TW202036018A - Multi-stage constant current charging method for optimizing output current value using the optimal output current value to charge after the optimal output current value is obtained - Google Patents

Abstract

A multi-stage constant current charging method for optimizing output current value includes the following steps: (a) according to a particle swarm algorithm, initializing a charge current value Icharge,s of each of multiple stages and a stage cut-off voltage value VT; (b) performing an operation on each charging current value Icharge,s of the stages and the stage cut-off voltage value VT to obtain a total charging time T and a total charging loss, wherein this operation includes: where s is the number of stages, Ceq is the equivalent capacitance of the battery, Ro is the series impedance of the battery, Rp is the parallel impedance of the battery, VCeq,s is a battery voltage of each stage, and Icharge,s is a charging current of each stage; (c) according to a current total charging time and total charging loss (Tnow, Lnow) and an ideal total charging time and total charging loss (Tmin, Lmin), using a fitness function to calculate; (d) updating an individual optimal value and a group optimal value for each charging current value (Icharge,s of the stages and the stage cut-off voltage value VT, so as to determine whether an optimal output current value is obtained; and (e) using the optimal output current value to charge, and then returns to step (b) for repetition.

Description

A multi-stage constant current charging method for optimizing output current value

The present invention relates to a method for charging a secondary battery, in particular to a method for charging a secondary battery based on a multi-stage constant current charging method.

Global warming and the intensification of the greenhouse effect have made the development and application of renewable energy and electric vehicles an inevitable trend. Because the development of these systems requires a large number of battery energy storage and power conversion management systems, the manufacturing material technology and charge balance of secondary batteries The development of technology, power conversion and management technology has attracted much attention.

With the advancement of secondary battery-related technologies, secondary batteries have been widely used in products such as mobile phones, notebook computers, and tablet computers. Among the secondary batteries, lithium ion can become the mainstream of secondary batteries due to its high energy density, long cycle life, light weight, no memory effect, high average working voltage and low self-discharge rate. In addition, many large power storage systems have also begun to invest in the research and development of large-capacity lithium-ion batteries.

In the past ten years, lithium-ion batteries with relatively excellent characteristics have become the first choice for power sources for mobile devices. The power batteries of electric vehicles and the energy storage devices used in renewable energy power generation systems are also mainly equipped with lithium batteries. At present, it is also widely used in 3C electronic products, renewable energy power generation and energy storage systems, electric vehicles and other industries that require high voltage and high power. Therefore, the application and demand for lithium batteries are increasing year by year.

The battery capacity of the secondary battery is limited in order to be lighter and thinner. Therefore, fast charging technology becomes very important. However, the high current charging in pursuit of fast charging will increase the temperature rise of the secondary battery and reduce the battery cycle life. short. Temperature rise is very important for batteries. Excessive temperature rise during charging may cause problems such as accelerated aging of the battery, imbalance between battery packs, capacity decline, and shortened cycle life. The above-mentioned aging phenomenon is caused by the excessively intense electrochemical reaction at a higher operating current, which causes the internal resistance of the battery to gradually increase. In addition, if the power of each battery in the battery pack in series can be maintained at any time, the battery pack will have more charge and discharge cycles. It can extend the use time of the built-in battery-type mobile device, thereby increasing the life and usage rate of the mobile device.

Therefore, charging technology is very important for secondary batteries. The charging technology is related to the charging speed, charging efficiency, battery temperature rise, battery cycle life and other factors of the secondary battery. The most commonly used lithium battery charging method is the constant current constant voltage charging method (Constant Current Constant Voltage, CC-CV). ), CC-CV charging starts with constant current charging until the battery's rated upper limit voltage, and then uses the rated upper limit voltage to charge the battery. At this time, the charging current will gradually decrease due to the difference between the battery terminal voltage and the internal voltage. It is fully charged when it drops to the rated cut-off current. The advantage of this method is that it is simple and easy to implement, but the disadvantage is that the charging time is longer.

