Equalization algorithm and device applied to energy storage battery management system

Abstract

The invention discloses an equalization algorithm and device applied to an energy storage battery management system, and relates to the technical field of energy storage batteries, and the algorithm comprises the steps: carrying out the real-time capturing of state parameters of an energy storage battery unit in the energy storage battery management system, and generating a unit state vector; analyzing the unit state vectors, identifying health state differences among the energy storage battery units, and calculating an equilibrium difference quantity; according to the health state difference, the balance difference quantity and the voltage, an energy transfer link is constructed through a balance path, and an efficient path with a small load is selected for balancing; and monitoring the state change of the energy storage battery unit, calculating the equilibrium transfer state, and dynamically adjusting the energy transfer link according to the change of the equilibrium transfer state. The state parameters of the energy storage battery units are dynamically monitored in real time, the health state is accurately evaluated, the energy transmission path is optimized, efficient and automatic balance adjustment is achieved, and therefore the stability and energy efficiency of the energy storage battery management system are improved, and the service life of the energy storage battery management system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage batteries, and in particular to an equalization algorithm and device applied to an energy storage battery management system. Background Art

[0002] In modern energy storage battery management systems, the performance balance and health status maintenance of energy storage battery cells is a key issue. Energy storage batteries are usually composed of multiple battery cells, which may have slight differences during the manufacturing process. In long-term use, these differences will further expand, resulting in inconsistent state parameters such as battery cell voltage, current, temperature and remaining capacity. This inconsistency may affect the overall performance of the energy storage battery, resulting in reduced system efficiency and may even cause safety issues.

[0003] In the prior art, the balancing algorithms for energy storage batteries are mostly based on static balancing strategies, that is, balancing adjustments are made only when significant differences in state parameters are detected between battery cells. This strategy has the following major deficiencies: Untimely response: The static balancing algorithm can only be adjusted after the battery state is obviously unbalanced, and fails to achieve real-time monitoring and dynamic balancing of the battery cell state. This may cause the state differences of the battery cells to last for a long time, thereby affecting the health and service life of the battery; Lack of in-depth state analysis: Most existing balancing algorithms only focus on a single state parameter of the battery cell, such as voltage or current, and fail to comprehensively analyze the impact of multiple parameters on the health state of the battery cell, and lack accurate evaluation of the state between battery cells; Low balancing efficiency: Existing balancing strategies usually fail to make fine adjustments based on the specific characteristics and changes of the battery cells, resulting in poor balancing adjustment effects and failure to fully improve the stability and energy efficiency of the system. Summary of the invention

[0004] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a balancing algorithm and device applied to an energy storage battery management system to solve the above-mentioned technical problems.

[0005] To achieve the above object, the present invention provides the following technical solution: a balancing algorithm applied to an energy storage battery management system, comprising:

[0006] Capture the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generate a unit state vector to describe the health status of the energy storage battery unit;

[0007] The cell state vector of the energy storage battery cell is analyzed through the difference monitoring module, the health state difference between the energy storage battery cells is identified, and the balance difference amount is calculated;

[0008] According to the health status difference, balance difference and voltage of the energy storage battery cells, an energy transfer link is constructed through a balance path, and an efficient and lightly loaded path is selected for balancing;

[0009] Monitor the state changes of energy storage battery cells, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

[0010] The present invention is further configured such that the state parameters include voltage, current, temperature and remaining power, and the captured state parameters are normalized so that the state parameters are in the same dimensional range; the processed state parameters are combined into a unit state vector in a predetermined order.

[0011] The present invention is further configured to analyze the cell state vector of the energy storage battery cell through the difference monitoring module, identify the health state difference between the energy storage battery cells, and calculate the balance difference amount, including:

[0012] Extracting energy morphological features and state response features according to a cell state vector of an energy storage battery cell;

[0013] Construct a health feature vector based on energy morphology features and state response features;

[0014] The health status difference and equilibrium difference between the energy storage battery cells are calculated by the health feature vector. The present invention is further configured such that the calculation logic of the energy form feature is: in, is the energy form characteristic of the i-th energy storage battery unit, V i is the voltage of the i-th energy storage battery unit, SOC i is the remaining power of the i-th energy storage battery unit, ∈ is a constant used to prevent division by zero; the calculation logic of the state response feature is: is the state response characteristic of the i-th energy storage battery unit, T i is the temperature of the i-th energy storage battery unit, I i is the current of the i-th energy storage battery unit;

[0015] According to the energy form characteristics and state response characteristics Construct the health feature vector H i , where H i is the health feature vector of the i-th energy storage battery unit;

[0016] The calculation logic of the health status difference between energy storage battery units is: D ijis the difference in health status between the i-th and j-th energy storage battery units, and m is the health feature vector H i The dimension is 2, which is the energy form characteristic. and state response characteristics So H ik and H jk That is, the feature of the kth dimension of the i-th and j-th energy storage battery units;

[0017] The calculation logic of the equilibrium difference is: Among them, BV is the balance difference, and n is the number of energy storage battery units.

