A method and apparatus for fast charging a lithium-ion battery

Abstract

The application discloses a kind of fast charging method and device of lithium ion battery, it is related to power battery charge-discharge technical field.The application aims at the problem that the fast charging method of existing lithium battery cannot better adapt to the state of lithium battery in different charging stages.The SOC interval is selected in the application, and the SOC interval is divided into several charging stages with the defined ΔSOC as interval;The residual life prediction value of lithium ion battery is obtained according to the temperature, state of charge, charge rate and charge cycle number of lithium ion battery;The characteristic curve of lithium ion battery is obtained, and the charge time weight α of each charging stage is obtained according to the charge characteristic curve;The charging current of each charging stage is obtained by inputting the charging time, charge time weight and residual life prediction value into target equation.The application effectively prolongs the service life of battery while ensuring safe and fast charging.

Description

TECHNICAL FIELD

[0001] The application relates to a power battery charging and discharging technology field, in particular to a fast charging method and device of a lithium ion battery. BACKGROUND

[0002] Energy and environmental problems are long-term problems faced by human society, in recent years, the electric vehicle industry has developed rapidly due to its advantages of small pollution and high energy efficiency, and power batteries as the main energy source of electric vehicles have also been closely watched by relevant professionals. Lithium ion batteries have the characteristics of long cycle life, high energy conversion efficiency and low self-discharge rate, and the market share is continuously increasing, and are widely used in the field of electric vehicles.

[0003] As an important power source of new energy vehicles, the charging and discharging process of lithium ion batteries is an important part of the normal operation of the vehicle. The charging and discharging technology of lithium ion batteries is one of the key problems of lithium ion battery research, which will have an important influence on the normal work and recycling of lithium ions. There are many influencing factors in the charging and discharging process of lithium ion batteries, such as charging rate, temperature, state of charge and aging degree, and at the same time, overcharging and overdischarging should be avoided. If the charging rate is too high in order to pursue the charging speed, it will also cause the chemical reaction in the battery to be too violent, thereby increasing the temperature of the battery and affecting the remaining life of the battery, so selecting a suitable charging strategy is of great significance to the application of lithium ion batteries.

[0004] In recent years, the users of new energy vehicles have gradually increased the demand for fast charging of power batteries, but long-term continuous and frequent fast charging will also cause damage to the battery, resulting in reduced battery life and increased use risk. Therefore, how to balance the charging time and the remaining life of the battery has become the focus of people's research. Although the traditional constant current and constant voltage charging method combines the advantages of constant current charging and constant voltage charging methods, it cannot meet the current needs of safe fast charging in the field of lithium ion batteries, so scholars have proposed pulse charging, multi-stage constant current charging, intelligent charging and other methods. Pulse charging can make the internal ion concentration uniform through a short discharging or intermittent time, effectively reducing the polarization voltage, and the battery produces less heat, and the charging efficiency is improved. The multi-stage constant current charging method divides the entire charging process into several stages and uses different charging rates in each stage, and the switching condition between stages is generally to reach the cut-off voltage or cut-off capacity, and the current of each stage is gradually reduced, which is beneficial to the protection of the battery life, but as the charging rate decreases, the charging speed will also decrease, so it is more suitable for non-full charging charging occasions. Intelligent charging usually adopts the method of modeling by using equivalent circuit model or electrochemical model, and calculates the current optimal charging rate in real time by combining temperature, state of charge and other factors, so as to realize the fast charging of lithium ion batteries.

[0005] Compared with the traditional charging method, these methods reduce the charging time and charging temperature rise to a certain extent, and realize the fast charging of lithium ion batteries on the basis of safety and reliability. However, the existing fast charging method of lithium battery cannot better adapt to the state of lithium battery in different charging stages. SUMMARY

[0006] In order to solve the above problems, the application provides a fast charging method and device for lithium ion battery, which combines the influence of fast charging process on the remaining life of lithium ion battery, prolongs the service life of the battery while ensuring safe and fast charging.

