An active balancing method for lithium-ion battery packs based on time optimization

Abstract

The present invention relates to a method for active balancing of lithium-ion battery packs based on time optimization, belonging to the field of lithium batteries. A hierarchical active balancing topology structure of module-to-module architecture and unit-to-module architecture is constructed; a relationship between the battery state of charge (SOC) and the pulse width modulation (PWM) duty cycle of the equalizer is established, and the balancing controller is made to act directly on the field effect transistor of the equalizer; by reducing the micro-cycle period between the charge and discharge of the intermediate battery, the SOC of the battery is ensured to converge to a point; a hierarchical balancing topology structure is also developed to simplify the battery pack structure and reduce computing costs. The present invention adopts a time-optimal model predictive control (MPC) strategy and designs a time-optimal method for reducing the micro-cycle of charge and discharge of the intermediate battery, overcoming the problems of large energy loss and rapid battery temperature rise when solving the problem of battery pack inconsistency, and improving the robustness of the system.

Description

Technical Field

[0001] The present invention belongs to the field of lithium batteries and relates to a time-optimized active balancing method for lithium-ion battery packs. Background Art

[0002] With the increasing demand for sustainable energy, energy storage technology plays an important role in electric vehicles and power systems, and is a means to reduce fossil fuel use and reduce global warming. As a representative of advanced energy storage technology, lithium-ion batteries have attracted great attention due to their high energy density, low self-discharge rate, and long life, and are widely used in many fields. Lithium-ion battery packs are composed of many cells connected in series to meet the higher power and energy requirements of practical applications. However, due to the inconsistency and heterogeneity of cells caused by different self-discharge rates, internal resistance, and temperature changes, the performance and battery life of the battery pack may deteriorate over time, even causing safety accidents. Therefore, in order to overcome these problems and improve the performance of lithium-ion battery packs, it is very necessary to develop an effective cell balancing system.

[0003] Addressing cell imbalance in battery packs is a challenge, as cell inconsistencies are caused by many factors. First, overcharging, overdischarging, and voltage imbalances between cells can cause serious problems in a battery pack over time. Cell balancing relies on estimating the state of all cells, such as SOC and voltage. Accurately estimating the state of all cells is very time-consuming for the battery management system. Therefore, an efficient cell balancing system is essential and urgent for lithium-ion battery packs. Summary of the Invention

[0004] In view of this, an object of the present invention is to provide a lithium-ion battery pack active balancing method based on time optimization.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A time-optimal active balancing method for a lithium-ion battery pack comprises the following steps:

[0007] S1: According to the battery pack architecture, a hierarchical active balancing topology is established between modules and batteries, and a bidirectional C'uk equalizer M is designed between modules. ei and battery to module balancer Realize mutual energy transfer;

[0008] S2: The relationship between the SOC of the coupled batteries and the PWM duty cycle of the balancer is studied. The balancing controller is directly applied to the FET of the balancer to calculate the state space expression of the inter-module balancing system and the battery-to-module architecture. The module-to-module and battery-to-module balancing systems are then established.

[0009] S3: Based on the time-optimal model predictive control algorithm, a time-optimal method to reduce the micro-cycle of intermediate battery charge and discharge is designed; by solving the following constrained linear optimal problem, a time-optimal balancing controller for module-to-module and battery-to-module architectures is designed.

[0010] Optionally, in S1, a balancing system consisting of a module-to-module and battery-to-module architecture is established; the battery pack in the system includes n battery modules, each battery module consists of m batteries connected in series; n-1 module-to-module equalizers are used to achieve energy balancing between battery modules, and m battery-to-module equalizers are used to achieve SOC balancing of batteries in each module; an improved bidirectional converter C′uk is used to achieve energy transfer in the module-to-module topology; α i Indicates equalizer Energy transmission efficiency; represents the current delivered to module i from all equalizers, Defined as the average SOC of all batteries in the i-th battery module, the balancing current output by the balancer is defined as Each battery can exchange energy with the entire module, the balancer For energy exchange between battery i and module, 1≤i≤m; They are the balancing currents on the battery / module side respectively, and the total balancing current of battery i is recorded as Including all battery side and module side The balancing current is expressed as

