A quick charging control method for electric vehicle charging piles

Abstract

The application belongs to the technical field of electric vehicle charging control, and particularly relates to a fast charging control method for electric vehicle charging piles, which comprises the following steps: step 1, analyzing the structure of a lithium battery: the lithium battery is composed of a positive electrode, a diaphragm, a negative electrode, an organic electrolyte and a battery shell; when the lithium battery is working, lithium ions are generated from the positive electrode of the battery, move from the positive electrode to the electrolyte, pass through the small holes on the diaphragm and move to the negative electrode, and combine with the electrons in the external circuit that move to the negative electrode; the complex electrochemical internal mechanism is considered for analysis. In the application, a recursive least square method based on a forgetting factor is used to identify the parameters of a second-order RC equivalent circuit model of the lithium battery, and the identification result is added to a transfer function of a mathematical model of the lithium battery, so that the charging and discharging change process of the lithium battery can be simulated more accurately.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging control, in particular to a quick charging control method for electric vehicle charging piles. BACKGROUND

[0002] In the face of the grim situation of global warming and the growing shortage of oil resources, under the influence of the low-carbon concept, electric vehicles have become an important direction of contemporary automobile development. According to the current actual situation, many countries have joined the promotion and use of electric vehicles, which makes the development of electric vehicles enter a white-hot stage. Under the trend of the comprehensive development of electric vehicles, the charging system is indispensable as a supporting system. Therefore, designing a universal, safe, and fast charging electric vehicle intelligent charging pile without damaging the battery life will have great significance for promoting the development of the electric vehicle industry. The electric vehicle charging pile control system has the characteristics of multiple input and output, strong coupling and nonlinearity. When large-current fast charging is performed on the power battery of the electric vehicle, the output current and voltage need to be adjusted in real time and online to meet the optimal charging curve of the battery. Most of the current electric vehicle charging pile control systems use traditional proportion integration differentiation (PID) control method, which has the problems of poor stability, poor anti-interference ability and poor robustness. SUMMARY

[0003] (I) Technical problems solved

[0004] In view of the deficiencies of the prior art, the present application provides a quick charging control method for electric vehicle charging piles, which solves the problems of poor stability, poor anti-interference ability and poor robustness of the electric vehicle charging pile control system obtained by the prior control method.

[0005] (II) Technical solutions

[0006] In order to achieve the above purpose, the present application specifically adopts the following technical solutions:

[0007] A quick charging control method for electric vehicle charging piles, comprising the following steps:

[0008] Step 1, analyzing the structure of lithium battery: the lithium battery is composed of a positive electrode, a separator, a negative electrode, an organic electrolyte, and a battery shell; when the lithium battery is working, lithium ions are generated from the positive electrode, move from the positive electrode to the electrolyte, pass through the small holes on the separator, move to the negative electrode, and combine with the electrons in the external circuit that run to the negative electrode; considering the complex electrochemical internal mechanism analysis, a second-order RC equivalent circuit model composed of commonly used electrical elements such as voltage source, resistance, capacitance, and inductance is used to simulate the charging and discharging reaction of the lithium battery, and according to Kirchhoff's law, the system equation and the observation equation can be obtained as follows:

[0009] System equation:

[0010]

[0011] wherein R1 and R2 respectively represent the transport impedance and the concentration impedance; C1 and C2 respectively represent the transport capacitance and the concentration capacitance; U1 and U2 respectively represent the polarization voltage at both ends of R1 and R2; dU1 represents the differential of U1; dU2 represents the differential of U2; dSOC represents the differential of the remaining capacity SOC of the battery; dt represents the differential of the charging and discharging time; Q n represents the capacity of the battery at time n; I L represents the charging and discharging current, and the direction of the discharging current is defined as positive, and the direction of the charging current is defined as negative;

[0012] Observation equation:

[0013] U L = U OC -R0I L -U1-U

[0014] wherein U L represents the terminal voltage of the battery; U oc represents the open-circuit voltage of the battery; R0 represents the ohmic internal resistance; I L represents the charging and discharging current, and the direction of the discharging current is defined as positive, and the direction of the charging current is defined as negative; U1 and U2 respectively represent the polarization voltage at both ends of R1 and R2;

