Battery pack equalization management control system of energy storage lithium battery module and method thereof

Abstract

The invention discloses a battery pack equalization management control system of an energy storage lithium battery module and a method thereof, and relates to the technical field of battery pack equalization management. The method comprises the following steps: calculating a consistency evaluation value of a battery pack at a t moment in an observation period T, dividing the consistency evaluation value into lambda states by utilizing a classification threshold value, describing a consistency state transformation process of the battery pack by utilizing a Markov chain model, and defining an influence factor state; obtaining a consistency evaluation value time sequence lambda u, v, w and r of the battery pack when the environment temperature is in the u state, the charging and discharging current is in the v state, the power grid voltage variation range is in the w state and the power grid frequency deviation is in the r state, and calculating the transition probability of the consistency of the battery pack from the state i to the state j through one step, and constructing a transition probability matrix to predict the current consistency state of the battery pack at the next moment, and when the predicted consistency state is abnormal, adjusting the current influence factors until the predicted consistency state is normal.

Description

Technical Field

[0001] The present invention relates to the technical field of battery pack balancing management, and particularly relates to a battery pack balancing management control system and method for an energy storage lithium battery module. Background Art

[0002] During the use of an energy storage lithium battery module, due to differences among battery cells (such as capacity, internal resistance, self-discharge rate, etc.) and different charge and discharge conditions, the state of charge (SOC) and state of health (SOH) of each single battery in the battery pack will become inconsistent. This inconsistency will affect the overall performance of the battery pack, reduce the energy utilization rate, and may even cause overcharging and over-discharging of the battery, leading to safety problems. Especially for distributed energy storage modules, due to the relatively dispersed distribution among modules, there will inevitably be a time delay in the information synchronization process, and there are a large number of battery cell data among modules, increasing the probability of inconsistency. Therefore, it is more necessary to maintain the consistency of single batteries in the battery pack through balancing management control to ensure the use performance of the battery pack, thereby improving the energy utilization rate and service life of the battery module.

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

[0004] The implementation of the above control strategies is based on the acquisition and analysis of the current operating state variable data of the battery module. In a distributed energy storage system, the operating state of the battery module will change in real time with the charge and discharge process, environmental state, and parameters of the battery cells. Relying solely on the currently collected operating data for analysis and control may lead to the lag of the control strategy and the inability to timely respond to the impact of the superposition mechanism of various factor changes on the consistency of the battery module, thereby reducing the timeliness and effectiveness of the balancing management, deteriorating the control effect, and further affecting the overall performance and life of the energy storage system. For this reason, we propose a battery pack balancing management control system and method for an energy storage lithium battery module. Summary of the Invention

[0005] The main object of the present invention is to provide a battery pack balancing management control system and method for an energy storage lithium battery module, which can effectively solve the problems in the background art.

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

[0007] A battery pack balancing management and control method for an energy storage lithium battery module, comprising:

[0008] Step 1: Collect the consistency evaluation index data of the battery pack within the observation period T and perform weighted processing to obtain the consistency evaluation value of the battery pack at time t t , set the classification thresholds of the consistency status of the battery pack from small to large as Consistency1, Consistency2, ..., Consistency λ-1 , using the classification threshold to divide the consistency evaluation value into λ states;

[0009] Step 2: Construct the consistent state space S of the battery pack = {s1, s2, ..., s λ},s λ Denote the consistency of the battery pack as the λth state, t∈T, and use the Markov chain model to describe the consistency state transformation process of the battery pack within the observation period T;

[0010] Step 3: Obtain the influencing factor data of the battery pack consistency state within the observation period T, and use the threshold method to define the influencing factor state, including the ambient temperature state, the charge and discharge current state, the grid voltage variation amplitude state and the grid frequency deviation state;

[0011] Step 4: Arrange the consistency evaluation values of the battery pack within the observation period T into a time series λ, classify the sequence λ according to the state of the influencing factors during the acquisition, and obtain the consistency evaluation value time series λ of the battery pack when the ambient temperature is in the uth state, the charge and discharge current is in the vth state, the grid voltage variation amplitude is in the wth state, and the grid frequency deviation is in the rth state. u,v,w,r ;

[0012] Step 5: Calculate the time series λ u,v,w,r The transition probability of the consistency evaluation value of the battery pack from state i to state j in one step is 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 until the predicted consistency state is normal.