[0007] The application provides a fast charging method for lithium ion battery, comprising the following steps:

[0008] S1, selecting an SOC interval, dividing the SOC interval into several charging stages with a ΔSOC as an interval;

[0009] S2, obtaining a remaining life prediction value of the lithium ion battery according to the temperature, state of charge, charging rate and charging cycle number of the lithium ion battery;

[0010] S3, obtaining a characteristic curve of the lithium ion battery, and obtaining a charging time weight α of each charging stage according to the charging characteristic curve;

[0011] S4, inputting the charging time, charging time weight and remaining life prediction value into a target equation to obtain a charging current of each charging stage.

[0012] Further, step S2 comprises:

[0013] The temperature, SOC, charging rate and charging cycle number of the current state of the lithium ion battery are input into the neural network as input parameters to obtain the remaining life prediction value of the lithium ion battery.

[0014] Further, the neural network is a BP neural network, comprising an input layer, a first hidden layer, a second hidden layer and an output layer.

[0015] The charging characteristic curve is a charging time-maximum allowable charging current curve.

[0016] The method for obtaining the charging time weight comprises:

[0017] According to the charging time-maximum allowable charging current curve, the numerical value of the charging time weight coefficient under different SOC conditions is obtained;

[0018] The weight coefficients in each SOC stage are averaged to obtain the time weight coefficient α of each SOC stage charging.

[0019] Step S4 comprises:

[0020] S41, input the charging time t, the charging time weight a and the remaining life prediction value RUL into a target equation, the target equation is:

[0021] J=aM1t-(1-a)M2RUL;

[0022] Wherein, t is the charging time; a is the weight of time in the target equation; M1 and M2 are measurement constants to ensure that the two targets are in the same order of magnitude; RUL is the remaining life prediction value;

[0023] S42, input the charging time t, the weight a and the remaining battery life prediction value RUL corresponding to different charging currents in each charging stage into the above charging target equation, obtain a plurality of equation results, the minimum value of the plurality of target equation results in each charging stage is the optimal solution of the target equation, and the corresponding charging current of the optimal solution is the optimal charging current of the charging stage.

[0024] The application provides a rapid charging device for a lithium ion battery, comprising a main controller, a sampling circuit and a current regulator, wherein the main controller comprises:

[0025] A charging stage acquisition unit selects an SOC interval, divides the SOC interval into a plurality of charging stages with a interval of ΔSOC;

[0026] A remaining life prediction unit obtains a remaining life prediction value of the lithium ion battery according to the temperature, the state of charge, the charging rate and the charging cycle number of the lithium ion battery;

[0027] A weight acquisition unit obtains a characteristic curve of the lithium ion battery, and obtains a charging time weight a of each charging stage according to the charging characteristic curve;

[0028] A charging current acquisition unit inputs the charging time, the charging time weight and the remaining life prediction value into a target equation, and obtains a charging current of each charging stage.

[0029] Further, the sampling circuit comprises a current sampling circuit and a voltage sampling circuit.

[0030] Further, the current regulator comprises a switch tube and a power supply circuit.

[0031] As described above, the application provides a rapid charging method and device for a lithium ion battery, which has the following effects:

[0032] 1. The application uses four influencing factors, temperature, charging cut-off voltage, charging rate and charging cycle number, to predict the remaining life of the lithium ion battery, and adds the prediction result to the designed charging target equation, changes the weight value according to the needs of different stages in the charging process, so as to determine the current value of each stage of the multi-stage constant current charging process.

[0033] 2. The improved constant current charging method of the application divides the whole constant current charging process into several stages, and charges at different rates according to the current state of the battery in each stage, comprehensively considers the remaining life and charging time of the lithium ion battery, and determines the charging current of each charging stage by changing the weight change curve in different charging stages. The trend of the charging current of each charging stage conforms to the lithium battery charging curve, and can better adapt to the current state of the lithium ion battery.

[0034] 3. The charging current of each charging stage of the application is more easy to balance the internal chemical reaction of the lithium ion battery and inhibit the temperature rise of the battery, reduces the charging time of the battery on the basis of ensuring the safety of the battery charging, and prolongs the service life of the battery.

