CN-122001043-A - Battery network dynamic reconstruction method and system based on entropy reduction model

CN122001043ACN 122001043 ACN122001043 ACN 122001043ACN-122001043-A

Abstract

The invention discloses a battery network dynamic reconstruction method and system based on an entropy reduction model, which belong to the technical field of new energy storage technology and battery management systems, and comprise the steps of collecting operation parameters of each battery module in a battery network in real time; the method comprises the steps of calculating a macroscopic state vector representing a consistency state of a battery network based on the operation parameters, calculating a unified entropy value of the battery network based on probability distribution of the macroscopic state vector, solving an optimal battery network topology reconstruction instruction by taking the minimized unified entropy value or the change rate of the unified entropy value as an optimization target, executing the battery network topology reconstruction instruction, and dynamically adjusting the topology relation among battery modules. According to the invention, by establishing an entropy reduction dynamics model fused by information thermodynamics and a control theory, the disorder degree of the system is quantized, and an optimal reconstruction strategy is generated according to the disorder degree, so that the entropy increase of the system is actively restrained, and the consistency, the service life and the safety of the energy storage system are cooperatively improved.

Inventors

ZHANG MING
LI KAI
LI XUEFENG
LI CHAOFAN
CAO XUEBIN
Bai Xuheng
TIAN XING
WANG YUNFANG
QIN WEI

Assignees

云储新能源科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (10)

1. The method for dynamically reconstructing the battery network based on the entropy reduction model is characterized by comprising the following steps of: Collecting operation parameters of each battery module in a battery network in real time; Calculating a macroscopic state vector representing a battery network consistency state based on the operating parameters; based on probability distribution of macroscopic state vectors, calculating a unified entropy value of the battery network; Taking the minimized unified entropy value or the change rate of the unified entropy value as an optimization target, and solving an optimal battery network topology reconstruction instruction; and executing a battery network topology reconstruction instruction, and dynamically adjusting the topology relation among the battery modules.
2. The method for dynamically reconstructing the battery network based on the entropy reduction model of claim 1, wherein the unified entropy value is calculated in a Gibbs entropy form, and the calculation formula is as follows: ; In the above-mentioned method, the step of, Is the total entropy of the system at time t, Is a boltzmann constant, The probability density of the battery network being in the macroscopic state vector m at time t.
3. The method for dynamically reconstructing the battery network based on the entropy reduction model according to claim 1 or 2, wherein the optimal battery network topology reconstruction command is a predictive optimization based on an information-driven main process, and the expression of the main process is: ; In the above-mentioned method, the step of, Represents a natural evolution term describing the decay of the spontaneous consistency of the system, At the rate of the natural transition, Is the pre-state of the macroscopic state vector m, For the time t the battery network is in the m prepositive state Is used to determine the probability density of (1), To describe the information feedback control term of the control action, P is a probability density function, In order to control the action of the device, As the measurement information at the present moment in time, Is the evolution rate of the probability distribution of the macroscopic state of the system.
4. The method for dynamically reconstructing a battery network based on an entropy reduction model as set forth in claim 3, wherein the information feedback control term has a calculation formula as follows: ; In the above-mentioned method, the step of, To control the flow velocity, its direction is determined by a cost function Is determined by the information diffusion coefficient Determining; is a partial differential operator.
5. The method for dynamically reconstructing a battery network based on an entropy reduction model as set forth in claim 4, wherein the control flow rate satisfies the following formula: ; In the above-mentioned method, the step of, Is an information diffusion coefficient for quantifying the change efficiency of the control action; is a gradient operator.
6. The method for dynamic reconstruction of a battery network based on an entropy reduction model according to claim 5, further comprising diffusing the information by a coefficient Performing identification, wherein the identification step comprises the following steps: Recording the change quantity of macroscopic state vector before and after each execution of topology reconstruction instruction ; Calculating gradients of the cost function at corresponding states prior to reconstruction ; According to linear regression relationship Estimating the information diffusion coefficient Is a value of (2).
7. The method for dynamically reconstructing a battery network based on an entropy reduction model according to claim 3, wherein the solving the optimal battery network topology reconstruction command adopts a model prediction control framework in the solving process, and the method comprises the following steps: Based on probability distribution at the current moment Predicting evolution tracks of unified entropy values of the system under different candidate topology reconstruction sequences in a future prediction period range with an information-driven main process; And selecting a sequence which can minimize the unified entropy value at the end of the prediction period from all candidate topology reconstruction sequences as an optimal topology reconstruction sequence, namely, taking a sequence which can minimize the unified entropy value at the end of the prediction period as an optimal battery network topology reconstruction instruction at the current moment by a corresponding first control instruction.
8. A method for dynamic reconstruction of a battery network based on an entropy reduction model as claimed in claim 3, further comprising the step of applying a natural transition rate to said main equation A step of performing identification, the step of identifying comprising: Collecting macroscopic state time sequence data when the battery network is in a fixed topological state; based on the time series data, a natural transition rate matrix is estimated by counting transition frequencies between states.
9. A battery network dynamic reconstruction system based on an entropy reduction model, characterized by being adapted to implement the method according to any of claims 1-8, comprising: The sensing measurement unit is used for collecting the operation parameters of each battery module in the battery network in real time; the entropy reduction model calculation unit is connected with the sensing measurement unit and is used for executing calculation and optimization steps and generating a topology reconstruction instruction; And the dynamic reconstruction executor is connected with the entropy reduction model calculation unit and is used for receiving and executing the topology reconstruction instruction so as to change the electric connection topology between the battery modules.
10. The battery network dynamic reconstruction system based on the entropy reduction model according to claim 9, wherein the entropy reduction model calculation unit performs numerical solution on the information driven main course through a finite volume method or a random simulation algorithm.