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

[0014] The evaluation index data acquisition module is used to collect the consistency evaluation index data of the battery pack within the observation period T, and 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 consistency state of the battery pack in ascending order as Consistency1, Consistency2,..., Consistency λ-1 , and use the classification thresholds to divide the consistency evaluation values into λ states;

[0016] The data processing module is used to construct the consistency state space S of the battery pack = {s1, s2,..., s λ}}, and use the Markov chain model to describe the process of the consistency state transformation of the battery pack within the observation period T, where s λ represents that the consistency of the battery pack is in the λ-th state, and t ∈ T;

[0017] The influencing factor data acquisition module is used to obtain the influencing factor data of the consistency state of the battery pack within the observation period T, and define the influencing factor states by the threshold method, including the environmental temperature state, the charge and discharge current state, the grid voltage change range state, and the grid frequency deviation state;

[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, classify the series λ according to the influencing factor states at the time of acquisition, and obtain the time series λ of the consistency evaluation values of the battery pack when the environmental temperature is in the u-th state, the charge and discharge current is in the v-th state, the grid voltage change range is in the w-th state, and the grid frequency deviation is in the r-th state u,v,w,r ;

[0019] The consistency state prediction module is used to calculate the transition probability that the consistency evaluation value of the battery pack in the time series λ u,v,w,r transfers from state i to state j in one step and construct a transition probability matrix Use the obtained transition probability matrix P u,v,w,r to 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 various influencing factors to make the predicted consistency state normal when the predicted consistency state is abnormal.

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

[0022] Furthermore, the consistency evaluation indicators include

[0023] Voltage consistency indicator: including the average voltage difference of the battery pack and the standard deviation of the voltage change rate of each single battery in the battery pack;

[0024] Capacity consistency index: including the standard deviation of the capacity of each single cell in the battery pack and the standard deviation of the capacity attenuation rate of each single 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 internal resistance change rate of each single cell in the battery pack;

[0026] Temperature consistency index: including the temperature difference of the battery pack and the standard deviation of the temperature change rate of each single cell in the battery pack;

[0027] State of charge consistency index: including the standard deviation of the state of charge of each single cell in the battery pack;

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

[0029] Furthermore, the consistency evaluation value Consistency of the battery pack at time t t is calculated as follows:

[0030]

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

[0032] Furthermore, the classification principle of the consistency state of the battery pack is as follows:

[0033] When Consistency t <Consistency1, the consistency of the battery pack at time t is in the first state;

[0034] When Consistency1≤Consistency t <Consistency2, the consistency of the battery pack at time t is in the second state;

[0035] And so on;

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

[0037] Furthermore, the calculation formula of the transition probability is as follows:

[0038]

[0039] In the formula, is expressed as the time series λ u,v,w,r the number of times that the consistency evaluation value of the battery pack in is expressed as the time series λ u,v,w,r the total number of times that the consistency evaluation value of the battery pack in

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

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

[0042] In the formula, S t+1 represents the consistency state of the battery pack at the (t + 1)-th moment within the observation period T; S t represents the consistency state of the battery pack at the t-th moment within the observation period T; P(S t+1 = j|S t = i) represents that under the condition that the consistency state of the battery pack at the t-th moment is S t = i, the probability that the consistency state of the battery pack at the (t + 1)-th moment is S t+1 = j after one-step transfer, and S t+1 、S t 、i、j ∈ S.

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

[0044] Set the current moment as t', the consistency state of the battery pack at the current moment as s t’ , and s t’ ∈ S, the environmental temperature is the state u t’ 、the charge and discharge current state is v t’ 、the grid voltage change amplitude state w t’ 、the grid frequency deviation state is r t’ ;

[0045] According to the state of influencing factors at the current moment, select the corresponding transition probability matrix Calculate the cumulative transition probability at the current t' moment The calculation formula is:

[0046] Set the next moment as t + 1', the consistency state of the battery pack at the next moment as 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’ is a random number generated according to the consistency state s t’ of the battery pack, the ambient temperature state u t’ , the charge and discharge current state v t’ , the grid voltage change amplitude state w t’ and the grid frequency deviation state r t’ , and follows a uniform distribution, and 0 ≤ η t’ ≤ 1.