Therefore, many documents propose improved charging methods. There are documents that use dual-loop control and use positive and negative feedback to control the battery voltage, which can obtain a charging curve similar to CC-CV, and does not need to use a current sensor, so it is simpler and lower in cost than the CC-CV charging method; Some literature proposes a step-up constant current-constant voltage charging method that initially uses a higher rated voltage of the battery (such as 4.3V) for charging, and then switches to the constant current and constant voltage charging method after the high constant voltage period. This method can be used in Charge the battery to 30% of the rated capacity in a short time, so the charging time is shorter, but the disadvantage is that the battery must be fully discharged before charging; there are also documents that propose active charging state combined with fuzzy control charging method and gray predictive control battery charging system. Charge more power in the same time; or some literature proposes a charging method based on phase-locked loop control, the reference phase is compared with the output phase to produce a phase error, and the error phase is transmitted to the current source to generate a suitable current Charge the battery; in order to improve the shortcomings of the phase-locked loop in the constant voltage mode, there are documents that propose a current pump battery charger, which uses current pump charging in the constant current mode, and uses pulse current charging in the constant voltage mode.

Some literature proposes to use (0,1)-Integer Linear Programming (ILP) to search for the best battery charging curve for multi-stage current and constant voltage modes; some literature proposes to use smart algorithms to determine the multi-stage constant current charging method The charging current of each stage; or some literature proposes an optimized multi-stage charging method based on the battery model. Its advantage is that it uses simple mathematical calculations to determine the charging time as the shortest charging current. The disadvantage is that it cannot optimize other factors such as charging loss, Charging efficiency, etc.; there are also documents that change the charging method by changing the current size and pulse width and the rest period between pulses; Some literature proposes a fuzzy five-stage lithium-ion battery charging method, and uses Taguchi method to determine the attribution function of fuzzy control, thereby optimizing the performance of the charging method.

However, the above-mentioned documents all require multiple actual experiments to find the optimal current setting. Therefore, a novel method for charging the secondary battery is urgently needed in the art.

One purpose of the present invention is to disclose a multi-stage constant current charging method for optimizing the output current value, which uses AC impedance analysis to establish a battery equivalent model, while considering the charging loss and charging time, and uses the particle swarm algorithm to find The best solution in a limited area, thereby realizing an optimized multi-stage charging method that minimizes charging loss and time.

Another object of the present invention is to disclose a multi-stage constant current charging method for optimizing the output current value. The particle swarm algorithm is used to find the optimized charging current setting value. It only needs to use the battery equivalent model and has no need The advantages of optimized current setting can be obtained by carrying out many time-consuming and labor-intensive actual experiments.

Another object of the present invention is to disclose a multi-stage constant current charging method that optimizes the output current value. When the stage cut-off voltage is 4.22V, the battery capacity can be charged to 98% of the conventional CC-CV charging method. Above, and before the temperature rise does not increase, the charging time can be shortened by 22.77%, and the charging efficiency can be improved by 0.43%.

，該運算包括：

In order to achieve the foregoing purpose, a multi-stage constant current charging method for optimizing the output current value is proposed, which is realized by a control circuit. The charging method includes the following steps: initializing the multiple stages of charging according to a particle swarm algorithm Current value I _charge,s and a stage cut-off voltage value V _T (step a); perform an operation on each charge current value I _charge,s of the plural stages and the stage cut-off voltage value V _T to obtain a total charge Time T and a total loss of charge

, The operation includes:

Among them, s is the number of stages, C _eq is the equivalent capacitance of the battery, R _o is the battery series impedance, R _p is the battery parallel impedance, V _Ceq,s is the battery voltage in each stage, and L _carge,s is the battery Charging current (step b); according to a current total charging time and total charging loss ( T _now , L _now ) and an ideal total charging time and total charging loss ( T _min , L _min ), use a fitness function to calculate (Step c); update each of the charging current values I _{charge, s} of the plurality of stages and one of the stage cut-off voltage values V _T for an individual optimal value and a group optimal value to determine whether to obtain an optimal output current Value (step d); and apply the best output current value for charging, and then return to step b and repeat (step e).

In an embodiment, the fitness function includes:

Among them, α is the weight value.

In an embodiment, the weight value α is 0.5.

