Control method and control system for lithium-ion battery active equalization at fusion temperature

Abstract

The application provides a control method and a control system for active balancing of lithium ion batteries at a fusion temperature. The method comprises: obtaining the initial temperature and the initial state of charge of each cell of the lithium ion battery when the lithium ion battery is in the balancing mode; constructing a linear state prediction model; constructing a cost function and converting it into a quadratic programming problem with linear constraints and solving to obtain the optimal battery balancing current; adjusting the energy flow of the bidirectional Cuk converter bidirectional switch tube according to the optimal battery balancing current to adjust the battery balancing current; updating the state of charge of each cell and the temperature of each cell through the LSTM model and the preset constraints. In this way, by constructing an active balancing control method targeting the state of charge and temperature deviation of the cells, combining the fine current regulation capability of the bidirectional Cuk converter, the dynamic prediction and intelligent optimization control of the lithium ion battery state are realized, the balancing speed of the lithium ion battery is improved, the temperature difference during balancing is reduced, and the safety is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery management, in particular to a temperature-fused lithium-ion battery active balancing control method and system. BACKGROUND

[0002] In a lithium-ion battery pack, due to cell inconsistency and thermal coupling effect, SOC and temperature often show distribution differences. When the difference is too large, it will cause capacity utilization rate to decrease, cycle life to shorten, and even thermal runaway. The existing passive balancing or simple voltage balancing method cannot consider both SOC and temperature indicators, and lacks forward-looking prediction and rapid response capability. SUMMARY

[0003] Therefore, a temperature-fused lithium-ion battery active balancing control method and system are provided to improve the inconsistency of lithium-ion battery cells in the prior art.

[0004] In a first aspect, a temperature-fused lithium-ion battery active balancing control method is provided, which includes:

[0005] In response to the lithium-ion battery being in a balancing mode, the initial temperature of each cell of the lithium-ion battery is obtained, and the initial state of charge of each cell is obtained.

[0006] A linear state prediction model is constructed according to the initial temperature and the initial state of charge.

[0007] A cost function is constructed, and the cost function is converted into a quadratic programming problem with linear constraints.

[0008] The quadratic programming problem is solved to obtain an optimal battery balancing current.

[0009] The optimal battery balancing current is used to adjust the control of energy flow of the bidirectional Cuk converter bidirectional switch tube, and the battery balancing current is adjusted.

[0010] The state of charge of each cell is updated through an LSTM model and a preset constraint, and the temperature of each cell is updated through a heat conservation equation.

[0011] Optionally, the linear state prediction model is constructed according to the initial temperature and the initial state of charge, which includes:

[0012] A state vector of the battery pack at time k is defined, which includes the initial state of charge of N cells at time k and the initial temperature of N cells at time k.

[0013] A linear state prediction model is constructed according to the state vector and a preset matrix.

[0014] The linear state prediction model satisfies:

[0015]

[0016] in, Let A represent the state vector of the j-th cell at time k, including the state of charge and temperature; A is the identity matrix; B is the control input coefficient matrix. Let represent the current of the j-th cell at time k.

[0017] Optionally, the cost function satisfies:

[0018]

[0019] in, J Represents the cost function, This represents the prediction time-domain step size, where N represents the total number of battery cells. The target weighting coefficient represents the deviation of the state of charge. Indicates the first m Each battery cell State of charge at time t, Indicates the target state of charge. The target weighting coefficient represents the temperature deviation. Indicates the first m Each battery cell Temperature at any moment Indicates the target temperature.

[0020] Optionally, the cost function is transformed into a quadratic programming problem with linear constraints, including:

[0021] The cost function is transformed into a quadratic programming problem, which satisfies the following:

[0022]

[0023] in, Let N represent the current sequence of the lithium-ion battery in the predicted time domain, where N represents the number of lithium-ion battery cells. This represents the current of the Nth cell, and T represents the transpose. for U transpose,

[0024] For the first j The current sequence of each cell in the predicted time domain. For the first j Each battery cell k Current at any moment Indicates the prediction time step. For the first j Each battery cell wherein, I represents the current of the battery at time t, H represents a symmetric positive definite matrix, and f represents a first-order term coefficient vector;

[0025] obtaining a current constraint, wherein the current constraint satisfies:

[0026]

[0027] minimizing the current of the constraint during the solving process, maximizing the current of the constraint during the solving process, is j the current of the battery at time t.