[0018] The present invention is further configured to construct an energy transfer link through a balancing path according to the health status difference, the balancing difference amount and the voltage of the energy storage battery unit, select an efficient and lightly loaded path for balancing, including:

[0019] Calculate the voltage potential according to the health status difference and voltage, construct the voltage potential matrix, and make threshold judgment on the voltage potential matrix and the equilibrium difference;

[0020] When the balance difference value BV is greater than or equal to the balance difference threshold, a global balance signal is generated;

[0021] When the balance difference BV is less than the balance difference threshold and the voltage potential g of the energy storage battery unit pair ij When it is greater than or equal to the voltage potential threshold, a local equilibrium signal is generated.

[0022] The present invention is further configured such that the calculation logic of the voltage potential is: Among them, g ij is the voltage potential between the i-th and j-th energy storage battery units, V i and V j are the voltages of the i-th and j-th energy storage battery units, respectively, and D ij is the difference in health status between the i-th and j-th energy storage battery units, T i and T j are the temperatures of the i-th and j-th energy storage battery units, respectively, and α is the temperature sensitivity coefficient;

[0023] Calculate the voltage potential g of all energy storage battery cell pairs ij and the equilibrium difference BV, according to the voltage potential g ij Construct the voltage potential matrix G.

[0024] The present invention is further configured to obtain the maximum voltage potential g in the voltage potential matrix G when generating a local equilibrium signal. ij The corresponding energy storage battery unit pairs are balanced;

[0025] When generating a global equilibrium signal, a pair of energy storage battery cells with high voltage potential is selected as a candidate transfer path according to the voltage potential matrix, the transfer path weight and load factor of the energy storage battery cell pair are calculated, and a path with a lower load factor is selected as the optimal transfer path;

[0026] The calculation logic of the transfer path weight is: Among them, p ij is the transfer path weight between the i-th and j-th energy storage battery units, SOC j is the remaining power of the jth energy storage battery unit, I i is the current of the i-th energy storage battery unit, β is the current regulation factor;

[0027] The calculation logic of the load factor is: Among them, λ ij is the load factor between the i-th and j-th energy storage battery units, I j is the current of the jth energy storage battery unit, γ is the current imbalance coefficient;

[0028] According to the load factor, the path with the lower load factor is selected as the optimal transmission path, an optimal path set is generated, and balancing is performed based on the optimal path set.

[0029] The present invention is further configured to monitor the state change of the energy storage battery unit, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the change of the balanced transfer state to ensure the stability and efficiency of the balancing process, including:

[0030] Calculate the change rate of the state parameter of the energy storage battery unit, that is, the change of the state parameter per unit time, and calculate the equilibrium transfer state according to the state parameter and the change rate of the state parameter;

[0031] In each time step, the stability of each energy transfer link is dynamically monitored by updating the value of the balanced transfer state in real time. When the balanced transfer state is less than the balanced transfer state threshold, the current intensity of the transfer link is dynamically adjusted. When the balance is completed, the monitoring is stopped. The balance completion condition is that the balance difference BV is less than the balance difference threshold and the voltage potential g of any energy storage battery unit pair is ij Less than the voltage potential threshold.

[0032] The present invention is further configured such that the calculation logic of the balanced transfer state is: Among them, Q ij (t) is the equilibrium transfer state between the i-th and j-th energy storage battery units at time t, V i (t) and V j (t) is the voltage of the i-th and j-th energy storage battery cells at time t, Ti (t) and T j (t) is the temperature of the i-th and j-th energy storage battery cells at time t, and is the current change rate of the i-th and j-th energy storage battery units at time t, α is the temperature sensitivity coefficient, and ζ is the current change suppression coefficient;

[0033] The dynamic adjustment logic of the current intensity of the transmission link is: I ij (t) = I ij (t-1)·(1-δ·(τ-Q ij (t))), where I ij (t) and I ij (t-1) is the current intensity of the transmission link between the i-th and j-th energy storage battery units at time t and time t-1 respectively, τ is the balanced transmission state threshold, and δ is the current adjustment coefficient.

