the maximum temperature of the monomer obtained by estimation is higher than 60 ℃ or the maximum temperature difference of the battery pack is higher than 5 ℃, continuing charging after the charging current multiplying power is reduced by 0.2C. If the above-mentioned limit condition is reached again, the charging is continued by decreasing the charging current of 0.2C. And at any time in the charging process of the battery pack, finishing charging when any single voltage reaches an upper limit cut-off voltage, wherein the upper limit cut-off voltage for charging is the highest charging voltage specified by a battery manufacturer manual.
In the embodiment, the charging process of the battery pack formed by connecting unbalanced monomers in series is optimized by analyzing the parameter change rules of the batteries under different stresses, combining the battery temperature real-time estimation model and optimizing the charging current in different SOC intervals.

Claims (5)

1. A multi-target charging method for a battery pack is characterized by comprising the following steps:
step 1, carrying out HPPC test with 5% SOC as an interval in a 5% -90% SOC range under different multiplying factors, and obtaining ohmic internal resistance, polarization capacitance and open-circuit voltage parameters required by a battery model;
step 2, estimating the SOC of the battery by using an ampere-hour integration method to obtain a relation curve of each parameter in the battery model with the charging rate and the SOC; the method specifically comprises the following steps:
utilizing the HPPC test results with different multiplying powers obtained in the step 1, calculating by an ampere-hour integration method to obtain SOC values, respectively identifying to obtain three-dimensional graphs which take the SOC as an x axis, take the charging multiplying power as a y axis, respectively take Ro, Rp, Cp and OCV as a z axis, establishing corresponding three-dimensional graphs, and utilizing a linear interpolation method to obtain corresponding interpolation three-dimensional graphs of 5% SOC at intervals and 1A current at intervals;
step 3, estimating the battery temperature in real time by combining a battery temperature estimation model;
step 4, optimizing the charging process by using a multi-objective optimization control strategy and combining with charging limiting factors and taking short charging time and small temperature difference in the charging process as targets, so as to determine charging current in each 5% SOC charging interval; the objective function and constraint conditions of the multi-objective optimization method are as follows:
the expression of the objective function is
minJw＝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,A,Ta)
in the formula, U represents a charging voltage, and SOC represents a battery state of charge;
the constraints include the following three 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 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.
2. The multi-target battery pack charging method according to claim 1, wherein the step 1 is specifically:
firstly, carrying out primary circulating charge and discharge on a battery monomer, after the battery monomer is fully charged with constant current and constant voltage, carrying out constant current discharge to reach a lower limit cut-off voltage, and measuring the battery capacity Q; standing for more than 1 hour, carrying out charging HPPC test, and identifying charging internal resistance and an OCV-SOC curve by adopting a least square method;
step 1-1, charging 5% of the battery capacity with 1C multiplying power, and standing for 2 hours; recording the battery terminal voltage as the open circuit voltage OCV at that point;
step 1-2, discharging at a specific multiplying power for 10s, standing for 40s, and charging at 0.75 times of the same specific multiplying power for 10 s;
step 1-3, returning to step 1-1, performing a pulse power test of a cycle 10%, 15% and 20% … … 90% SOC state, and ending when the cell voltage exceeds the upper limit cut-off voltage of charging at any time in the process of step 1-1 or step 1-2;
and 1-4, repeating the steps 1-2-1-3 under different multiplying powers to obtain HPPC test results under different multiplying powers.
3. The multi-target battery pack charging method according to claim 1, wherein the specific steps of calculating the temperature of each monomer in step 3 are as follows:
the thermal balance equation of the battery is
Figure FDA0003508926230000021
Where m is the cell mass, C is the cell heat capacity, TsIs the surface temperature, Q, of the batterygFor the heat-generating power of the battery, QdDissipating heat power for the battery;
Qg＝I2R (2)
wherein I is charging current, and R is equivalent charging internal resistance obtained in the step 1, and the value of the equivalent charging internal resistance is equal to the sum of ohmic internal resistance and polarization internal resistance;
Qd＝hA(Ts-Ta) (3)
where h is the heat transfer coefficient, A is the cell surface area, TaIs ambient temperature;
solving a linear differential equation according to the equations (1) - (3), and calculating a temperature iteration formula at the current moment according to the temperature at the previous moment;
Figure FDA0003508926230000022
wherein T issampleFor sample time, p denotes the p-th sample.
4. The method for multiple target charging of battery packs according to claim 1, wherein the charging limiting factors in step 4 include maximum charging current, upper and lower voltage limits, and battery temperature.
5. The method for multiple target charging of battery packs according to claim 1, characterized in that: in step 4, the optimization targets of the optimization method include short charging time and small charging temperature rise, and an ideal solution between the charging time and the charging temperature rise is found by using a TOPSIS algorithm on the basis of obtaining a Pareto front edge, and the optimization method specifically comprises the following steps:
(1) respectively corresponding the temperature rise and the charging time in the charging process to a y axis and an x axis, thus constructing a 2-dimensional space, and corresponding each non-inferior optimal solution to a coordinate point in the 2-dimensional space according to the data;
(2) selecting all non-inferior optimal solutions according to each indexThe optimal value and the worst value of the index are used for sequentially calculating the distances d from the coordinate points of the non-inferior optimal solution to the optimal coordinate point and the worst coordinate point respectively*And d0；
(3) Constructing an evaluation reference value:
Figure FDA0003508926230000031
the larger the f value, the more excellent the evaluation result.
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CN110492572A (en) * 2019-08-30 2019-11-22 联动天翼新能源有限公司 A kind of the fast charge method and fast charge control system of lithium battery
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CN115593277B (en) * 2022-10-19 2024-10-01 重庆长安新能源汽车科技有限公司 Low-temperature charging parameter acquisition method and charging control method for lithium iron phosphate power battery

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018079164A1 (en) * 2016-10-26 2018-05-03 日立オートモティブシステムズ株式会社 Battery control device

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105552465B (en) * 2015-12-03 2018-05-08 北京交通大学 It is a kind of that charging method is optimized based on the lithium ion battery of time and temperature
CN107039696B (en) * 2016-11-29 2019-04-05 北京交通大学 A kind of track traffic vehicle carries the optimization charging method of energy storage lithium ion battery
CN107171035B (en) * 2017-05-24 2019-10-22 上海交通大学 The charging method of lithium ion battery
CN107831441B (en) * 2017-09-20 2019-11-05 哈尔滨理工大学 Prediction technique, forecasting system and a kind of charging unit of lithium battery charging current
CN107834620B (en) * 2017-09-30 2020-05-29 浙江大学 Multi-objective optimization control lithium battery pack charging method
CN107799843B (en) * 2017-09-30 2019-07-02 南京理工大学 A kind of unbalanced method for charging battery pack considering temperature
CN108445406B (en) * 2018-03-13 2021-05-25 桂林电子科技大学 Power battery state of health estimation method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018079164A1 (en) * 2016-10-26 2018-05-03 日立オートモティブシステムズ株式会社 Battery control device

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