Description

Battery network dynamic reconstruction method and system based on entropy reduction model Technical Field The invention relates to the technical field of new energy storage technologies and battery management systems, in particular to a method and a system for dynamically reconstructing a battery network based on an entropy reduction model. Background With the continuous increase of the renewable energy source duty ratio, a large-scale battery energy storage system has become a key infrastructure for peak shaving, frequency modulation and peak clipping and valley filling of a power grid on the user side. However, in the long-term operation of the energy storage system, due to the inherent differences of the battery cells in the aspects of capacity, internal resistance, aging rate and the like, consistency attenuation occurs in the system, and the consistency attenuation is expressed as divergence of parameters such as voltage, temperature and the like. From the thermodynamic point of view, this phenomenon is an entropy increase caused by irreversible processes inside the system, i.e. the inevitable trend of the system going from ordered to disordered. Traditional battery management systems mainly employ passive equalization or simple active equalization strategies. Passive equalization dissipates the energy of high-energy battery cells through resistors, which is inefficient and aggravates the thermal management burden of the system, while active equalization can transfer energy through devices such as DC/DC converters, which are typically costly, topologically complex, and difficult to deploy on a large scale at the battery pack or cluster level. These methods are essentially local, hysteretic corrections to the "entropy increase" result, failing to actively suppress entropy generation from the system dynamics level. The dynamic reconfigurable battery network technology changes the serial-parallel topology of the battery module in real time through the power electronic switch matrix, and provides a revolutionary means for realizing real-time and non-dissipative equalization of energy. Although prior applications have proven their effectiveness, existing control strategies rely mostly on rule-based threshold decisions or empirical algorithms, lacking a unified theoretical framework that can profoundly reveal the inherent mechanisms of achieving "entropy reduction". The existing theoretical model is used for simply and linearly superposing thermodynamic entropy and information entropy, has inherent defects of physical mechanism fracture, lack of dynamic description, limited guiding significance and the like, and cannot answer the core problem of 'what control strategy can most effectively realize entropy reduction'. Therefore, there is an urgent need for a control system that, based on the first principle, can represent the dynamic reconfiguration process as an information-driven feedback control process, and can uniformly describe and quantify the theoretical model of the "entropy-decreasing" mechanism thereof and thus construct, so as to guide the design and optimization of the next-generation intelligent energy storage system. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a battery network dynamic reconstruction method and a system based on an entropy reduction model, which are characterized in that the disorder degree of a system is quantified by establishing an entropy reduction dynamic model fused by information thermodynamics and a control theory, and an optimal reconstruction strategy is generated according to the disorder degree, so that the entropy increase of the system is actively restrained, and the consistency, the service life and the safety of an energy storage system are cooperatively improved. The technical scheme for solving the technical problems is as follows: in a first aspect, the present invention provides a method for dynamically reconstructing a battery network based on an entropy reduction model, including: Collecting operation parameters of each battery module in a battery network in real time; Calculating a macroscopic state vector representing a battery network consistency state based on the operating parameters; based on probability distribution of macroscopic state vectors, calculating a unified entropy value of the battery network; Taking the minimized unified entropy value or the change rate of the unified entropy value as an optimization target, and solving an optimal battery network topology reconstruction instruction; and executing a battery network topology reconstruction instruction, and dynamically adjusting the topology relation among the battery modules. Further, the unified entropy is calculated in a Gibbs entropy form, and a calculation formula is as follows: ; In the above-mentioned method, the step of, Is the total entropy of the system at time t,Is a boltzmann constant,The probability density of the battery network being