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

[0048] Set the demarcation state in the consistency state space S = {s1, s2,..., s λ} of the battery pack to s q , where q is an integer between 1 and λ;

[0049] According to the set demarcation state s q , further classify the consistency state space S of the battery pack into a normal state space S1 = {s1, s2,..., s q} and an 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 the state space S1, determine it as a normal state;

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

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

[0053] Compared with the prior art, by collecting and weighting the consistency evaluation index data of the battery pack within the observation period T, the consistency evaluation value Consistency of the battery pack at time t is obtained. t, the consistency evaluation values are divided into λ states using a classification threshold, and the consistency state space of the battery pack is constructed. The Markov chain model is used to describe the process of consistency state transformation of the battery pack within the observation period T. The threshold method is adopted to define the states of influencing factors. The consistency evaluation values of the battery pack within the observation period T are sorted into a time series sequence λ and classified to obtain the time series sequence λ of the consistency evaluation values of the battery pack when the ambient temperature is in the u-th state, the charge-discharge current is in the v-th state, the fluctuation range of the grid voltage is in the w-th state, and the grid frequency deviation is in the r-th state. u,v,w,r , calculate the time series sequence λ u,v,w,r of the battery pack in u,v,w,r . Calculate the transition probability that the consistency of the battery pack transfers from state i to state j in one step, and construct a transition probability matrix. Use the obtained transition probability matrix to predict the consistency state of the battery pack at the next moment. When the predicted consistency state is abnormal, adjust the current influencing factors to make the predicted consistency state normal. Description of the Drawings

[0054] Figure 1 is a schematic flow chart of the battery pack equalization management control method for an energy storage lithium battery module of the present invention;

[0055] Figure 2 is a schematic structural diagram of the battery pack equalization management control system for an energy storage lithium battery module of the present invention. Detailed Embodiments

[0056] The following further describes the present invention in conjunction with the detailed embodiments. Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as limiting the present invention. In order to better illustrate the detailed embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual products.

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

[0058] Step 1: Collect and weight the consistency evaluation index data of the battery pack within the observation period T to obtain the consistency evaluation value Consistency of the battery pack at time t t , where the consistency evaluation value Consistency t has the following calculation formula:

[0059]

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

[0061] The consistency evaluation index includes

[0062] Voltage consistency index: including the average voltage difference of the battery pack and the standard deviation of the voltage change rate of each single battery in the battery pack;

[0063] Among them, the average voltage difference of the battery pack refers to the average value of the difference between the voltage of each single battery in the battery pack and the average voltage of the battery pack. The calculation process is as follows:

[0064] Measure the voltage of each single battery: Assume that the battery pack has n single batteries, measure the voltage of each single battery at the same moment, denoted as Uq, q = 1, 2,..., n;

[0065] Calculate the average voltage of the battery pack

[0066] Calculate the voltage difference ΔUq of each single battery:

[0067] Calculate the average voltage difference, where,

[0068] The voltage change rate refers to the change amount of the battery voltage per unit time. The standard deviation of the voltage change rate is used to evaluate the consistency of the voltage change of each single battery in the battery pack. The calculation process is as follows:

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

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

[0071] Calculate the average value of the voltage change rates of all single batteries:

[0072]

[0073] Calculate the deviation of the voltage change rate of the q-th single battery:

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

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

[0076]

[0077] Capacity consistency index: including the standard deviation of the capacity of each single battery in the battery pack and the standard deviation of the capacity attenuation rate of each single battery in the battery pack;

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

[0079] Measure the capacity of each individual cell: Assume there are n individual cells in the battery pack, and 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 capacity standard deviation:

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

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

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

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

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

[0088] Internal resistance consistency index: It includes the average internal resistance difference of the battery pack and the standard deviation of the internal resistance change rate of individual cells within the battery pack;

