CN110297452B - A battery pack adjacent balancing system and its predictive control method - Google Patents

Abstract

The invention relates to a battery pack adjacent balance system and a predictive control method thereof. Based on the battery pack adjacent balance topology, the invention mainly designs a battery pack adjacent balance topology main circuit, a battery pack voltage acquisition circuit, a two-way boost The voltage converter current detection module, the MPC-FPGA controller and the power tube floating drive circuit; and the predictive equalization control strategy is designed according to the energy transfer relationship of the equalization system to complete the equalization current distribution; finally, the adaptive control method of the bidirectional boost converter is applied to realize Balanced current tracking control process.

Description

A battery pack adjacent balancing system and its predictive control method

technical field

The invention relates to the technical field of battery energy storage, in particular to a battery pack adjacent balance system and a predictive control method thereof.

Background technique

With the increasing consumption of fossil energy and the urgency to solve the problem of environmental pollution, it is urgent to develop a clean and efficient sustainable energy. At present, batteries, especially lithium-ion batteries, have the characteristics of high energy density, low self-discharge rate and high cell voltage, and are increasingly used in industry and life, such as mobile devices, electric vehicles, power station energy storage systems, etc. , so it has important research value.

When batteries are applied to pure electric vehicles or large-scale energy storage systems, it is often necessary to connect multiple battery cells in series and parallel to form a giant battery with high voltage and high energy. This leads to the inconsistency of capacity, internal resistance and volt-ampere characteristic curve between single cells, which will cause the performance of battery life and available capacity to decrease exponentially. In order to prevent overvoltage/undervoltage caused by battery cell inconsistency under excessive use, and to improve battery life reduction and safety hazards caused by overvoltage/undervoltage, the battery management system is applied to large battery packs. Consistency and its safety management work to increase the effective capacity of the battery pack, keeping individual cells within a pre-defined safe working area.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide an adjacent battery pack equalization system and a predictive control method thereof, which can make each single cell of the battery pack reach the equilibrium state efficiently and quickly.

The present invention adopts the following scheme to realize: an adjacent battery pack equalization system, comprising a battery pack, a signal acquisition and processing module, an MPC-FPGA controller, an adaptive controller, a bidirectional boost converter type equalization circuit, and a drive circuit;

A bidirectional boost converter is connected between every two adjacent smallest series cells of the battery pack to control the mutual transfer of the energy of each cell;

The signal acquisition and processing module collects and processes the voltage, current and temperature signals of the battery pack and the current variation of the bidirectional boost converter, and converts them into signals that can be identified by the MPC-FPGA controller and the adaptive controller;

The MPC-FPGA controller predicts the overall state of the battery pack to generate the optimal control quantity, and the adaptive controller uses the parameter adaptive law and the control law to estimate the system parameters and generate the control signal u, and then through the driving The circuit controls the bidirectional boost converter to realize the energy transfer between the cells of the battery pack and realize the balance process of the battery pack.

Further, the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the battery pack, specifically: establishing a balanced system state space model; The deviation of the state of charge is the smallest, and the control variable corresponding to the minimum value of the objective function is solved; the obtained control variable is converted and applied to the controlled system. In the next sampling period, the collected new state variables and other information will be collected. Reload into the constrained optimization problem and do a new round of solving.

Further, the self-adaptive controller adopts the parameter self-adaptation law and the control law to estimate the system parameters and generate the control signal u, and then controls the bidirectional boost converter through the driving circuit to realize the energy transfer between the cells of the battery pack, so as to realize the The specific process of battery pack balancing is as follows: first, the mathematical model of the bidirectional boost converter is established, and the state equation of the bidirectional boost converter is obtained; then the system adaptive parameters are introduced, and the above state equation is matrixed, and then the tracking control error and the system are combined. The sliding mode surface and the Lyapunov function are designed for the state quantity. Finally, the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function are eliminated respectively, and the parameter adaptive law and the control law are obtained.

