CN110297452A - A kind of equal balance system of the adjacent type of battery group and its forecast Control Algorithm - Google Patents
A kind of equal balance system of the adjacent type of battery group and its forecast Control Algorithm Download PDFInfo
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- H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
- H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
- H02M3/10—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode
- H02M3/145—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal
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Abstract
The present invention relates to a kind of equal balance system of the adjacent type of battery group and its forecast Control Algorithms, based on the balanced topology of the adjacent type of battery group, the major design adjacent type equilibrium topological main circuit of battery group, accumulator battery voltage Acquisition Circuit, bi-directional boost converters current detection module, MPC-FPGA controller and power tube floating driving circuit；And prediction Balance route strategy is designed according to balanced system capacity transfer relationship, complete euqalizing current distribution；Final application bi-directional boost converters self-adaptation control method realizes the control process of euqalizing current tracking.
Description
Technical Field
The invention relates to the technical field of battery energy storage, in particular to a storage battery adjacent type equalization system and a prediction control method thereof.
Background
With the increasing consumption of fossil energy and the urgent need for solving the environmental pollution problem, the development of a clean and efficient sustainable energy is urgently needed. At present, storage batteries, particularly lithium ion batteries, have the characteristics of high energy density, low self-discharge rate and higher single voltage, are increasingly applied to industries and lives, such as mobile equipment, electric automobiles, power station energy storage systems and the like, and therefore have important research values.
When the storage battery is applied to a pure electric vehicle or a large energy storage system, a plurality of storage battery cells are often required to be connected in series and in parallel to form a giant battery with high voltage and high energy, but due to differences of the battery in various aspects in the manufacturing process and the using process, inconsistency of capacity, internal resistance, volt-ampere characteristic curve and the like exists among the single batteries, and performance such as battery life, available capacity and the like is exponentially decreased. In order to prevent the over-voltage/under-voltage phenomenon caused by the inconsistency of the single batteries under the condition of over-use and improve the phenomena of battery service life reduction and potential safety hazard caused by over-voltage/under-voltage, a battery management system is applied to the consistency and safety management work of a large-scale storage battery pack so as to improve the effective capacity of the battery pack and keep each single battery in a predefined safe working area.
Disclosure of Invention
In view of the above, the present invention provides an adjacent equalization system for a storage battery and a predictive control method thereof, which can enable each single battery of the storage battery to efficiently and quickly reach an equalization state.
The invention is realized by adopting the following scheme: a storage battery adjacent type equalization system comprises a storage battery, a signal acquisition processing module, an MPC-FPGA controller, a self-adaptive controller, a bidirectional boost converter type equalization circuit and a drive circuit;
a bidirectional boost converter is connected between every two adjacent minimum series monomers of the storage battery pack and is used for controlling mutual transfer of energy of the monomer batteries;
the signal acquisition processing module acquires and processes voltage, current and temperature signals of the storage battery pack and current change conditions of the bidirectional boost converter, and converts the signals into signals which can be identified by the MPC-FPGA controller and the adaptive controller;
the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the storage battery pack, the self-adaptive controller estimates system parameters and generates a control signal u by adopting a parameter self-adaptation law and a control law respectively, and then the drive circuit controls the bidirectional boost converter to realize energy transfer among the single batteries of the storage battery pack and realize the equalization process of the storage battery pack.
Further, the MPC-FPGA controller pre-determines the overall state of the storage battery pack to generate an optimal control quantity, specifically: establishing a state space model of the equilibrium system; constructing a target function to minimize the state of charge deviation among the single batteries of the storage battery pack, and solving the corresponding control quantity when the target function is the minimum; and converting the obtained control quantity, acting on the controlled system, reloading the collected information such as new state quantity into the constraint optimization problem in the next sampling period, and performing a new round of solution.
Further, the adaptive controller estimates system parameters and generates a control signal u by adopting a parameter adaptive law and a control law respectively, and then controls the bidirectional boost converter through the driving circuit to realize energy transfer among the single batteries of the storage battery pack, and the equalization process of the storage battery pack is specifically as follows: firstly, establishing a mathematical model of a bidirectional boost converter to obtain a state equation of the bidirectional boost converter; then introducing system self-adaptive parameters, matrixing the state equation, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities; and finally, respectively eliminating the estimation error and the control deviation of the system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law.
