CN111797568A - Lithium battery charging method based on minimum energy consumption - Google Patents

Lithium battery charging method based on minimum energy consumption Download PDF

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CN111797568A
CN111797568A CN202010546882.7A CN202010546882A CN111797568A CN 111797568 A CN111797568 A CN 111797568A CN 202010546882 A CN202010546882 A CN 202010546882A CN 111797568 A CN111797568 A CN 111797568A
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专祥涛
黄柯
姜涵
陈武
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Guangdong Changbai Electric Appliance Industry Co ltd
Shenzhen Research Institute of Wuhan University
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Abstract

The invention discloses a lithium battery charging method based on minimum energy consumption, which comprises the following steps: 1) establishing a first-order RC equivalent circuit model of the lithium ion battery; 2) testing the lithium battery to be charged, and identifying by using test data and a genetic algorithm toolbox to obtain battery parameters; 3) establishing a loss model of the battery according to the first-order RC equivalent circuit model of the lithium ion battery established in the step 1) and the battery parameters obtained in the step 2); 4) according to the loss model of the battery, under the condition of not increasing the charging time, calculating to obtain an optimal charging current curve; 5) and charging the battery according to the optimal charging current curve. According to the invention, the optimal charging current curve is obtained by establishing the battery charging loss model under the condition of meeting the minimum loss, and the charging method can achieve the aim of minimizing the charging loss on the basis of not increasing the charging time, so that the charging energy loss is effectively reduced.

Description

Lithium battery charging method based on minimum energy consumption
Technical Field
The invention relates to a battery charging technology, in particular to a lithium battery charging method based on minimum energy consumption.
Background
The increasing shortage of petroleum resources and the environmental pollution of automobiles compel people to reconsider the power problems of future automobiles. The electric automobile integrating a plurality of high and new technologies has the characteristics of no emission pollution, low noise, low maintenance and operation cost and the like, is causing a revolution of the automobile industry in the world, and will replace a fuel automobile to become the mainstream of the future automobile.
The power battery is a main power source of the electric automobile, and occupies the most central position in the electric automobile.
The driving range, the acceleration performance and the braking energy recovery rate of the whole vehicle are closely inseparable with the performance of the power battery. The key function of the power battery is to enable the electric automobile to have strong climbing capability, acceleration capability and cruising capability. In a pure electric vehicle, the power battery is the only power source of the electric vehicle, and the power battery is used more to make the driving range of the electric vehicle longer, and the large-current discharge is used as an auxiliary for starting, accelerating and climbing the electric vehicle, so that the requirement for the long-time continuous discharge capacity of the battery is higher, and the power battery is required to have high specific energy. The power battery generally needs to have the following requirements: the energy density is high; the power density is high; the cycle life is long; the self-discharge is less; the safety and the reliability are high; environmental protection and no pollution.
At present, the main performance comparison of power batteries in common electric vehicles shows that the lithium ion batteries have the advantages of high specific energy, high specific power, high voltage platform, low self-discharge rate, long service life and the like, so that the lithium ion power batteries become the optimal power source for the electric vehicles. At present, relatively long charging time is a main reason for restricting the wide application of lithium ion batteries. However, rapid charging necessarily results in a large amount of energy loss. Therefore, how to minimize the energy loss without increasing the charging time is a major issue in current research.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lithium battery charging method based on minimum energy consumption aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a lithium battery charging method based on minimum energy consumption comprises the following steps:
1) establishing a first-order RC equivalent circuit model of the lithium ion battery;
2) testing the lithium battery to be charged, and identifying by using test data and a genetic algorithm toolbox to obtain battery parameters; the battery parameters include: open circuit voltage Em(SoC), direct Current resistance R0(SoC), polarization resistance Rp(SoC) and polarization capacitance Cp(SoC)；
3) Establishing a loss model of the battery according to the first-order RC equivalent circuit model of the lithium ion battery established in the step 1) and the battery parameters obtained in the step 2);
4) according to the loss model of the battery, under the condition of not increasing the charging time, calculating to obtain an optimal charging current curve;
5) and charging the battery according to the optimal charging current curve.
According to the scheme, the first-order RC equivalent circuit model of the lithium ion battery in the step 1) is expressed as follows:
Figure BDA0002541011540000031
in the formula of UbattIs terminal voltage; u shapeocvIs an open circuit voltage source; u shapepIs a polarization voltage; cpIs a polarization capacitor; rpIs a polarization resistance; r0Is a direct current resistance; i is a charging current;
during charging, the lithium ion battery state of charge SoC of the battery is expressed as:
Figure BDA0002541011540000032
in the formula, SoC0Is the initial state of charge of the battery before charging begins; cbIs the nominal capacity of the battery.