The present invention also provides a control method based on the above-mentioned battery pack adjacent balancing system, comprising the following steps:

Step S1: Considering the state-of-charge changes of all single cells of the battery pack, the state space model of the equilibrium system is obtained and discretized, as follows:

A_C和C都是一单位阵，u表示系统控制变量，T_s为控制系统采样步长，

C_Q、I_C是对角矩阵，分别表示蓄电池组各个单体电池容量、相邻型均衡系统最大工作电流，T代表相邻型均衡拓扑结构关系，A_C、C和T分别如下所示：In the formula,

Both A _C and C are a unit matrix, u represents the system control variable, T _s is the sampling step size of the control system,

C _Q and I _C are diagonal matrices, which represent the capacity of each single cell of the battery pack and the maximum operating current of the adjacent balanced system, respectively, and T represents the relationship between the adjacent balanced topology. A _C , C and T are shown as follows:

According to the conditional constraints of the equilibrium system and for the system to operate reliably and stably, each variable in the state space model of the equilibrium system satisfies the following constraints:

0≤x(k)≤1;

-1≤u(k)≤1.

Step S2: Set the prediction time domain and the control time domain to be NP and _NC respectively; and satisfy the control variables outside the control time domain are unchanged, that is, Δu(k+i) ₌ 0, i= _NC , _NC +1 ,..., _NP -1; therefore, the output in the time domain is predicted for the balanced system by the following formula:

In the formula, Δx(k)=x(k)-x(k-1) represents the state increment of the system at time k, ΔU( _k ) is the control increment of the system, in addition, S _x , Γ, Su are as follows shown:

Step S3: Predictive control is to use the optimal control quantity to achieve the optimal result of the target control, usually by solving the control quantity corresponding to the minimum value of the objective function, so the following objective function is constructed to make the difference between the cells of the battery pack The state of charge deviation is minimal:

In the formula, R _i (i=1, 2,... _NP ) is the error weight matrix; y(k) is the battery state-of-charge matrix predicted at time k, and y _ref is the reference value of the given controlled variable trajectory , setting the target value of y(k)-y _ref can reduce the fluctuation of the state of charge of the battery pack; the optimal control matrix U ^* (k) of the objective function in the prediction time domain at time k is calculated by the constrained optimal solution method, And select u(k) corresponding to time k in the matrix as the control quantity to control the conduction time of the switch tube in the bidirectional boost converter;

The actual work of solving the above constrained optimization problem is to solve a linear programming problem with constraints, and finally obtain the solution corresponding to the minimum value by solving the minimum value of the objective function in the feasible region.

In the next sampling period, the collected new state quantities are reloaded into the constrained optimization problem, and a new round of solution is carried out. Therefore, the predictive control strategy of the equilibrium system is defined as:

Δu(k)=[Ι _N-1 ×Ι _N-1 0 … 0]ΔU ^* (k);

Considering the limit condition IC of the balanced topology circuit, _Δu (k) is transformed into a series of current control tracking values, and finally the design process of the balanced predictive control strategy is completed.

Further, the present invention adopts an adaptive control method to control the bidirectional boost converter to work efficiently and stably, so as to realize the mutual transfer of the energy of each single battery, which specifically includes the following steps:

Step S4: establish a mathematical model of the bidirectional boost converter, and obtain the state equation of the bidirectional boost converter, as shown below:

为变换器状态参考变量，

u₁与u₂分别为双向升压变换器中两个功率管的控制信号，i_L为电感电流V_Bat1与V_Bat2分别为双向升压变换器输入、输出两端的电压，L为双向升压变换器电路电感值，R_Bat1、R_Bat2分别为双向升压变换器中两端等效电阻，C_Bat1、C_Bat2分别为双向升压变换器中V_Bat1与V_Bat2端电容。In the formula,

is the transformer state reference variable,

u ₁ and u ₂ are the control signals of the two power tubes in the bidirectional boost converter respectively, i _L is the inductor current V _Bat1 and V _Bat2 are the voltages at the input and output ends of the bidirectional boost converter respectively, L is the bidirectional boost Converter circuit inductance values, R _Bat1 and R _Bat2 are the equivalent resistances at both ends of the bidirectional boost converter, respectively, and C _Bat1 and C _Bat2 are the capacitances of V _Bat1 and V _Bat2 in the bidirectional boost converter, respectively.