[0015] Step 2, identifying the parameters of the equivalent circuit model: using the recursive least squares method based on the forgetting factor to perform online parameter identification on the second-order RC equivalent circuit model described in step 1, obtaining the circuit parameters of the second-order RC equivalent circuit, and further verifying and correcting the model;

[0016] Step 3, constructing a mathematical model of the lithium battery: performing Laplace transform on the second-order RC equivalent circuit model in step 1, taking the voltage difference as the input and the current as the output, and adding the existing time delay of the system to obtain the mathematical model of the lithium battery;

[0017] Step 4, design fuzzy PID controller: fuzzy PID controller is based on the conventional PID plus fuzzy control, using fuzzy rules to optimize PID parameters in real time, so that the desired value and the actual output value of the system at different times meet the control conditions;

[0018] Step 5, establish particle swarm optimization algorithm to optimize fuzzy PID control system: particle swarm optimization algorithm is a kind of swarm intelligence optimization algorithm which simulates the foraging behavior of birds. By establishing fitness function, the fuzzy PID parameters in step 4 are optimized to form the particle swarm optimization algorithm to optimize fuzzy PID control system.

[0019] Further, in step 1, the lithium battery second-order RC equivalent circuit model is composed of open-circuit voltage, terminal voltage, ohmic resistance, transport impedance, concentration impedance, transport capacitance and concentration capacitance. The ohmic resistance is composed of battery electrolyte, positive aluminum foil and negative aluminum foil. The sudden change characteristics of voltage during lithium battery discharge are described. The polarization resistance and polarization capacitance describe the gradual change characteristics of voltage during lithium battery charge and discharge.

[0020] Further, in step 2, the forgetting factor in the recursive least squares method based on the forgetting factor is set to a fixed value. The data is time-varying weighted, the past collected data is constantly weakened, the role of the current newly collected data is enhanced, and finally the more accurate identification parameters of the equivalent circuit model are obtained, providing a better model selection for the realization of the estimation algorithm.

[0021] Further, in step 3, the lithium battery mathematical model is obtained by pull-type transformation of the second-order RC equivalent circuit. The input voltage difference is E(S)-U(S), and the output current is I(S). According to the parameters identified by the second-order RC equivalent circuit, the transfer function expression of the lithium battery mathematical model is obtained.

[0022] Further, in step 4, the PID algorithm is a linear control system, including proportion, integration and differentiation. The control strategy is composed of the error between the actual value and the target value of the controlled object, the cumulative error and the error increment, which can quickly and stably track the target value of the controlled object. The fuzzy rules in fuzzy PID include fuzzification, fuzzy rules and defuzzification. The use of fuzzy PID for control can effectively enhance the robustness of the control system and improve the anti-interference ability of the system.

[0023] Further, in step 5, in the particle swarm optimization algorithm, each solution in the target space can be represented by a particle. The food source to be solved by the bird swarm is needed. In the process of finding the optimal solution, each particle has individual behavior and group behavior, learns the flight experience of the companion and uses its own flight experience to find the optimal solution. The optimal fitness function is used to optimize the fuzzy PID algorithm.

[0024] (III) Beneficial Effects

[0025] Compared with the prior art, the application provides a fast charging control method for electric vehicle charging piles, and has the following beneficial effects:

[0026] 1. The recursive least square method based on the forgetting factor is used to identify the parameters of the lithium battery second-order RC equivalent circuit model, and the identification result is added to the transfer function of the established lithium battery mathematical model, so that the lithium battery charging and discharging change process can be better and more accurately simulated.

[0027] 2. The particle swarm optimization fuzzy PID parameter is proposed, and the particle swarm optimization algorithm is simple, has few parameters, and can search for the optimal value through iteration, so that the data of the proportion, integral and differential are optimized, and the whole system effect is better.

