A battery pack equalization management and control system and method for energy storage lithium battery modules

Abstract

This invention discloses a battery pack equalization management and control system and method for an energy storage lithium battery module, relating to the field of battery pack equalization management technology. By calculating the consistency evaluation value of the battery pack at time t within an observation period T, the consistency evaluation value is divided into λ states using a classification threshold. A Markov chain model is used to describe the consistency state transformation process of the battery pack, and influencing factor states are defined. The time series sequence λ of the battery pack consistency evaluation value is obtained when the ambient temperature is in state u, the charging / discharging current is in state v, the grid voltage fluctuation is in state w, and the grid frequency deviation is in state r. u,v,w,r The transition probability of the battery pack's consistency from state i to state j in one step is calculated. A transition probability matrix is ​​constructed to predict the consistency state of the battery pack at the next moment. When the predicted consistency state is abnormal, the current influencing factors are adjusted to the predicted consistency state as normal.

Description

Technical Field

[0001] This invention relates to the field of battery pack equalization management technology, and in particular to a battery pack equalization management control system and method for energy storage lithium battery modules. Background Technology

[0002] During use, differences in individual batteries (such as capacity, internal resistance, and self-discharge rate) and varying charging and discharging conditions can lead to inconsistencies in the state of charge (SOC) and state of health (SOH) of individual cells within a battery pack. This inconsistency affects the overall performance of the battery pack, reduces energy utilization, and may even cause overcharging or over-discharging, resulting in safety issues. This is especially true for distributed energy storage modules, where the relatively dispersed distribution and the inherent time delays in information synchronization, coupled with the large amount of individual battery data across modules, increase the probability of inconsistencies. Therefore, it is crucial to maintain the consistency of individual cells within the battery pack through balanced management and control to ensure the overall performance of the battery pack, thereby improving the energy utilization and lifespan of the battery modules.

[0003] Currently, the main equalization management and control strategies for distributed energy storage lithium battery modules include: single-variable equalization strategies, such as using only voltage or SOC as the equalization variable, which are simple to implement but cannot fully reflect the battery status; multi-variable equalization strategies, such as "voltage + capacity" or "SOC + SOH + temperature", which can more comprehensively reflect the battery status and improve the consistency and safety of the battery pack; and intelligent algorithm optimization, such as dynamically adjusting the equalization threshold based on fuzzy control algorithms and optimizing the equalization path based on genetic algorithms to reduce energy loss.

[0004] The implementation of the aforementioned control strategy is based on the collection and analysis of current operating state variable data of the battery modules. In distributed energy storage systems, the operating state of the battery modules changes in real time with the charging and discharging process, environmental conditions, and individual battery parameters. Relying solely on currently collected operating data for analysis and control may lead to a lag in the control strategy, failing to promptly address the impact of the combined effects of various factors on the consistency of the battery modules. This results in a decrease in the timeliness and effectiveness of equalization management, a deterioration in control performance, and ultimately affects the overall performance and lifespan of the energy storage system. Therefore, we propose a battery pack equalization management and control system and method for energy storage lithium battery modules. Summary of the Invention

[0005] The main objective of this invention is to provide a battery pack equalization management and control system and method for energy storage lithium battery modules, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A battery pack equalization management and control method for an energy storage lithium battery module includes:

[0008] Step 1: Collect the consistency evaluation index data of the battery pack within the observation period T, perform weighted processing, and obtain the consistency evaluation value of the battery pack at time t. t Set the classification thresholds for the battery pack's consistency state from smallest to largest as Consistency1, Consistency2, ..., Consistency λ-1 The consistency evaluation value is divided into λ states using a classification threshold.

[0009] Step 2: Construct the consistent state space of the battery pack S = {s1, s2, ..., s} λ}, s λ Let the consistency of the battery pack be represented as the λth state, t∈T. The consistency state transformation process of the battery pack within the observation period T is described using a Markov chain model.

[0010] Step 3: Obtain data on factors affecting the consistency state of the battery pack within the observation period T, and define the state of the influencing factors using the threshold method, including the ambient temperature state, the charging and discharging current state, the grid voltage fluctuation amplitude state, and the grid frequency deviation state.