The invention also provides a control method based on the storage battery adjacent equalization system, which comprises the following steps:
step S1: and (3) considering the charge state changes of all the single batteries of the storage battery pack, obtaining a state space model of the equalizing system and discretizing the state space model, wherein the state space model comprises the following steps:
in the formula,ACand C are both a unit matrix, u represents the system control variable, TsIn order to control the sampling step size of the system,CQ、ICis a diagonal matrix which respectively represents the capacity of each single battery of the storage battery and the maximum working current of an adjacent type equalizing system, T represents the adjacent type equalizing topological structure relationship, ACC and T are as follows:
according to the condition limitation of the equilibrium system and for the reliable and stable operation of the system, each variable in the state space model of the equilibrium system meets the following limiting conditions:
0≤x(k)≤1；
-1≤u(k)≤1。
step S2: let the prediction time domain and the control time domain be N respectivelyP、NC(ii) a And the control quantity outside the control time domain is constant, namely delta u (k + i) is 0, i is NC，NC+1,…,NP-1; the output in the prediction time domain of the equalization system is thus given by:
YNP(k+1|k)＝SxΔx(k)+Γy(k)+SuΔU(k)；
where Δ x (k) ═ x (k) — x (k-1) represents the state increment of the system at time k, Δ u (k) represents the control increment of the system, and S represents the control increment of the systemx，Γ，SuRespectively as follows:
step S3: the predictive control is to adopt the optimal control quantity to realize the optimal result of the target control, and usually by solving the corresponding control quantity when the target function is the minimum, so the following target function is constructed to minimize the state of charge deviation among the single batteries of the storage battery pack:
in the formula, Ri(i＝1，2，…NP) Is an error weight matrix; y (k) is the predicted battery state of charge matrix at time k, yrefSetting y (k) -y for a given reference value of the controlled variable trajectoryrefThe target value can reduce the state of charge fluctuation of the storage battery pack; calculating an optimal control quantity matrix U of the objective function in a prediction time domain at the k moment by a constrained optimal solution method*(k) And u (k) corresponding to the k time in the matrix is selected as a control quantity to control the conduction time of a switching tube in the bidirectional boost converter;
the practical work for solving the constraint optimization problem is to solve a linear programming problem with constraint conditions, and finally obtain a 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 quantity is reloaded into the constraint optimization problem and a new round of solution is performed, so that the predictive control strategy of the equilibrium system is defined as:
Δu(k)＝[ΙN-1×ΙN-1 0 … 0]ΔU*(k)；
considering the constraint condition I of balanced topology circuitCAnd converting the delta u (k) into a series of current control tracking values, and finally completing the design process of the balanced predictive control strategy.
Furthermore, the invention adopts a self-adaptive control method to control the bidirectional boost converter to work efficiently and stably, thereby realizing the mutual transfer of the energy of each single battery, and specifically comprises the following steps:
step S4: establishing a mathematical model of the bidirectional boost converter, and obtaining a state equation of the bidirectional boost converter, wherein the state equation is as follows:
in the formula,a reference variable for the state of the converter,u1and u2Control signals, i, of two power transistors in a bidirectional boost converterLIs an inductive current VBat1And VBat2Voltage at input and output ends of the bidirectional boost converter, L is inductance value of the bidirectional boost converter circuit, RBat1、RBat2Respectively an equivalent resistance C at two ends of the bidirectional boost converterBat1、CBat2Respectively V in bidirectional boost converterBat1And VBat2And a terminal capacitance.
Step S5: introducing system self-adaptive parameters, matrixing a state equation of the bidirectional boost converter, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities;
step S6: respectively eliminating the estimation error and the control deviation of system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law as follows:
where ψ is a positive definite matrix, c1，c2And alpha, beta are both normal numbers, s＝c2e1+e2as a slip form face, e1＝x1-iref＝iL-irefIs the error in the tracking of the track,is a second order inversion variable; x ═ x1 x2 x3]T＝[iLVBat1 VBat2]TRepresenting the system state quantity.
The invention is based on an adjacent topology equalization system composed of bidirectional boost converters, and realizes energy transfer among all single batteries of the storage battery by adopting a model prediction control strategy and a self-adaptive control method, so that all the single batteries of the storage battery can efficiently and quickly reach an equalization state.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a specific solution of an equalizing system with adjacent storage battery packs, and the designed circuit structure is also suitable for other equalizing systems.