[0028] 3. The particle swarm optimization fuzzy PID control system has stronger stability and anti-interference, better robustness, and can achieve the purpose of optimizing the charging curve when the lithium ion battery of the electric vehicle is charged at a large current. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is the flowchart of the application;

[0030] Figure 2 is the lithium battery second-order RC equivalent circuit model diagram of the application;

[0031] Figure 3 is the particle swarm optimization algorithm flowchart of the application;

[0032] Figure 4 is the particle swarm optimization fuzzy PID controller flowchart of the application;

[0033] Figure 5 is the current index comparison diagram of the traditional PID control algorithm, the fuzzy PID control algorithm and the method proposed in the application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0035] Embodiment 1

[0036] As Figures 1-5As shown in the figure, an embodiment of the present invention proposes a fast charging control method for electric vehicle charging piles, which specifically includes the following steps:

[0037] Step 1: Analyze the lithium battery structure: A lithium battery consists of a positive electrode, a separator, a negative electrode, an organic electrolyte, and a battery casing. Common equivalent models for lithium batteries include first-order, second-order, and even n-order RC networks. The first-order RC model has a relatively large error, the third-order RC model has a complex calculation process, and RC models above the fifth order have even larger errors. Therefore, this invention selects a network such as... Figure 2 The second-order RC model shown is the equivalent circuit model of a lithium battery. The second-order RC model includes the open-circuit voltage U. oc Terminal voltage U L Given the ohmic internal resistance R0, charge transfer impedance R1, charge transfer capacitance C1, concentration impedance R2, and concentration capacitance C2, the system equations and observation equations can be obtained using Kirchhoff's laws:

[0038] According to Kirchhoff's laws, the system equations and observation equations can be obtained as follows:

[0039] System equations:

[0040]

[0041] Where R1 and R2 represent the charge transfer impedance and concentration impedance, respectively; C1 and C2 represent the charge transfer capacitance and concentration capacitance, respectively; U1 and U2 represent the polarization voltages across R1 and R2, respectively; dU1 represents the derivative of U1; dU2 represents the derivative of U2; dSOC represents the derivative of the remaining battery charge SOC; dt represents the derivative of the charge / discharge time; Q n I represents the battery capacity at time n; L This represents the charging and discharging current, with the direction of the discharging current defined as positive and the direction of the charging current as negative.

[0042] Observation equation:

[0043] U L =U OC -R0I L -U1-U2

[0044] Among them, U L Indicates the battery terminal voltage; U oc R0 represents the battery open-circuit voltage; I represents the internal resistance in ohms; L This represents the charging and discharging current, with the direction of the discharging current defined as positive and the direction of the charging current as negative; U1 and U2 represent the polarization voltages across R1 and R2, respectively.

[0045] Discretizing the system equations and observation equations at time k yields:

[0046]

[0047]

[0048] wherein U1(k) represents the value of U1 at time k; U1(k-1) represents the value of U1 at time k-1; T s represents the sampling time; R1, R2 represent the transport impedance and the concentration impedance, respectively; C1, C2 represent the transport capacitance and the concentration capacitance, respectively; U1, U2 represent the polarization voltage across R1, R2, respectively; U2(k) represents the value of U2 at time k; U2(k-1) represents the value of U2 at time k-1; U2(k-2) represents the value of U2 at time k-2; SOC(k) represents the value of SOC at time k; SOC(k-1) represents the value of SOC at time k-1; SOC(k-2) represents the value of SOC at time k-2; Q n represents the capacity of the battery at time n; I L represents the charge and discharge current, with the discharge current direction defined as positive and the charge current direction defined as negative;

[0049]

[0050] wherein U1(k) represents the value of U1 at time k; U1(k-1) represents the value of U1 at time k-1; U1(k-2) represents the value of U1 at time k-2; U L represents the battery terminal voltage; U oc represents the open circuit voltage; R0 represents the ohmic internal resistance; I L (k) represents the charge and discharge current at time k, with the discharge current direction defined as positive and the charge current direction defined as negative; I L (k-1) represents the charge and discharge current at time k-1; I L (k-2) represents the charge and discharge current at time k-2; U1, U2 represent the polarization voltage across R1, R2, respectively; U2(k) represents the value of U2 at time k; U2(k-1) represents the value of U2 at time k-1; U2(k-2) represents the value of U2 at time k-2;

[0051] The system equation is simplified to obtain:

[0052]