[0011] Optionally, in S2, the balancing current is determined to depend on the duty cycle of the field effect tube according to the relationship between the balancing current between the joint modules and the PWM equalizer; m is the inductance of the C′uk type balancer, and the balancing current I flowing into n battery modules m (k) and PWM signal duty cycle D m The relationship is expressed as:

[0012]

[0013] Defining state variables and the control input vector in is the average rated capacity of all batteries in module i; η is the coulombic efficiency, T s is the sampling period; for the module-to-module equilibrium system, the state space expression of the inter-module equilibrium system is expressed as:

[0014] x m (k+1)=x m (k)+B m u m (k)+d m I n I MS (k)

[0015] in and

[0016] L c is the balancing current of the inductor and battery The PWM duty cycle D applied to the field effect tube c The relationship between them is derived as follows:

[0017]

[0018] The state variable is and the control input variables are Therefore, the state space of the battery module balancing system is expressed as:

[0019] x c (k+1)=x c (k)+B c u c (k)+d c I m I CS (k).

[0020] Optionally, in S3, the time-optimal model-based predictive control algorithm is to design a time-optimal balancing controller for module-to-module and battery-to-module architectures by solving multiple constrained linear optimal problems; for the battery balancing model, the final balanced SOC is expressed as:

[0021]

[0022] in is a constant input; I S is the external current, and is the final equilibrium SOC; 0 n is a zero vector with n columns; the rated capacity of all batteries in the battery pack is roughly the same, and Ld is a smaller value, then the constraint condition is simplified to:

[0023] Lx(k)+LBu(k)θ+LdI n I S (k)θ=0 n

[0024] Define a new column vector: The module-to-module balanced control is designed by solving the following constrained linear optimal problem:

[0025]

[0026] The battery module structure balance controller is executed at discrete time K moments. By solving the minimum time problem at each moment, the optimal PWM duty cycle is obtained. Similarly, by defining a new column vector: By solving the constrained linear optimization problem, the time-optimal balancing controller for the battery-to-module architecture is obtained:

[0027]

[0028] There is no coupling between different modules, and the balancing algorithms of n controllers are calculated in parallel, which improves the calculation speed of the battery balancing algorithm.

[0029] The beneficial effects of the present invention are as follows: the present invention simplifies the structural model of the battery pack and has a significant effect in suppressing the increase in the temperature of the battery pack and achieving battery SOC balance.

[0030] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0032] Figure 1 is a flow chart of the present invention;

[0033] Figure 2 Battery pack schematic; (a) Architecture of module-to-module and cell-to-module balancing systems for battery packs; (b) Schematic diagram of a bidirectional equalizer; (c) Schematic diagram of a bidirectional flyback transformer equalizer;

[0034] Figure 3 The structure of the hierarchical battery balancing control strategy;

[0035] Figure 4 is the external working current of the battery pack;

[0036] Figure 5 is the initial SOC of the battery;

[0037] Figure 6 is the equalization result of battery SOC;

[0038] Figure 7 is the difference in the battery SOC root mean square;

[0039] Figure 8 is the battery temperature;

[0040] Figure 9 is the battery current and control result;

[0041] Figure 10 Comparison results of battery SOC and battery average temperature RMS; (a) is battery SOC RMS; (b) is battery average temperature RMS. DETAILED DESCRIPTION

[0042] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0043] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0044] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0045] Implementation Method 1

[0046] like Figure 1 As shown, the active balancing method for a lithium-ion battery pack based on time optimization described in this embodiment includes the following steps:

[0047] Step 1: Use an isolated bidirectional flyback transformer-based converter to achieve energy transfer in the battery-to-module topology. To prevent excessive current from burning out the balancer, the current range should be limited. The balancer ensures that the battery's state of charge converges to a single point, reducing the battery's microcycle of charge and discharge.

[0048] Step 2: For a battery pack consisting of serially connected battery modules, the battery cells in each battery module share the same balancing current provided by the module-to-module equalizer. By solving a constrained linear optimization problem, a module-to-module time-optimal balancing equalizer can be designed to act between the battery packs.

[0049] Step 3: The balancing controller can act directly on the FET of the equalizer without the need for another inner loop controller to track the balancing current. There is no coupling between different modules, and the balancing algorithm of the equalizer can be calculated in parallel, which can greatly improve the calculation speed of the unit balancing algorithm.