2. The invention adopts model predictive control, can realize the rolling optimization and correction of the model, improves the accuracy of the model and increases the stability of the control.
3. The invention adopts a self-adaptive control method, realizes the stable and efficient control of the bidirectional boost converter, and ensures that the equalizing current can well track the current reference value distributed by the predictive control strategy.
Drawings
Fig. 1 is a schematic diagram of a battery pack adjacent equalization prediction control system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of the overall power supply of the battery pack balancing system according to the embodiment of the present invention.
Fig. 3 is a circuit diagram of a battery pack voltage and temperature signal acquisition module according to an embodiment of the invention.
Fig. 4 is a schematic diagram of a current acquisition circuit of a main circuit of an adjacent type balanced topology according to an embodiment of the present invention.
Fig. 5 is a circuit diagram of a MOS transistor driving circuit according to an embodiment of the invention.
FIG. 6 is a flow chart of a predictive control strategy according to an embodiment of the invention.
FIG. 7 is a block diagram of an MPC-FPGA implementation process 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 a diagram illustrating adaptive control effects under the start-up condition and the tracking current step condition according to an embodiment of the present invention.
Fig. 10 is a diagram illustrating the SOC variation trend of each single battery in the battery pack balancing process according to the embodiment of the present invention.
Fig. 11 is a diagram illustrating a variation trend of equalization current in the equalization process of the battery pack according to the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, 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 is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides an adjacent equalization system for a storage battery pack, which includes a storage battery pack, a signal acquisition and processing module, an MPC-FPGA controller, an adaptive controller, a bidirectional boost converter type equalization circuit, and a driving circuit;
a bidirectional boost converter is connected between every two adjacent minimum series monomers of the storage battery pack and is used for controlling mutual transfer of energy of the monomer batteries;
the signal acquisition processing module acquires and processes voltage, current and temperature signals of the storage battery pack and current change conditions of the bidirectional boost converter, and converts the signals into signals which can be identified by the MPC-FPGA controller and the adaptive controller;
the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the storage battery pack, the self-adaptive controller estimates system parameters and generates a control signal u by adopting a parameter self-adaptation law and a control law respectively, and then the drive circuit controls the bidirectional boost converter to realize energy transfer among the single batteries of the storage battery pack and realize the equalization process of the storage battery pack.
In this embodiment, the MPC-FPGA controller generates an optimal control amount after pre-judging the overall state of the storage battery pack, specifically: establishing a state space model of the equilibrium system; constructing a target function to minimize the state of charge deviation among the single batteries of the storage battery pack, and solving the corresponding control quantity when the target function is the minimum; and converting the obtained control quantity, acting on the controlled system, reloading the collected information such as new state quantity into the constraint optimization problem in the next sampling period, and performing a new round of solution.
In this embodiment, the adaptive controller estimates system parameters and generates a control signal u by using a parameter adaptive law and a control law, and then controls the bidirectional boost converter through the driving circuit to realize energy transfer between the single batteries of the storage battery pack, and the process of realizing the equalization of the storage battery pack specifically includes: firstly, establishing a mathematical model of a bidirectional boost converter to obtain a state equation of the bidirectional boost converter; then introducing system self-adaptive parameters, matrixing the state equation, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities; and finally, respectively eliminating the estimation error and the control deviation of the system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law.