[0028] converting the current constraint into an inequality constraint;

[0029] combining the quadratic programming problem with the inequality constraint to obtain the quadratic programming problem with linear constraints.

[0030] Optionally, the updating the state of charge of each battery cell through the LSTM model and the preset constraint comprises:

[0031] inputting the state vector of each battery cell at the current time into the LSTM model to obtain the state of charge of each battery cell at the next time;

[0032] determining a state of charge deviation according to the state of charge of each battery cell at the next time and the state of charge of each battery cell at the current time;

[0033] if the state of charge deviation is greater than a preset deviation threshold, then limiting the state of charge of each battery cell at the current time to obtain the updated state of charge of each battery cell.

[0034] Optionally, after the state of charge of each battery cell is updated through the LSTM model and the preset constraint and the temperature of each battery cell is updated through the heat conservation equation, the control method further comprises:

[0035] determining whether the updated state of charge of each battery cell is greater than a preset state of charge threshold;

[0036] if the updated state of charge of each battery cell is greater than the preset state of charge threshold, reconstructing a cost function to update the state of charge and the temperature of each battery cell until the state of charge of each battery cell is less than the preset state of charge threshold.

[0037] In a second aspect, a control system for active balancing of a lithium-ion battery considering temperature is provided, and the control system comprises:

[0038] ​The acquisition module is configured to acquire initial temperatures of the cells of the lithium ion battery and initial states of charge of the cells in response to the lithium ion battery being in the equalization mode.

[0039] The first construction module is configured to construct a linear state prediction model according to the initial temperatures and the initial states of charge.

[0040] The second construction module is configured to construct a cost function and convert the cost function into a quadratic programming problem with linear constraints.

[0041] The solving module is configured to solve the quadratic programming problem to obtain an optimal battery equalization current.

[0042] The adjustment module is configured to adjust the energy flow of the bidirectional Cuk converter bidirectional switch according to the optimal battery equalization current to adjust the battery equalization current.

[0043] The updating module is configured to update the states of charge of the cells by using an LSTM model and preset constraints and update the temperatures of the cells by using a heat conservation equation.

[0044] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and used to run on the processor, and the processor implements the method described above when executing the computer program.

[0045] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method described above.

[0046] According to the above-mentioned control method for the lithium ion battery active equalization based on the fusion temperature, in response to the lithium ion battery being in the equalization mode, the initial temperatures of the cells of the lithium ion battery and the initial states of charge of the cells are acquired; a linear state prediction model is constructed according to the initial temperatures and the initial states of charge; a cost function is constructed, and the cost function is converted into a quadratic programming problem with linear constraints; the quadratic programming problem is solved to obtain an optimal battery equalization current; the energy flow of the bidirectional Cuk converter bidirectional switch is adjusted according to the optimal battery equalization current to adjust the battery equalization current; the states of charge of the cells are updated by using an LSTM model and preset constraints, and the temperatures of the cells are updated by using a heat conservation equation. In this way, by constructing an active equalization control method with the cell state of charge and temperature deviation as the target and combining the fine current adjustment capability of the bidirectional Cuk converter, the dynamic prediction and intelligent optimization control of the lithium ion battery state are realized, and the equalization speed of the lithium ion battery is improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1is a flowchart of a control method of a lithium ion battery active equalization with temperature fusion provided by an embodiment of the present application.

[0048] Figure 2 is a bidirectional Cuk converter equalization circuit diagram of a battery cell provided by an embodiment of the present application.

[0049] Figure 3 is a battery overall equalization circuit diagram provided by an embodiment of the present application.

[0050] Figure 4 is a structural diagram of a control device of a lithium ion battery active equalization with temperature fusion provided by an embodiment of the present application.

[0051] Figure 5 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0053] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually a category and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally means that the front and rear associated objects are in an "or" relationship.

[0054] The control method and control system of the lithium ion battery active equalization with temperature fusion provided by the embodiments of the present application will be described in detail below with reference to the drawings, specific embodiments and application scenarios.

[0055] Please refer to Figure 1 is a flowchart of a control method of a lithium ion battery active equalization with temperature fusion provided by an embodiment of the present application. As Figure 1 shown, the control method comprises:

[0056] Step S100: In response to the lithium ion battery being in an equalization mode, obtaining the initial temperature of each battery cell of the lithium ion battery, and the initial state of charge of each battery cell.