Step S5: introducing system adaptive parameters, matrixing the state equation of the bidirectional boost converter, and then designing the sliding mode surface and the Lyapunov function in combination with the tracking control error and the system state quantity;

Step S6: respectively eliminate the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function, and obtain the parameter adaptation law and the control law as follows:

s＝c₂e₁+e₂为滑模面，e₁＝x₁-i_ref＝i_L-i_ref是追踪误差，

是二阶反演变量；x＝[x₁ x₂ x₃]^T＝[i_LV_Bat1 V_Bat2]^T表示系统状态量。In the formula, ψ is a positive definite matrix, c ₁ , c ₂ and α, β are all positive numbers,

s=c ₂ e ₁ +e ₂ is the sliding mode surface, e ₁ =x ₁ -i _ref =i _L -i _ref is the tracking error,

is the second-order inversion variable; x=[x ₁ x ₂ x ₃ ] ^T =[i _L V _Bat1 V _Bat2 ] ^T represents the system state quantity.

The invention is based on an adjacent topology equalization system composed of a bidirectional boost converter, and adopts a model prediction control strategy and an adaptive control method to realize the energy transfer between the individual cells of the storage battery pack, so that the single cells of the storage battery pack are efficient and efficient. Equilibrium is reached quickly.

Compared with the prior art, the present invention has the following beneficial effects:

1. The present invention proposes a specific solution for the adjacent battery pack equalization system, and the designed circuit structure is also applicable to other equalization systems.

2. The present invention adopts the model predictive control, which can realize the rolling optimization and correction of the model, improve the accuracy of the model, and increase the stability of the control.

3. The present invention adopts an adaptive control method to realize stable and efficient control of the bidirectional boost converter, so that the balanced current can well track the current reference value allocated by the predictive control strategy.

Description of drawings

FIG. 1 is a schematic diagram of a battery pack adjacent balanced predictive control system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of a total power supply of a battery pack balancing system according to an embodiment of the present invention.

3 is a circuit diagram of a battery pack voltage and temperature signal acquisition module according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of a current collection circuit of an adjacent balanced topology main circuit according to an embodiment of the present invention.

FIG. 5 is a diagram of a driving circuit of a MOS transistor according to an embodiment of the present invention.

FIG. 6 is a flowchart of a predictive control strategy according to an embodiment of the present invention.

FIG. 7 is a block diagram of an implementation process of an MPC-FPGA according to an embodiment of the present invention.

FIG. 8 is a block diagram of adaptive control of a bidirectional boost converter according to an embodiment of the present invention.

FIG. 9 is an effect diagram of adaptive control under a startup condition and a tracking current step condition according to an embodiment of the present invention.

FIG. 10 is a trend diagram of the SOC changes of each single cell in the battery pack balancing process according to an embodiment of the present invention.

FIG. 11 is a change trend diagram of the balancing current in the balancing process of the battery pack according to the embodiment of the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

As shown in FIG. 1, this embodiment provides a battery pack adjacent equalization system, including a battery pack, a signal acquisition and processing module, an MPC-FPGA controller, an adaptive controller, a bidirectional boost converter type equalization circuit, Drive circuit;

A bidirectional boost converter is connected between every two adjacent smallest series cells of the battery pack to control the mutual transfer of the energy of each cell;

The signal acquisition and processing module collects and processes the voltage, current and temperature signals of the battery pack and the current variation of the bidirectional boost converter, and converts them into signals that can be identified by the MPC-FPGA controller and the adaptive controller;

The MPC-FPGA controller predicts the overall state of the battery pack to generate the optimal control quantity, and the adaptive controller uses the parameter adaptive law and the control law to estimate the system parameters and generate the control signal u, and then through the driving The circuit controls the bidirectional boost converter to realize the energy transfer between the cells of the battery pack and realize the balance process of the battery pack.

In this embodiment, the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the battery pack, specifically: establishing a balanced system state space model; building an objective function to make each cell of the battery pack The state of charge deviation between the batteries is the smallest, and the corresponding control variable is obtained when the objective function is at the minimum value; the obtained control variable is converted and applied to the controlled system. In the next sampling period, the collected new state The information such as quantity is reloaded into the constrained optimization problem, and a new round of solution is carried out.