The embodiment also provides a control method based on the storage battery pack adjacent equalization system, which comprises the following steps:
step S1: and (3) considering the charge state changes of all the single batteries of the storage battery pack, obtaining a state space model of the equalizing system and discretizing the state space model, wherein the state space model comprises the following steps:
in the formula,ACand C are both a unit matrix, u represents the system control variable, TsIn order to control the sampling step size of the system,CQ、ICis a diagonal matrix which respectively represents the capacity of each single battery of the storage battery and the maximum working current of an adjacent type equalizing system, T represents the adjacent type equalizing topological structure relationship, ACC and T are as follows:
according to the condition limitation of the equilibrium system and for the reliable and stable operation of the system, each variable in the state space model of the equilibrium system meets the following limiting conditions:
0≤x(k)≤1；
-1≤u(k)≤1。
step S2: let the prediction time domain and the control time domain be N respectivelyP、NC(ii) a And the control quantity outside the control time domain is constant, namely delta u (k + i) is 0, i is NC，NC+1,…,NP-1; the output in the prediction time domain of the equalization system is thus given by:
YNP(k+1|k)＝SxΔx(k)+Γy(k)+SuΔU(k)；
where Δ x (k) ═ x (k) — x (k-1) represents the state increment of the system at time k, Δ u (k) represents the control increment of the system, and S represents the control increment of the systemx，Γ，SuRespectively as follows:
step S3: the predictive control is to adopt the optimal control quantity to realize the optimal result of the target control, and usually by solving the corresponding control quantity when the target function is the minimum, so the following target function is constructed to minimize the state of charge deviation among the single batteries of the storage battery pack:
in the formula, Ri(i＝1，2，…NP) Is an error weight matrix; y (k) is the predicted battery state of charge matrix at time k, yrefSetting y (k) -y for a given reference value of the controlled variable trajectoryrefThe target value can reduce the state of charge fluctuation of the storage battery pack; calculating an optimal control quantity matrix U of the objective function in a prediction time domain at the k moment by a constrained optimal solution method*(k) And u (k) corresponding to the k time in the matrix is selected as a control quantity to control the conduction time of a switching tube in the bidirectional boost converter;
the practical work for solving the constraint optimization problem is to solve a linear programming problem with constraint conditions, and finally obtain a 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 quantity is reloaded into the constraint optimization problem and a new round of solution is performed, so that the predictive control strategy of the equilibrium system is defined as:
Δu(k)＝[ΙN-1×ΙN-1 0 … 0]ΔU*(k)；
considering the constraint condition I of balanced topology circuitCConverting Δ u (k) into a seriesAnd finally finishing the design process of the balanced predictive control strategy by using the current control tracking value.
In this embodiment, an adaptive control method is adopted to control a bidirectional boost converter to work efficiently and stably, so as to realize mutual transfer of energy of each single battery, and the method specifically includes the following steps:
step S4: establishing a mathematical model of the bidirectional boost converter, and acquiring a state equation of the bidirectional boost converter as shown in the following;
in the formula,a reference variable for the state of the converter,u1and u2Control signals, i, of two power transistors in a bidirectional boost converterLIs an inductive current VBat1And VBat2Voltage at input and output ends of the bidirectional boost converter, L is inductance value of the bidirectional boost converter circuit, RBat1、RBat2Respectively an equivalent resistance C at two ends of the bidirectional boost converterBat1、CBat2Respectively V in bidirectional boost converterBat1And VBat2And a terminal capacitance.
Step S5: introducing system self-adaptive parameters, matrixing a state equation of the bidirectional boost converter, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities;
step S6: respectively eliminating the estimation error and the control deviation of system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law as follows:
where ψ is a positive definite matrix, c1，c2And alpha, beta are both normal numbers, s＝c2e1+e2as a slip form face, e1＝x1-iref＝iL-irefIs the error in the tracking of the track,is a second order inversion variable; x ═ x1 x2 x3]T＝[iLVBat1 VBat2]TRepresenting the system state quantity.
Preferably, the following detailed description is made with reference to the accompanying drawings. As shown in fig. 1, in the present embodiment, based on an adjacent topology equalization system composed of bidirectional boost converters, a model predictive control strategy and an adaptive control method are used to implement energy transfer between individual cells of a storage battery pack, so that the individual cells of the storage battery pack efficiently and quickly reach an equalization state. The system mainly comprises a system power supply circuit, a voltage acquisition circuit of each single battery of a storage battery pack, a bidirectional boost converter current detection module, an adjacent type balanced topology main circuit, an MPC-FPGA controller, a main controller system and an MOS drive circuit. The specific working process is roughly divided into three steps: firstly, signals such as voltage, current, temperature and the like of a storage battery pack are collected and processed by a signal collection processing module and are converted into signals which can be recognized by an MPC-FPGA and an adaptive controller; secondly, an MPC-FPGA controller generates an optimal control signal after pre-judging the overall state of the storage battery pack, and the concrete implementation processes of software and hardware are respectively shown in FIGS. 6 and 7; and thirdly, controlling an adjacent type balancing topology circuit by the self-adaptive controller according to the control quantity u (k) distributed by the control strategy to realize energy transfer among the single batteries of the storage battery pack and realize the balancing process of the storage battery pack.