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

[0090] Measure the internal resistance of each individual cell: Assume there are n individual cells in the battery pack, and measure the internal resistance of each individual cell at the same moment, 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 single cell:

[0093] Calculate the average internal resistance difference, where

[0094] The standard deviation of the internal resistance change rate is used to evaluate the consistency of the internal resistance changes of each single cell in the battery pack. The calculation process is as follows:

[0095] Calculate the internal resistance change rate of each single cell: Assume that the internal resistance change rate of the q-th single cell at the n-th measurement is ΔR q,n , and its calculation formula is: ΔR q,n = (R q,n - R q,0 ) / R q,0 × 100%; where R q,0 is the initial internal resistance of the q-th single cell, and R q,n is the internal resistance of the q-th single cell at the n-th measurement.

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

[0097] Calculate the deviation of the internal resistance change rate of the q-th single cell

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

[0099] Temperature consistency index: It includes the temperature difference of the battery pack and the standard deviation of the temperature change rate of each single cell in the battery pack;

[0100] The temperature difference is used to evaluate the consistency of the temperatures of each single cell in the battery pack. The calculation process is as follows:

[0101] Measure the temperatures of each single cell: Assume that the battery pack has n single cells, and measure the temperature Tq of each single cell respectively, where q = 1, 2,..., n;

[0102] Calculate the average temperature of the battery pack

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

[0104] Calculate the average temperature difference:

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

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

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

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

[0109]

[0110] Calculate the deviation of the temperature change rate of the q-th individual cell:

[0111] Temperature change rate deviation q = Temperature change rate q - Average value of temperature change rates;

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

[0113] State of charge consistency index: Includes the standard deviation of the state of charge of individual cells within the battery pack;

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

[0115] Calculate the SOC of each individual cell:

[0116] The battery pack has n individual cells. Measure the state of charge (SOC) value SOC q , q = 1, 2,..., n; where the state of charge (SOC) value = Current battery capacity / Total battery capacity × 100%;

[0117] Calculate the average state of charge value of all individual cells

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

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

[0120] Self-discharge rate consistency index: Includes the standard deviation of the self-discharge rates of individual cells within the battery pack.

[0121] The self-discharge rate is an indicator to measure the natural loss of battery power during storage, usually expressed as the percentage of capacity loss per day or per hour. Calculating the standard deviation of the self-discharge rates of individual cells within a battery pack can evaluate the consistency of the self-discharge characteristics of the individual cells within the battery pack. The calculation process is as follows:

[0122] For a battery pack with n individual cells, measure the self-discharge rate SDR of the q-th individual cell respectively q , where q = 1, 2,..., n; among them, the self-discharge rate can be measured by the following methods:

[0123] Voltage drop method: Estimate the self-discharge rate by measuring the voltage drop rate of the battery during storage.

[0124] Capacity attenuation method: Calculate the self-discharge rate by measuring the percentage of capacity loss of the battery during storage.

[0125] Self-discharge current method: Deduce the self-discharge rate 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 q-th individual cell q ,

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

[0129] Step 2: Set the classification thresholds for the consistency status of the battery pack to be Consistency1, Consistency2,..., Consistency in ascending order λ-1 , and use the classification thresholds to divide the consistency evaluation values into λ states. The classification principle is:

[0130] When Consistency t <Consistency1, the consistency of the battery pack at time t is in the first state;

[0131] When Consistency1 ≤ Consistency t <Consistency2, the consistency of the battery pack at time t is in the second state;

[0132] And so on;

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

[0134] Step 3: Construct the consistency state space S of the battery pack = {s1, s2,..., s λ}, s λ represents that the consistency of the battery pack is in the λ-th state, t ∈ T. Use the Markov chain model to describe the transformation process of the consistency state of the battery pack within the observation period T. Among them, the expression of the Markov chain model is:

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

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

[0137] Step 4: Obtain the data of the influencing factors of the consistency state of the battery pack within the observation period T, and use the threshold method to define the states of the influencing factors, including the ambient temperature state, the charge and discharge current state, the grid voltage fluctuation amplitude state, and the grid frequency deviation state;

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

[0139] By setting the step classification thresholds of the ambient temperature, denoted as T1, T2,..., T U-1 in turn, use the set step classification thresholds to classify the states of the ambient temperature. Specifically:

[0140] When the obtained ambient temperature is less than T1, define the ambient temperature state as the 1st state;

[0141] When the obtained ambient temperature is within the range from T1 to T2, define the ambient temperature state as the 2nd state;

[0142] And so on;

[0143] When the obtained ambient temperature is greater than T U-1 , define the ambient temperature state as the U-th state;

[0144] The definition methods of the remaining influencing factor states are the same as above and will not be elaborated 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 states of influencing factors during acquisition, and obtain the time series λ of the consistency evaluation values of the battery pack when the ambient temperature is in the u-th state, the charge and discharge current is in the v-th state, the grid voltage fluctuation range is in the w-th state, and the grid frequency deviation is in the r-th state u,v,w,r ;

[0146] Step 6: Calculate the transition probability that the consistency evaluation value of the battery pack in the time series λ u,v,w,r transfers from state i to state j in one step and construct a transition probability matrix where the transition probability is calculated by the formula:

[0147]

[0148] In the formula, represents the number of times that the consistency evaluation value of the battery pack in the time series λ u,v,w,r transfers from state i to state j in one step; represents the total number of times that the consistency evaluation value of the battery pack in the time series λ u,v,w,r transfers from state i in one step

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

[0150] Set the current moment as t’, and the consistency state of the battery pack at the current moment as s t’ , and s t’ ∈S, the ambient temperature is in state u t’ , the charge and discharge current state is v t’ , the grid voltage fluctuation range state w t’ , the grid frequency deviation state is r t’ ;

[0151] Select the corresponding transition probability matrix according to the states of influencing factors at the current moment Calculate the cumulative transition probability at the current t’ moment The calculation formula is:

[0152] Set the next moment as t + 1’, and the consistency state of the battery pack at the next moment as 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’ is a random number generated according to the consistency state s t’ of the battery pack, the environmental temperature state u t’ , the charge and discharge current state v t’ , the grid voltage variation range state w t’ and the grid frequency deviation state r t’ and follows a uniform distribution, and 0 ≤ η t’ ≤ 1.

[0153] Step 8: When the predicted consistency state is abnormal, adjust the current influencing factors until the predicted consistency state becomes normal.

[0154] Among them, the determination process for whether the consistency state is abnormal is as follows:

[0155] Set the demarcation state in the consistency state space S = {s1, s2,..., s λ} of the battery pack to s q , where q is an integer between 1 and λ;

[0156] According to the set demarcation state s q further classify the consistency state space S of the battery pack into a normal state space S1 = {s1, s2,..., s q} and an 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 the state space S1, determine it as a normal state;

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

[0159] Specifically, when the predicted consistency state is abnormal, it means that at the current moment, the consistency state of the battery pack is s t’ , the environmental temperature is state u t’ , the charge and discharge current state is v t’ , the grid voltage variation range state is w t’ , and the grid frequency deviation state is r t’In this case, the consistency state of the battery pack changes to an abnormal state at the next moment. At this time, the influencing factor value can be adjusted at the current moment. When any influencing factor state changes, return to step 5, and repeat steps 5-8 until the predicted consistency state is normal, so as to realize the equalization control of the battery pack and keep the battery monomers in the battery pack highly consistent.

[0160] The above shows and describes 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 by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A battery pack balancing management and control method for an energy storage lithium battery module, characterized in that, Including: Step 1: Collect the data of the consistency evaluation indicators of the battery pack during the observation period T, perform weighted processing, and obtain the consistency evaluation value Consistency of the battery pack at time t t , and set the classification thresholds of the consistency status of the battery pack to be Consistency1, Consistency2,..., Consistency in ascending order λ-1 , and use the classification threshold to divide the consistency evaluation value into λ states; Step 2: Construct the consistency state space S of the battery pack = {s1, s2,..., s λ}, s λ represents that the consistency of the battery pack is in the λ-th state, t ∈ T, and the Markov chain model is used to describe the transformation process of the consistency state of the battery pack within the observation period T; Step 3: Obtain the data of the influencing factors of the battery pack consistency state within the observation period T, and use the threshold method to define the states of the influencing factors, including the ambient temperature state, the charge and discharge current state, the grid voltage variation amplitude state, and the grid frequency deviation state; Step 4: Organize the consistency evaluation values of the battery pack within the observation period T into a time series sequence λ, classify the sequence λ according to the influencing factor states during acquisition, and obtain the time series sequence λ of the consistency evaluation values of the battery pack when the ambient temperature is in the u-th state, the charge and discharge current is in the v-th state, the grid voltage variation range is in the w-th state, and the grid frequency deviation is in the r-th state u,v,w,r ; Step Five: Calculate the time series λ u,v,w,r The transition probability that the consistency evaluation value of the battery pack in u,v,w,r transfers 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 until the predicted consistency state becomes normal.