In this embodiment, the self-adaptive controller adopts the parameter self-adaptation law and the control law respectively to estimate the system parameters and generate the control signal u, and then controls the bidirectional boost converter through the driving circuit to realize the energy between the cells of the battery pack The specific process of realizing the battery pack balance is as follows: firstly, the mathematical model of the bidirectional boost converter is established, and the state equation of the bidirectional boost converter is obtained; then the system adaptive parameters are introduced, and the above state equation is matrixed, and then combined with the tracking control Errors and system state quantities are designed with sliding mode surface and Lyapunov function; finally, the estimation error and control deviation of system parameters in the derivative of Lyapunov function are eliminated respectively, and the parameter adaptive law and control law are obtained.

The present embodiment also provides a control method based on the above-mentioned battery pack adjacent balancing system, comprising the following steps:

Step S1: Considering the state-of-charge changes of all single cells of the battery pack, the state space model of the equilibrium system is obtained and discretized, as follows:

A_C和C都是一单位阵，u表示系统控制变量，T_s为控制系统采样步长，

C_Q、I_C是对角矩阵，分别表示蓄电池组各个单体电池容量、相邻型均衡系统最大工作电流，T代表相邻型均衡拓扑结构关系，A_C、C和T分别如下所示：In the formula,

Both A _C and C are a unit matrix, u represents the system control variable, T _s is the sampling step size of the control system,

C _Q and I _C are diagonal matrices, which represent the capacity of each single cell of the battery pack and the maximum operating current of the adjacent balanced system, respectively, and T represents the relationship between the adjacent balanced topology. A _C , C and T are shown as follows:

According to the conditional constraints of the equilibrium system and for the system to operate reliably and stably, each variable in the state space model of the equilibrium system satisfies the following constraints:

0≤x(k)≤1;

-1≤u(k)≤1.

Step S2: Set the prediction time domain and the control time domain to be NP and _NC respectively; and satisfy the control variables outside the control time domain are unchanged, that is, Δu(k+i) ₌ 0, i= _NC , _NC +1 ,..., _NP -1; therefore, the output in the time domain is predicted for the balanced system by the following formula:

In the formula, Δx(k)=x(k)-x(k-1) represents the state increment of the system at time k, ΔU( _k ) is the control increment of the system, in addition, S _x , Γ, Su are as follows shown:

Step S3: Predictive control is to use the optimal control quantity to achieve the optimal result of the target control, usually by solving the control quantity corresponding to the minimum value of the objective function, so the following objective function is constructed to make the difference between the cells of the battery pack The state of charge deviation is minimal:

In the formula, R _i (i=1, 2,... _NP ) is the error weight matrix; y(k) is the battery state-of-charge matrix predicted at time k, and y _ref is the reference value of the given controlled variable trajectory , setting the target value of y(k)-y _ref can reduce the fluctuation of the state of charge of the battery pack; the optimal control matrix U ^* (k) of the objective function in the prediction time domain at time k is calculated by the constrained optimal solution method, And select u(k) corresponding to time k in the matrix as the control quantity to control the conduction time of the switch tube in the bidirectional boost converter;

The actual work of solving the above constrained optimization problem is to solve a linear programming problem with constraints, and finally obtain the solution corresponding to the minimum value by solving the minimum value of the objective function in the feasible region.

In the next sampling period, the collected new state quantities are reloaded into the constrained optimization problem, and a new round of solution is carried out. Therefore, the predictive control strategy of the equilibrium system is defined as:

Δu(k)=[Ι _N-1 ×Ι _N-1 0 … 0]ΔU ^* (k);

Considering the limit condition IC of the balanced topology circuit, _Δu (k) is transformed into a series of current control tracking values, and finally the design process of the balanced predictive control strategy is completed.