[0057] In the embodiments of the present application, the core of the equalization mode of the lithium ion battery is to eliminate the voltage / capacity difference of the cells in the series battery pack through active or passive means, avoid overcharging and overdischarging of some batteries, thereby prolonging the service life of the battery pack, ensuring safety and improving overall capacity utilization. The equalization mode is started when the state of charge and temperature difference of the lithium ion battery is greater than a threshold. In response to the lithium ion battery being in the equalization mode, the initial temperature of each cell of the lithium ion battery is obtained, and the initial state of charge of each cell is obtained. The initial temperature of each cell of the lithium ion battery can be collected by a temperature sensor. The initial state of charge of each cell is estimated by an OCV-SOC curve.

[0058] The OCV-SOC curve represents the curve of open-circuit voltage and state of charge, and is one of the most important characteristic curves of the lithium ion battery, which describes the corresponding relationship between the open-circuit voltage (OCV) and the state of charge (SOC) of the battery in the open-circuit state (no charging and discharging current). It is the core basis for the battery management system (BMS) to estimate SOC, and is also a key tool for understanding the electrochemical characteristics of the battery. Specifically, the initial state of charge satisfies: . Wherein, represents the initial state of charge of the jth cell at time k, j represents the initial temperature of the jth cell at time k, represents the inverse function relationship between OCV and SOC, represents the voltage of the jth cell at time k. j

[0059] Step S200: constructing a linear state prediction model according to the initial temperature and the initial state of charge.

[0060] In the embodiments of the present application, MPC (Model Predictive Control) internally uses a linear state prediction model. When constructing the linear state prediction model, first define the state vector of the battery pack at time k, which includes the initial state of charge of the N cells at time k and the initial temperature of the N cells at time k; construct the linear state prediction model according to the state vector and a preset matrix. Specifically, the linear state prediction model can satisfy: . Wherein, represents the state vector of the jth cell at time k, including the state of charge and the temperature; A is an identity matrix; B is a control input coefficient matrix; represents the current of the jth cell at time k.

[0061] Step S300: constructing a cost function, and converting the cost function into a quadratic programming problem with linear constraints.

[0062] ​In this embodiment, the cost function, also commonly referred to as the loss function, is a mathematical function that quantifies the difference between the model's predicted result and the true target. Its core function is to provide the algorithm with an "optimization direction." By minimizing (or maximizing) the value of the cost function, the model output gradually approaches the true target, ultimately improving performance. The cost function is then transformed into a quadratic programming problem with linear constraints. The core of a quadratic programming problem is that the objective function is quadratic and the constraints are linear equations or inequalities.

[0063] In this embodiment of the application, the cost function satisfies:

[0064]

[0065] in, J Represents the cost function, This represents the prediction time-domain step size, where N represents the total number of battery cells. The target weighting coefficient represents the deviation of the state of charge. Indicates the first m Each battery cell State of charge at time t, Indicates the target state of charge. The target weighting coefficient represents the temperature deviation. Indicates the first m Each battery cell Temperature at any moment Indicates the target temperature.

[0066] In this embodiment, the cost function is transformed into a quadratic programming problem, which satisfies:

[0067]

[0068] in, Let N represent the current sequence of the lithium-ion battery in the predicted time domain, where N represents the number of lithium-ion battery cells. This represents the current of the Nth cell, and T represents the transpose. for U transpose,

[0069] For the first j The current sequence of each cell in the predicted time domain For the first j Each battery cell k Current at any moment Indicates the prediction time step. For the first j Each battery cell is the current at time k, H represents a symmetric positive definite matrix, f represents a first-order term coefficient vector, and reflects the influence of the current system state on the optimization direction of the control input, and is determined by the system state and the weighted matrix.

[0070] In the embodiment of the present application, the current constraint is reacquired, and the current constraint satisfies: The current constraint is converted into the form of an inequality constraint, the quadratic programming problem is combined with the inequality constraint, and a quadratic programming problem with linear constraints is obtained.

[0071] Step S400: solving the quadratic programming problem to obtain an optimal battery equalization current.