[0011] Step 4: Organize the consistency evaluation values ​​of the battery pack within the observation period T into a time series λ. Classify the series λ according to the state of the influencing factors at the time of acquisition, and obtain the time series λ of the battery pack consistency evaluation values ​​when the ambient temperature is in state u, the charging and discharging current is in state v, the grid voltage fluctuation is in state w, and the grid frequency deviation is in state r. u,v,w,r ;

[0012] Step 5: Calculate the time series λ u,v,w,r The consistency evaluation value of the battery pack is the transition probability from state i to state j in one step. And construct the transition probability matrix Using the obtained transition probability matrix P u,v,w,r Predict the consistency state of the battery pack at the next moment, and when the predicted consistency state is abnormal, adjust the current influencing factors to the predicted consistency state as normal.

[0013] A battery pack equalization management and control system for an energy storage lithium battery module, comprising:

[0014] The evaluation index data acquisition module is used to collect consistency evaluation index data of the battery pack within the observation period T, and to perform weighted processing on the collected data to obtain the consistency evaluation value of the battery pack at time t.t ;

[0015] The battery pack state classification module is used to set the classification thresholds for the battery pack's consistency state, from smallest to largest: Consistency1, Consistency2, ..., Consistency... λ-1 The consistency evaluation value is divided into λ states using a classification threshold.

[0016] The data processing module is used to construct the consistent state space S = {s1, s2, ..., s} of the battery pack. λ The uniform state transformation process of the battery pack within the observation period T is described using a Markov chain model, where s λ The consistency of the battery pack is represented by the λth state, t∈T;

[0017] The influencing factor data acquisition module is used to acquire data on influencing factors of battery pack consistency status within the observation period T. The threshold method is used to define the influencing factor status, including ambient temperature status, charge and discharge current status, grid voltage fluctuation amplitude status, and grid frequency deviation status.

[0018] The data time series acquisition module is used to organize the consistency evaluation values ​​of the battery pack within the observation period T into a time series λ. The sequence λ is then classified according to the state of influencing factors at the time of acquisition, and the time series λ of the battery pack consistency evaluation values ​​is obtained when the ambient temperature is at state u, the charging / discharging current is at state v, the grid voltage fluctuation is at state w, and the grid frequency deviation is at state r. u,v,w,r ;

[0019] The consistency state prediction module is used to calculate the time series λ. u,v,w,r The consistency evaluation value of the battery pack is the transition probability from state i to state j in one step. And construct the transition probability matrix Using the obtained transition probability matrix P u,v,w,r Predict the consistency state of the battery pack at the next moment;

[0020] The influencing factor data adjustment module is used to adjust the current influencing factors to a normal consistency state when the consistency state of the prediction is abnormal.

[0021] The system also includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0022] Furthermore, the consistency evaluation indicators include

[0023] Voltage consistency metrics include the average voltage difference of the battery pack and the standard deviation of the voltage change rate of each individual cell within the battery pack.

[0024] Capacity consistency metrics include the standard deviation of the capacity of each individual cell in the battery pack and the standard deviation of the capacity decay rate of each individual cell in the battery pack.

[0025] Internal resistance consistency index: including the average internal resistance difference of the battery pack and the standard deviation of the rate of change of internal resistance of each individual cell in the battery pack;

[0026] Temperature consistency indicators include the temperature difference of the battery pack and the standard deviation of the temperature change rate of each individual cell within the battery pack.

[0027] Charge consistency index: including the standard deviation of charge of each individual cell in the battery pack;

[0028] Self-discharge rate consistency index: includes the standard deviation of the self-discharge rate of each individual cell in the battery pack.

[0029] Furthermore, the consistency assessment value of the battery pack at time t. t The calculation formula is:

[0030]

[0031] In the formula, Ci tk Let θ represent the k-th consistency evaluation index value at time t; k Let θ represent the weight of the k-th consistency evaluation index at time t, and θ tk ∈(0,1), K represents the type of consistency evaluation index.

[0032] Furthermore, the consistency state classification principle for battery packs is as follows:

[0033] When Consistency t When Consistency1 is less than 1, the battery pack consistency at time t is in state 1.

[0034] When Consistency1≤Consistency t When Consistency2 is less than 2, the battery pack consistency at time t is in state 2.

[0035] And so on;

[0036] When Consistency t ≥Consistency λ-1 At time t, the consistency of the battery pack is in state λ.

[0037] Furthermore, the transition probability The calculation formula is:

[0038]

[0039] In the formula, Represented as a time series λ u,v,w,r The number of times the consistency evaluation value of the battery pack transitions from state i to state j in one step; Represented as a time series λ u,v,w,r The total number of times the consistency evaluation value of the battery pack transitions from state i in one step.