The specific implementation flow of the storage battery adjacent equalization system and the prediction control method thereof is as follows:
the circuit structure comprises a storage battery pack, an adjacent balanced topology main circuit, a signal acquisition system and a control system. Specifically, the first: a bidirectional boost converter is designed between the adjacent minimum series-connected monomers of the storage battery pack to form an adjacent type balanced topology main circuit, and energy transfer between the adjacent monomers can be realized, as shown in FIG. 1, a partial balanced topology main circuit diagram is shown in the diagram, and other parts are rationality expansion of the circuit schematic diagram; II, secondly: the minimum series monomer connection nodes adjacent to the storage battery pack are respectively connected into the voltage acquisition circuits of the single batteries of the storage battery pack through certain impedances, as shown in fig. 3, the storage battery pack integrally forms a storage battery pack voltage and temperature acquisition module, the LTC6804 chip is mainly arranged in the storage battery pack, and voltage and temperature signals are transmitted to the main controller through the isolation SPI communication module; thirdly, a current detection resistor is connected in series with an internal circuit of each bidirectional boost converter, and the current detection resistor is input into a controller after signal isolation and amplification to form a bidirectional boost converter current detection module for detecting the change condition of current in the energy transfer process, as shown in fig. 4, the invention adopts an IIC bus hanging mode to reduce the use of pins, and a single circuit can collect 16 current signals at most; fourthly, a predictive controller (MPC) adopts a Field-Programmable gate array (FPGA) to realize the designed logic function; fifthly, a main controller system accessed by the detection signals mainly comprises a main control chip and a peripheral minimum system circuit; and sixthly, the MOS drive circuit is mainly used for isolating and amplifying the PWM signal generated by the main controller system and then sending the PWM signal into the MOS tube in the adjacent type balanced topology main circuit to realize the transfer process of balanced energy, wherein the drive circuit is shown in fig. 5.
The present embodiment proposesThe strategy flow is shown in fig. 6, the system firstly inputs the current, voltage and temperature signals of the storage battery into a prediction model, the state of charge of the battery is immediately estimated by a storage battery state estimation model, and then N is estimated by a state space model of the equalization systemPThe method includes the following steps that at a moment, the state of the storage battery pack is solved through constraint optimization to obtain the control quantity of an objective function at the minimum, and the method specifically includes the following steps:
the first step of the predictive control strategy is the modeling of an adjacent type equilibrium topology system of the storage battery pack, in order to explain the implementation process of the invention in detail, the embodiment adopts five strings of single storage batteries to form a group, namely N is 5, and firstly, a state space model of a controlled object of the equilibrium system is established, so that a foundation is laid for a battery pack state of charge prediction model.
Considering the state of charge changes of all the single batteries of the storage battery pack, obtaining a state space model of the equalization system and discretizing the state space model, as follows:
wherein,ACand C are both a unit matrix, u represents the system control variable, TsIn order to control the sampling step size of the system,CQ，ICis a diagonal matrix which respectively represents the capacity of each single battery of the storage battery and the maximum working current of an adjacent type equalizing system, and T represents the adjacent type equalizing topological structure relationshipCC and T are shown below:
depending on the condition constraints of the equalization system and for a reliable and stable operation of the system, the following constraints should be satisfied for the variables in the state space model:
0≤x(k)≤1
-1≤u(k)≤1
based on a state space model of an equilibrium system, a prediction time domain and a control time domain are respectively NP＝10、NC3. And the control quantity is unchanged when the control time domain is not satisfied, namely, the control quantity is that the delta u (k + i) is 0, and the i is NC，NC+1,…,NP-1; thus, the output in the prediction time domain of the equalization system can be obtained by:
where Δ x (k) ═ x (k) — x (k-1) represents the state increment of the system at time k, Δ u (k) represents the control increment of the system, and S represents the control increment of the systemx，Γ，SuRespectively as follows:
the second step of the predictive control strategy is to adopt the optimal control quantity to realize the optimal result of the target control, and generally solve the corresponding control quantity when the minimum value of the objective function is solved. The invention firstly constructs the following objective function to minimize the state of charge deviation among the single batteries of the storage battery pack:
wherein R isi(i＝1，2，…NP) Is an error weight matrix. y (k) is the predicted battery state of charge matrix at time k, yrefSetting y (k) -y for a given reference value of the controlled variable trajectoryrefThe target value may reduce battery pack state of charge fluctuations. Calculating an optimal control quantity matrix U of the objective function in a prediction time domain at the k moment by a constrained optimal solution method*(k) And selecting u (k) corresponding to k time in the matrix to be input to the adjacent type balanced topology main circuit controlAnd the conduction time of the switching tube.