In this embodiment, the adaptive control method is used to control the bidirectional boost converter to work efficiently and stably, so as to realize the mutual transfer of the energy of each single battery, which specifically includes the following steps:

Step S4: establishing a mathematical model of the bidirectional boost converter, and obtaining the state equation of the bidirectional boost converter, as shown below;

为变换器状态参考变量，

u₁与u₂分别为双向升压变换器中两个功率管的控制信号，i_L为电感电流V_Bat1与V_Bat2分别为双向升压变换器输入、输出两端的电压，L为双向升压变换器电路电感值，R_Bat1、R_Bat2分别为双向升压变换器中两端等效电阻，C_Bat1、C_Bat2分别为双向升压变换器中V_Bat1与V_Bat2端电容。In the formula,

is the transformer state reference variable,

u ₁ and u ₂ are the control signals of the two power tubes in the bidirectional boost converter respectively, i _L is the inductor current V _Bat1 and V _Bat2 are the voltages at the input and output ends of the bidirectional boost converter respectively, L is the bidirectional boost Converter circuit inductance values, R _Bat1 and R _Bat2 are the equivalent resistances at both ends of the bidirectional boost converter, respectively, and C _Bat1 and C _Bat2 are the capacitances of V _Bat1 and V _Bat2 in the bidirectional boost converter, respectively.

Step S5: introducing system adaptive parameters, matrixing the state equation of the bidirectional boost converter, and then designing the sliding mode surface and the Lyapunov function in combination with the tracking control error and the system state quantity;

Step S6: respectively eliminate the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function, and obtain the parameter adaptation law and the control law as follows:

s＝c₂e₁+e₂为滑模面，e₁＝x₁-i_ref＝i_L-i_ref是追踪误差，

是二阶反演变量；x＝[x₁ x₂ x₃]^T＝[i_LV_Bat1 V_Bat2]^T表示系统状态量。In the formula, ψ is a positive definite matrix, c ₁ , c ₂ and α, β are all positive numbers,

s=c ₂ e ₁ +e ₂ is the sliding mode surface, e ₁ =x ₁ -i _ref =i _L -i _ref is the tracking error,

is the second-order inversion variable; x=[x ₁ x ₂ x ₃ ] ^T =[i _L V _Bat1 V _Bat2 ] ^T represents the system state quantity.

Preferably, a specific description is given below with reference to the accompanying drawings. As shown in Figure 1, this embodiment is based on an adjacent topology equalization system composed of a bidirectional boost converter, and adopts a model predictive control strategy and an adaptive control method to realize the energy transfer between the individual cells of the battery pack, so that the battery Each single cell of the group reaches the equilibrium state efficiently and quickly. It is mainly composed of the system power supply circuit, the voltage acquisition circuit of each single cell of the battery pack, the current detection module of the bidirectional boost converter, the adjacent balanced topology main circuit, the MPC-FPGA controller, the main controller system and the MOS drive circuit. The specific working process is roughly divided into three steps: first, the signal acquisition and processing module collects and processes the voltage, current, temperature and other signals of the battery pack, and converts them into signals that can be recognized by MPC-FPGA and the adaptive controller; second, the main The MPC-FPGA controller generates the optimal control signal after prejudging the overall state of the battery pack. The specific implementation process of software and hardware is shown in Figures 6 and 7 respectively; the third is the specific allocation of the adaptive controller according to the control strategy. The control quantity u(k) controls the adjacent balanced topological circuit to realize the energy transfer between the individual cells of the battery pack and realize the balance process of the battery pack.

Among them, the specific implementation process of the adjacent battery pack equalization system and its predictive control method is as follows:

Its circuit structure includes a battery pack, an adjacent balanced topology main circuit, a signal acquisition system and a control system. Specifically, 1: A bidirectional boost converter is designed between the adjacent smallest series cells of the battery pack, forming an adjacent balanced topology main circuit, which can realize energy transfer between adjacent cells, as shown in Figure 1. The figure shows part of the main circuit diagram of the balanced topology, and the other parts are the rational expansion of this circuit schematic diagram; 2: The connection nodes of the smallest series cell adjacent to the battery pack are respectively connected to each cell of the battery pack through a certain impedance. In the battery voltage acquisition circuit, as shown in Figure 3, the battery pack voltage and temperature acquisition module is formed as a whole. The interior is mainly composed of LTC6804 chip, and the voltage and temperature signals are transmitted to the main controller through the isolated SPI communication module; three, each bidirectional The internal circuit of the boost converter is connected with a current-sensing resistor in series. After signal isolation and amplification, it is input to the controller to form a bidirectional boost converter current detection module, which is used to detect the current change during the energy transfer process, as shown in Figure 4. The invention adopts the form of IIC bus connection to reduce the use of pins, and a single channel can collect up to 16 current signals; Fourth, the prediction controller (Model Predict Control, MPC) adopts a Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) realizes the designed logic function; 5. The main controller system connected to the above detection signals is mainly composed of the main control chip and the peripheral minimum system circuit; 6. The MOS drive circuit mainly converts the PWM signal generated by the main controller system After isolation and amplification, it is sent to the MOS tube in the main circuit of the adjacent balanced topology to realize the transfer process of the balanced energy. The drive circuit is shown in Figure 5. The present invention adopts the floating ground form to meet the requirements of the MOS tube in the adjacent balanced topology circuit. conduction requirements.