[0072] In the embodiment of the present application, a quadratic programming (QP) solver can be called to perform real-time solving to obtain an optimal control current sequence.

[0073] Step S500: adjusting the energy flow of the bidirectional Cuk converter bidirectional switch according to the optimal battery equalization current to adjust the battery equalization current.

[0074] In the embodiment of the present application, after the optimal battery equalization current is obtained, the energy flow of the bidirectional Cuk converter bidirectional switch is adjusted according to the optimal battery equalization current to adjust the single battery equalization current. Figure 2 is a bidirectional Cuk converter equalization circuit diagram of the battery cell, and in Figure 2 , B1 is a battery, L1 and L2 are corresponding inductors, C1 and C2 are corresponding capacitors, and Q1 and Q2 are corresponding switches. Figure 3 is an equalization circuit diagram of the battery as a whole, wherein is the first battery cell, is the n-1th battery cell, is the nth battery cell, is the state variable current of the first battery cell, is the state variable current of the n-1th battery cell, is the state variable current of the nth battery cell, that is, the optimal current calculated, is the actual output current of the first battery cell, is the actual output current of the n-1th battery cell, is the actual output current of the nth battery cell. In the case where the loss is not considered:

[0075]

[0076] wherein, is the average current to be output by the converter to which the jth battery cell belongs at the k+1 moment, The first data of the control variable for the j-th cell is output by the MPC, which is the optimal battery balancing current.

[0077] Step S600: Update the state of charge of each cell using the LSTM model and preset constraints, and update the temperature of each cell using the heat conservation equation.

[0078] In this embodiment of the application, the updated state of charge of each cell can be obtained by using an LSTM model and applying preset constraints. The temperature change is determined by the heat generated by internal resistance and the heat exchange with the surrounding environment. The temperature update can be performed using the heat conservation equation.

[0079] Considering the nonlinear and hysteresis characteristics of the battery, a trained LSTM neural network is used as the state estimation model in the actual control process:

[0080]

[0081]

[0082] In the formula: It is the temperature of the j-th cell at time k-L+1. It is the temperature of the j-th cell at time k. It is the current of the j-th cell at time k-L+1. It is the current of the j-th cell at time k. It represents the state of charge of the j-th cell at time k+1. The sampling period is Let be the voltage of the j-th cell at time k. It is the voltage of battery j at time k-L+1. The time window length, For the j-th cell at time... forward Step-by-step input sequence.

[0083] The equation for the conservation of heat is expressed as:

[0084]

[0085] In the formula: For the first j Each battery cell k Temperature at any moment For the first j Each battery cell k Current at any moment m For battery quality, As for the specific heat capacity of the battery As an ambient temperature This refers to the battery heat exchange number. R is the internal resistance of the battery, S A is the surface area of the battery, the formula gives the relationship between the battery current and its temperature, the updated SOC, feedback to the MPC controller to form a closed-loop control.

[0086] Through the above steps S100-S600, in response to the lithium ion battery being in the equalization mode, the initial temperature of each cell of the lithium ion battery and the initial state of charge of each cell are obtained; a linear state prediction model is constructed according to the initial temperature and the initial state of charge; a cost function is constructed, the cost function is converted into a quadratic programming problem with linear constraints; the quadratic programming problem is solved to obtain an optimal battery equalization current; the optimal battery equalization current is used to adjust the control of energy flow of the bidirectional Cuk converter bidirectional switch tube, and the battery equalization current is adjusted; the state of charge of each cell is updated through the LSTM model and the preset constraints, and the temperature of each cell is updated through the heat conservation equation. In this way, by constructing an active equalization control method with the cell state of charge and temperature deviation as the target, and combining the fine current regulation capability of the bidirectional Cuk converter, dynamic prediction and intelligent optimization control of the lithium ion battery state are realized, the equalization speed of the lithium ion battery is improved, the temperature difference during equalization is reduced, and the safety is improved.

[0087] The application scheme fuses temperature factors to introduce model predictive control (MPC) and an LSTM model, constructs an active equalization control method with the minimum battery pack (cell) state of charge and temperature deviation as the target, can realize dynamic prediction and intelligent optimization control of the battery state; at the same time, combining the fine current regulation capability of the bidirectional Cuk converter, bidirectional accurate control of the charging and discharging current of each battery is realized, the safety of equalization is effectively improved, has higher adaptability and practicality, and the temperature-current double closed-loop control can also prevent thermal runaway.