[0053] wherein U1(k) represents the value of U1 at time k; U1(k-1) represents the value of U1 at time k-1; T s represents the sampling time; R1, R2 represent the transport impedance and the concentration impedance, respectively; C1, C2 represent the transport capacitance and the concentration capacitance, respectively; U1, U2 represent the polarization voltage across R1, R2, respectively; U2(k) represents the value of U2 at time k; U2(k-1) represents the value of U2 at time k-1; SOC(k) represents the value of SOC at time k; SOC(k-1) represents the value of SOC at time k-1; Qn represents the capacity of the battery at n time; I L (k) represents the charge-discharge current at k time, defining the discharge current direction as positive and the charge current direction as negative; I L (k-1) represents the charge-discharge current at k-1 time; I L (k-2) represents the charge-discharge current at k-2 time;

[0054] Let x = [U1, U2, SOC], y = U L The following formula can be obtained:

[0055]

[0056] where T s represents the sampling time; R1 and R2 represent the transport impedance and the concentration impedance, respectively; C1 and C2 represent the transport capacitance and the concentration capacitance, respectively; I L (k) represents the charge-discharge current at k time, defining the discharge current direction as positive and the charge current direction as negative; U1 and U2 represent the polarization voltages at the two ends of R1 and R2, respectively; Q n represents the capacity of the battery at n time;

[0057] Step 2, identification of the equivalent circuit model: the least square method is the most basic and most commonly used estimation method in system identification, and the classical least square method cannot realize online estimation of model parameters, so the recursive least square method is used, but the recursive least square method has the problem of “data saturation”. With the growth of the covariance matrix, the updating effect of the newly collected input and output data on the parameter estimation value is weakened, thereby causing the real-time parameter estimation to fail. Therefore, the recursive least square method based on the forgetting factor is used to perform online parameter identification on the second-order RC equivalent circuit model described in step 1, which can overcome this phenomenon and obtain the circuit parameters of the second-order RC equivalent circuit, thereby further verifying and correcting the model;

[0058] The meaning of y(k) is as follows:

[0059] y(k) = U oc (k) - U L (k)

[0060] where y(k) represents the difference between the open-circuit voltage and the terminal voltage of the battery at k time; U oc (k) represents the open-circuit voltage value of the battery at k time; U L (k) represents the terminal voltage value of the battery at k time;

[0061] The meaning of y(k) is as follows:

[0062] ​

[0063] wherein, is an observation vector; y(k-1) represents the difference between the open-circuit voltage and the terminal voltage at k-1 time; y(k-2) represents the difference between the open-circuit voltage and the terminal voltage at k-2 time; I(k) represents the circuit current value at k time; I(k-1) represents the circuit current value at k-1 time; I(k-2) represents the circuit current value at k-2 time;

[0064] The parameters to be estimated are θ = [α1 α2 α3 α4 α5] T ;

[0065] The recursive least square method based on the forgetting factor is as follows:

[0066]

[0067] wherein e(k) is the prediction error of y(k); θ(k) is the estimated value of θ; K(k) is the algorithm gain; P(k) is the covariance matrix; λ is the forgetting factor;

[0068] By identifying the values of α1, α2, α3, α4, and α5, the values of R0, R1, R2, C1, and C2 can be derived, and the specific steps are as follows:

[0069]

[0070] a = α0α2

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] wherein T s represents the sampling time; a = τ1τ2; b = τ1+τ2; c = R1+R2+R0; d = R1τ2+R2τ1+R0(τ1+τ2);

[0077] Based on the α1, α2, α3, α4, α5, and T s , the values of R0, R1, R2, C1, and C2 are calculated as follows:

[0078]

[0079] R1 = (τ1c+τ2R0-d) / (τ1-τ2) ​

[0080] R2 = c - R1 - R0

[0081] C1 = τ1 / R1

[0082] C2 = τ2 R2

[0083] wherein, R0 represents ohmic internal resistance; R1 represents transport impedance; R2 represents concentration impedance; C1 represents transport capacitance; C2 represents concentration capacitance;

[0084] Through the above formula, the difference equation of the identification system can be obtained:

[0085] U(k) = a0 + a1U(k-1) + a2U(k-2) + a3I(k) + a4I(k-1) + a5I(k-2)

[0086] + a5I(k-2)

[0087] wherein, a0 = (1 - a1 - a2)U oc ; θ = [a1 a2 a3 a4 a5] T As direct identification parameters, the circuit model parameters are calculated by the identification results of a1, a2, a3, a4, and a5.