[0034] The present invention also provides an equalization device for use in an energy storage battery management system, the device comprising:

[0035] Parameter capture module: captures the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generates a unit state vector to describe the health status of the energy storage battery unit;

[0036] Difference calculation module: The difference monitoring module analyzes the cell state vector of the energy storage battery cell, identifies the health state difference between the energy storage battery cells, and calculates the balance difference;

[0037] Path construction module: According to the health status difference, balance difference and voltage of the energy storage battery cells, the energy transfer link is constructed through the balance path, and the efficient and lightly loaded path is selected for balancing;

[0038] State monitoring module: monitors the state changes of energy storage battery units, calculates the balanced transfer state, and dynamically adjusts the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

[0039] The present invention provides a balancing algorithm and device for an energy storage battery management system. The algorithm captures the state parameters of energy storage battery cells in the energy storage battery management system in real time to generate a cell state vector for describing the health state of the energy storage battery cells; analyzes the cell state vector of the energy storage battery cells through a difference monitoring module, identifies the health state difference between the energy storage battery cells, and calculates the balancing difference amount; constructs an energy transfer link through a balancing path according to the health state difference, balancing difference amount and voltage of the energy storage battery cells, selects an efficient and lightly loaded path for balancing; monitors the state change of the energy storage battery cells, calculates the balancing transfer state, and dynamically adjusts the energy transfer link according to the change of the balancing transfer state to ensure the stability and efficiency of the balancing process, and the beneficial effects produced include:

[0040] 1. Dynamic balancing adjustment to improve system stability: By real-time monitoring of the state parameters of the energy storage battery unit, including voltage, temperature, remaining power, etc., the unit health feature vector is generated, which can accurately analyze the state differences between the units. Unlike the existing static balancing algorithm, the present invention can make real-time adjustments based on the dynamic changes of the battery state to ensure that the battery unit is always in a relatively balanced state during operation, avoiding the negative impact of long-term state imbalance on battery health, thereby greatly improving the stability of the energy storage battery management system;

[0041] 2. Multi-dimensional health status assessment to improve the accuracy of balanced adjustment: The present invention not only evaluates the battery cell through single parameters such as voltage and current, but also comprehensively considers multiple parameters to construct a health feature vector, thereby making a more comprehensive and accurate assessment of the health status of the battery cell. Through this multi-dimensional health status monitoring and analysis, it is possible to more accurately identify the state differences between battery cells, calculate the amount of balanced differences, and then formulate a refined balanced strategy, effectively improving the effect of balanced adjustment;

[0042] 3. Optimize the energy transmission path and improve the balancing efficiency: The present invention calculates the state difference and change rate between battery cells, dynamically adjusts the balancing transmission link, and selects the best transmission path, making the energy transmission between battery cells more efficient. This method not only reduces unnecessary energy loss, but also ensures the stability of energy transmission of battery cells during the balancing process, thereby improving the energy utilization efficiency of the overall balancing process;

[0043] 4. Rapid response to abnormal conditions and extended battery life: The present invention can monitor the state changes of energy storage battery cells through the difference module, identify and respond to abnormal conditions, such as a sharp decline in the health of the battery cells or excessive balance differences. When an abnormality is identified, corresponding balance adjustment measures can be quickly taken to restore the normal state of the battery cells in time, effectively avoiding damage to the battery cells caused by long-term abnormalities, thereby extending the service life of the entire energy storage battery management system.

[0044] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0046] Figure 1 A flowchart of a balancing algorithm applied to an energy storage battery management system is shown as an exemplary embodiment of the present invention;

[0047] Figure 2 The present invention is a schematic structural diagram of an balancing device applied to an energy storage battery management system according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0049] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0050] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0051] Embodiment 1

[0052] A balancing algorithm applied to energy storage battery management system, such as Figure 1 As shown, including:

[0053] Capture the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generate a unit state vector to describe the health status of the energy storage battery unit;

[0054] The cell state vector of the energy storage battery cell is analyzed through the difference monitoring module, the health state difference between the energy storage battery cells is identified, and the balance difference amount is calculated;