2. The battery pack equalization management control method of an energy storage lithium battery module according to claim 1, wherein The consistency evaluation indicators include Voltage consistency indicator: including the average voltage difference of the battery pack and the standard deviation of the voltage change rate of each single battery in the battery pack; Capacity consistency indicator: including the standard deviation of the capacity of each single battery in the battery pack and the standard deviation of the capacity attenuation rate of each single battery in the battery pack; Internal resistance consistency indicator: including the average internal resistance difference of the battery pack and the standard deviation of the internal resistance change rate of each single battery in the battery pack; Temperature consistency indicator: including the temperature difference of the battery pack and the standard deviation of the temperature change rate of each single battery in the battery pack; State of charge consistency indicator: including the standard deviation of the state of charge of each single battery in the battery pack; Self-discharge rate consistency indicator: including the standard deviation of the self-discharge rate of each single battery in the battery pack.

3. A battery pack equalization management control method for an energy storage lithium battery module according to claim 1, characterized in that, In step one, the consistency evaluation value Consistency of the battery pack at time t t is calculated by the formula: Wherein, Ci tk represents the data value of the k-th consistency evaluation index at time t; θ k represents the weight of the k-th consistency evaluation index at time t, and θ tk ∈(0, 1), K is the type of consistency evaluation index.

4. A battery pack equalization management control method for an energy storage lithium battery module according to claim 1, characterized in that, In Step 1, the classification principle of the battery pack consistency state is: When Consistency t When it is less than Consistency 1, the consistency of the battery pack at time t is in the first state; When Consistency1 ≤ Consistency t < Consistency2, the consistency of the battery pack at time t is in the second state; And so on; When Consistency t ≥ Consistency λ-1 At time t, the consistency of the battery pack is in the λ-th state.

5. A battery pack equalization management control method for an energy storage lithium battery module according to claim 1, characterized in that, In Step 2, the expression of the Markov chain model for describing the transformation process of the battery pack consistency state within the observation period T is: P(S t+1 = j | S t = i) Where S t+1 represents the consistency state of the battery pack at the (t + 1)-th moment within the observation period T; S t represents the consistency state of the battery pack at the t-th moment within the observation period T; P(S t+1 = j|S t = i) represents that under the condition that the consistency state of the battery pack at the t-th moment is S t = i, the probability that after one-step transition, the consistency state of the battery pack at the (t + 1)-th moment is S t+1 = j, and S t+1 , S t , i, j ∈ S.

6. The battery pack equalization management control method of an energy storage lithium battery module according to claim 1, wherein, In step five, the transfer probability is calculated by the following formula: In the formula, represents the number of times that the consistency evaluation value of the battery pack in the time series sequence λ u,v,w,r transfers from state i to state j in one step; represents the total number of times that the consistency evaluation value of the battery pack in the time series sequence λ u,v,w,r transfers from state i in one step.

7. A battery pack equalization management control method for an energy storage lithium battery module according to claim 1, characterized in that, In Step 5, the prediction process of the battery pack consistency state at the next moment includes the following steps: Set the current time as t’, and the consistency state of the battery pack at the current time as s t’ , and s t’ ∈S, the ambient temperature is the state u t’ , the charge and discharge current state is v t’ , the grid voltage change range state is w t’ , the grid frequency deviation state is r t’ ; Select the corresponding transition probability matrix according to the influencing factor state at the current moment Calculate the cumulative transition probability at the current moment t' The calculation formula is as follows: Set the next moment as t+1’, and the consistency state of the battery pack at the next moment is 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’ is a random number generated according to the consistency state s t’ of the battery pack, the environmental temperature as state u t’ , the charge and discharge current state v t’ , the grid voltage variation amplitude state w t’ , and the grid frequency deviation state r t’ , and follows a uniform distribution, and 0 ≤ η t’ ≤ 1.