[0040] Furthermore, the expression for the Markov chain model describing the uniform state transformation process of the battery pack within the observation period T is as follows:

[0041] P(S t+1 =j|S t =i)

[0042] In the formula, S t+1 S represents the uniformity state of the battery pack at time t+1 within the observation period T; t P(S) represents the uniformity state of the battery pack at time t within the observation period T; t+1 =j|S t =i) represents the consistency state of the battery pack at time t as S t Under the condition that = i, the consistency state of the battery pack at time t+1 after one step transition is S t+1 =j probability, and S t+1 S t , i, j∈S.

[0043] Furthermore, the next-moment consistency state prediction process for the battery pack includes the following steps:

[0044] Let the current time be t', and the consistency state of the battery pack at the current time be s. t’ , and s t’ ∈S, the ambient temperature is state u t’ The charging and discharging current state is v t’ , Status of grid voltage fluctuations w t’ The power grid frequency deviation is r t’ ;

[0045] Select the corresponding transition probability matrix based on the current state of the influencing factors. Calculate the cumulative transition probability at time t'. The calculation formula is:

[0046] Let the next time step be t+1', and the consistency state of the battery pack at the next time step be s. t+1’ , and s t+1’∈S, if Then s t+1’ =s1; if Then s t+1’ =s φ+1 ; where s t+1’ s1 and s φ+1 ∈S; φ is an integer greater than or equal to 1 and less than λ; η t’ To determine the consistency state s of the battery pack t’ The ambient temperature is state u t’ State of charge / discharge current v t’ , Status of grid voltage fluctuations w t’ and the power grid frequency deviation state r t’ The generated random numbers follow a uniform distribution, and 0 ≤ η t’ ≤1.

[0047] Furthermore, the process for determining whether a consistency state is abnormal is as follows:

[0048] Define the consistent state space of the battery pack as S = {s1, s2, ..., s}. λ The boundary state in} is s q where q is an integer between 1 and λ;

[0049] According to the set boundary state s q The consistent state space S of the battery pack is further classified into a normal state space S1 = {s1, s2, ..., s}. q} and the abnormal state space S2={s q s q+1 , ..., s λ};

[0050] When the predicted consistency state of the battery pack at the next moment belongs to state space S1, it is determined to be a normal state.

[0051] When the predicted consistency state of the battery pack at the next moment belongs to state space S2, it is determined to be an abnormal state.

[0052] The present invention has the following beneficial effects:

[0053] Compared with existing technologies, this method obtains the consistency evaluation value of the battery pack at time t by collecting and weighting the consistency evaluation index data of the battery pack within the observation period T. tThe consistency evaluation values ​​are divided into λ states using a classification threshold to construct the consistency state space of the battery pack. A Markov chain model is used to describe the consistency state transformation process of the battery pack within the observation period T. A threshold method is employed to define the states of influencing factors. The consistency evaluation values ​​of the battery pack within the observation period T are organized into a time series λ and classified. The time series λ of the battery pack's consistency evaluation values ​​is obtained when the ambient temperature is in state u, the charging / discharging current is in state v, the grid voltage fluctuation is in state w, and the grid frequency deviation is in state r. u,v,w,r Calculate the time series λ u,v,w,r The consistency of the battery pack is determined by the transition probability from state i to state j in one step, and a transition probability matrix is ​​constructed. The obtained transition probability matrix is ​​used to predict the consistency state of the battery pack at the next moment. When the predicted consistency state is abnormal, the current influencing factors are adjusted to the predicted consistency state as normal. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a battery pack equalization management and control method for an energy storage lithium battery module according to the present invention.

[0055] Figure 2 This is a schematic diagram of the battery pack equalization management and control system of an energy storage lithium battery module according to the present invention. Detailed Implementation

[0056] The present invention will be further described below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size.

[0057] The specific implementation process of the technical solution of this invention includes the following steps:

[0058] Step 1: Collect the consistency evaluation index data of the battery pack within the observation period T, perform weighted processing, and obtain the consistency evaluation value of the battery pack at time t. t Among them, the consistency assessment value is Consistency. t The calculation formula is:

[0059]

[0060] In the formula, Ci tk Let θ represent the k-th consistency evaluation index value at time t; k Let θ represent the weight of the k-th consistency evaluation index at time t, and θ tk ∈(0,1), K represents the type of consistency evaluation index;

[0061] Consistency evaluation indicators include

[0062] Voltage consistency metrics include the average voltage difference of the battery pack and the standard deviation of the voltage change rate of each individual cell within the battery pack.