According to the scheme, the battery parameters are obtained by using the test data and the genetic algorithm toolbox identification in the step 2), and the method specifically comprises the following steps:
performing OCV test and HPPC test on the battery to obtain battery open-circuit voltage Em and direct-current resistance R under different SoCs0Polarization resistance RpAnd a polarization capacitor Cp(ii) a The OCV test is to obtain battery open-circuit voltage Em under different SoCs by taking 5% SoC as an interval when the battery is open-circuit; the HPPC test is to perform a pulse power test every 5% SoC to obtain direct current resistances R under different SoCs0Polarization resistance RpAnd a polarization capacitor Cp。
According to the scheme, in the step 2), the battery parameters are obtained by using the test data and the genetic algorithm toolbox for identification, and the specific process is as follows:
2.1) identifying the battery parameters, before the identification begins, giving an R every 5% SoC according to the test data0(SoC)，Cp(SoC)，Rp(SoC) and Em(SoC) obtaining a group of battery parameters related to SoC, and obtaining battery parameters of other SoC states through interpolation;
2.2) substituting the battery parameters into a first-order RC equivalent circuit model to calculate to obtain a model output voltage;
and 2.3) calculating the error between the actually measured voltage and the model output voltage during sampling, if the error is larger than a set value, obtaining new battery parameters through a genetic algorithm, and repeating the steps until the error is smaller than the set value to obtain the identified battery parameters.
According to the scheme, in the step 3), a loss model of the battery is established, and the method specifically comprises the following steps:
R0and RpAs the SoC changes, the total charge loss during charging is:
Figure BDA0002541011540000041
wherein, I1Is flowed through RpCurrent of (t)chFor charging time, R0(SoC) is the DC resistance corresponding to the SoC state, Rp(SoC) is a polarization capacitance corresponding to the SoC state;
suppose that in each time interval Δ t, the currents I and I1All are constant, and a loss model of the k +1 time interval is obtained:
Figure BDA0002541011540000042
according to the scheme, in the step 4), an optimal charging current curve is calculated according to the loss model of the battery under the condition that the charging time is not increased, and the method specifically comprises the following steps:
the whole charging process of the lithium ion battery is divided into a plurality of sections according to the parameter precision of the battery and the charging and discharging cycle interval of the battery, a segmented constant current charging method is adopted, and the current value of each section is adjusted by taking the minimum value of the loss model of each section as a target under the condition of ensuring that the whole charging time is not prolonged and the charging electric quantity is not reduced, so that the aim of optimizing energy consumption is achieved.
According to the scheme, the precision of the battery parameter in the step 4) is 0.05SoC, and the battery charging and discharging cycle interval is 0.2 SoC-0.8 SoC), and the charging process is divided into 12 stages;
and solving by using the minimum value of the loss model of each stage as a target and adopting a genetic algorithm to obtain the current of each stage in the charging process.
The invention has the following beneficial effects:
according to the invention, through a battery charging loss model, an optimal charging current curve is calculated and obtained by utilizing a genetic algorithm under the condition of meeting the minimum loss, the charging method can achieve the aim of minimizing the charging loss on the basis of not increasing the charging time, and the charging mode can effectively reduce the charging energy loss.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a first-order RC lithium-ion battery equivalent circuit diagram employed in an embodiment of the present invention;
FIG. 3 is a block diagram of a battery parameter identification process according to an embodiment of the present invention;
FIG. 4 is a flow chart of a genetic algorithm for optimal current calculation according to an embodiment of the present invention;
FIG. 5 is a graph of the optimum current versus resistance for different charging times in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a method for charging a lithium battery based on minimum energy consumption, as shown in fig. 1, includes the following steps:
step 1, for a power battery, to analyze a charging process, a correct lithium ion battery model needs to be established, and the model must be capable of reflecting static and dynamic changes in the battery charging process, namely, the charging characteristic and the polarization characteristic of the lithium ion battery, so that a first-order RC equivalent circuit model is adopted in the invention, as shown in FIG. 2.
The mathematical model can be expressed as:
Figure BDA0002541011540000061
in the formula: u shapebattIs terminal voltage; u shapeocvIs an open circuit voltage source; u shapepIs a polarization voltage; cpIs a polarization capacitor; rpIs a polarization resistance; r0Is a direct current resistance; i is the charging current.
During charging, the SoC (lithium ion battery state of charge) of the battery can be expressed as:
Figure BDA0002541011540000071
in the formula: SoC (system on chip)0Is the initial state of charge of the battery before charging begins; cbIs the nominal capacity of the battery.
Step 2, an OCV test can show the relationship between the electric quantity and the terminal voltage of the lithium ion battery; the HPPC test may indicate dynamic changes in the battery charging process; from the experimental data of these two tests, battery parameters can be identified using the MATLAB toolkit.