其中，Req為等效阻抗，SOC為電池剩餘容量。 In one embodiment, it further includes performing calculations using a fitting function of equivalent impedance to ensure that the voltage in the battery does not exceed a preset rated voltage, and the fitting function of equivalent impedance includes:

Among them, Req is the equivalent impedance, and SOC is the remaining battery capacity.

In one embodiment, the preset rated voltage is 4.2V.

In one embodiment, the plural stages are 5 stages.

In order to enable your reviewer to further understand the structure, features and purpose of the present invention, drawings and detailed descriptions of preferred specific embodiments are attached as follows.

Step a‧‧‧Initialize the charge current value I _{charge, s} and the first-stage cut-off voltage value V _{T of the} complex number stage according to a particle swarm algorithm

Step b‧‧‧ Perform an operation on each charge current value I _{charge, s} of the complex number stage and the stage cut-off voltage value V _T to obtain a total charge time T and a total charge loss

Step c‧‧‧Using a fitness function to perform calculations based on a current total charging time and total charging loss ( T _now , L _now ) and an ideal total charging time and total charging loss ( T _min , L _min )

Step d‧‧‧Update the respective optimal value of each charge current value I _{charge, s} and the stage cut-off voltage value V _T of the plurality of stages and a group optimal value to determine whether to obtain an optimal output current value

Step e‧‧‧ and apply the best output current value for charging, then go back to step b and repeat

Fig. 1 shows the flow chart of the multi-stage constant current charging method with optimized output current value in this case.

Fig. 2 shows a charging schematic diagram of the conventional multi-stage constant current charging method.

Figure 3 shows a schematic diagram of an equivalent model of Thevenin battery.

Figure 4 shows a Nyquist diagram showing the AC impedance characteristics of the battery.

Figure 5 shows a schematic diagram of the setup of the AC impedance analysis experimental system.

Figure 6 shows the Nyquist impedance diagram for different remaining capacities.

Figure 7a shows the measured series impedance ( R _o ), parallel impedance ( R _p ) and equivalent impedance ( R _eq ) as a function of the percentage of battery remaining capacity.

Fig. 7b shows a graph of the relationship between the measured parallel capacitance ( C _p ) and the percentage of remaining battery capacity.

Fig. 7c illustrates the relationship between the measured open circuit voltage ( V _oc ) and the percentage of remaining battery capacity.

Figure 8 shows a schematic diagram of particles moving in the solution space of the particle swarm algorithm.

FIG. 9 shows a curve diagram of the fitting relationship between the remaining battery capacity (SOC) and the equivalent impedance ( R _eq ) of this case.

Figure 10 shows the definition diagram of the fitness function in this case.

Fig. 11 is a schematic diagram showing the trajectory of particle swarms in space.

Figure 12 shows a schematic diagram of the experimental platform architecture of this case.

Figure 13 shows the measured current and voltage waveforms of the conventional constant current and constant voltage charging method.

Figure 14 shows the measured current and voltage waveforms of the cut-off voltage (V _T =4.22V) in this case.

Please refer to FIG. 1, which shows the flow chart of the multi-stage constant current charging method for optimizing the output current value of this case.

，該運算包括：

As shown in the figure, the multi-stage constant current charging method for optimizing the output current value of this case is realized by a control circuit. The charging method includes the following steps: initializing the charging current values of the multiple stages according to a particle swarm algorithm I _{charge, s} and a stage cut-off voltage value V _T ; (step a); perform an operation on each charge current value I _{charge, s} of the plural stages and the stage cut-off voltage value V _T to obtain a total charging time T and a total loss of charge

, The operation includes:

Where s is the number of stages, C _eq is the equivalent capacitance of the battery, R _o is the battery series impedance, R _p is the battery parallel impedance, V _{Ceq, s} is the battery voltage in each stage, I _{charge, s} is the battery Charging current; (step b); based on a current total charging time and total charging loss ( T _now , L _now ) and an ideal total charging time and total charging loss ( T _min , L _min ), using a fitness function Operation; (Step c); Update the individual best value of each charge current value I _{charge, s} and the phase cutoff voltage value V _T of the complex number stage and a group best value to determine whether to obtain an optimal value Output current value; (step d); and apply the best output current value for charging, and then return to step b to repeat the execution; (step e).