[0088] In some embodiments, constructing a linear state prediction model according to the initial temperature and the initial state of charge includes:

[0089] defining a state vector of the battery pack at time k, the state vector including the initial state of charge of the N cells at time k and the initial temperature of the N cells at time k;

[0090] constructing a linear state prediction model according to the state vector and a preset matrix;

[0091] the linear state prediction model satisfies:

[0092]

[0093] wherein, xk represents the state vector of the jth cell at time k, including the state of charge and the temperature; A is an identity matrix; B is a control input coefficient matrix; denotes the current of the jth cell at time k.

[0094] Specifically, in constructing the linear state prediction model, first define the state vector of the battery pack at time k, the state vector contains the state of charge and temperature. In one example, the state vector at time k is expressed as:

[0095]

[0096] wherein, denotes the state of charge of the 1st cell at time k, denotes the temperature of the 1st cell at time k, and N denotes the number of cells.

[0097] Then, according to the state vector and the preset matrix, the linear state prediction model is constructed, and the linear state prediction model can satisfy:

[0098]

[0099] wherein, denotes the state vector of the jth cell at time k, containing the state of charge and temperature; A is a unit matrix; B is a control input coefficient matrix; denotes the current of the jth cell at time k.

[0100] In some embodiments, the cost function satisfies:

[0101]

[0102] wherein, J denotes the cost function, denotes the prediction time domain step, N denotes the total number of cells, denotes the target weight coefficient of the state of charge deviation, denotes the state of charge of the jth cell at time k, m denotes the target state of charge, denotes the target weight coefficient of the temperature deviation, denotes the temperature of the jth cell at time k, denotes the target temperature. m wherein,

[0103]

[0104]

[0105]

[0106] ​​​​

[0107] In the formula, , These are the unnormalized weighting parameters for state of charge and temperature, respectively. For temperature regulation sensitivity coefficient, Let be the current temperature of the i-th cell. Let n be the average temperature of all battery cells, and n be the total number of battery cells. The sensitivity coefficient for adjusting the state of charge. Let i be the current state of charge of the i-th cell. This represents the current average state of charge (SOC) of all battery cells.

[0108] In some implementations, transforming the cost function into a quadratic programming problem with linear constraints includes:

[0109] The cost function is transformed into a quadratic programming problem, which satisfies the following:

[0110]

[0111] in, Let N be the predicted current sequence of the battery pack in the time domain, where N represents the number of lithium-ion battery cells. This represents the current of the Nth cell, and T represents the transpose. for U transpose,

[0112] For the first j The current sequence of each cell in the predicted time domain For the first j Each battery cell k Current at any moment Indicates the prediction time step. For the first j Each battery cell The current at time t, where H represents a symmetric positive definite matrix and f represents the coefficient vector of the first-order term;

[0113] Obtain current constraints that satisfy:

[0114]

[0115] This represents the minimum current constrained during the solution process. This represents the maximum constrained current during the solution process. for j Each battery cell Current at any given moment.

[0116] Convert the current constraint into the form of an inequality constraint;

[0117] The quadratic programming problem is solved together with the inequality constraints to obtain the quadratic programming problem with linear constraints.

[0118] In some embodiments, the updating the state of charge of each of the battery cells by the LSTM model and the preset constraint comprises:

[0119] inputting the state vector of each of the battery cells at the current time into the LSTM model to obtain the state of charge of each of the battery cells at the next time;

[0120] determining a state of charge deviation according to the state of charge of each of the battery cells at the next time and the state of charge of each of the battery cells at the current time;

[0121] in a case where the state of charge deviation is greater than a preset deviation threshold, limiting the state of charge of each of the battery cells at the current time to obtain the state of charge of each of the battery cells after updating.

[0122] Specifically, the state vector of each of the battery cells at the current time is input into the LSTM model to obtain the state of charge of each of the battery cells at the next time. That is, .

[0123] wherein: is the state of charge of the jth battery cell at the k+1 time, is a sampling period, is the voltage of the jth battery cell at the k time, is a time window length, is the voltage of the jth battery cell at the k time, is the sequence input of the previous step.