[0035] The application has the advantages of ensuring safety, prolonging the remaining life of the battery, shortening the charging time and improving the charging and discharging efficiency of the battery, and better meets the use demand of new energy vehicles. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The flow chart of the lithium ion power battery fast charging method of the embodiment of the application;

[0037] Figure 2 The BP neural network structure diagram of the embodiment of the application;

[0038] Figure 3 The hardware structure diagram of the charging device of the embodiment of the application;

[0039] Figure 4 The current and voltage change curve of the lithium ion power battery fast charging method of the embodiment of the application;

[0040] Figure 5 The current and voltage change curve of the lithium ion power battery using the traditional constant current constant voltage charging method. DETAILED DESCRIPTION

[0041] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0042] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0043] like Figure 1 As shown, the present invention provides a fast charging method for lithium-ion batteries, comprising the following steps:

[0044] S1. Select a SOC range and divide the SOC range into several charging stages with ΔSOC as the interval;

[0045] In one specific embodiment, the selected SOC range is 0-0.9. The size of the interval ΔSOC determines the number of charging stages. The smaller the interval ΔSOC, the better it can adapt to the current state of the lithium-ion battery. As the interval ΔSOC decreases, the corresponding amount of calculation also increases. In this embodiment, the interval ΔSOC is 0.1.

[0046] S2. Based on the lithium-ion battery's temperature, state of charge, charging rate, and number of charging cycles, the remaining lifespan of the lithium-ion battery is predicted, specifically including the following steps:

[0047] S21. Conduct charge and discharge experiments on the battery to obtain the remaining battery life under different temperatures, different states of charge, different charging rates and different number of charging cycles, and record the data to obtain 500 sets of data. Use 400 sets of data as the training set and the remaining 100 sets of data as the test set.

[0048] The temperature of the battery changes during the charging and discharging process of the battery. When the temperature of the battery rises, the chemical reaction inside the battery intensifies, causing damage to the battery plate and affecting the service life of the battery. The charging speed requirement is different under different battery capacity conditions, especially in the initial and final stages of charging. If the acceptable current of the battery is exceeded, polarization will occur, which will accelerate the service life decay. Therefore, the charging speed is one of the factors affecting the service life of the battery, and the charging speed is largely determined by the charging rate. Therefore, the charging rate is also an important factor affecting the service life of the battery. With the decay of the battery capacity, even if the battery capacity is the same, the state of charge of the lithium ion battery is different, so the state of charge can also reflect the aging of the service life. The capacity of the battery will inevitably decay during the continuous charging and discharging process. Therefore, the number of charging cycles of the battery that has been operated is also an important parameter for measuring the remaining service life. Other parameters such as charging and discharging voltage during the charging and discharging process are usually used within the parameters specified by the manufacturer, and the impact on the remaining service life of the battery is relatively small. Therefore, in the embodiment, the remaining service life prediction value of the battery is obtained according to the temperature, charging rate, state of charge and charging cycle number of the battery.

[0049] S22, construct a neural network and input a training set for training, and test by a test set.

[0050] In a specific embodiment, the neural network is a BP neural network with double hidden layers, as shown in Figure 2 The BP neural network includes an input layer, a first hidden layer, a second hidden layer and an output layer. The input variables of the input layer include x1, x2, x3 and x4, which are the current temperature, state of charge, charging rate and charging cycle number of the lithium ion battery, respectively. The output result y of the output layer is the remaining service life RUL of the lithium ion battery.

[0051] The determination method of the BP neural network includes:

[0052] A BP neural network with double hidden layers is constructed, the test set is input into the BP neural network for training, the mapping relationship between the current temperature, state of charge, charging rate and charging cycle number of the lithium ion battery of the input layer and the remaining service life prediction value of the lithium ion battery of the output layer is obtained, the validation set is used to compare the error between the battery remaining service life prediction value and the actual remaining service life to verify the accuracy of the neural network prediction model, and the trained BP neural network is obtained.

