A hybrid energy storage system state prediction equalization control method and system

Abstract

The application relates to the technical field of battery management, in particular to a mixed energy storage system state prediction and equalization control method and system, which comprises the following steps: collecting real-time electrical data of a battery pack, calculating the health state of each battery through an internal resistance model and temperature history; predicting the dynamic time constant of each battery based on the health degree and the rated time constant, and predicting the future voltage value of each battery by using an open-circuit voltage model and a relaxation effect; comparing the deviation of the future voltage and the group average value, and calculating the comprehensive equalization urgency by combining the battery health degree; finally, inputting the urgency and the total current of the system into a dynamic decision model based on an activation energy model, the dynamic decision model nonlinearly coupling the equalization demand and the system load through an exponential function, and adaptively outputting smooth and accurate equalization current of each battery, so that accurate judgment of real inconsistency and preferential protection of aging batteries are realized. The application improves the accuracy of the mixed energy storage system state prediction and equalization control.

Description

Technical Field

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

[0002] Battery energy storage systems are core components of modern energy systems. However, due to differences in manufacturing processes, material properties, and usage conditions, individual cells within a battery pack inevitably exhibit inconsistencies in capacity, internal resistance, and aging rates. During series charging and discharging, these inconsistencies cause the state of charge of each individual cell to gradually diverge, leading to overcharging or over-discharging of some cells. This severely restricts the usable capacity of the battery pack, accelerates overall aging, and poses safety hazards. To address this issue, voltage threshold-based equalization control technologies (including passive and active equalization) are commonly used to maintain consistency by monitoring battery voltage and redistributing energy.

[0003] However, this type of traditional equalization method has three fundamental drawbacks: First, it relies solely on instantaneous terminal voltage as the criterion, making it highly susceptible to sudden changes in operating current and temperature fluctuations. This can lead to frequent false triggering of equalization actions, wasting energy and potentially exacerbating inconsistencies. Second, the method completely ignores the differences in the health status of individual batteries, failing to identify and protect aging "weak" batteries and accelerating the lifespan degradation of the entire battery pack. Finally, its control strategy is rigid, employing fixed thresholds and equalization currents. It cannot adaptively adjust based on the severity of the imbalance and the real-time load of the system, resulting in sluggish response, low efficiency, and a tendency to generate switching oscillations and electromagnetic interference near the threshold.

[0004] In summary, existing equalization technologies suffer from low control precision and insufficient intelligence due to their reliance on single decision parameters and failure to integrate dynamic battery operating conditions and historical health status information. This makes it impossible to avoid malfunctions or provide proactive protection for weaker batteries, ultimately becoming a key bottleneck restricting the improvement of overall battery pack performance, safety, and lifespan. Summary of the Invention

[0005] This invention provides a state prediction equalization control method and system for hybrid energy storage systems, which solves the problems of existing battery equalization control methods that rely on a single voltage parameter, ignore battery health status, and have rigid control strategies, resulting in frequent malfunctions, low equalization efficiency, inability to protect aging batteries, and accelerated degradation of the entire battery pack lifespan.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] The first aspect of this invention is to provide a state prediction equilibrium control method for a hybrid energy storage system, comprising:

[0008] Obtain the real-time voltage and current of each cell in the battery pack of the hybrid energy storage system, as well as the total current of the battery pack.

[0009] Based on the real-time current and real-time voltage of each battery, the estimated real-time internal resistance of each battery is obtained; the temperature aging coefficient is obtained by fitting the battery accelerated aging test data; and the battery health status of each battery is obtained based on the estimated real-time internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery.

[0010] Based on the battery's rated time constant and the battery's state of health, the predicted time constant of each battery is obtained; based on the battery's real-time current and real-time voltage, the current theoretical open-circuit voltage of each battery is obtained; based on the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span, the future voltage prediction value of each battery is obtained.

[0011] The forecast urgency for each battery is determined by the difference between the predicted future voltage of each battery and the mean of the predicted future voltage of all batteries, and the state health of each battery.

[0012] Based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency, the dynamic equalization activation energy of each battery cell is obtained; based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum allowable equalization current of the system, the equalization current of each battery cell is obtained.