The fitness function includes:

Wherein, α is a weight value, and the weight value α is, for example, but not limited to 0.5.

其中，R _eq 為等效阻抗，SOC為電池剩餘容量。 It further includes using a fitting function of equivalent impedance to perform calculations to ensure that the voltage in the battery does not exceed a preset rated voltage. The fitting function of the equivalent impedance includes:

Among them, R _eq is the equivalent impedance, and SOC is the remaining battery capacity.

The preset rated voltage is, for example, but not limited to 4.2V; the plurality of stages is, for example, but not limited to, 5 stages.

The principle of this case will be explained as follows: Please refer to FIG. 2, which shows a charging diagram of the conventional multi-stage constant current (Multi-Stage Constant Current, MSCC) charging method.

As shown in the figure, when the multi-stage constant current charging method is used for charging, when the battery voltage reaches the upper limit rated voltage 4.2V, the current will switch to the next stage, and then continue to charge with a charging current smaller than the previous stage. The charging time of the multi-stage constant current charging method is shorter and the battery temperature rise is low, so the battery can be increased Cycle life. Because the internal electrochemical characteristics of the battery are quite complicated, it is difficult to use the conventional technology to optimize the charging current. The conventional technology can optimize the charging current at each stage, such as using the fuzzy control method to charge the battery Temperature rise is used as the input of fuzzy control, after fuzzy control calculation, output charging current; or use Taguchi Methods (TM) to find the best charging current; or use Ant colony system (ACS) to obtain Optimize charging current. However, since the above methods all require complex calculations, they must be implemented by computers.

Lithium ion battery equivalent model:

Establishing a correct battery equivalent model is conducive to the analysis of the experiment, because the battery's internal chemical materials will cause its output voltage to drop with the increase of use time. Therefore, an accurate battery equivalent model must consider factors such as self-discharge, battery internal resistance, and transient response.

Please refer to FIG. 3, which shows a schematic diagram of an equivalent model of Thevenin battery.

As shown in the figure, the difference between this model and the linear battery model is the addition of a set of parallel capacitors ( C _p ) and parallel impedance ( R _p ), because the dynamic response of the battery can be simulated by C _p and R _p , and the battery is simulated The charging and discharging process is more realistic. This case will use this model to derive the optimal charging mode of the multi-stage constant current charging method.

Then derive the charging time and charging capacity of the multi-stage constant current charging method. The battery equivalent model includes the equivalent battery capacitance ( C _eq ), parallel impedance ( R _p ) and parallel capacitance ( C _p ) and series impedance ( R _o ), where R _p and C _p represent the transient response of the battery, and the initial voltage V _Ceq on C _eq is the open-circuit voltage (OCV) of the battery. In addition, the relevant parameters in the model are related to the remaining battery The capacity (state of charge, SOC) is related to temperature.

Among them, V _T is the battery terminal voltage (terminal voltage), which is also the stage cut-off voltage of the multi-stage constant current charging method, V _Cp = V _Rp , according to Kirchhoff's voltage law, V _{T is} as shown in equation (1) Show.

為電池之內電壓，如方程示(2)所示。 among them,

Is the internal voltage of the battery, as shown in equation (2).

為電池初 始開路電壓。因各階段為固定電流充電，其充電電流頻率極小，故並聯電容(C _p )容抗極大可視為開路，則I _RP

I _charge ，由於多階段定電流充電法之充電時間係由每一個階段之充電電流決定，因此所需總充電時間T如方程示(3)所示。 Among them, s is the number of stages, when s=1, I ₀ =0,

Is the initial open circuit voltage of the battery. Since each stage is charged with a fixed current, the frequency of the charging current is very small, so the parallel capacitor ( C _p ) can be regarded as an open circuit when the capacitive reactance is large, then I _RP

I _charge , since the charging time of the multi-stage constant current charging method is determined by the charging current of each stage, the required total charging time T is shown in equation (3).