[0055] According to the health status difference, balance difference and voltage of the energy storage battery cells, an energy transfer link is constructed through a balance path, and an efficient and lightly loaded path is selected for balancing;

[0056] Monitor the state changes of energy storage battery cells, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

[0057] Specifically, the energy storage battery management system is a system used to monitor and manage energy storage battery packs. Its main function is to monitor the battery status parameters in real time, control the battery charging and discharging process, ensure the balance and optimization between battery cells, and prevent abnormal situations such as overcharging and over-discharging. The goal of this system is to improve the operating efficiency of the entire energy storage battery pack, extend battery life, and ensure the safety of the system. It is widely used in electric vehicles, home energy storage devices, and large-scale power grid energy storage systems; energy storage battery cells are the basic components of energy storage battery systems, usually referring to a single or a group of electrochemical devices with energy storage functions, such as lithium-ion battery cells or modules. Each battery cell is responsible for storing and releasing electrical energy, and its health and performance directly affect the overall efficiency and life of the entire energy storage battery system. Multiple energy storage battery cells are usually connected in series or in parallel to form a battery pack. When the system is running, the state differences of each cell may affect the overall balance, so a management system is required to monitor and adjust it. The present invention is further configured such that the state parameters include voltage, current, temperature and remaining power, and the captured state parameters are normalized so that the state parameters are in the same dimensional range; the processed state parameters are combined into a unit state vector in a predetermined order. In a feasible embodiment of the present invention, the predetermined order may be: [voltage, current, temperature, remaining power]. Other orders that can be used to describe the health status of energy storage battery cells may also be used as embodiments, and no specific restrictions are made here.

[0058] The present invention is further configured to analyze the cell state vector of the energy storage battery cell through the difference monitoring module, identify the health state difference between the energy storage battery cells, and calculate the balance difference amount, including:

[0059] The energy morphology characteristics and state response characteristics are extracted according to the cell state vector of the energy storage battery cell; the present invention is further configured such that the calculation logic of the energy morphology characteristics is: in, is the energy form characteristic of the i-th energy storage battery unit, V i is the voltage of the i-th energy storage battery unit, SOC i is the remaining power of the i-th energy storage battery unit, ∈ is a constant used to prevent division by zero; the calculation logic of the state response feature is: is the state response characteristic of the i-th energy storage battery unit, T i is the temperature of the i-th energy storage battery unit, I iis the current of the i-th energy storage battery cell; specifically, in the calculation logic of the energy form feature, the voltage and remaining power of the battery cell are processed by the natural logarithm function ln(). When the remaining power is close to 0, the influence of the voltage and remaining power is amplified, so that the balancing algorithm can more sensitively perceive the state difference of the battery cell. At the same time, the +1 term prevents the negative output of the logarithmic function; in the calculation logic of the state response feature, the thermal effect of the battery during operation is reflected by the temperature and current ratio of the battery cell. A higher ratio may mean that the battery has a large temperature rise at a relatively small current, which is usually unfavorable and may indicate that the battery has insufficient heat dissipation capacity or increased internal impedance. Conversely, a lower ratio means that the battery temperature rises more moderately even at a higher current, which usually indicates that the battery is in good working condition.

[0060] Construct a health feature vector based on energy form characteristics and state response characteristics; and state response characteristics Construct the health feature vector H i , where H i is the health feature vector of the i-th energy storage battery unit; specifically, when constructing the health feature vector according to the energy form characteristics and the state response characteristics, no specific restriction is made on the order of the energy form characteristics and the state response characteristics.

[0061] The health status difference and equilibrium difference between energy storage battery cells are calculated through the health feature vector; the calculation logic of the health status difference between energy storage battery cells is: D ij is the difference in health status between the i-th and j-th energy storage battery units, and m is the health feature vector H i The dimension is 2, which is the energy form characteristic. and state response characteristics So H ik and H jk That is, the feature of the kth dimension of the i-th and j-th energy storage battery units;

[0062] The calculation logic of the equilibrium difference is: Among them, BV is the balance difference, and n is the number of energy storage battery cells; specifically, through the quantification of the health status difference, the present invention can make personalized adjustments to the differences between energy storage battery cells, reduce the imbalance state within the system, and thus improve the operating efficiency and life of the entire energy storage system; the real-time calculation of the health status difference enables the system to adjust the balance strategy in time when there is a significant state difference between battery cells, avoiding long-term unbalanced operation from causing adverse effects on battery life; the size of the balance difference directly affects the system's energy transmission optimization strategy. When the balance difference is large, a more balanced transmission path can be selected to reduce energy loss and improve transmission efficiency.