8. A battery pack equalization management control method for an energy storage lithium battery module according to claim 1, characterized in that, In Step 5, the determination process of whether the consistency state is abnormal is: Set the boundary state in the consistency state space S = {s1, s2,..., s λ} to be s q , where q is an integer between 1 and λ; According to the set demarcation 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 battery pack consistency state at the next moment belongs to the state space S1, it is determined to be in a normal state; When the predicted battery pack consistency state at the next moment belongs to the state space S2, it is determined to be in an abnormal state.

9. A battery pack equalization management and control system for an energy storage lithium battery module, characterized in that, The system is used to implement the steps of a battery pack equalization management control method for an energy storage lithium battery module described in any one of claims 1-8, including: The evaluation index data acquisition module is used to collect the consistency evaluation index data of the battery pack within the observation period T, and use the formula: to perform weighted processing on the collected data to obtain the consistency evaluation value Consistency of the battery pack at time t t , where Ci tk represents the data value of the k-th consistency evaluation index at time t; θ k represents the weight of the k-th consistency evaluation index at time t, and θ tk ∈(0,1), K is the type of consistency evaluation index; The battery pack state classification module is used to set the classification thresholds for the consistency state of the battery pack from small to large as Consistency1, Consistency2,..., Consistency λ-1 , and the consistency evaluation values are divided into λ states by using the classification thresholds; among them, the consistency state classification principle is: When Consistency t When it is less than Consistency1, the consistency of the battery pack at time t is in the first state; When Consistency1 ≤ Consistency t < Consistency2, the consistency of the battery pack at time t is in the second state; And so on; When Consistency t ≥ Consistency λ-1 At time t, the consistency of the battery pack is in the λ-th state; A data processing module is used to construct a consistency state space S = {s1, s2,..., s λ}, and use a Markov chain model to describe the consistency state transformation process of the battery pack within the observation period T. Among them, s λ represents that the consistency of the battery pack is in the λ-th state, t ∈ T; the expression of the Markov chain model is: P(S t+1 = j|S t = i); in the formula, S t+1 represents the consistency state of the battery pack at the t + 1 moment within the observation period T; S t represents the consistency state of the battery pack at the t moment within the observation period T; P(S t+1 = j|S t = i) represents that under the condition that the consistency state of the battery pack at the t moment is S t = i, after one-step transfer, the probability that the consistency state of the battery pack at the t + 1 moment is S t+1 = j, and S t+1 , S t , i, j ∈ S; An influencing factor data acquisition module, configured to obtain the data of the influencing factors of the battery pack consistency state within the observation period T, and use the threshold method to define the states of the influencing factors, including the ambient temperature state, the charge and discharge current state, the grid voltage variation amplitude state, and the grid frequency deviation state; 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, classify the series λ according to the influencing factor states during acquisition, and obtain the time series λ of the consistency evaluation values of the battery pack when the ambient temperature is in the u-th state, the charge and discharge current is in the v-th state, the grid voltage change range is in the w-th state, and the grid frequency deviation is in the r-th state. u,v,w,r ; A consistency status prediction module for calculating the time series λ u,v,w,r The transition probability that the consistency evaluation value of the battery pack in u,v,w,r transfers from state i to state j in one step And construct a transition probability matrix Using the obtained transition probability matrix P u,v,w,r Predict the consistency status of the battery pack at the next moment An influencing factor data adjustment module, configured to adjust the current influencing factors to make the predicted consistency state normal when the predicted consistency state is abnormal.

10. The battery pack equalization management control system of a energy storage lithium battery module according to claim 9, characterized in that, The system further includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it can implement the steps of a battery pack equalization management control method for an energy storage lithium battery module described in any one of claims 1-8.

Images