Derived from the formula, the total charge capacity Ah _{T of the} multi-stage constant current charging method is shown in equation (4).

Generally speaking, the five-stage constant current charging method needs to fully charge the battery, and the fifth stage current is shown in equation (5).

則為電池在充飽之後的內 電壓(如4.2V)。 Among them, V _{T_cut-off} is the stage cut _-off voltage,

It is the internal voltage of the battery after being fully charged (such as 4.2V).

The total loss of charging energy can also be obtained as shown in equation (6).

AC impedance analysis of lithium battery:

In the charging and discharging technology of secondary batteries, alternating current impedance (Alternating Current Internal Resistance, ACIR) analysis is a method of electrochemical analysis, mainly to analyze the chemical reaction of the battery in different states, so as to obtain the battery in different states. Equivalent impedance. And the AC impedance analysis department uses The small amplitude AC sine wave voltage or current disturbs the terminal electrodes of the battery, from which the AC impedance data is obtained, and an equivalent circuit is established from this data. In addition, by changing the frequency of the AC sine wave, the response curve of real and imaginary impedance to frequency can be obtained, which is called Electrochemical Impedance Spectrum (EIS) or Nyquist-plot.

Please refer to FIG. 4, which shows a Nyquist diagram of the AC impedance characteristic of the battery.

As shown in the figure, the Nyquist diagram of the AC impedance characteristics of the battery can be divided into three parts: low frequency, intermediate frequency and high frequency for discussion. They are the mass transport effects of low frequency (Mass Transport Effects), the charge transfer of intermediate frequency and Double-Layer Effects and High Frequency Electromagnetic Effects (Electric and Magnetic Effects).

To measure the AC impedance parameters of the battery, the AC impedance analyzer (Bio-Logic’s multifunctional modular potentiostat) generates a set of variable frequency sine wave voltage signals. The voltage active signal is an active variable frequency voltage signal to the battery Do disturbance until all set disturbance frequencies are processed. In the constant voltage mode, the disturbance voltage should not be too large to avoid disturbing the balance state of the battery and causing measurement distortion. In the active constant potential voltage perturbation experiment, the battery generates a corresponding current due to the perturbed voltage, so the amplitude and phase angle of the current can be detected, and the current parameters can be signal adjusted and converted.

As shown in equation (7), the impedance and phase angle difference can be calculated by the magnitude of the disturbance voltage and the corresponding current. After the response measurement of all frequencies is completed, the AC impedance parameter analysis can be performed.

After confirming that the battery specifications meet the experimental requirements, the AC impedance analysis experiment can be carried out. Please refer to Figure 5 which shows the schematic diagram of the AC impedance analysis experiment system.

As shown in Figure 5, first connect the computer to the VSP. After confirming that the connection is correct, add a battery test circuit and set a custom test procedure, then confirm the setting parameters and limits again, and start the test if there are no errors. EC-Lab can perform constant current charging/discharging, constant voltage charging, constant potential AC impedance measurement and other tests; in the limit part, you can also select the voltage, current or time limit in each test step; in the data storage part, EC- Lab can automatically store the voltage, current, charge/discharge of each test Parameters such as capacity and time can be used to generate graphics. The remaining capacity of the battery has a corresponding relationship with the AC impedance of the battery. During the experiment, the accuracy of the remaining capacity of the battery and the time required for the experiment need to be weighed, because each measurement needs to stand still for about an hour before you can Therefore, it takes at least 100 hours to stand still for all data measured at 1% as an interval.

Please refer to Figure 6, which shows the Nyquist impedance diagram of different remaining capacities.

In this case, considering the accuracy of the battery equivalent model, each 1% of the remaining battery capacity will be used as the accuracy of the AC impedance analysis. After the AC impedance of the battery is measured, the Nyquist impedance diagrams of different remaining capacities can be obtained. As shown in the figure, because the Thevenin battery equivalent model is used in this case, the positive value area of the ordinate axis is composed of two resistors R and one capacitor C. The following only conducts parameter analysis of AC impedance for the positive value area.