[0063] The average voltage difference of the battery pack refers to the average of the differences between the voltage of each individual cell in the battery pack and the average voltage of the battery pack. The calculation process is as follows:

[0064] Measure the voltage of each individual cell: Assume that the battery pack has n individual cells. Measure the voltage of each individual cell at the same time, denoted as Uq, where q = 1, 2, ..., n;

[0065] Calculate the average voltage of the battery pack

[0066] Calculate the voltage difference ΔUq for each individual cell:

[0067] Calculate the average voltage difference, where,

[0068] Voltage change rate refers to the change in battery voltage per unit time. The standard deviation of the voltage change rate is used to assess the consistency of voltage changes among individual cells within a battery pack. The calculation process is as follows:

[0069] Measure the voltage change rate of each individual cell: Assuming that the voltage of the q-th individual cell changes from Uq(t) to Uq(t+Δt) within a time interval Δt, then the voltage change rate of this individual cell is:

[0070] Voltage change rate q = [Uq(t+Δt)-Uq(t)] / Δt;

[0071] Calculate the average voltage change rate of all individual cells:

[0072]

[0073] Calculate the voltage change rate deviation of the qth individual cell:

[0074] Voltage change rate deviation q = voltage change rate q - average voltage change rate;

[0075] Calculate the standard deviation of the rate of change of voltage:

[0076]

[0077] Capacity consistency metrics include the standard deviation of the capacity of each individual cell in the battery pack and the standard deviation of the capacity decay rate of each individual cell in the battery pack.

[0078] The capacity standard deviation is used to assess the consistency of capacity among individual cells within a battery pack. The calculation process is as follows:

[0079] Measure the capacity of each individual cell: Assume the battery pack has n individual cells, measure the capacity Cq of each individual cell, where q = 1, 2, ..., n;

[0080] Calculate the average capacity of the battery pack

[0081] Calculate the capacity deviation ΔCq of the q-th individual cell:

[0082] Calculate the standard deviation of the volume:

[0083] The standard deviation of capacity degradation rate is used to assess the consistency of capacity degradation among individual cells within a battery pack. The calculation process is as follows:

[0084] Calculate the capacity decay rate of each individual cell: Assume the capacity decay rate of the q-th individual cell in the nth cycle is R. q,n Its calculation formula is: R q,n =(C 0,q -C q,n ) / C 0,q ×100%; where C 0,q C is the initial capacity of the q-th individual cell. q,n It is the capacity of the q-th individual cell in the nth cycle.

[0085] Calculate the average capacity degradation rate of all individual cells.

[0086] Calculate the capacity decay rate deviation of the q-th individual cell.

[0087] Calculate the standard deviation of capacity decay rate:

[0088] Internal resistance consistency index: including the average internal resistance difference of the battery pack and the standard deviation of the rate of change of internal resistance of each individual cell in the battery pack;

[0089] The average internal resistance difference is used to evaluate the consistency of the internal resistance of individual cells within a battery pack. The calculation process is as follows:

[0090] Measure the internal resistance of each individual cell: Assume that the battery pack has n individual cells. Measure the internal resistance of each individual cell at the same time, denoted as Rq, where q = 1, 2, ..., n;

[0091] Calculate the average internal resistance of the battery pack

[0092] Calculate the internal resistance difference ΔRq of the q-th individual cell:

[0093] Calculate the average internal resistance difference, where,

[0094] The standard deviation of the rate of change of internal resistance is used to assess the consistency of the internal resistance changes among individual cells in a battery pack. The calculation process is as follows:

[0095] Calculate the rate of change of internal resistance for each individual cell: Assume the rate of change of internal resistance of the q-th individual cell during the nth measurement is ΔR. q,n Its calculation formula is: ΔR q,n =(R q,n -R q,0 ) / R q,0 ×100%; where R q,0 R is the initial internal resistance of the q-th individual cell. q,n It is the internal resistance of the q-th cell in the nth measurement.

[0096] Calculate the average internal resistance change rate of all individual cells.

[0097] Calculate the deviation of the internal resistance change rate of the qth individual cell.