[0124] The state of charge deviation is determined according to the state of charge of each of the battery cells at the next time and the state of charge of each of the battery cells at the current time, and in a case where the state of charge deviation is greater than a preset deviation threshold, the state of charge of each of the battery cells at the current time is limited to obtain the state of charge of each of the battery cells after updating. In a case where the state of charge deviation is less than or equal to the preset deviation threshold, the adjustment can be temporarily not performed.

[0125] In some embodiments, after the state of charge of each of the battery cells is updated by the LSTM model and the preset constraint and the temperature of each of the battery cells is updated by the heat conservation equation, the control method further comprises:

[0126] determining whether the state of charge of each of the battery cells after updating is greater than a preset state of charge threshold;

[0127] In a case where the updated state of charge of each of the battery cells is greater than the preset state of charge threshold, the cost function is reconstructed to update the state of charge and the temperature of each of the battery cells until the state of charge of each of the battery cells is less than the preset state of charge threshold.

[0128] Specifically, after updating the state of charge and the temperature of each battery cell, it is determined whether the updated state of charge of each battery cell is greater than the preset state of charge threshold; in a case where the updated state of charge of each battery cell is greater than the preset state of charge threshold, the cost function is reconstructed to update the state of charge and the temperature of each battery cell until the state of charge of each battery cell is less than the preset state of charge threshold, so as to realize closed-loop control.

[0129] Please refer to Figure 4 FIG. 1 is a structural schematic diagram of a control system for active balancing of lithium ion batteries in fusion of temperature, and a second aspect of the present application provides a control system for active balancing of lithium ion batteries in fusion of temperature, which comprises:

[0130] An acquisition module is configured to acquire initial temperatures of battery cells of a lithium ion battery and initial states of charge of the battery cells in response to the lithium ion battery being in an equalization mode;

[0131] A first construction module is configured to construct a linear state prediction model according to the initial temperatures and the initial states of charge;

[0132] A second construction module is configured to construct a cost function and convert the cost function into a quadratic programming problem with linear constraints;

[0133] A solving module is configured to solve the quadratic programming problem to obtain an optimal battery equalization current;

[0134] An adjustment module is configured to adjust a bidirectional Cuk converter bidirectional switch tube to control energy flow according to the optimal battery equalization current and adjust a battery equalization current;

[0135] An updating module is configured to update the state of charge of each battery cell through an LSTM model and a preset constraint and update the temperature of each battery cell through a heat conservation equation.

[0136] Please refer to Figure 5 FIG. 1 is a structural schematic diagram of a control system for active balancing of lithium ion batteries in fusion of temperature, and a second aspect of the present application provides a control system for active balancing of lithium ion batteries in fusion of temperature, which comprises:

[0137] The control system for the fusion temperature lithium ion battery active balancing provided by the second aspect of the embodiments of the present application can achieve the same beneficial effects as the control method for the fusion temperature lithium ion battery active balancing, and thus, for the sake of brevity, will not be described again.

[0138] In some embodiments, the embodiments of the present application provide a machine readable storage medium, on which instructions are stored, and the instructions are executed by a processor to enable the processor to implement the control method for the fusion temperature lithium ion battery active balancing described above.

[0139] In some embodiments, the embodiments of the present application also provide a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the control method for the fusion temperature lithium ion battery active balancing according to the embodiments described above.

[0140] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device implemented in accordance with the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The device that implements the function specified in one or more flows and / or blocks. These computer program instructions can also be stored in a computer readable memory that can cause the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction devices, which implement the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1The computer program instructions can 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 block or blocks. Figure 1 The computer program instructions can 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 block or blocks. Figure 1 The computer program instructions can 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 block or blocks.

[0142] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0143] The memory can include non-persistent memory and / or persistent memory, such as flash memory, read-only memory (ROM), and / or other types of nonvolatile memory. The memory is an example of computer-readable media.

[0144] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0145] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without more limitations, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0146] The above merely provides examples of the present application, but is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

[0147] Furthermore, any combination of the various embodiments of the present application can be made, as long as it does not deviate from the idea of the present application, it shall be considered as disclosed by the present application.