[0013] Furthermore, the battery health status of each battery is obtained based on its real-time estimated internal resistance, historical average temperature, reference temperature, temperature aging coefficient, and rated internal resistance. The battery health status of each battery is specifically expressed by the following formula:

[0014]

[0015] In the formula, This indicates the battery's rated internal resistance. Indicates the first Real-time estimation of the internal resistance of the battery. Indicates the temperature aging coefficient. Indicates the first Historical average temperature of batteries Indicates reference temperature. This represents an exponential function with the natural constant as its base. Indicates the first The battery's health status.

[0016] Further, the process of obtaining the predicted time constant for each battery based on its rated time constant and the state health of each battery, and obtaining the current theoretical open-circuit voltage of each battery based on its real-time current and real-time voltage, includes:

[0017] The predicted time constant for each battery cell is specifically expressed by the following formula:

[0018]

[0019] In the formula, This indicates the battery's rated time constant. Indicates the first Battery health status of the battery. Indicates the first Predicted time constant of the battery;

[0020] The specific process for obtaining the current theoretical open-circuit voltage of each battery cell is as follows:

[0021] Based on the real-time current and real-time voltage of the battery, the estimated state of charge (SOC) value of the battery is obtained by combining the ampere-hour integration method with the voltage correction method, or by using Kalman filtering. Based on the estimated SOC value of the battery, the current theoretical open-circuit voltage of each battery cell is obtained through the OCV-SOC curve.

[0022] Furthermore, based on the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span, the predicted future voltage value of each battery is obtained. The predicted future voltage value of each battery is specifically expressed by the following formula:

[0023]

[0024] In the formula, Indicates the first Real-time voltage of the battery. Indicates the first The current theoretical open-circuit voltage of the battery, Indicates the first The predicted time constant of the battery. Indicates the preset prediction time span. Indicates the first Predicted future voltage of the battery Represents the natural constant.

[0025] Furthermore, based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery, the predicted urgency of balancing is obtained for each battery. The predicted urgency of balancing for each battery is specifically expressed by the following formula:

[0026]

[0027] In the formula, Indicates the first Predicted future voltage of the battery This represents the average of the predicted future voltage values ​​for all batteries. Indicates the first Battery health status of the battery. Indicates the first The urgency of predicting battery equalization It is the absolute value symbol.

[0028] Furthermore, based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency, the dynamic equalization activation energy of each battery cell is obtained. The dynamic equalization activation energy of each battery cell is specifically expressed by the following formula:

[0029]

[0030] In the formula, This represents the theoretical reference equilibrium activation energy. This indicates the total current of the battery pack. This represents a reference value for the total current. Indicates the first The urgency of predicting battery equalization A reference value indicating the urgency of predicting equilibrium. Indicates the first The dynamic equilibrium activation energy of the battery. It is the absolute value symbol;

[0031] The activation energy corresponding to the charge transfer resistance obtained through electrochemical impedance spectroscopy is used as the theoretical reference equilibrium activation energy.

[0032] Furthermore, the equalization current of each battery is obtained based on the dynamic equalization activation energy, energy reference value, and maximum allowable equalization current of each battery. The equalization current of each battery is specifically expressed by the following formula:

[0033]

[0034] In the formula, Indicates the first The dynamic equilibrium activation energy of the battery. Indicates the energy reference value. Indicates the maximum allowable equalization current of the system. Indicates the first The equalization current of the battery, This represents an exponential function with the natural constant as its base.

[0035] A second aspect of the present invention is to provide a state prediction and equalization control system for a hybrid energy storage system, comprising:

[0036] Data acquisition module: used to acquire the real-time voltage, real-time current of each cell in the battery pack of the hybrid energy storage system, and the total current of the battery pack;

[0037] Health assessment module: used to obtain the real-time estimated internal resistance of each battery based on the real-time current and real-time voltage of each battery; to obtain the temperature aging coefficient by fitting the battery accelerated aging test data; and to obtain the battery health status of each battery based on the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery.

[0038] Voltage prediction module: used to obtain the predicted time constant of each battery based on the rated time constant of the battery and the state health of each battery; to obtain the current theoretical open circuit voltage of each battery based on the real-time current and real-time voltage of the battery; and to obtain the future voltage prediction value of each battery based on the real-time voltage of each battery, the current theoretical open circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span.

[0039] Urgency Analysis Module: Used to obtain the predicted equilibrium urgency of each battery based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery.

[0040] The equalization current decision module is used to obtain the dynamic equalization activation energy of each battery cell based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency; and to obtain the equalization current of each battery cell based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum allowable equalization current of the system.