Please refer to Figures 7a to 7c together, where Figure 7a shows the measured series impedance ( R _o ), parallel impedance ( R _p ), and equivalent impedance ( R _eq ) versus the remaining battery capacity percentage; 7b is a graph showing the relationship between the measured parallel capacitance ( C _p ) and the percentage of remaining battery capacity; Fig. 7c is a graph showing the relationship between the measured open circuit voltage ( V _oc ) and the percentage of remaining battery capacity.

As shown in the figure, after the battery is fully charged and discharged at 1% capacity, the open circuit voltage and AC impedance are obtained after 1 hour of rest. The equivalent impedance ( R _eq ) = series impedance ( R _o ) + Parallel impedance ( R _p ).

In this case, the particle swarm algorithm is used to find the optimal charging current setting value:

The basic spirit of the particle swarm algorithm comes from the predation behavior of group animals, and it is used to search for the best solution to related problems. In the solution space, all particles have their own fitness value, and each particle knows its best fitness value and its best position so far, which is called the best value of individual particles (Particle best value, pBest ), this information is the experience of each particle, and each particle also knows the best solution and its position of all particles, which is called the best value of the group particle (gBest). After each iteration update, the particles will use individual experience and group experience as a reference, and update the individual's speed and position.

Please refer to Figure 8, which shows a schematic diagram of particles moving in the solution space of the particle swarm algorithm.

As shown in the figure, at the beginning, all particles are randomly scattered in the solution space. If there are particles that can approach the best solution in the region, the particles in this region will search for the best solution in this region, but this region may only be The regional optimal solution, therefore, the optimal fitness value and position of the population must be corrected by the search results of each particle swarm to approach the global optimal solution.

The moving speed of particles is shown in equation (8).

v _ij ( t +1) = w * v _ij ( t )+ C ₁ * rand 1*[ pBest _ij ( t )- x _ij ( t )]+ C ₂ * rand 2*[ gBest _ij ( t )- x _ij ( t )](8)

The particle position update mechanism is shown in equation (9).

x _ij ( t +1) = x _ij ( t )+ v _ij ( t +1) (9)

Wherein, w is a weight value, C ₁ and C ₂ for the individual and community learning factor, x _ij table position of the i-th particle j dimensions, velocity v _ij Table i particles j dimensions, RAND1 and rand2 table nonce, pBest _ij It represents the best position of individual particles, and gBest _ij represents the best position of the population.

After obtaining the AC impedance of the lithium battery, the particle swarm algorithm is used to solve the multi-stage optimization current setting problem. Before applying the particle swarm algorithm, it is necessary to define the optimization problem to be solved. The goal of this case is to reduce the charging loss and shorten the charging time, so it is necessary to find out what factors will affect this goal. From the derivation of the multi-stage constant current charging mode, it can be seen that the charging time and the charging loss affect the charging current (I1~I5) and the stage cut-off voltage (VT_cut-off).

Please refer to FIG. 9, which shows a curve diagram of the fitting relationship between the remaining battery capacity (SOC) and the equivalent impedance ( R _eq ) of this case.

As shown in the figure, the charging time calculation method of this case is as shown in equation (3), and the charging loss is related to the internal resistance of the battery. Therefore, this case uses the measured AC impedance data to use the curve fitting function to establish a simulation of the remaining capacity and impedance. With the resultant curve, equation (6) can be used to find the loss during charging. The curve fitting in this case uses Gaussian summation function model as shown in equation (10).

Among them, SOC is the remaining capacity of the battery, and R _eq is the equivalent impedance.

The definition of fitness value function is the most important part of the particle swarm algorithm. The definition method will affect the final output result. In this case, the charging loss and charging time are considered as two parameters of different units. Therefore, a method must be proposed. Its regularization.

Please refer to Figure 10, which illustrates the definition diagram of the fitness function in this case.

As shown in the figure, the fitness value function in this case is defined as the number using the straight line distance between two points as the fitness value. Point A in the figure is the current total charging time and total charging loss. When point A ( T _now , L _The smaller the distance d between _now ) and the ideal optimal solution ( T _min , L _min ) is, the smaller the total charging time and the total loss of charging, the better the objective evaluation of its fitness value, and the distance between the two points is as in equation (11) Shown.