Claims

1. A method for controlling active balancing of lithium-ion batteries at fusion temperature, characterized in that, The control method includes: In response to the lithium-ion battery being in equalization mode, the initial temperature of each cell of the lithium-ion battery and the initial state of charge of each cell are obtained. A linear state prediction model is constructed based on the initial temperature and the initial state of charge. Construct a cost function and transform it into a quadratic programming problem with linear constraints; Solving the quadratic programming problem yields the optimal battery balancing current; The bidirectional switching transistors of the bidirectional Cuk converter are adjusted according to the optimal battery balancing current to control energy flow and regulate the battery balancing current. The state of charge of each cell is updated using an LSTM model and preset constraints, and the temperature of each cell is updated using the heat conservation equation.

2. The control method according to claim 1, characterized in that, A linear state prediction model is constructed based on the initial temperature and the initial state of charge, including: Define the state vector of the battery pack at time k, which includes the initial state of charge of N cells at time k and the initial temperature of N cells at time k; A linear state prediction model is constructed based on the state vector and the preset matrix; The linear state prediction model satisfies: in, This represents the state vector of the j-th cell at time k+1. Let A represent the state vector of the j-th cell at time k, and the state vector includes the state of charge and temperature; A is the identity matrix; B is the control input coefficient matrix. Let represent the current of the j-th cell at time k.

3. The control method according to claim 1, characterized in that, The cost function satisfies: in, J Represents the cost function, This represents the prediction time-domain step size, where N represents the total number of battery cells. The target weighting coefficient represents the deviation of the state of charge. Indicates the first m Each battery cell k+i State of charge at time t, Indicates the target state of charge. The target weighting coefficient represents the temperature deviation. Indicates the first m Each battery cell k+i Temperature at any moment Indicates the target temperature.

4. The control method according to claim 1, characterized in that, Transforming the cost function into a quadratic programming problem with linear constraints includes: The cost function is transformed into a quadratic programming problem, which satisfies the following: in, Let N represent the current sequence of the lithium-ion battery in the predicted time domain, where N represents the number of lithium-ion battery cells. This represents the current of the Nth cell, and T represents the transpose. for U transpose, For the first j The current sequence of each cell in the predicted time domain For the first j Each battery cell k Current at any moment Indicates the prediction time step. For the first j Each battery cell The current at time t, where H represents a symmetric positive definite matrix and f represents the coefficient vector of the first-order term; Obtain current constraints that satisfy: This represents the minimum current constrained during the solution process. This represents the maximum constrained current during the solution process. for j Each battery cell k+i Current at any given moment; Convert the current constraint into the form of an inequality constraint; Combining the quadratic programming problem with the inequality constraints yields the quadratic programming problem with linear constraints.

5. The control method according to claim 1, characterized in that, The process of updating the state of charge of each battery cell using an LSTM model and preset constraints includes: Input the state vector of each cell at the current time into the LSTM model to obtain the state of charge of each cell at the next time. The state of charge deviation is determined based on the state of charge of each cell at the next time step and the state of charge of each cell at the current time step. If the state of charge deviation is greater than a preset deviation threshold, the state of charge of each cell at the current moment is limited to obtain the updated state of charge of each cell.

6. The control method according to claim 1, characterized in that, After updating the state of charge of each cell using an LSTM model and preset constraints, and updating the temperature of each cell using a heat conservation equation, the control method further includes: Determine whether the updated state of charge of each of the battery cells is greater than a preset state of charge threshold; If the updated state of charge of each of the cells is greater than a preset state of charge threshold, the cost function is reconstructed to update the state of charge and temperature of each cell until the state of charge of each cell is less than the preset state of charge threshold.

7. A control system for active balancing of lithium-ion batteries based on temperature integration, characterized in that, The control system includes: The acquisition module is used to acquire the initial temperature of each cell of the lithium-ion battery and the initial state of charge of each cell in response to the lithium-ion battery being in the equalization mode. The first construction module is used to construct a linear state prediction model based on the initial temperature and the initial state of charge. The second construction module is used to construct the cost function and transform the cost function into a quadratic programming problem with linear constraints. The solution module is used to solve the quadratic programming problem to obtain the optimal battery balancing current; The adjustment module is used to adjust the bidirectional switching transistors of the bidirectional Cuk converter to control energy flow and adjust the battery equalization current according to the optimal battery equalization current. The update module is used to update the state of charge of each of the cells using an LSTM model and preset constraints, and to update the temperature of each of the cells using a heat conservation equation.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the control method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control method according to any one of claims 1 to 6.

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