The predictive control strategy based on the adjacent battery pack equalization system proposed in this embodiment is shown in Figure 6. The system first inputs the battery pack current, voltage and temperature signals into the predictive model, and the battery pack status is determined by the battery pack state. The estimation model immediately evaluates the state of charge of the battery, and then estimates the state of the battery pack at the time of _NP through the equilibrium system state space model, and finally obtains the control quantity under the minimum objective function through the constrained optimization solution, which includes:

The first step of the predictive control strategy is to model the adjacent balanced topology system of the battery group. In order to describe the implementation process of the present invention in detail, this embodiment will use five strings of single batteries to form a group, that is, N=5. The state space model of the controlled object lays the foundation for the prediction model of the state of charge of the battery pack.

Considering the state-of-charge changes of all cells in the battery pack, the state space model of the equilibrium system is obtained and discretized, as follows:

A_C和C都是一单位阵，u表示系统控制变量，T_s为控制系统采样步长，

C_Q，I_C是对角矩阵，分别表示蓄电池组各个单体电池容量、相邻型均衡系统最大工作电流，T代表相邻型均衡拓扑结构关系，在本发明具体实施案例中，A_C，C和T分别如下所示：in,

Both A _C and C are a unit matrix, u represents the system control variable, T _s is the sampling step size of the control system,

C _Q , I _C are diagonal matrices, which respectively represent the capacity of each single cell of the battery pack and the maximum operating current of the adjacent balanced system, and T represents the relationship between the adjacent balanced topological structure. In the specific implementation case of the present invention, A _C , C and T are respectively as follows:

According to the conditional constraints of the equilibrium system and for the system to operate reliably and stably, each variable in the state space model should satisfy the following constraints:

0≤x(k)≤1

-1≤u(k)≤1

Based on the state space model of the equilibrium system, the prediction time domain and the control time domain are _NP =10 and _NC =3, respectively. And it satisfies that outside the control time domain, the control quantity remains unchanged, that is, Δu(k+i)=0, i=N _C , N _C +1,...,N _P -1; therefore, the output in the time domain is predicted for the equalization system can be obtained by:

Among them, Δx(k)=x(k)-x(k-1) represents the state increment of the system at time k, ΔU(k) is the control increment of the system, in addition, S _x , Γ, S _u are respectively as follows Show:

The second step of the predictive control strategy is to use the optimal control quantity to achieve the optimal result of the target control, usually by solving the control quantity corresponding to the minimum value of the objective function. The present invention first constructs the following objective function to minimize the state-of-charge deviation between the individual cells of the battery pack:

Among them, R _i (i=1, 2, . . . _NP ) is an error weight matrix. y(k) is the predicted battery state of charge matrix at time k, and _yref is the reference value of the given controlled quantity trajectory. Setting the y(k) _-yref target value can reduce the fluctuation of the battery pack state of charge. Calculate the optimal control matrix U ^* (k) of the objective function in the prediction time domain at time k by the constrained optimal solution method, and select the u(k) corresponding to time k in the matrix to input it to the adjacent balanced topology The main circuit controls the conduction time of the switch tube.

The actual work of solving the above constrained optimization problem is to solve a linear programming problem with constraints, and finally obtain the solution corresponding to the minimum value by solving the minimum value of the objective function in the feasible region.