Among them, T represents charging time and L represents charging loss.

In order to emphasize the respective importance of the two parameters, the weight α is introduced to adjust the above equation, and equation (11) can be rewritten as shown in equation (12).

This case uses the measured AC impedance data to simulate the constant current and constant voltage charging method with MATLAB software, and the simulation conditions are the results obtained from the standard charging current provided by the technical manual of the battery to the maximum charging current, and the charging cut-off current is 0.02C. , The reference data points of normalization are shown in Table 1.

It can be seen from Table 1 that under the condition of 0.5C, the charging time is the maximum value ( T _max ) 8980 seconds and the charging loss is the minimum value ( L _min ) 819.34 joules; under the condition of 2C, the charging time is the minimum value ( T _min ) 3582 seconds, the maximum charge loss ( L _max ) is 2803.94 Joules.

Simulation results:

Table 2 shows the specifications and fitness evaluation parameters of the batteries selected in the simulation of this case.

Among them, the weight value α of this case is set to 0.5, indicating that the charging time and the charging loss have the same importance.

The factors that affect the charging time and loss of the multi-stage constant current charging method include the current ( I ₁ ~ I ₅ ) and the stage cut-off voltage ( V _T ). Generally, the current value of each stage is between 0 and 1C, and the stage cut-off voltage is set It is 4.2V. It is mentioned in the literature that if you want to ensure that the last stage current can fully charge the battery, and the internal voltage does not exceed 4.2V, you can use a higher stage cut-off voltage for charging.

The particle swarm range setting in this case is shown in Table 3.

table 3

With the fitting curve of the equivalent impedance ( R _eq ), the change of the equivalent impedance during the charging process can be simulated, so it can ensure that the battery voltage does not exceed the rated voltage of 4.2V.

Please refer to FIG. 11, which shows a schematic diagram of the movement trajectory of the particle swarm in the solution space.

As shown in the figure, this case is first simulated with two different phase cut-off voltages V _T (4.22V, 4.25V). It can be seen that the higher the V _{T, the} better the adaptation value.

The search results of the particle swarm algorithm in this case and the simulation results of the relevant performance parameters of the phase cutoff voltage (VT=4.22V) are shown in Table 4.

Table 5 shows the simulation results of the relevant performance parameters of the cut-off voltage ( V _T =4.25V) in this case.

Please refer to Figure 12, which shows a schematic diagram of the experimental platform architecture of this case.

As shown in the figure, the charging and discharging experiment of the battery in this case uses the programmable charging and discharging machine WBCS3000 produced by WonATech and the monitoring interface, and the monitoring interface can set a variety of charging rules, and can monitor the voltage, current and temperature of the battery in real time. They are all placed in a CIL-100 incubator and controlled at 25 degrees Celsius.

Experimental results and comparison between this case and conventional technology:

The following compares the constant current constant voltage charging method and the optimized multi-stage constant current charging method for this case and the conventional technology. The items include charging time, charging and discharging capacity, and charging efficiency to verify the feasibility and performance improvement of this case.

For fair comparison, the current in the constant current phase of the constant current and constant voltage charging method is set to be the same as the five phase I1.

Please refer to FIG. 13, which shows the measured current and voltage waveforms of the conventional constant current and constant voltage charging method.

As shown in the figure, the conventional constant current and constant voltage charging method adopts 0.864C charging conditions and the total charging time is 7847 seconds.

Please refer to Figure 14, which shows the current and voltage measured waveforms of the cut-off voltage ( V _T =4.22V) in this case.

As shown in the figure, in this case, the phase cut-off voltage (VT=4.22V) adopts 0.864C charging conditions and the total charging time is 6060 seconds. The experimental data is shown in Table 6.

The experimental charging performance parameters of the two methods are compared as shown in Table 7. It can be seen that the charging time in this case can be shortened by 22.77%, and the charging efficiency can be improved by 0.43%.