The practical work for solving the constraint optimization problem is to solve a linear programming problem with constraint conditions, and finally obtain a solution corresponding to the minimum value by solving the minimum value of the objective function in the feasible region.
And the third step of the predictive control strategy is to convert the obtained control quantity and then act on the controlled system, reload the collected information such as new state quantity and the like into the constraint optimization problem in the next sampling period, and carry out a new round of solution. Therefore, the predictive control strategy of the equalization system is defined as:
Δu(k)＝[Ι4×Ι4 0 … 0]ΔU*(k)
further, consider the constraint I of the balanced topology circuitCAnd converting the delta u (k) into a series of current control tracking values, and finally completing the design process of the balanced predictive control strategy.
In particular, the method of the adjacent equalization system for a storage battery pack and the predictive control method thereof according to the present embodiment is as shown in fig. 8, and the adaptive control block diagram principle of the bidirectional boost converter is that the adaptive control method is adopted to control the bidirectional boost converter to work efficiently and stably according to the working characteristics of the adjacent equalization topology main circuit, so as to realize mutual transfer of energy of each single battery.
Firstly, establishing a mathematical model of a bidirectional boost converter to obtain a state equation of the bidirectional boost converter; then introducing system self-adaptive parameters, matrixing the state equation, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities; finally, respectively eliminating the estimation error and the control deviation of the system parameters in the derivative of the Lyapunov function V to obtain a parameter self-adaptive law and a control law as follows:
where ψ is a positive definite matrix, c1，c2And alpha, beta are both normal numbers, s＝c2e1+e2as a slip form face, e1＝x1-iref＝iL-irefIs the error in the tracking of the track,is a second order inversion variable; x ═ x1 x2 x3]T＝[iLVBat1 VBat2]TRepresenting the system state quantity.
In the above formula, the first and second polymers are,-a converter state reference variable,u1and u2Respectively represent power tubes S1、S2The control signal of (a) is,energy is transferred from battery 1 to battery 2 at 1,for-1 energy transfer from battery 2 to battery 1, iLInductor current, VBat2-a capacitance CBat2Voltage across, VBat1-a capacitance CBat1Voltage at both ends, defined as L as the circuit inductance value, RBat1、CBat1Is a VBat1The equivalent resistance and capacitance of the end cell,RBat2、CBat2is a VBat2End cell equivalent resistance and capacitance.
In this embodiment, the stability and robustness of the algorithm are tested according to the adaptive control method, and the results are shown in fig. 9, which respectively show the simulation results of the bidirectional boost converter under the starting condition and the tracking current step condition of the control method shown in fig. 8, and it can be seen that the equalizing current is rapidly stabilized at the tracking current reference value.
In the embodiment, the storage battery pack adjacent equalization system shown in fig. 1 is verified, and as shown in fig. 10 and 11, the states of charge of the individual cells of the storage battery pack are respectively 0.75, 0.78, 0.70, 0.80, and 0.67, and finally reach consistency in about 262 seconds, and the distributed equalization current changes smoothly in the constraint current, so that the storage battery pack adjacent equalization system and the predictive control method thereof are finally implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or 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, and the like) 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 application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (5)
1. A storage battery adjacent type equalizing system is characterized by comprising a storage battery, a signal acquisition and processing module, an MPC-FPGA controller, a self-adaptive controller, a bidirectional boost converter type equalizing circuit and a driving circuit;
a bidirectional boost converter is connected between every two adjacent minimum series monomers of the storage battery pack and is used for controlling mutual transfer of energy of the monomer batteries;
the signal acquisition processing module acquires and processes voltage, current and temperature signals of the storage battery pack and current change conditions of the bidirectional boost converter, and converts the signals into signals which can be identified by the MPC-FPGA controller and the adaptive controller;
the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the storage battery pack, the self-adaptive controller obtains a parameter self-adaptation law and a control law estimation system parameter respectively by adopting a balance control strategy and generates a control signal u, and then the control drive circuit controls the bidirectional boost converter to realize energy transfer among the single batteries of the storage battery pack and realize the balance process of the storage battery pack.