[0050] Step 4: Apply the equalizer obtained in step 3 between different modules of different battery packs, and ensure that the lithium-ion battery pack is actively balanced in the shortest possible time by measuring the SOC and temperature data of individual batteries;

[0051] In this implementation, a module-to-module and unit-to-module hierarchical active balancing topology is constructed, making large battery packs easier to maintain and reducing computational complexity. Secondly, a relationship between the unit SOC and the equalizer PWM duty cycle is established, allowing the balancing controller to act directly on the equalizer's field-effect transistors, facilitating practical implementation. Furthermore, a time-optimized method for reducing the charge and discharge microcycles of the intermediate cells is designed, effectively minimizing energy loss in the intermediate cells and suppressing battery temperature rise when the battery SOC reaches equilibrium. Extensive experimental results demonstrate that the proposed time-optimized method achieves excellent hierarchical active balancing performance, while maintaining fast balancing times and ensuring a safe unit current range.

[0052] Implementation Method 2

[0053] This embodiment further illustrates the first embodiment. A simulation platform was built to test the SOC of a lithium-ion battery pack, and the specific implementation of this method was described in detail using simulation data. First, a battery pack with n = 3 battery modules, each with m = 4 series cells, was simulated using Simulink in MATLAB. The main parameters of the designed control algorithm are shown in Table 1.

[0054] Table 1 External operating current of battery pack

[0055]

[0056] Figure 2 Battery pack schematic; (a) Architecture of module-to-module and cell-to-module balancing systems for battery packs; (b) Schematic diagram of a bidirectional equalizer; (c) Schematic diagram of a bidirectional flyback transformer equalizer;

[0057] The embodiment of the present invention is based on the battery pack simulated above, and its specific diagnostic process includes the following steps:

[0058] Step 1: First, build a virtual simulation environment for the battery pack model on the MATLAB simulation platform; first, use Simulink to create a battery pack with n = 3 battery modules, each module has m = 4 series-connected batteries; the standard capacity of each battery is 1.1Ah and the voltage is 3.2V; build a balancing model for the battery pack, and the battery SOC range is from 0% to 100%. In order to make the converter work in DCM, the maximum PWM duty cycle is set to D max =50%. The battery current should be limited to [-4.1A, 2.1A], such as Figure 4 shown.

[0059] Step 2: Initialize the virtual simulation environment, select the correct SOC initial value and error value σ; use represents the average SOC of the battery at time k. In order to effectively avoid excessive repeated iterations during calculation and reduce the energy supply cost of the battery pack balancing topology, the error σ is set to 0.2%; Figure 5 shown.

[0060] Step 3: Use a hierarchical active balancing optimal algorithm based on lithium-ion battery pack to calculate the balancing result of battery SOC, battery SOC root mean square, battery temperature and battery, such as Figures 6 to 9 shown.

[0061] Implementation Method 3

[0062] This embodiment further explains the first embodiment; in order to illustrate the excellent performance of the proposed time optimization method in active balancing of battery pack layers, the present invention is compared with the balancing control strategy that minimizes the difference in battery state of charge. Different weight coefficients are considered to minimize the strategy of battery SOC difference. The comparison results of battery SOC and battery average temperature root mean square are shown as follows: Figure 10 (a) and 10(b).

[0063] In summary, the following conclusions can be drawn from the constructed simulation platform and comparison:

[0064] from Figure 6 It can be seen that when the balancing time is 1156 seconds, the SOC of all batteries can converge to the same value. In particular, it can be observed that after 1156 seconds, the difference in the root mean square of the battery SOC quickly converges from the initial 13.8% to 0.2%, as shown in Figure 2. Figure 7 As shown; at the same time, Figure 8 During the entire balancing process, when the battery is at an ambient temperature of 25°C, the maximum temperature of the battery is less than 30.9°C, indicating that the proposed method can suppress the temperature rise of the battery; the battery current can be well limited to a safe range of [-4.1A, 2.1A], as shown in Figure 9 All results demonstrate the effectiveness and good performance of our proposed balancing strategy. Because cell imbalance can affect the performance and lifespan of a battery pack, a time-optimal approach to tiered active balancing of lithium-ion battery packs addresses this problem. Extensive experimental results demonstrate that the proposed time-optimal approach delivers excellent tiered active balancing performance, while maintaining a fast balancing time and a safe battery current range.