The third step of the predictive control strategy is to convert the control quantities obtained above and act on the controlled system. In the next sampling period, the collected new state quantities and other information are reloaded into the constrained optimization problem, and new information is carried out. One round of solution. Therefore, the predictive control strategy of the equilibrium system is defined as:

Δu(k)=[Ι ₄ ×Ι ₄ 0 … 0]ΔU ^* (k)

Further, considering the limiting condition IC of the balanced topology circuit, _Δu (k) is transformed into a series of current control tracking values, and finally the design process of the balanced predictive control strategy is completed.

In particular, a battery pack adjacent balancing system and its predictive control method proposed in this embodiment are shown in Figure 8. The principle of the adaptive control block diagram of the bidirectional boost converter is based on the adjacent balancing topology. Main circuit working characteristics The adaptive control method is used to control the bidirectional boost converter to work efficiently and stably, so as to realize the mutual transfer of the energy of each single battery. The details are as follows.

Firstly, the mathematical model of the bidirectional boost converter is established, and the state equation of the bidirectional boost converter is obtained; then the system adaptive parameters are introduced, and the above state equation is matrixed, and then the sliding mode surface and Lyapunov function; finally, the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function V are eliminated respectively, and the parameter adaptive law and control law are obtained as follows:

s＝c₂e₁+e₂为滑模面，e₁＝x₁-i_ref＝i_L-i_ref是追踪误差，

是二阶反演变量；x＝[x₁ x₂ x₃]^T＝[i_LV_Bat1 V_Bat2]^T表示系统状态量。Among them, ψ is a positive definite matrix, c ₁ , c ₂ and α, β are all positive numbers,

s=c ₂ e ₁ +e ₂ is the sliding mode surface, e ₁ =x ₁ -i _ref =i _L -i _ref is the tracking error,

is the second-order inversion variable; x=[x ₁ x ₂ x ₃ ] ^T =[i _L V _Bat1 V _Bat2 ] ^T represents the system state quantity.

-变换器状态参考变量，

u₁与u₂分别代表功率管S₁、S₂的控制信号，

为1时能量从电池1转移到电池2，

为-1能量从电池2转移到电池1，i_L-电感电流，V_Bat2-电容C_Bat2两端的电压，V_Bat1-电容C_Bat1两端的电压，定义L为电路电感值，R_Bat1、C_Bat1为V_Bat1端电池等效电阻和电容，R_Bat2、C_Bat2为V_Bat2端电池等效电阻和电容。In the above formula,

- Inverter state reference variable,

u ₁ and u ₂ represent the control signals of the power tubes S ₁ and S ₂ respectively,

When it is 1, energy is transferred from battery 1 to battery 2,

For -1 energy is transferred from battery 2 to battery 1, i _L - inductor current, V _Bat2 - voltage across capacitor C _Bat2 , V _Bat1 - voltage across capacitor C _Bat1 , define L as the inductance value of the circuit, R _Bat1 , C _Bat1 are the equivalent resistance and capacitance of the battery at the end of V _Bat1 , and R _Bat2 and C _Bat2 are the equivalent resistance and capacitance of the battery at the end of V _Bat2 .

In this embodiment, the stability and robustness of the algorithm are tested according to the adaptive control method designed above, and the results are shown in Fig. 9, respectively. According to the simulation results under the condition and the tracking current step condition, it can be seen that the equilibrium current quickly stabilizes at the tracking current reference value.

In this embodiment, the above-mentioned balancing system of the adjacent battery pack shown in Fig. 1 has been verified. The results are shown in Figs. 10 and 11. , 0.80 and 0.67, and finally reach the same value in about 262 seconds, the distributed balancing current changes smoothly within the constraint current, and finally the proposed battery pack adjacent balancing system and its predictive control method are realized.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications to equivalent changes. Example. However, any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solutions of the present invention still belong to the protection scope of the technical solutions of the present invention.