2. The adjacent storage battery pack type equalization system according to claim 1, wherein the MPC-FPGA controller generates an optimal control quantity after pre-judging the overall state of the storage battery pack, and specifically comprises: establishing a state space model of the equilibrium system; constructing a target function to minimize the state of charge deviation among the single batteries of the storage battery pack, and solving the corresponding control quantity when the target function is the minimum; and converting the obtained control quantity, acting on the controlled system, reloading the collected information such as new state quantity into the constraint optimization problem in the next sampling period, and performing a new round of solution.
3. The adjacent equalization system of the storage battery pack according to claim 1, wherein the adaptive controller estimates system parameters and generates a control signal u by using a parameter adaptive law and a control law, and further controls the bidirectional boost converter through the driving circuit to realize energy transfer among the single batteries of the storage battery pack, and the equalization process of the storage battery pack is specifically as follows: firstly, establishing a mathematical model of a bidirectional boost converter to obtain a state equation of the bidirectional boost converter; then introducing system self-adaptive parameters, matrixing the state equation, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities; and finally, respectively eliminating the estimation error and the control deviation of the system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law.
4. A control method for the adjacent equalization system of battery pack according to any of claims 1 to 3, characterized by comprising the steps of:
step S1: and (3) considering the charge state changes of all the single batteries of the storage battery pack, obtaining a state space model of the equalizing system and discretizing the state space model, wherein the state space model comprises the following steps:
in the formula,ACand C are both a unit matrix, u represents the system control variable, TsIn order to control the sampling step size of the system,CQ、ICis a diagonal matrix which respectively represents the capacity of each single battery of the storage battery and the maximum working current of an adjacent type equalizing system, T represents the adjacent type equalizing topological structure relationship, ACC and T are as follows:
each variable in the state space model of the equilibrium system meets the following limiting conditions:
0≤x(k)≤1；
-1≤u(k)≤1。
step S2: let the prediction time domain and the control time domain be N respectivelyP、NC(ii) a And the control quantity outside the control time domain is constant, namely delta u (k + i) is 0, i is NC，NC+1,…,NP-1; the output in the prediction time domain of the equalization system is thus given by:
wherein Δ x (k) -x (k)k-1) represents the state increment of the system at time k, Δ U (k) is the control increment of the system, and Sx，Γ，SuRespectively as follows:
step S3: the following objective function is constructed to minimize the state of charge deviation among the single batteries of the storage battery pack:
in the formula, Ri(i＝1，2，…NP) Is an error weight matrix; y (k) is the predicted battery state of charge matrix at time k, yrefSetting y (k) -y for a given reference value of the controlled variable trajectoryrefThe target value can reduce the state of charge fluctuation of the storage battery pack; calculating an optimal control quantity matrix U of the objective function in a prediction time domain at the k moment by a constrained optimal solution method*(k) And u (k) corresponding to the k time in the matrix is selected as a control quantity to control the conduction time of a switching tube in the bidirectional boost converter; in the next sampling period, the collected new state quantity is reloaded into the constraint optimization problem and a new round of solution is performed, so that the predictive control strategy of the equilibrium system is defined as:
Δu(k)＝[ΙN-1×ΙN-1 0 … 0]ΔU*(k)。
5. the method of controlling a battery pack adjacent equalization system according to claim 4, further comprising the steps of:
step S4: establishing a mathematical model of the bidirectional boost converter and obtaining a state equation of the bidirectional boost converter
Step S5: introducing system self-adaptive parameters, matrixing a state equation of the bidirectional boost converter, and further designing a sliding mode surface and a Lyapunov function by combining tracking control errors and system state quantities;