In the OCV test in step 2, when the battery is disconnected, the battery static voltages under different socs are obtained at intervals of 5% SoC. The HPPC test is a pulse power test every 5% SoC. After obtaining the experimental data, the battery parameters can be identified as shown in fig. 3. Before recognition begins, every 5% SoC is given an R0(SoC)，Cp(SoC)，Rp(SoC) and Em(SoC), a group of battery parameters related to SoC can be obtained, and battery parameters of other SoC states are obtained by interpolation. And substituting the battery parameters into an RC circuit model to calculate to obtain the output voltage of the model, and obtaining the root mean square error F according to the formula (3). And if the F is more than 0.01, obtaining new battery parameters through selection, crossing and variation of a genetic algorithm, repeating the steps until the root mean square error F is less than 0.01, and exiting the circulation to obtain the identified battery parameters.
Figure BDA0002541011540000072
In the formula: n is the number of samples in one HPPC test, Vexp(k) Measured voltage V at k times of samplingsim(k) Is the model output voltage at k samples.
And 3, quantifying the energy loss of the charging process according to the battery model in the step 1 and the battery parameters in the step 2.
In the first-order RC equivalent circuit model, R0And RpThe power loss is represented as:
Figure BDA0002541011540000081
in the formula: i is1Is flowed through RpThe current of (a); plossIt loses power.
Due to R0And RpAs the SoC changes, the total charge loss during charging is:
Figure BDA0002541011540000082
suppose that during each time interval, the currents I and I1All invariant, then the loss model can be approximated:
Figure BDA0002541011540000083
discretization according to the formula (2) can obtain
Figure BDA0002541011540000084
In the formula: i is1It can be derived from ohm's law:
Figure BDA0002541011540000085
in the formula: voltage U across the polarization capacitorpThe discretization can be obtained by the formula (1):
Figure BDA0002541011540000086
and 4, calculating to obtain an optimal charging current curve by using a genetic algorithm on the basis of the loss model in the step 3 under the condition of not increasing the charging time.
As shown in fig. 4, before the calculation starts, the relevant parameters of the genetic algorithm are initialized, the parent samples are generated in the feasible domain, the fitness of the parent samples is calculated and ranked from large to small, the optimal 1 sample is retained in the next generation, and a new set of samples is obtained through selection, crossover and mutation. And judging whether the optimal sample of the child is superior to the optimal sample of the parent, if so, repeating the steps until the optimal sample is not evolved continuously.
Finally, the optimal current distribution and the corresponding resistance R of charging from 0.2SoC to 0.8SoC under different charging time are obtained by calculation of the genetic algorithm0+RpDistribution diagram of (c). As shown in fig. 5, it can be found that, except for the first constant current phase, the following relationship is satisfied: the smaller the battery resistance, the larger the charging current; the greater the battery resistance, the smaller the charging current. In the first phase, the current is mainly applied to the polarization capacitor C in the RC parallel circuit due to the just entered charging statepFlows through a polarization resistor RpThe charging current can be slightly larger at this stage.
And designing a comparison experiment to verify the feasibility of the optimal charging method.
Under different charging time, corresponding voltage curves can be obtained by respectively charging the lithium ion battery in a constant current mode and the current in an optimal mode, the total energy consumption under the traditional CC mode and the optimal current charging mode under different charging time can be obtained by the formula (10), the charging energy consumption saved by the optimal charging current mode can be obtained by the formula (12), and further the saved charging loss ratio can be known, and the specific result is shown in table 1. Under different charging time, the energy-saving amplitude of the algorithm is 0.73% -1.23%, and it can be seen that the algorithm can reduce energy loss, and the energy-saving amplitude can be further increased along with the increase of the charging current to a certain extent. The proposed charging method saves less energy when the battery is charged with a smaller current than when it is charged with a larger current. The reason is that when the charging current is low, the battery charging energy loss itself becomes small, and the space for improving the charging efficiency is limited.
The charging intervals are all 0.2 SoC-0.8 SoC, and the energy consumption in the process can be expressed as follows:
Figure BDA0002541011540000101
in the formula: t is tchaTo end time of charging, Icha(t) is the current that changes with time during charging; vcha(t) is the voltage over time during charging; etotalFor the total energy consumption in the charging process, the energy mainly consists of two parts:
Etotal＝Ebat+Eloss(11)
in the formula: elossEnergy loss during charging; ebatThe energy charged to the battery during the charging process. The charging interval is set to be 0.2 SoC-0.8 SoC, and E is set in the two charging modesbatAre the same. The charging power saved by the optimal charging current mode can be expressed as:
Figure BDA0002541011540000102
in the formula:
Figure BDA0002541011540000103
for the total energy loss in the conventional CC mode,
Figure BDA0002541011540000104
the total energy loss in the optimal current charging mode is achieved.
The experimental results are shown in table 1, and compared with the CC constant current charging mode, the charging mode of the present invention can effectively save the charging energy loss.