Claims (3)

1. a battery pack adjacent type equalization system, is characterized in that, comprises battery pack, signal acquisition and processing module, MPC-FPGA controller, self-adaptive controller, bidirectional boost converter type equalization circuit, drive circuit; A bidirectional boost converter is connected between every two adjacent smallest series cells of the battery pack to control the mutual transfer of the energy of each cell; The signal acquisition and processing module collects and processes the voltage, current and temperature signals of the battery pack and the current variation of the bidirectional boost converter, and converts them into signals that can be identified by the MPC-FPGA controller and the adaptive controller; The MPC-FPGA controller prejudges the overall state of the battery pack to generate the optimal control quantity, and the adaptive controller adopts the balanced control strategy to obtain the parameter adaptive law and the control law to estimate the system parameters and generate the control signal u. , and then control the bidirectional boost converter by controlling the driving circuit to realize the energy transfer between the individual cells of the battery pack, and realize the battery pack balancing process; Which includes the following steps: Step S1: Considering the state-of-charge changes of all single cells of the battery pack, the state space model of the equilibrium system is obtained and discretized, as follows:

In the formula,

Both A _C and C are a unit matrix, u represents the system control variable, T _s is the sampling step size of the control system,

C _Q and I _C are diagonal matrices, which represent the capacity of each single cell of the battery pack and the maximum operating current of the adjacent balanced system, respectively, and T represents the relationship between the adjacent balanced topology. A _C , C and T are shown as follows:

Each variable in the equilibrium system state space model satisfies the following constraints: 0≤x(k)≤1; -1≤u(k)≤1; Step S2: Set the prediction time domain and the control time domain to be NP and NC respectively; and satisfy the control amount outside the control time domain is unchanged, that is, Δu(k+i) ₌ 0, i= _NC , _NC + ₁ , ..., N _P -1; therefore, the output in the time domain is predicted for the balanced system by the following formula:

In the formula, Δx(k)=x(k)-x(k-1) represents the state increment of the system at time k, ΔU(k) is the control increment of the system, in addition, s _x , Γ, S _u are as follows shown:

Step S3: Construct the following objective function to minimize the state-of-charge deviation between the individual cells of the battery pack:

In the formula, R _i (i=1, 2,... _NP ) is the error weight matrix; y(k) is the battery state-of-charge matrix predicted at time k, and y _ref is the reference value of the given controlled variable trajectory , setting the target value of y(k)-y _ref can reduce the fluctuation of the state of charge of the battery pack; the optimal control matrix U ^* (k) of the objective function in the prediction time domain at time k is calculated by the constrained optimal solution method, And select u(k) corresponding to time k in the matrix as the control quantity to control the on-time of the switch in the bidirectional boost converter; in the next sampling period, the new state quantity collected is reloaded into the constrained optimization problem , and a new round of solution is carried out. Therefore, the predictive control strategy of the equilibrium system is defined as: Δu(k)=[I _N-1 ×I _N-1 0 … 0]ΔU ^* (k); Also includes steps: Step S4: establishing a mathematical model of the bidirectional boost converter, and obtaining the state equation of the bidirectional boost converter; Step S5: introducing system adaptive parameters, matrixing the state equation of the bidirectional boost converter, and then designing the sliding mode surface and the Lyapunov function in combination with the tracking control error and the system state quantity; Step S6: respectively eliminate the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function, and obtain the parameter adaptation law and the control law . 2. A battery pack adjacent type balancing system according to claim 1, wherein the MPC-FPGA controller generates an optimal control amount after pre-judging the overall state of the battery pack, specifically: establishing Balance the state space model of the system; construct an objective function to minimize the state-of-charge deviation between the individual cells of the battery pack, and solve the control quantity corresponding to the minimum value of the objective function; convert the obtained control quantity to function For the controlled system, in the next sampling period, the new state quantity information collected is reloaded into the constrained optimization problem, and a new round of solution is performed. 3 . The adjacent battery pack equalization system according to claim 1 , wherein the adaptive controller adopts a parameter adaptive law and a control law to estimate system parameters and generate a control signal u, and then drives the The circuit controls the bidirectional boost converter to realize the energy transfer between the cells of the battery pack, and the process of realizing the balance of the battery pack is as follows: firstly, the mathematical model of the bidirectional boost converter is established, and the state equation of the bidirectional boost converter is obtained; then the introduction of The system adaptive parameters, and the above state equation is matrixed, and then combined with the tracking control error and the system state quantity to design the sliding mode surface and the Lyapunov function; finally, the estimation error and control deviation of the system parameters in the derivative of the Lyapunov function are eliminated respectively. , the parameter adaptation law and control law are obtained.

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