step S6: respectively eliminating the estimation error and the control deviation of system parameters in the derivative of the Lyapunov function to obtain a parameter self-adaptive law and a control law as follows:
where ψ is a positive definite matrix, c1，c2And alpha, beta are both normal numbers, s＝c2e1+e2as a slip form face, e1＝x1-iref＝iL-irefIs the error in the tracking of the track,is a second order inversion variable; x ═ x1 x2 x3]T＝[iLVBat1 VBat2]TRepresenting a system state quantity;
wherein,a reference variable for the state of the converter,u1and u2Control signals, i, of two power transistors in a bidirectional boost converterLIs an inductor current irefFor reference purposesCurrent, VBat1And VBat2Voltage at input and output ends of the bidirectional boost converter, L is inductance value of the bidirectional boost converter circuit, RBat1、RBat2Respectively an equivalent resistance C at two ends of the bidirectional boost converterBat1、CBat2Respectively V in bidirectional boost converterBat1And VBat2The capacitance of the terminal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112067925A (en) * | 2020-09-07 | 2020-12-11 | 淮阴工学院 | Real-time weighted fault detection method for boost converter circuit |
| CN112117792A (en) * | 2020-07-31 | 2020-12-22 | 西安交通大学 | Passive equalization system based on model and equalization current estimation method |
| CN113346571A (en) * | 2021-05-12 | 2021-09-03 | 万克能源科技有限公司 | Event-driven alternating-current micro-grid energy storage SOC balance control method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200623581A (en) * | 2004-12-31 | 2006-07-01 | Jason Auto Technology Co Ltd | Method and device for battery charger and diagnosis with detectable battery energy barrier |
| CN101966820A (en) * | 2010-08-26 | 2011-02-09 | 清华大学 | On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge |
| CN107134827A (en) * | 2017-05-27 | 2017-09-05 | 重庆大学 | Bus type lithium battery group equalizing system forecast Control Algorithm |
| CN107276171A (en) * | 2017-07-12 | 2017-10-20 | 浙江大学 | A kind of battery pack equilibrium method based on sliding formwork control |
| CN107591991A (en) * | 2017-07-25 | 2018-01-16 | 华南理工大学 | A kind of bicyclic forecast Control Algorithm of band control error compensation |
| CN108594135A (en) * | 2018-06-28 | 2018-09-28 | 南京理工大学 | A kind of SOC estimation method for the control of lithium battery balance charge/discharge |
| CN109378881A (en) * | 2018-11-30 | 2019-02-22 | 福州大学 | A kind of power battery pack bidirectional self-adaptive balance control method |
| CN109617151A (en) * | 2018-11-19 | 2019-04-12 | 浙江大学 | Lithium battery group active equalization control method based on Model Predictive Control |
-
2019
- 2019-07-15 CN CN201910634595.9A patent/CN110297452B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200623581A (en) * | 2004-12-31 | 2006-07-01 | Jason Auto Technology Co Ltd | Method and device for battery charger and diagnosis with detectable battery energy barrier |
| CN101966820A (en) * | 2010-08-26 | 2011-02-09 | 清华大学 | On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge |
| CN107134827A (en) * | 2017-05-27 | 2017-09-05 | 重庆大学 | Bus type lithium battery group equalizing system forecast Control Algorithm |
| CN107276171A (en) * | 2017-07-12 | 2017-10-20 | 浙江大学 | A kind of battery pack equilibrium method based on sliding formwork control |
| CN107591991A (en) * | 2017-07-25 | 2018-01-16 | 华南理工大学 | A kind of bicyclic forecast Control Algorithm of band control error compensation |
| CN108594135A (en) * | 2018-06-28 | 2018-09-28 | 南京理工大学 | A kind of SOC estimation method for the control of lithium battery balance charge/discharge |
| CN109617151A (en) * | 2018-11-19 | 2019-04-12 | 浙江大学 | Lithium battery group active equalization control method based on Model Predictive Control |
| CN109378881A (en) * | 2018-11-30 | 2019-02-22 | 福州大学 | A kind of power battery pack bidirectional self-adaptive balance control method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112117792A (en) * | 2020-07-31 | 2020-12-22 | 西安交通大学 | Passive equalization system based on model and equalization current estimation method |
| CN112117792B (en) * | 2020-07-31 | 2022-07-12 | 西安交通大学 | Passive equalization system based on model and equalization current estimation method |
| CN112067925A (en) * | 2020-09-07 | 2020-12-11 | 淮阴工学院 | Real-time weighted fault detection method for boost converter circuit |
| CN113346571A (en) * | 2021-05-12 | 2021-09-03 | 万克能源科技有限公司 | Event-driven alternating-current micro-grid energy storage SOC balance control method |
| CN113346571B (en) * | 2021-05-12 | 2022-04-01 | 万克能源科技有限公司 | Event-driven alternating-current micro-grid energy storage SOC balance control method |
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