With the design disclosed above, the present invention has the following advantages:

1. The present invention discloses a multi-stage constant current charging method for optimizing the output current value, which uses AC impedance analysis to establish an equivalent battery model, while taking into account the charging loss and charging time, and uses the particle swarm algorithm to find a limited area The best solution in this way can realize an optimized multi-stage charging method that minimizes charging loss and time.

2. The present invention discloses a multi-stage constant current charging method for optimizing the output current value. The particle swarm algorithm is used to find the optimized charging current setting value. Only the battery equivalent model is used, and there is no need to perform multiple times Time-consuming and labor-intensive actual experiments can get the advantages of optimized current settings.

3. The present invention discloses a multi-stage constant current charging method with optimized output current value. When the stage cut-off voltage is 4.22V, the battery capacity can be charged to more than 98% of the conventional constant current and constant voltage charging method. And before the temperature rise does not increase, the charging time can be shortened by 22.77%, and the charging efficiency can be improved by 0.43%.

The disclosure of the present invention is a preferred embodiment, and any partial changes or modifications that are derived from the technical idea of the present invention and can be easily inferred by those familiar with the art will not depart from the scope of the patent right of the present invention.

In summary, no matter the purpose, means, and effects of the present invention, it is showing its technical characteristics that are very different from the conventional ones, and its first invention is suitable for practical use, and it is also in compliance with the patent requirements of the invention. I sincerely ask your examiner to observe it carefully, and Pray that the patent will be granted as soon as possible to benefit the society.

Step a‧‧‧Initialize the charge current value I _charge,s and the first-stage cut-off voltage value V _{T of the} complex number stage according to a particle swarm algorithm

Step b‧‧‧ Perform an operation on each charge current value I _charge,s of the complex number stage and the stage cut-off voltage value V _T to obtain a total charge time T and a total charge loss

Step c‧‧‧Using a fitness function to perform calculations based on a current total charging time and total charging loss ( T _now , L _now ) and an ideal total charging time and total charging loss ( T _min , L _min )

Step d‧‧‧Update the individual best value of each charge current value I _charge,s and the phase cutoff voltage value V _T of the plurality of stages and a group best value to determine whether to obtain an optimal output current value

Step e‧‧‧ and apply the best output current value for charging, then go back to step b and repeat

Claims (6)

A multi-stage constant current charging method for optimizing the output current value, which includes the following steps: step a) according to a particle swarm algorithm to initialize each charging current value I _{charge, s} and a stage cut-off voltage value V _{T in the} plural stages; Step b) Perform an operation on each charge current value I _{charge, s} of the complex number stage and the stage cut-off voltage value V _T to obtain a total charge time T and a total charge loss

, The operation includes:

Among them, s is the number of stages, C _eq is the equivalent capacitance of the battery, R _o is the series impedance, R _p is the parallel impedance, V _{Ceq, s} is the battery voltage in each stage, I _{charge, s} is the charging current of each stage Step c) Perform calculations based on a current total charging time and total charging loss ( T _now , L _now ) and an ideal total charging time and total charging loss ( T _min , L _min ) using an fitness function; step d ) Updating each of the charging current values I _{charge, s} and the stage cut-off voltage value V _T in the plurality of stages, an individual optimal value and a group optimal value, to determine whether to obtain an optimal output current value; and step e) Apply the best output current value to charge, and then return to step b and repeat. The multi-stage constant current charging method for optimizing the output current value described in item 1 of the scope of patent application, wherein the fitness value function includes:

Among them, α is the weight value. The multi-stage constant current charging method for optimizing the output current value described in item 2 of the scope of the patent application, wherein the weight value α is 0.5. For example, the multi-stage constant current charging method for optimizing the output current value described in the scope of patent application, which further includes the use of an equivalent impedance fitting function to perform calculations to ensure that the battery voltage does not exceed a preset rating Voltage, the fitting function of the equivalent impedance includes:

Among them, R _eq is the equivalent impedance, and SOC is the remaining battery capacity. The multi-stage constant current charging method with optimized output current value described in item 4 of the scope of patent application, wherein the preset rated voltage is 4.2V. The multi-stage constant current charging method for optimizing the output current value described in item 1 of the scope of patent application, wherein the plural stages are 5 stages.

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