TABLE 1
Figure BDA0002541011540000111
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (7)

1. A lithium battery charging method based on minimum energy consumption is characterized by comprising the following steps:
1) establishing a first-order RC equivalent circuit model of the lithium battery;
2) testing the lithium battery to be charged, and identifying by using test data and a genetic algorithm toolbox to obtain battery parameters; the battery parameters include: open circuit voltage Em(SoC), direct Current resistance R0(SoC), polarization resistance Rp(SoC) and polarization capacitance Cp(SoC)；
Identifying by using test data and a genetic algorithm toolbox to obtain battery parameters;
3) establishing a loss model of the battery according to the first-order RC equivalent circuit model of the lithium battery established in the step 1) and the battery parameters obtained in the step 2);
4) according to the loss model of the battery, under the condition of not increasing the charging time, calculating to obtain an optimal charging current curve;
5) and charging the battery according to the optimal charging current curve.
2. The method for charging a lithium battery based on minimum energy consumption according to claim 1, wherein the first-order RC equivalent circuit model of the lithium battery in the step 1) is represented as:
Figure FDA0002541011530000011
in the formula of UbattIs terminal voltage; u shapeocvIs an open circuit voltage; u shapepIs a polarization voltage; cpIs a polarization capacitor; rpIs a polarization resistance; r0Is a direct current resistance; i is a charging current;
during charging, the lithium ion battery state of charge SoC of the battery is expressed as:
Figure FDA0002541011530000021
in the formula, SoC0Is the initial state of charge of the battery before charging begins; cbIs the nominal capacity of the battery。
3. The method for charging a lithium battery based on minimum energy consumption according to claim 1, wherein the lithium battery to be charged in the step 2) is tested to obtain test data, and the test data are as follows:
performing OCV test and HPPC test on the battery to obtain battery static voltage, open-circuit voltage and direct-current internal resistance under different SoCs; the OCV test is to obtain the static voltages of the batteries under different SoCs by taking 5% SoC as an interval when the batteries are disconnected; the HPPC test is to perform a pulse power test every 5% of SoC to obtain open-circuit voltage and direct-current internal resistance under different SoC.
4. The method for charging a lithium battery based on minimum energy consumption according to claim 1 or 3, wherein in the step 2), the battery parameters are obtained by using test data and a genetic algorithm toolbox identification, and the method comprises the following steps:
2.1) identifying the battery parameters, before the identification begins, giving an R every 5% SoC according to the test data0(SoC)，Cp(SoC)，Rp(SoC) and Em(SoC) obtaining a group of battery parameters related to SoC, and obtaining battery parameters of other SoC states through interpolation;
2.2) substituting the battery parameters into a first-order RC equivalent circuit model to calculate to obtain a model output voltage;
and 2.3) calculating the error between the actually measured voltage and the model output voltage during sampling, if the error is larger than a set value, obtaining new battery parameters through a genetic algorithm, and repeating the steps until the error is smaller than the set value to obtain the identified battery parameters.
5. The method for charging a lithium battery based on minimum energy consumption according to claim 1, wherein in the step 3), a loss model of the battery is established, specifically as follows:
R0and RpAs the SoC changes, the total charge loss during charging is:
Figure FDA0002541011530000031
wherein, I1Is flowed through RpCurrent of (t)chFor charging time, R0(SoC) is the DC resistance corresponding to the SoC state, Rp(SoC) is a polarization capacitance corresponding to the SoC state;
suppose that in each time interval Δ t, the currents I and I1All are constant, and a loss model of the k +1 time interval is obtained:
Figure FDA0002541011530000032
6. the method for charging a lithium battery based on minimum energy consumption according to claim 1, wherein the optimal charging current curve is calculated in step 4) according to the loss model of the battery without increasing the charging time, and specifically the following steps are performed:
the whole charging process of the lithium ion battery is divided into a plurality of sections according to the parameter precision of the battery and the charging and discharging cycle interval of the battery, a segmented constant current charging method is adopted, and the current value of each section is adjusted by taking the minimum value of the loss model of each section as a target under the condition of ensuring that the whole charging time is not prolonged and the charging electric quantity is not reduced, so that the aim of optimizing energy consumption is achieved.
7. The lithium battery charging method based on the minimum energy consumption of claim 6, wherein the precision of the battery parameters in the step 4) is 0.05SoC, the battery charging and discharging cycle interval is 0.2 SoC-0.8 SoC, and the charging process is divided into 12 stages;
and solving by using the minimum value of the loss model of each stage as a target and adopting a genetic algorithm to obtain the current of each stage in the charging process.
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CN113036846A (en) * 2021-03-08 2021-06-25 山东大学 Lithium ion battery intelligent optimization quick charging method and system based on impedance detection
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