[0063] The present invention is further configured to construct an energy transfer link through a balancing path according to the health status difference, the balancing difference amount and the voltage of the energy storage battery unit, select an efficient and lightly loaded path for balancing, including:

[0064] The voltage potential is calculated based on the health status difference and the voltage, a voltage potential matrix is ​​constructed, and a threshold judgment is performed on the voltage potential matrix and the equilibrium difference amount; the present invention is further configured that the calculation logic of the voltage potential is: Among them, g ij is the voltage potential between the i-th and j-th energy storage battery units, V i and V j are the voltages of the i-th and j-th energy storage battery units, respectively, and D ij is the difference in health status between the i-th and j-th energy storage battery units, T i and T j are the temperatures of the i-th and j-th energy storage battery cells, respectively, and α is the temperature sensitivity coefficient; specifically, the above calculation logic calculates the voltage difference between the energy storage battery cells i and j |V i -V j |, this difference reflects the voltage imbalance between the two, according to the difference in the health status of the battery cells D ij , the voltage difference is corrected, and the correction coefficient is proportional to the difference in health status between the two, through the exponential term exp(-α·|T i -T j |) to introduce the effect of temperature on voltage potential, ensuring that the adjustment of voltage potential by temperature is taken into account. α is a constant used to adjust the effect of temperature difference. A higher α value means that temperature has a greater impact on battery performance. The common range of temperature sensitivity coefficient is between 0.01 and 0.1.

[0065] Calculate the voltage potential g of all energy storage battery cell pairs ij and the equilibrium difference BV, according to the voltage potential g ijThe voltage potential matrix G is constructed; by introducing health status difference and temperature correction, the calculation of voltage potential not only takes into account the voltage difference between battery cells, but also combines the health status and temperature difference of the battery, so that the unbalanced state between battery cells is quantified more accurately; by constructing the voltage potential matrix, the balanced state of the battery cells can be comprehensively evaluated, and corresponding balancing strategies can be adopted according to the difference in voltage potential to ensure that the state between battery cells gradually tends to be balanced, thereby improving the overall stability of the system.

[0066] When the balance difference amount BV is greater than or equal to the balance difference threshold, a global balance signal is generated; when the global balance signal is generated, a pair of energy storage battery cells with high voltage potential is selected as a candidate transmission path according to the voltage potential matrix, the transmission path weight and load factor of the energy storage battery cell pair are calculated, and a path with a lower load factor is selected as the optimal transmission path; specifically, when the balance difference amount is greater than or equal to the balance difference threshold, a global balance signal is generated, and by selecting an energy storage battery cell pair with a higher voltage potential as a candidate transmission path, further by calculating the path weight and load factor, a path with a smaller transmission load is selected as the optimized transmission path, thereby completing energy balance regulation.

[0067] The calculation logic of the transfer path weight is: Among them, p ij is the transfer path weight between the i-th and j-th energy storage battery units, SOC j is the remaining power of the jth energy storage battery unit, I i is the current of the i-th energy storage battery unit, β is the current regulation factor; specifically, the transfer path weight p ij is the priority weight of the transfer path between energy storage battery cells i and j. The larger the value, the more suitable the energy transfer path between the pair of battery cells is in the balancing operation. The smaller the weight, the higher the load or inefficiency may be caused by transferring energy on this path, so the priority is lower. The value of the current regulation factor β is usually between 0.01 and 0.1. A higher value indicates that the current has a greater impact on the weight of the transfer path. The larger the value, the stronger the inhibitory effect of the current on the path selection, and the weight of the path with large current will be reduced. The formula ensures that the best path for energy transfer can be accurately judged when selecting the balancing path by integrating multiple factors such as voltage potential, remaining power and current, thereby improving the efficiency and accuracy of energy balancing regulation. Through the current regulation factor, energy transfer on paths with large loads can be effectively avoided, reducing the overload or instability that may be caused during the energy transfer process, and ensuring the safety of the energy storage system. Through the calculation of the path weight, the priority of the transfer path can be dynamically adjusted according to the actual state of the battery cell, ensuring the optimal efficiency during the energy transfer process and reducing unnecessary energy loss.