[0098] Calculate the standard deviation of the rate of change of internal resistance:

[0099] Temperature consistency indicators include the temperature difference of the battery pack and the standard deviation of the temperature change rate of each individual cell within the battery pack.

[0100] Temperature difference is used to assess the temperature consistency of individual cells within a battery pack. The calculation process is as follows:

[0101] Measure the temperature of each individual cell: Assuming the battery pack has n individual cells, measure the temperature Tq of each individual cell, where q = 1, 2, ..., n;

[0102] Calculate the average temperature of the battery pack

[0103] Calculate the temperature deviation ΔTq of the q-th individual cell:

[0104] Calculate the average temperature difference:

[0105] The standard deviation of the temperature change rate is used to assess the consistency of temperature changes among individual cells within a battery pack. The calculation process is as follows:

[0106] Measure the temperature change rate of each individual cell: Assuming that the temperature of the q-th individual cell changes from Tq(t) to Tq(t+Δt) within a time interval Δt, then the temperature change rate of this individual cell is:

[0107] The rate of temperature change q = [Tq(t+Δt)-Tq(t)] / Δt;

[0108] Calculate the average temperature change rate of all individual cells:

[0109]

[0110] Calculate the temperature change rate deviation of the qth individual cell:

[0111] Temperature change rate deviation q = temperature change rate q - average temperature change rate;

[0112] Calculate the standard deviation of the rate of temperature change:

[0113] Charge consistency index: including the standard deviation of charge of each individual cell in the battery pack;

[0114] The standard deviation of state of charge (SOC) is used to assess the consistency of the state of charge (SOC) of individual cells within a battery pack. The calculation process is as follows:

[0115] Calculate the SOC of each individual cell:

[0116] The battery pack has n individual cells. The state of charge (SOC) value of the q-th individual cell is measured. q q = 1, 2, ..., n; where, State of Charge (SOC) value = current battery capacity / total battery capacity × 100%;

[0117] Calculate the average state of charge (SOC) of all individual cells.

[0118] Calculate the state-of-charge deviation ΔSOC of the q-th individual cell. q ,

[0119] Calculate the standard deviation of the state of charge:

[0120] Self-discharge rate consistency index: includes the standard deviation of the self-discharge rate of each individual cell in the battery pack.

[0121] Self-discharge rate is a measure of the natural loss of charge a battery undergoes during storage, typically expressed as a percentage of capacity loss per day or hour. Calculating the standard deviation of the self-discharge rate of each individual cell within a battery pack can assess the consistency of the self-discharge characteristics of the individual cells within the pack. The calculation process is as follows:

[0122] The battery pack has n individual cells. The self-discharge rate (SDR) of the q-th individual cell is measured. q q = 1, 2, ..., n; where the self-discharge rate can be measured by the following methods:

[0123] Voltage drop method: This method estimates the self-discharge rate by measuring the rate at which the battery voltage drops during storage.

[0124] Capacity decay method: The self-discharge rate is calculated by measuring the percentage of capacity loss of the battery during storage.

[0125] Self-discharge current method: The self-discharge rate is estimated by measuring the self-discharge current of the battery during storage.

[0126] Calculate the average self-discharge rate of all individual cells.

[0127] Calculate the self-discharge rate deviation ΔSDR of the qth individual cell. q ,

[0128] Calculate the standard deviation of self-discharge rate:

[0129] Step 2: Set the classification thresholds for the battery pack's consistency state from smallest to largest as Consistency1, Consistency2, ..., Consistency λ-1 The consistency evaluation value is divided into λ states using a classification threshold, and the classification principle is as follows:

[0130] When Consistency t When Consistency1 is less than 1, the battery pack consistency at time t is in state 1.

[0131] When Consistency1≤Consistency t When Consistency2 is less than 2, the battery pack consistency at time t is in state 2.

[0132] And so on;

[0133] When Consistency t ≥Consistency λ-1 At time t, the consistency of the battery pack is in state λ.

[0134] Step 3: Construct the consistent state space of the battery pack S = {s1, s2, ..., s} λ}, s λ Let the consistency of the battery pack be represented by the λ-th state, t∈T. The consistency state transition process of the battery pack within the observation period T is described using a Markov chain model; where the expression of the Markov chain model is:

[0135] P(S t+1 =j|S t =i)

[0136] In the formula, S t+1 S represents the uniformity state of the battery pack at time t+1 within the observation period T; t P(S) represents the uniformity state of the battery pack at time t within the observation period T; t+1 =j|S t =i) represents the consistency state of the battery pack at time t as S t Under the condition that = i, the consistency state of the battery pack at time t+1 after one step transition is S t+1 =j probability, and S t+1 S t , i, j∈S.