[0053] The training set is input into the BP neural network to train the double hidden layer BP neural network to achieve accurate prediction effect. The test set data verifies whether the training result meets the requirements by comparing the error between the verification value and the actual value. When the verification result shows that the prediction error is within the allowable range, the result is accurate.

[0054] In the embodiment, the state of charge SOC in the training set data is divided into nine stages of 0-0.1, 0.1-0.2,..., 0.8-0.9 with 0.1 as an interval, and the temperature, the charge rate and the charge cycle number of the battery are also divided into intervals as the data set. The values of the charge rate and the temperature of the training set are the values in the range of the allowed charge current and temperature of the battery. The division of the temperature, the charge rate and the charge cycle number is determined by the control variable experiment, and the corresponding remaining life of the battery is determined under different temperature intervals, different charge rate intervals and different cycle charge number intervals. In a specific embodiment, the interval of the charge rate is 0.1C, the interval of the temperature is 1℃, and the cycle number is selected from the batteries that have not failed under different remaining life, such as the batteries just out of the factory in the same batch and the batteries that have been cycled for dozens of weeks, hundreds of weeks or even thousands of weeks. The different conditions of the batteries are predicted according to a large number of historical experimental data samples.

[0055] S23, inputting the related parameters of the battery to be charged into the BP neural network to obtain the remaining battery life of the battery.

[0056] The related parameters of the battery to be charged include the battery temperature, the charge rate, the charge cycle number and the SOC value, and the above parameters as the input layer are also represented in the form of intervals. The remaining life of the battery is obtained through the trained BP neural network.

[0057] S3, obtaining the characteristic curve of the lithium ion battery, and obtaining the charge time weight a of each charge stage according to the charge characteristic curve.

[0058] During the whole charging process, the state of the lithium ion battery is constantly changing. Within the range of the allowed charging current variation of the lithium battery, at the beginning of the charging, the concentration of the lithium battery reaction substance is large, and a large current is allowed to be charged, that is, in the initial charging stage, the importance of the charging time is greater than the consideration of the remaining life, and even if the battery temperature rises, it will not have a great impact on the battery life. The appropriate increase in temperature will increase the activity of the chemical substances inside the lithium battery, promote the chemical reaction, reduce the internal resistance of the lithium battery, and is beneficial to the charging process, so the charging current should be appropriately increased; when the lithium battery has been charged for a period of time, the internal chemical reaction of the lithium battery tends to be intense, at this time, more consideration should be given to the remaining life of the battery, and the charging rate should be appropriately reduced to avoid damage to the battery caused by continuous large current charging; in the late charging stage, the concentration of the internal reaction substance of the lithium battery decreases, and large current charging will have a great impact on the life of the lithium battery, so the importance of battery loss is greater, and the charging current should be reduced to slow down the charging speed. Therefore, a charging method that can better meet the actual situation of the lithium ion battery needs to be used in the battery charging process. In the embodiment, the characteristic curve of the lithium ion battery is first obtained, and the charging time weight variation curve of each charging stage is obtained according to the charging characteristics of the lithium ion battery, so that the current charging current and the lithium battery characteristics are more matched.

[0059] The charging characteristic curve is a charging time-maximum allowed charging current curve determined by experimental data combined with expert experience. The maximum allowed charging current curve gradually decreases with the change of the charging time, that is, the limit current. When the limit current is exceeded, the polarization phenomenon may be intensified, and adverse effects such as gas evolution may occur.

[0060] In the embodiment, the limit current is regarded as a manifestation of the charging time, and the limit current variation curve is scaled down to obtain the numerical value of the charging time weight coefficient under different SOC conditions. The weight coefficients in the SOC stage are averaged to obtain the time weight coefficient a of each SOC stage charging.

[0061] S4, input the charging time, charging time weight and remaining life prediction value into the target equation to obtain the charging current of each charging stage.

[0062] When charging a battery with low power, the charging time is the main optimization factor. As the battery power increases during the charging process, the influence of the battery remaining life gradually increases. Therefore, the charging time is regarded as the main optimization factor in the initial charging stage, and the battery life is considered more in the latter stage to delay the aging of the battery. Then, the target equation is established to adjust the current in different charging stages, so as to achieve the effect of fast charging considering the charging time and the remaining battery life.