[0068] The calculation logic of the load factor is: Among them, λ ij is the load factor between the i-th and j-th energy storage battery units, I j is the current of the jth energy storage battery unit, and γ is the current imbalance coefficient; specifically, the load factor indicates the degree of load imbalance between energy storage battery units i and j. The larger the load factor, the greater the current imbalance between the battery cells, or the lower the weight of the transfer path, and therefore it is not suitable as a path for energy transfer. Conversely, a path with a smaller load factor is more suitable for energy transfer; the control factor γ is used to control the effect of the current difference on the load factor, and the value range is 0.01 to 0.1. When the energy storage system is more sensitive to current imbalance, the value of γ may be higher to increase the effect of the current difference on the load factor.

[0069] According to the load factor, the path with the lower load factor is selected as the optimal transmission path, an optimal path set is generated, and balancing is performed based on the optimal path set.

[0070] When the balance difference BV is less than the balance difference threshold and the voltage potential g of the energy storage battery unit pair ij When the voltage potential is greater than or equal to the voltage potential threshold, a local equilibrium signal is generated; when a local equilibrium signal is generated, the maximum voltage potential g in the voltage potential matrix G is obtained. ij The corresponding energy storage battery unit pairs are balanced.

[0071] The present invention is further configured to monitor the state change of the energy storage battery unit, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the change of the balanced transfer state to ensure the stability and efficiency of the balancing process, including:

[0072] The state parameter change rate of the energy storage battery unit, that is, the change amount of the state parameter per unit time, is calculated, and the equilibrium transfer state is calculated according to the state parameter and the state parameter change rate; the present invention is further configured that the calculation logic of the equilibrium transfer state is: Among them, Q ij (t) is the equilibrium transfer state between the i-th and j-th energy storage battery units at time t, V i (t) and V j (t) is the voltage of the i-th and j-th energy storage battery cells at time t, T i (t) and T j (t) is the temperature of the i-th and j-th energy storage battery cells at time t, and is the current change rate of the i-th and j-th energy storage battery units at time t, α is the temperature sensitivity coefficient, and ζ is the current change suppression coefficient; Q ij(t) represents the energy transfer equilibrium state of energy storage battery cells i and j at time t. The balanced transfer state between battery cells is comprehensively calculated by voltage difference, temperature difference and current change rate. The larger the value, the better the balance of energy transfer between the two, and the more suitable it is for balanced adjustment. The current change suppression coefficient ζ is used to control the impact of current change on the balanced transfer state. The value range is between 0.01 and 0.1. A larger ζ indicates that the current change has a greater impact on the transfer state, and a smaller ζ indicates that the system has a higher tolerance for current changes. By combining the voltage difference, temperature difference and current change rate, the state changes between energy storage battery cells can be monitored in real time, and the energy transfer path can be dynamically adjusted to ensure the accuracy of balanced adjustment. The formula comprehensively considers multiple factors such as voltage, temperature and current changes to ensure that the energy transfer path can not only effectively transfer energy, but also avoid instability caused by temperature or current fluctuations, and optimize operating efficiency.

[0073] In each time step, the stability of each energy transfer link is dynamically monitored by updating the value of the balanced transfer state in real time. When the balanced transfer state is less than the balanced transfer state threshold, the current intensity of the transfer link is dynamically adjusted. When the balance is completed, the monitoring is stopped. The balance completion condition is that the balance difference BV is less than the balance difference threshold and the voltage potential g of any energy storage battery unit pair is ij Less than the voltage potential threshold; the dynamic adjustment logic of the current intensity of the transmission link is: I ij (t) = I ij (t-1)·(1-δ·(τ-Q ij (t))), where I ij (t) and I ij(t-1) are the current intensities of the transmission links between the i-th and j-th energy storage battery cells at time t and time t-1 respectively, τ is the balanced transmission state threshold, and δ is the current adjustment coefficient; specifically, the above logic dynamically monitors the balanced transmission state of the energy storage battery cells, and adjusts the current intensity of the energy transmission link according to the balanced transmission state value updated in real time, thereby ensuring the stability of the system and the effect of balanced regulation. When the balanced transfer state is lower than a certain threshold, the current of the transfer link is dynamically adjusted until the balance is completed. The balanced transfer state threshold is a preset threshold used to determine whether the balancing process is completed. The correction factor δ is a parameter used to control the current adjustment rate, and its value range is between 0.01 and 0.1. By real-time monitoring of the balanced transfer state and dynamically adjusting the current intensity, it can more flexibly respond to state changes between energy storage battery cells and improve the efficiency and accuracy of energy balancing adjustment. By introducing correction factors and balanced transfer state thresholds, the current intensity can be gradually adjusted to ensure the smoothness of the energy transfer process and avoid instability caused by drastic changes in current. Through balanced adjustment, the system can timely correct the state differences between battery cells and avoid damage to battery cells caused by long-term excessive imbalance, thereby extending the service life of the battery.