[0065] It is observed that, regardless of the weight coefficient, our proposed method can make the RMS of the battery SOC converge quickly to a tolerance range, and the average temperature of the battery is lower than the strategy that minimizes the battery SOC difference.

[0066] In contrast, the proposed balancing strategy can make the average temperature of the battery vary within a small range while achieving the fastest balancing of the battery SOC.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for active balancing of a lithium-ion battery pack based on time optimization, the method comprising the following steps: S1: According to the battery pack architecture, establish the module-to-module and battery-to-module hierarchical active balancing topology, and design the bidirectional C′ between modules. uk Type Equalizer M ei and battery to module balancer Realize mutual energy transfer; S2: The relationship between the SOC of the coupled batteries and the PWM duty cycle of the balancer is studied. The balancing controller is directly applied to the FET of the balancer to calculate the state space expression of the inter-module balancing system and the battery-to-module architecture. The module-to-module and battery-to-module balancing systems are then established. S3: Based on a time-optimal model predictive control algorithm, a time-optimal method for reducing the charge and discharge microcycles of the intermediate batteries was designed. By solving the following constrained linear optimization problem, time-optimal balancing controllers for module-to-module and battery-to-module architectures were designed. The time-optimal model-based predictive control algorithm solves multiple constrained linear optimization problems to design a time-optimal balancing controller for module-to-module and battery-to-module architectures. For the battery balancing model, the final balanced SOC is expressed as: in is a constant input; I S is the external current, and is the final equilibrium SOC; 0 n is a zero vector with n columns; the rated capacity of all batteries in the battery pack is roughly the same, and Ld is a smaller value, then the constraint condition is simplified to: <h2 style=";text-align:left;direction:ltr">Lx(k)+LBu(k)θ+LdI<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> I<h2 style=";text-align:left;direction:ltr"> S <h2 style=";text-align:left;direction:ltr"> (k)θ = 0<h2 style=";text-align:left;direction:ltr"> n Define a new column vector: The module-to-module balanced control is designed by solving the following constrained linear optimal problem: The battery module balance controller is executed at discrete time K moments. By solving the minimum time problem at each moment, the optimal PWM duty cycle is obtained. Similarly, by defining a new column vector: By solving the constrained linear optimization problem, the time-optimal balancing controller for the battery-to-module architecture is obtained: There is no coupling between different modules, and the balancing algorithms of n controllers are calculated in parallel, which improves the calculation speed of the battery balancing algorithm.

2. The method for active balancing of lithium-ion battery packs based on time optimization according to claim 1, characterized in that: In S1, a balancing system consisting of a module-to-module and battery-to-module architecture is established; the battery pack in the system includes n battery modules, each battery module consists of m batteries connected in series; n-1 module-to-module equalizers are used to achieve energy balancing between battery modules, and m battery-to-module equalizers are used to achieve SOC balancing of batteries in each module; an improved bidirectional converter C′ is used uk Enables energy transfer in module-to-module topologies; α i Indicates equalizer Energy transmission efficiency; represents the current delivered to module i from all equalizers, Defined as the average SOC of all batteries in the i-th battery module, the balancing current output by the balancer is defined as Each battery can exchange energy with the entire module, the balancer For energy exchange between battery i and module, 1≤i≤m; They are the balancing currents on the battery / module side respectively, and the total balancing current of battery i is recorded as Including all battery side and module side The balancing current is expressed as 3. The method for active balancing of lithium-ion battery packs based on time optimization according to claim 1, characterized in that: In said S2, according to the relationship between the balanced current between the joint modules and the PWM equalizer, it is determined that the balanced current depends on the duty cycle of the field effect tube; K m is the inductance of the C′uk type balancer, and the balancing current I flowing into n battery modules m (k) and PWM signal duty cycle D m The relationship is expressed as: Defining state variables and the control input vector in is the average rated capacity of all batteries in module i; η is the Coulomb efficiency, T s is the sampling period; for the module-to-module equilibrium system, the state space expression of the inter-module equilibrium system is expressed as: x m (k+1)=x m (k)+B m u m (k)+d m I n I MS (k) in and L c is the balancing current of the inductor and battery The PWM duty cycle D applied to the field effect tube c The relationship between them is derived as follows: The state variable is and the control input variables are Therefore, the state space of the battery module balancing system is expressed as: x c (k+1)=x c (k)+B c u c (k)+d c I m I CS (k)。

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