[0063] S41, input the charging time t, the charging time weight a and the remaining life prediction value RUL into the target equation, and the target equation is:

[0064] J = aM1t - (1-a)M2RUL

[0065] In the target equation, the first term represents the speed of charging, and the second term represents the length of the remaining life, and the charging time and the predicted remaining life in each charging stage are taken as decision factors and are given different weights in the equation, wherein t is the charging time, a is the weight of time in the target equation, M is a metric constant, in order to make the charging time and the remaining life be in the same order of magnitude, both of the two variables are normalized, so as to achieve the trade-off of the two optimization targets in the target equation, the metric constant M is used to ensure that the charging time and the remaining life are in the same order of magnitude, and RUL is the predicted value of the remaining life. The charging target equation is for different charging rates in different stages, and the charging current of each constant-current stage is determined according to the weight change curve, and the weight values of the charging time and the remaining life in different constant-current charging stages are changed through the weight change curve, so that the charging current of each charging stage is recalculated, that is, the charging rate of each charging stage is changed, so as to better adapt to the current state of the lithium ion battery.

[0066] S42, input the charging time t, the weight a and the predicted value of the remaining battery life RUL corresponding to different charging currents in each charging stage into the above charging target equation, obtain a plurality of equation results, and the minimum value of the plurality of results of the target equation in each charging stage is the optimal solution of the target equation, and the corresponding charging current of each optimal solution is the optimal charging current of the charging stage.

[0067] Input the charging time t, the weight a and the predicted value of the remaining battery life RUL corresponding to different charging currents in each charging stage into the above charging target equation, obtain a plurality of equation results J, and the minimum value of the plurality of results of the target equation in each charging stage is the optimal solution of the target equation, and the optimal solution of the target equation of the nine charging stages in the embodiment is {J1, J2, J3, J4, J5, J6, J7, J8, J9}, and the corresponding charging current of each optimal solution is the optimal charging current of the charging stage.

[0068] The present application uses the weight change curve to obtain the charging current of each stage, and since the multi-stage constant-current charging method divides the entire charging process into several stages, and the optimization targets of each stage are also different, therefore, the weight values of the charging time and the remaining life in each stage should be reasonably considered, so as to better meet the charging demand.

[0069] In a specific embodiment, a rapid charging device for a lithium ion battery is provided, as shown in Figure 3As shown, including current regulator 100, drive circuit 200, sampling circuit 300, main controller 400 and communication circuit 500, the current regulator 100 includes power supply circuit 101 and switch tube 102, sampling circuit 300 includes voltage sampling circuit and current sampling circuit, sampling circuit 300 is connected with lithium ion battery, the voltage, current, temperature signal of lithium ion battery is converted into corresponding voltage which can be read by main controller 400, output voltage is connected with main controller 400, main controller 400 carries out ADC conversion, calculates actual voltage, current and temperature value, to realize real-time monitoring of the current state of battery, and executes the above charging method, main controller 400 changes the duty cycle of output PWM according to the real-time data collected by sampling circuit 300, the PWM signal controls the drive signal of drive circuit 200, the drive signal is used to control the opening and closing of switch tube, to realize the regulation of charging current;The communication circuit 500 realizes the transmission of data between host computer and main controller through serial port.The power supply circuit 101 is connected with the mains, and is converted into the required DC power supply of the charging device through the rectification and step-down of power supply circuit 600, and the power supply circuit 101 is responsible for the power supply of the whole main circuit, and provides corresponding power supply voltage for the main controller and other auxiliary circuits.The sampling circuit includes voltage sampling, current sampling, temperature sampling, etc., to collect voltage, current and other signals transmitted to the single-chip microcomputer, the communication circuit realizes the transmission of data between host computer and lower computer through serial port, and monitors the current state of battery in real time.The voltage sampling generally collects the voltage across the sampling resistor, and selects the voltage dividing resistor according to the voltage size, and transmits the voltage signal to the single-chip microcomputer;The current sampling usually selects a small resistance such as constantan wire to collect the voltage across the resistance, but if the voltage signal is small, the voltage signal needs to be amplified first and then followed by the operational amplifier to ensure the stability of the voltage signal;The temperature sampling usually uses the thermal resistance method, which uses the property that the resistance value of conductor or semiconductor changes with temperature, and reflects the change of resistance value with display instrument, so as to achieve the purpose of temperature measurement.Main controller 400 is 51 single-chip microcomputer, DSP28335 or STM32 series single-chip microcomputer, etc., to realize the control function of the whole circuit.Communication module 500 can select RS232 communication module, RS485 communication module, Ethernet communication and other ways for signal transmission.Power supply circuit 101 rectifies the mains into DC power supply, and provides corresponding power supply for lithium ion battery charging device through Buck, Buck-boost or Sepic circuit topology.