[0088] Step 3, constructing a lithium battery mathematical model: the lithium battery mathematical model takes the voltage difference E(S)-U(S) as input and the current I(S) as output, and performs a pull-type transformation on the second-order RC equivalent circuit in step 2 to obtain the following formula:

[0089]

[0090] wherein, R0 represents ohmic internal resistance; R1 represents transport impedance; R2 represents concentration impedance; C1 represents transport capacitance; C2 represents concentration capacitance; U oc represents the open-circuit voltage of the battery; I represents the circuit current of the battery;

[0091] And the system has a certain time delay, so the transfer function of the simplified lithium battery mathematical model with time delay is:

[0092]

[0093] wherein, R0 represents ohmic internal resistance; R1 represents transport impedance; R2 represents concentration impedance; τ1 = R1C1; τ2 = R2C2; τ represents the system time delay;

[0094] Step 4, design fuzzy PID controller: fuzzy PID is based on the conventional PID, the input and output and fuzzy control theory combined control algorithm, including fuzzy, fuzzy rule and defuzzification, using fuzzy rules for real-time optimization of PID parameters, so that the desired value and the actual output value at different times of the system meet the control conditions, while the PID algorithm is a linear control system, the expression of PID control transfer function is:

[0095]

[0096] Where, K p is the proportional coefficient; T i is the time integral constant; τ is the differential constant;

[0097] Then the control differential equation of the system is:

[0098]

[0099] Where, u(t) is the controller output control amount; K p represents the proportional time constant; e(t) is the control system input; T i represents the integral time constant; T d is the differential time constant;

[0100] In the PID algorithm, the proportion can speed up the response speed of the system, improve the regulation accuracy, the integral can eliminate the speed of the system steady-state error; The differential can improve the dynamic characteristics of the system, and suppress the deviation change direction; However, the PID control algorithm has poor control effect on the non-linear and time-varying system, so fuzzy control is added, the fuzzy subset of input and output parameter language value is {NB, NM, NS, ZO, PS, PM, PB}, the meaning is negative big, negative medium, negative small, zero, positive small, positive medium, positive big, positive small, two input and three output variables are established, the Gaussian membership function is used, and 49 languages are written, so the fuzzy PID control can enhance the robustness and anti-interference ability of the non-linear system;

[0101] Step 5, establish particle swarm optimization algorithm to optimize fuzzy PID control system: particle swarm optimization algorithm is a kind of swarm intelligence optimization algorithm by simulating the foraging behavior of bird swarm, as shown in Figure 3 When the particle swarm algorithm is optimized to solve, the random position and speed of the initial population particles are evaluated, and the fitness value of the current particles is evaluated. In each iteration search, the individual optimal solution and group optimal solution are selected, the learning mode to the optimal solution, each particle can constantly update its speed and position, so that all particles in the whole group tend to the optimal solution of the objective function, and finally meet the optimization requirements. The principle of particle swarm optimization algorithm is as follows:

[0102] Assuming the search space is D-dimensional, and the number of particles is m, it can be expressed as:

[0103]

[0104] Where X is a population containing m particles; x i is a D-dimensional vector indicating a possible position in the search space; v is the flight speed of the particle in the next step; P best is the individual extreme value; G best is the global extreme value;

[0105] The particle updates its speed and position according to the two extreme values, as follows:

[0106]

[0107] Where c1 and c2 are two positive constants; r1 and r2 are two random numbers between 0 and 1; n is the iteration number; x i is a D-dimensional vector indicating a possible position in the search space; v is the flight speed of the particle in the next step; P best is the individual extreme value; G best is the global extreme value;

[0108] The ITAF evaluation index is used as the fitness function to comprehensively evaluate the dynamic and static performance of the control system, and its expression is:

[0109]

[0110] As Figure 4 shown, the particle swarm optimization algorithm is used to iteratively optimize the quantization factors K e , K ec and the proportional factor K u . The fitness function is used to iteratively update the position and speed of the quantization factor, and after each iteration, the fitness function is used to determine whether the optimal value required by the system has been reached, and whether further iteration optimization processing is needed. Then, the input quantity is input into the fuzzy controller through the optimized quantization factor, and the fuzzy control quantity is converted into the actual PID parameter adjustment increment through the optimized proportional factor, forming a particle swarm optimization fuzzy PID control system.