[0074] Embodiment 2

[0075] See also Figure 2 , the exemplary balancing device applied to the energy storage battery management system includes:

[0076] Parameter capture module: captures the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generates a unit state vector to describe the health status of the energy storage battery unit;

[0077] Difference calculation module: The difference monitoring module analyzes the cell state vector of the energy storage battery cell, identifies the health state difference between the energy storage battery cells, and calculates the balance difference;

[0078] Path construction module: According to the health status difference, balance difference and voltage of the energy storage battery cells, the energy transfer link is constructed through the balance path, and the efficient and lightly loaded path is selected for balancing;

[0079] State monitoring module: monitors the state changes of energy storage battery units, calculates the balanced transfer state, and dynamically adjusts the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

[0080] It should be noted that the equalization device for energy storage battery management system provided in the above embodiment and the equalization algorithm for energy storage battery management system provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual application, the equalization device for energy storage battery management system provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0082] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0083] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0084] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A balancing algorithm applied to an energy storage battery management system, characterized in that: include: Capture the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generate a unit state vector to describe the health status of the energy storage battery unit; The cell state vector of the energy storage battery cell is analyzed through the difference monitoring module, the health state difference between the energy storage battery cells is identified, and the balance difference amount is calculated; According to the health status difference, balance difference and voltage of the energy storage battery cells, an energy transfer link is constructed through a balance path, and an efficient and lightly loaded path is selected for balancing; Monitor the state changes of energy storage battery cells, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

2. The equalization algorithm applied to the energy storage battery management system according to claim 1 is characterized in that: The state parameters include voltage, current, temperature and remaining power. The captured state parameters are normalized so that the state parameters are in the same dimensional range; the processed state parameters are combined into a unit state vector in a predetermined order.

3. The equalization algorithm applied to the energy storage battery management system according to claim 2 is characterized in that: The cell state vector of the energy storage battery cell is analyzed through the difference monitoring module, the health state difference between the energy storage battery cells is identified, and the balance difference amount is calculated, including: Extracting energy morphological features and state response features according to a cell state vector of an energy storage battery cell; Construct a health feature vector based on energy morphology features and state response features; The health status difference and balance difference between energy storage battery units are calculated through the health feature vector.

4. The equalization algorithm applied to the energy storage battery management system according to claim 3 is characterized in that: The calculation logic of the energy morphology feature is: in, is the energy form characteristic of the i-th energy storage battery unit, V i is the voltage of the i-th energy storage battery unit, SOC i is the remaining power of the i-th energy storage battery unit, ∈ is a constant used to prevent division by zero; the calculation logic of the state response feature is: is the state response characteristic of the i-th energy storage battery unit, T i is the temperature of the i-th energy storage battery unit, I i is the current of the i-th energy storage battery unit; According to the energy form characteristics and state response characteristics Construct the health feature vector H i , where H i is the health feature vector of the i-th energy storage battery unit; The calculation logic of the health status difference between energy storage battery units is: D ij is the difference in health status between the i-th and j-th energy storage battery units, and m is the health feature vector H i The dimension is 2, which is the energy form characteristic. and state response characteristics So H ik and H jk That is, the feature of the kth dimension of the i-th and j-th energy storage battery units; The calculation logic of the equilibrium difference is: Among them, BV is the balance difference, and n is the number of energy storage battery units.

5. The equalization algorithm applied to the energy storage battery management system according to claim 1 is characterized in that: According to the health status difference, balance difference and voltage of the energy storage battery cells, an energy transfer link is constructed through a balance path, and an efficient and lightly loaded path is selected for balancing, including: Calculate the voltage potential according to the health status difference and voltage, construct the voltage potential matrix, and make threshold judgment on the voltage potential matrix and the equilibrium difference; When the balance difference value BV is greater than or equal to the balance difference threshold, a global balance signal is generated; When the balance difference BV is less than the balance difference threshold and the voltage potential g of the energy storage battery unit pair ij When it is greater than or equal to the voltage potential threshold, a local equilibrium signal is generated.