[0070] In a specific embodiment, the switch tube 102 adopts a bridge inverter circuit composed of MOS tubes, the main controller 400 adopts an STM32F103 single-chip microcomputer, the driving circuit 200 selects an IR2104 or IR2110 driving chip as a main driving chip to provide a driving signal for the MOS tube, the sampling circuit selects a method of collecting a voltage across a sampling resistor, the main chips include an AD8210 and an LF353, and the communication circuit selects a serial communication mode. A PWM signal output by the single-chip microcomputer controls a driving signal of the driving circuit, and the driving signal is applied to a gate of the MOS tube to control on-off of the MOS tube.

[0071] The main controller comprises:

[0072] The charging phase acquisition unit divides the SOC interval into a plurality of charging phases with a ΔSOC as an interval.

[0073] The remaining life prediction unit obtains a remaining life prediction value of the lithium ion battery according to the temperature, the state of charge, the charging rate and the charging cycle number of the lithium ion battery.

[0074] The weight acquisition unit obtains a characteristic curve of the lithium ion battery, and obtains a charging time weight a of each charging phase according to the charging characteristic curve.

[0075] The charging current acquisition unit inputs the charging time, the charging time weight and the remaining life prediction value into a target equation to obtain a charging current of each charging phase.

[0076] The working processes of the units are described in the specific steps of the charging method described in the above embodiment, and will not be described here again. In a specific embodiment, the functions of the units in the main controller can be realized by a hardware circuit, a software program or a combination of software and hardware.

[0077] In order to verify the charging method, in a specific embodiment, the rapid charging device described in the embodiment is built to realize the charging method. An LS18650 lithium iron phosphate battery is selected, and the rated capacity is 1600mAh. Two charging and discharging experiments are performed by using the lithium ion power battery charging device. In the experiment process, the discharging process is selected to be 1C discharging to a cut-off voltage of 2V, the charging process is selected to be a constant current and constant voltage charging method and the variable weight multi-stage constant current charging method described in the above embodiment, and the voltage, current and temperature information in the charging process are collected.

[0078] The charging time weight a of each charging phase is obtained according to the charging characteristics of the lithium battery, and is input into the target equation to obtain the charging current of each charging phase. The specific embodiment is shown in Table 1.

[0079] Table 1 shows the corresponding weight and optimized charging current in each SOC charging phase.

[0080] SOC 0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 weight a 0.75 0.52 0.45 0.45 0.42 0.40 0.37 0.36 0.35 charging current 1.6A 0.98A 0.91A 0.93A 0.88A 0.78A 0.74A 0.75A 0.72A

[0081] The current value of the constant current charging phase of the constant current constant voltage charging method is the average value of the current of each phase of the multi-stage constant current charging method proposed by the present design. Compared with the charging method described in the embodiment of the present application, the results are shown in Figure 4 and Figure 5 Figure 4 The voltage and current values obtained by charging using the variable weight multi-stage constant current charging method proposed by the present design are shown in the figure, and the charging process takes 86 min. Figure 5 The voltage and current values obtained by charging using the constant current constant voltage charging method are shown in the figure, and the charging process takes 92 min. It can be seen that the rapid charging method of the present application saves the charging time, and the current value of each stage gradually decreases. This gradually decreasing mechanism makes the charging strategy easier to balance the chemical reaction and inhibit the temperature rise of the battery, which also has certain beneficial effects on prolonging the service life of the battery. It can be seen that, by using the charging strategy of the present application and charging the battery by the charging device proposed, compared with the constant current charging result, the superiority of the variable weight multi-stage constant current rapid charging method proposed is proved.