[0111] Example 2

[0112] A fast charging control method for an electric vehicle charging pile, which specifically comprises the following steps:

[0113] Step 1, analyze the structure of lithium battery: lithium battery is composed of positive electrode, separator, negative electrode, organic electrolyte and battery shell, so the lithium battery is equivalent to a second-order RC circuit, including open circuit voltage U oc, terminal voltage U L , ohmic resistance R0, charge transfer resistance R1, charge transfer capacitance C1, concentration difference resistance R2, concentration difference capacitance C2, and discretize the formula obtained by Kirchhoff's law,

[0114] Let x = [U1, U2, SOC], y = U L , the following formula can be obtained:

[0115]

[0116] Where, T s represents the sampling time; R1 and R2 represent the charge transfer resistance and the concentration difference resistance, respectively; C1 and C2 represent the charge transfer capacitance and the concentration difference capacitance, respectively; I L (k) represents the charge and discharge current at time k, and the discharge current direction is defined as positive and the charge current direction is defined as negative; U1 and U2 represent the polarization voltage across R1 and R2, respectively; Q n represents the capacity of the battery at time n;

[0117] According to the circuit diagram as Figure 2 shown, a second-order RC equivalent circuit model of the lithium battery is built in Matlab / simulink.

[0118] Step 2, identify the equivalent circuit model parameters: according to the second-order RC equivalent circuit model in step 1, the parameters of the equivalent circuit model are identified by using the least squares method based on the forgetting factor, and the pulse charge and discharge data of the 18650-20R type lithium battery at 25°C in the dynamic battery experiment database established by the Kalman battery research group laboratory are used to identify the parameters of the equivalent circuit model. The identification steps are as follows:

[0119]

[0120] a = α0α2

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] Where, T s represents the sampling time; a = τ1τ2; b = τ1+τ2; c = R1+R2+R0; d = R1τ2+R2τ1+R0(τ1+τ2);

[0127] Based on the α1, α2, α3, α4, α5 and T s The values of R0, R1, R2, C1 and C2 are calculated as follows:

[0128]

[0129] R1 = (τ1c + τ2R0 - d) / (τ1 - τ2)

[0130] R2 = c - R1 - R0

[0131] C1 = τ1 / R1

[0132] C2 = τ2R2

[0133] Wherein, R0 represents ohmic resistance; R1 represents charge transfer impedance; R2 represents concentration impedance; C1 represents charge transfer capacitance; C2 represents concentration capacitance;

[0134] According to the calculation, the SOC at the 80% stage obtains the identification parameter values R0 = 0.01, R1 = 3.9, R2 = 0.0073, C1 = 233.6, C2 = 5.31;

[0135] Step 3, constructing a mathematical model of lithium battery: the transfer function of the mathematical model of lithium battery is obtained by pull transformation and simplification of the second-order RC equivalent circuit in step 2:

[0136]

[0137] Wherein, R0 represents ohmic resistance; R1 represents charge transfer impedance; R2 represents concentration impedance; τ1 = R1C1; τ2 = R2C2; τ represents the delay time of the system, which is 1s;

[0138] According to the parameter identification result in step 2, the mathematical model of lithium battery is obtained:

[0139]

[0140] Step 4, design fuzzy PID controller: on the basis of traditional PID, add fuzzy rules, the principle of traditional fuzzy PID is as follows:

[0141]

[0142] Wherein, u(t) is the control amount of the output of the controller; K p represents proportional time constant; e(t) is the input of the control system; T i represents integral time constant; T d is the differential time constant;

[0143] The proportion can accelerate the response speed of the system, improve the regulation accuracy, the integral can eliminate the steady-state error of the system, the differential can improve the dynamic characteristic of the system, and suppress the deviation change direction, but the PID control algorithm has poor control effect on the system with strong nonlinearity and time variation, therefore, the fuzzy control is added, the fuzzy subsets of the input and output parameter language values are {NB, NM, NS, ZO, PS, PM, PB}, the meanings are negative big, negative medium, negative small, zero, positive small, positive medium, positive big, positive small, two input and three output variables are established, the Gaussian membership function is used, and 49 languages are written, therefore, the fuzzy PID control can enhance the robustness and anti-interference ability of the nonlinear system.