[0137] Step 4: Obtain data on factors affecting the consistency state of the battery pack within the observation period T, and define the state of the influencing factors using the threshold method, including the ambient temperature state, the charging and discharging current state, the grid voltage fluctuation amplitude state, and the grid frequency deviation state.

[0138] Specifically, taking the definition of ambient temperature state as an example:

[0139] By setting a tiered classification threshold for ambient temperature, and denoting them sequentially as T1, T2, ..., T... U-1 The ambient temperature state is classified using a set tiered classification threshold, specifically as follows:

[0140] When the acquired ambient temperature is less than T1, the ambient temperature state is defined as state 1.

[0141] When the acquired ambient temperature is within the range of T1 to T2, the ambient temperature state is defined as state 2.

[0142] And so on;

[0143] When the acquired ambient temperature is greater than T U-1 At that time, the ambient temperature state is defined as state U;

[0144] The definition methods for the other influencing factors are the same as above, and will not be repeated here.

[0145] Step 5: Organize the consistency evaluation values ​​of the battery pack within the observation period T into a time series λ. Classify the series λ according to the state of the influencing factors at the time of acquisition, and obtain the time series λ of the consistency evaluation values ​​of the battery pack when the ambient temperature is in state u, the charging and discharging current is in state v, the grid voltage fluctuation is in state w, and the grid frequency deviation is in state r. u,v,w,r ;

[0146] Step 6: Calculate the time series λ u,v,w,r The consistency evaluation value of the battery pack is the transition probability from state i to state j in one step. And construct the transition probability matrix Among them, the transition probability The calculation formula is:

[0147]

[0148] In the formula, Represented as a time series λ u,v,w,r The number of times the consistency evaluation value of the battery pack transitions from state i to state j in one step; Represented as a time series λ u,v,w,r The total number of times the consistency evaluation value of the battery pack transitions from state i in one step.

[0149] Step 7: Use the obtained transition probability matrix P u,v,w,r Predicting the consistency state of the battery pack at the next moment; the prediction process includes the following steps:

[0150] Let the current time be t', and the consistency state of the battery pack at the current time be s. t’ , and s t’ ∈S, the ambient temperature is state u t’ The charging and discharging current state is v t’ , Status of grid voltage fluctuations w t’ The power grid frequency deviation is r t’ ;

[0151] Select the corresponding transition probability matrix based on the current state of the influencing factors. Calculate the cumulative transition probability at time t'. The calculation formula is:

[0152] Let the next time step be t+1', and the consistency state of the battery pack at the next time step be s. t+1’ , and s t+1’ ∈S, if Then s t+1’ =s1; if Then s t+1’ =s φ+1 ; where s t+1’ s1 and s φ+1 ∈S; φ is an integer greater than or equal to 1 and less than λ; η t’ To determine the consistency state s of the battery pack t’ The ambient temperature is state u t’ State of charge / discharge current v t’ , Status of grid voltage fluctuations w t’ and the power grid frequency deviation state r t’ The generated random numbers follow a uniform distribution, and 0 ≤ η t’ ≤1.

[0153] Step 8: When the consistency of the prediction is abnormal, adjust the current influencing factors until the consistency of the prediction is normal.

[0154] The process for determining whether a consistency status is abnormal is as follows:

[0155] Define the consistent state space of the battery pack as S = {s1, s2, ..., s}. λ The boundary state in} is s q where q is an integer between 1 and λ;

[0156] According to the set boundary state s q The consistent state space S of the battery pack is further classified into a normal state space S1 = {s1, s2, ..., s}. q} and the abnormal state space S2={s q s q+1 , ..., s λ};

[0157] When the predicted consistency state of the battery pack at the next moment belongs to state space S1, it is determined to be a normal state.

[0158] When the predicted consistency state of the battery pack at the next moment belongs to state space S2, it is determined to be an abnormal state.