6. The equalization algorithm applied to the energy storage battery management system according to claim 5 is characterized in that: The calculation logic of the voltage potential is: Among them, g ij is the voltage potential between the i-th and j-th energy storage battery units, V i and V j are the voltages of the i-th and j-th energy storage battery units, respectively, and D ij is the difference in health status between the i-th and j-th energy storage battery units, T i and T j are the temperatures of the i-th and j-th energy storage battery units, respectively, and α is the temperature sensitivity coefficient; Calculate the voltage potential g of all energy storage battery cell pairs ij and the equilibrium difference BV, according to the voltage potential g ij Construct the voltage potential matrix G.

7. The equalization algorithm applied to the energy storage battery management system according to claim 6 is characterized in that: When generating a local equilibrium signal, obtain the maximum voltage potential g in the voltage potential matrix G ij The corresponding energy storage battery unit pairs are balanced; When generating a global equilibrium signal, a pair of energy storage battery cells with high voltage potential is selected as a candidate transfer path according to the voltage potential matrix, the transfer path weight and load factor of the energy storage battery cell pair are calculated, and a path with a lower load factor is selected as the optimal transfer path; The calculation logic of the transfer path weight is: Among them, p ij is the transfer path weight between the i-th and j-th energy storage battery units, SOC j is the remaining power of the jth energy storage battery unit, I i is the current of the i-th energy storage battery unit, β is the current regulation factor; The calculation logic of the load factor is: Among them, λ ij is the load factor between the i-th and j-th energy storage battery units, I j is the current of the jth energy storage battery unit, γ is the current imbalance coefficient; According to the load factor, the path with the lower load factor is selected as the optimal transmission path, an optimal path set is generated, and balancing is performed based on the optimal path set.

8. The equalization algorithm applied to the energy storage battery management system according to claim 1 is characterized in that: Monitor the state changes of energy storage battery units, calculate the balanced transfer state, and dynamically adjust the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process, including: Calculate the change rate of the state parameter of the energy storage battery unit, that is, the change of the state parameter per unit time, and calculate the equilibrium transfer state according to the state parameter and the change rate of the state parameter; In each time step, the stability of each energy transfer link is dynamically monitored by updating the value of the balanced transfer state in real time. When the balanced transfer state is less than the balanced transfer state threshold, the current intensity of the transfer link is dynamically adjusted. When the balance is completed, the monitoring is stopped. The balance completion condition is that the balance difference BV is less than the balance difference threshold and the voltage potential g of any energy storage battery unit pair is ij Less than the voltage potential threshold.

9. The equalization algorithm applied to the energy storage battery management system according to claim 8 is characterized in that: The calculation logic of the equilibrium transfer state is: Among them, Q ij (t) is the equilibrium transfer state between the i-th and j-th energy storage battery units at time t, V i (t) and V j (t) is the voltage of the i-th and j-th energy storage battery cells at time t, T i (t) and T j (t) is the temperature of the i-th and j-th energy storage battery cells at time t, and is the current change rate of the i-th and j-th energy storage battery units at time t, α is the temperature sensitivity coefficient, and ζ is the current change suppression coefficient; The dynamic adjustment logic of the current intensity of the transmission link is: I ij (t) = I ij (t-1)·(1-δ·(τ-Q ij (t))), where I ij (t) and I ij (t-1) is the current intensity of the transmission link between the i-th and j-th energy storage battery units at time t and time t-1 respectively, τ is the balanced transmission state threshold, and δ is the current adjustment coefficient.

10. An equalization device for energy storage battery management system, used to implement an equalization algorithm for energy storage battery management system according to any one of claims 1 to 9, characterized in that: include: Parameter capture module: captures the state parameters of the energy storage battery unit in the energy storage battery management system in real time and generates a unit state vector to describe the health status of the energy storage battery unit; Difference calculation module: The difference monitoring module analyzes the cell state vector of the energy storage battery cell, identifies the health state difference between the energy storage battery cells, and calculates the balance difference; Path construction module: According to the health status difference, balance difference and voltage of the energy storage battery cells, the energy transfer link is constructed through the balance path, and the efficient and lightly loaded path is selected for balancing; State monitoring module: monitors the state changes of energy storage battery units, calculates the balanced transfer state, and dynamically adjusts the energy transfer link according to the changes in the balanced transfer state to ensure the stability and efficiency of the balancing process.

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