[0144] Step 5, the particle swarm optimization fuzzy PID control system is established: based on the fuzzy PID controller in step 4, the particle swarm algorithm is added to optimize the quantization factor, and the principle of the particle swarm algorithm is as follows:

[0145]

[0146] Wherein, c1=c2=1.5; r1 and r2 are two random numbers between 0 and 1; n is the iteration number; x i is a D-dimensional vector, indicating a possible position in the search space; v is the flight speed of the next step of the particle; P best is the individual extreme value; G best is the global extreme value;

[0147] The ITAE evaluation index is used as the fitness function to comprehensively evaluate the dynamic and static performance of the control system, and the expression is as follows:

[0148]

[0149] Through the particle swarm algorithm, the quantization factors K e , K ec and the proportional factor K u are iteratively optimized, the position and speed of the quantization factor are iteratively updated by using the fitness function, and whether the optimal value required by the system has been reached or whether the iterative optimization process needs to be performed again is judged by the fitness function after each iteration; subsequently, the input quantity enters the fuzzy controller through the optimized quantization factor, and the fuzzy control quantity is output, and the fuzzy control quantity is converted into the actual PID parameter adjustment increment through the optimized proportional factor, so that the particle swarm optimization fuzzy PID control system is formed.

[0150] By comparing the particle swarm optimization fuzzy PID control algorithm with the traditional PID control algorithm and the fuzzy PID control algorithm, the feasibility and superiority of the present application are further verified. The current index comparison of the traditional PID control algorithm, the fuzzy PID control algorithm and the method proposed in the present application is as follows: Figure 5As shown, it can be seen that the method has stronger stability, anti-interference ability and robustness.

[0151] To sum up, the application is aimed at the electric vehicle charging pile fast charging control system, considers its stability, anti-interference ability and robustness, and proposes an electric vehicle charging pile fast charging control method, analyzes the structure of lithium battery, adopts the least square method based on forgetting factor to identify the parameters of the second-order RC model of lithium battery, obtains the identification parameters, constructs the mathematical model of lithium battery, establishes the particle swarm optimization fuzzy PID control system, so that the stability and anti-interference ability of the electric vehicle charging pile control system are stronger, and the system robustness is improved.