[0159] Specifically, when the predicted consistency state is abnormal, it means that the consistency state of the battery pack at the current moment is s. t’ The ambient temperature is state u t’ The charging and discharging current state is v t’ , Status of grid voltage fluctuations w t’ The power grid frequency deviation is r t’In the case of an abnormal state, the consistency state of the battery pack changes to an abnormal state at the next moment. At this time, the value of the influencing factor can be adjusted at the current moment. When the state of any influencing factor changes, return to step 5 and repeat steps 5-8 until the predicted consistency state is normal. This can achieve balanced control of the battery pack and keep the individual battery cells in the battery pack highly consistent.

[0160] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A battery pack equalization management control method of an energy storage lithium battery module, characterized by, Comprise: Step one: collect the consistency evaluation index data of the battery pack in the observation period T for weighted processing to obtain the consistency evaluation value of the battery pack at time t , the classification threshold of the consistency state of the battery pack is set from small to large in turn , ,..., , the consistency evaluation value is divided into λ states by using the classification threshold; Step two: Construct the consistency state space S = {s1, s2,..., s λ} of the battery pack, s λ represents the consistency of the battery pack is the λ state, t ∈ T, and the Markov chain model is used to describe the consistency state transformation process of the battery pack within the observation period T; Step three: obtain the influencing factor data of the battery pack consistency state in the observation period T, define the influencing factor state by threshold method, including the environment temperature state, the charging and discharging current state, the power grid voltage variation amplitude state and the power grid frequency deviation state; Step 4: Organize the consistency evaluation values ​​of the battery pack within the observation period T into a time series. The sequence is divided according to the state of influencing factors at the time of collection. Classify and obtain the time series of consistency evaluation values ​​of the battery pack when the ambient temperature is in state u, the charging / discharging current is in state v, the grid voltage fluctuation is in state w, and the grid frequency deviation is in state r. ; Step five: calculate the timing sequence The transition probability of the consistency evaluation value of the battery pack from state i to state j at one step , and construct the transition probability matrix = ( ), , ∈S, use the obtained transition probability matrix to predict the consistency state of the battery pack at the next moment, and when the predicted consistency state is abnormal, adjust the current influencing factors to the predicted consistency state to be normal.

2. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, Consistency evaluation index includes Voltage consistency index: including the average voltage difference of the battery pack and the voltage variation rate standard deviation of each single battery in the battery pack; Capacity consistency index: including the capacity standard deviation of each single battery in the battery pack and the capacity attenuation rate standard deviation of each single battery in the battery pack; Internal resistance consistency index: including the average internal resistance difference of the battery pack and the internal resistance variation rate standard deviation of each single battery in the battery pack; Temperature consistency index: including the temperature difference of the battery pack and the temperature variation rate standard deviation of each single battery in the battery pack; Charge consistency index: including the charge standard deviation of each single battery in the battery pack; Self-discharge rate consistency index: including the self-discharge rate standard deviation of each single battery in the battery pack.

3. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step one, the consistency evaluation value of the battery pack at time t The calculation formula is: = In the formula, represents the kth consistency evaluation index data value at time t; represents the weight of the kth consistency evaluation index at time t, and ∈ (0, 1), ; is the consistency evaluation index type.

4. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step one, the classification principle of the consistency state of the battery pack is: When the consistency of the battery pack at time t is the first state.​ When ≤ < at time t, the consistency of the battery pack is in the 2nd state. By analogy; When ≥ the consistency of the battery pack at time t is the λth state.

5. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step two, the expression of the Markov chain model describing the consistency state transformation process of the battery pack in the observation period T is: wherein, represents the state of uniformity of the battery pack at time t+1 within the observation period T; represents the state of uniformity of the battery pack at time t within the observation period T; represents the state of uniformity of the battery pack at time t+1 within the observation period T; represents the state of uniformity of the battery pack at time t+1 within the observation period T; , , , , ∈ S.

6. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step five, the transition probability is calculated as: = 1 - exp(-β · (V - V0) / V0) (1) wherein, is a time series is the number of times the consistency evaluation value of the battery pack in the state i transitions to the state j by one step; is a time series is the total number of times the consistency evaluation value of the battery pack in the state i transitions by one step.

7. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step five, the consistency state prediction process of the battery pack at the next time includes the following steps: Let the current time be t', the consistency state of the battery pack at the current time be s t’ , and s t’ ∈S, the ambient temperature be u t’ , the charging and discharging current state be v t’ , the grid voltage variation amplitude state be w t’ , and the grid frequency deviation state be r t’ ; According to the influence factor state of the current time, a corresponding transition probability matrix is selected = ( ), the cumulative transition probability at the current t' time is calculated , and the calculation formula is: = ; Let the next time be t+1', and the consistency state of the battery pack at the next time be s t+1’ , and s t+1’ ∈S, if 0≤η t’ ≤ , then s t+1’ =s1; if <η t’ ≤ , then s t+1’ =s φ+1 ; wherein s t+1’ , s1 and s φ+1 ∈S; φ is an integer greater than or equal to 1 and less than λ; η t’ is a random number subject to uniform distribution generated according to the consistency state s t’ of the battery pack, the ambient temperature state u t’ , the charging and discharging current state v t’ , the grid voltage variation range state w t’ and the grid frequency deviation state r t’ , and 0≤η t’ ≤1.

8. The battery pack equalization management control method of the energy storage lithium battery module according to claim 1, characterized in that, In step five, the judgment process of whether the consistency state is abnormal or not is: The boundary states in the consistency state space S = {s1, s2,..., s λ} of the battery pack are set to s q where q is an integer between 1 and λ. According to the set boundary state s q The consistency state space S of the battery pack is further classified into a normal state space S1={s1, s2,..., s q} and an abnormal state space S2={s q , s q+1 ,..., s λ}; When the predicted consistency state of the battery pack at the next time belongs to state space S1, it is judged to be normal state; When the predicted consistency state of the battery pack at the next time belongs to state space S2, it is judged to be abnormal state.

9. A battery pack equalization management control system for an energy storage lithium battery module, characterized by, The system is used to realize the steps of the battery pack equalization management control method of the energy storage lithium battery module according to any one of claims 1-8, comprising: The evaluation index data acquisition module is configured to acquire the consistency evaluation index data of the battery pack within an observation period T, and utilize the formula: = The acquired data is weighted to obtain the consistency evaluation value of the battery pack at time t , wherein represents the kth consistency evaluation index data value at time t; represents the weight of the kth consistency evaluation index at time t, and ∈ (0, 1), ; is the number of consistency evaluation indexes. The battery pack state division module is configured to set classification thresholds of consistency states of the battery pack in ascending order as follows , ,..., , divide the consistency evaluation value into λ states by using the classification thresholds; wherein, the consistency state classification principle is: When < the consistency of the battery pack at time t is in the first state. When ≤ < the consistency of the battery pack at time t is in the 2nd state. By analogy; When ≥ the consistency of the battery pack at time t is the λth state. A data processing module is configured to construct a consistency state space S={s1, s2,..., sN} of the battery pack, and describe a consistency state transition process of the battery pack in an observation period T by using a Markov chain model, wherein sλ represents that the consistency of the battery pack is in the λth state, t∈T; an expression of the Markov chain model is as follows: λ λ ; in the formula, ; in the formula, represents the consistency state of the battery pack at t+1 in the observation period T; represents the consistency state of the battery pack at t in the observation period T; represents the probability that the consistency state of the battery pack at t+1 is sλ+1 under the condition that the consistency state of the battery pack at t is sλ, and , , , ∈S;​​​ The influencing factor data acquisition module is used to obtain the influencing factor data of the battery pack consistency state in the observation period T, and the influencing factor state is defined by threshold method, including the environment temperature state, the charging and discharging current state, the power grid voltage variation amplitude state and the power grid frequency deviation state; The data time sequence acquisition module is configured to arrange the consistency evaluation value of the battery pack in the observation period T into a time sequence , classify the sequence according to the influence factor state at the time of collection, and acquire the time sequence of the consistency evaluation value of the battery pack under the condition that the ambient temperature is the u-th state, the charging and discharging current is the v-th state, the grid voltage variation amplitude is the w-th state, and the grid frequency deviation is the r-th state . ; A consistency state prediction module is configured to calculate a time series A transition probability of a consistency evaluation value of the battery pack from a state i to a state j in one step , and a transition probability matrix is constructed = ( ), , ∈S, using the obtained transition probability matrix to predict a consistency state of the battery pack at a next time instant The influencing factor data adjustment module is used to adjust the current influencing factors to the predicted normal consistency state when the predicted consistency state is abnormal.

10. The battery pack equalization management control system of a stored- energy lithium battery module of claim 9, wherein, The system further comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor can realize the steps of the battery pack equalization management control method of the energy storage lithium battery module according to any one of claims 1-8 when executing the program. The system further comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor can realize the steps of the battery pack equalization management control method of the energy storage lithium battery module according to any one of claims 1-8 when executing the program.

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