[0082] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought disclosed by the present application should be covered by the claims of the present application.​

Claims

1. A method of fast charging a lithium-ion battery, characterized in that, The method comprises the following steps: S1, selecting an SOC interval, dividing the SOC interval into several charging stages with a ΔSOC as an interval; S2, obtaining a remaining useful life prediction value of the lithium ion battery according to the temperature, state of charge, charging rate and charging cycle number of the lithium ion battery; S3, obtaining a charging time-maximum allowed charging current curve of the lithium ion battery, obtaining a value of a charging time weight coefficient under different SOC conditions according to the charging time-maximum allowed charging current curve, and obtaining a time weight coefficient of each SOC stage by averaging weight coefficients in each SOC stage α ; S4, inputting the charging time, charging time weight and remaining useful life prediction value into a target equation to obtain a charging current of each charging stage, comprising: S41、the charging time t, the charging time weight α and the remaining life prediction value RUL in the input target equation, the target equation being: J = αM1t-(1-α) M2RUL; wherein, t is the charging time; α is the weight of time in the target equation; M1 and M2 are metric constants to ensure that the two targets are in the same order of magnitude; RUL is the remaining life prediction value; S42, the charging time t , the weight α and the remaining battery life prediction value RUL corresponding to different charging current in each charging phase are input into the above target equation, and a number of equation results are obtained. The minimum value of the number of target equation results in each charging phase is the optimal solution of the target equation, and the corresponding charging current at the optimal solution is the optimal charging current of the charging phase.

2. The method of claim 1, wherein the charging is performed at a rate of 1C or more. Step S2 comprises: inputting the temperature, SOC, charging rate and charging cycle number of the current state of the lithium ion battery into the neural network as input parameters to obtain the remaining useful life prediction value of the lithium ion battery.

3. The method of claim 2, wherein the charging is performed at a rate of 1 C or more. The neural network is a BP neural network, comprising an input layer, a first hidden layer, a second hidden layer and an output layer.

4. A rapid charging device for a lithium-ion battery, characterized by The charging device comprises a main controller, a sampling circuit and a current regulator, wherein the main controller comprises: a charging stage acquisition unit for selecting an SOC interval and dividing the SOC interval into several charging stages with a ΔSOC as an interval; a remaining useful life prediction unit for obtaining a remaining useful life prediction value of the lithium ion battery according to the temperature, state of charge, charging rate and charging cycle number of the lithium ion battery; The weight acquisition unit acquires a charging time-maximum allowed charging current curve of the lithium ion battery, obtains the value of the charging time weight coefficient under different SOC conditions according to the charging time-maximum allowed charging current curve, and obtains the charging time weight coefficient of each SOC stage by averaging the weight coefficients in each SOC stage α ; A charging current acquisition unit acquires a charging time t, a charging time weight α and a remaining life prediction value RUL In the input target equation, the target equation is: J = αM1t-(1-α) M2RUL; wherein, t is the charging time; α is the weight of time in the target equation; M1 and M2 are metric constants to ensure that the two targets are in the same order of magnitude; RUL is the remaining life prediction value; charging time t , weight α and the remaining battery life prediction value RUL corresponding to different charging current in each charging phase are input into the above target equation, and a number of equation results are obtained. The minimum value of the number of target equation results in each charging phase is the optimal solution of the target equation, and the corresponding charging current at the optimal solution is the optimal charging current of the charging phase.

5. The fast charging device of a lithium-ion battery according to claim 4, wherein, The sampling circuit comprises a current sampling circuit and a voltage sampling circuit.

6. The device for fast charging of a lithium-ion battery according to claim 4, characterized in that, The current regulator comprises a switch tube and a power supply circuit.

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