[0152] Finally, it should be noted that: the above only for the preferred embodiments of the application and not for the purpose of limiting the application, although the application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for fast charging control of an electric vehicle charging pile, characterized in that: The method comprises the following steps: Step 1, analyzing the structure of the lithium battery: the lithium battery is composed of a positive electrode, a diaphragm, a negative electrode, an organic electrolyte and a battery shell; when the lithium battery works, lithium ions are generated from the positive electrode of the battery, the generated lithium ions move from the positive electrode to the electrolyte, pass through the small holes on the diaphragm and move to the negative electrode, and combine with the electrons in the external circuit that run to the negative electrode; considering the complex electrochemical internal mechanism analysis, a second-order RC equivalent circuit model composed of a voltage source, a resistor, a capacitor and an inductor is used to simulate the charging and discharging reaction of the lithium battery, and according to Kirchhoff's law, the system equation and the observation equation can be obtained as follows: System equation: wherein R1, R2 represent the transport impedance and the concentration impedance respectively; C1, C2 represent the transport capacitance and the concentration capacitance respectively; U1, U2 represent the polarization voltage at the two ends of R1, R2 respectively; dU1 represents the differential of U1; dU2 represents the differential of U2; dSOC represents the differential of the remaining capacity SOC of the battery; dt represents the differential of the charging and discharging time; Q n represents the capacity of the battery at time n; I L represents the charging and discharging current, with the definition that the direction of discharging current is positive and the direction of charging current is negative; Observation equation: U L = U OC - R0I L - U1-U2 wherein U L represents the battery terminal voltage; U oc represents the battery open-circuit voltage; R0represents the ohmic internal resistance; I L represents the charge and discharge current, with the discharge current direction defined as positive and the charge current direction defined as negative; U1, U2represent the polarization voltage across R1, R2, respectively; Step 2, identifying the equivalent circuit model parameters: the recursive least squares method based on the forgetting factor is used to identify the parameters of the second-order RC equivalent circuit model in step 1, and the circuit parameters of the second-order RC equivalent circuit are obtained, so that the model is further verified and corrected; Step 3, constructing a mathematical model of the lithium battery: the Laplace transform is performed on the second-order RC equivalent circuit model in step 2, the voltage difference is taken as the input, the current is taken as the output, and the delay existing in the system is added, so that the mathematical model of the lithium battery is obtained; the transfer function G(S) of the mathematical model is: Wherein, R0 represents the ohmic resistance; R1 represents the transport impedance; R2 represents the concentration impedance; τ1=R1C1; τ2=R2C2; τ represents the delay time existing in the system, which is 1s; According to the parameter identification result in step 2, the mathematical model of the lithium battery is obtained: Step 4, designing a fuzzy PID controller: the fuzzy PID controller is based on the conventional PID and adds fuzzy control, which optimizes the PID parameters in real time by using fuzzy rules, so that the expected value and the actual output value of the system at different times meet the control conditions; Step 5, establishing a particle swarm optimization fuzzy PID control system: the particle swarm optimization algorithm is a kind of swarm intelligence optimization algorithm which simulates the foraging behavior of birds, and the fuzzy PID parameters in step 4 are optimized by establishing an adaptive function, so that the particle swarm optimization fuzzy PID control system is formed.

2. The fast charging control method of electric vehicle charging piles according to claim 1, characterized in that: In step 1, the second-order RC equivalent circuit model of the lithium battery is composed of the open-circuit voltage, the terminal voltage, the ohmic resistance, the transport impedance, the concentration impedance, the transport capacitance and the concentration capacitance, the ohmic resistance is composed of the battery electrolyte, the positive electrode aluminum foil and the negative electrode aluminum foil, the sudden change characteristics of the voltage in the discharging process of the lithium battery are described, and the polarization resistance and the polarization capacitance describe the gradual change characteristics of the voltage in the charging and discharging process of the lithium battery.

3. The fast charging control method of electric vehicle charging pile according to claim 1, characterized in that: In step 2, the forgetting factor in the recursive least squares method based on the forgetting factor is set to a fixed value, the data is time-varying weighted, the past collected data is constantly weakened, the effect of the current newly collected data is enhanced, and finally more accurate identification parameters of the equivalent circuit model are obtained, which provides a better model selection for the realization of the estimation algorithm.

4. The fast charging control method of electric vehicle charging pile according to claim 1, characterized in that: In step 3, the mathematical model of the lithium battery is obtained by performing Laplace transform on the second-order RC equivalent circuit, the input voltage difference is E(S)-U(S), the output current is I(S), and the transfer function expression of the mathematical model of the lithium battery is obtained according to the parameters identified by the second-order RC equivalent circuit.

5. The fast charging control method of electric vehicle charging pile according to claim 1, characterized in that: In step 4, the PID algorithm is a linear control system, including proportion, integration and differentiation, using the error, cumulative error and error increment between the actual value and the target value of the controlled object to constitute the control strategy, which can quickly and stably track the target value of the controlled object; the fuzzy rules in fuzzy PID include fuzzification, fuzzy rules and defuzzification, and the use of fuzzy PID for control can effectively enhance the robustness of the control system and improve the anti-interference ability of the system.

6. The fast charging control method of electric vehicle charging piles according to claim 1, characterized in that: In step 5, in the particle swarm algorithm, each solution in the target space can be represented by a particle, and the food source to be found by the bird swarm needs to be solved. In the process of finding the optimal solution, each particle has individual behavior and group behavior, learns the flight experience of the companion and uses the flight experience of itself to find the optimal solution. An optimal fitness function is used to optimize the fuzzy PID algorithm.

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