[0041] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the state prediction and equalization control method for a hybrid energy storage system.

[0042] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the state prediction and equalization control method for a hybrid energy storage system.

[0043] Compared with existing technologies, the beneficial effects of this invention are: acquiring the real-time voltage, real-time current, and total current of each cell in the hybrid energy storage system battery pack; providing an accurate and comprehensive input basis for subsequent intelligent state assessment and prediction by collecting multi-dimensional real-time data; obtaining the real-time estimated internal resistance of each cell based on its real-time current and real-time voltage; obtaining the temperature aging coefficient by fitting battery accelerated aging test data; obtaining the battery health status of each cell based on its real-time estimated internal resistance, historical average temperature, reference temperature, temperature aging coefficient, and rated internal resistance; combining internal resistance and historical temperature to quantify battery health, enabling early diagnosis of battery aging status and identification of "weak" cells; obtaining the predicted time constant of each cell based on its rated time constant and battery health status; obtaining the current theoretical open-circuit voltage of each cell based on its real-time current and real-time voltage; obtaining the future voltage prediction value of each cell based on its real-time voltage, current theoretical open-circuit voltage, predicted time constant, and preset prediction time span; and based on health... The system dynamically corrects the time constant and predicts future voltage, effectively eliminating instantaneous operating condition interference and accurately identifying true inconsistencies. Based on the difference between the predicted future voltage of each cell and the average of all predicted future voltages, and the health status of each cell, the predicted balancing urgency of each cell is obtained. The urgency is calculated by integrating future voltage deviation and battery health status, allowing balancing resources to be prioritized for the highest-risk and weakest cells. The dynamic balancing activation energy of each cell is obtained based on theoretical reference balancing activation energy, total battery pack current, reference value of total current, predicted balancing urgency of each cell, and reference value of predicted balancing urgency. The balancing current of each cell is obtained based on its dynamic balancing activation energy, energy reference value, and the maximum allowable balancing current of the system. An activation energy model couples urgency with system load, adaptively generating a smooth, non-linear balancing current, achieving precise, flexible, and collaborative intelligent control. This solves the problems of existing battery balancing control methods that rely on a single voltage parameter, ignore battery health status, and have rigid control strategies, leading to frequent malfunctions, low balancing efficiency, inability to protect aging batteries, and accelerated overall battery life degradation. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1This invention provides a flowchart illustrating the steps of a state prediction and equilibrium control method for a hybrid energy storage system.

[0046] Figure 2 This invention provides a schematic flowchart of a state prediction and equilibrium control system for a hybrid energy storage system. Detailed Implementation

[0047] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] To address the problems existing in the background technology, a state prediction and equilibrium control method and system for hybrid energy storage systems have been researched and designed, which has important practical significance.

[0050] like Figure 1 As shown, the first aspect of the present invention is to provide a state prediction and equilibrium control method for a hybrid energy storage system, comprising the following steps:

[0051] Step S001: Obtain the real-time voltage, real-time current, and total current of each cell in the hybrid energy storage system battery pack.

[0052] It should be noted that, in order to completely solve the problems of incorrect equalization, neglect of battery health, and rigid control caused by the reliance on a single voltage signal in traditional methods, a smart system capable of predicting the real state, assessing internal health, and adaptively adjusting must be constructed. This requires moving beyond single data acquisition and comprehensively acquiring multi-dimensional information: battery terminal voltage, total current, and estimated SOC need to be collected to eliminate transient interference and predict future trends; voltage and current transient data and temperature history need to be collected to calculate real-time internal resistance, quantifying battery health and identifying aging bottlenecks; simultaneously, total current needs to be continuously collected to sense system load in real time, providing a basis for dynamically adjusting the equalization intensity; and the manufacturer's rated parameters are used as an evaluation benchmark. Ultimately, by fusing these data, precise, forward-looking, and adaptive intelligent equalization control can be achieved.

[0053] Specifically, the real-time voltage, real-time current, and total current of each cell in the hybrid energy storage system battery pack are obtained.

[0054] Step S002: Obtain the real-time estimated internal resistance of each battery based on the real-time current and real-time voltage of each battery; obtain the temperature aging coefficient by fitting the battery accelerated aging test data; obtain the battery state health of each battery based on the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery.

[0055] It is important to note that in order to accurately identify and proactively protect aging batteries during the balancing control process, and to avoid the "one-size-fits-all" approach of traditional methods that accelerates the "weakest link effect," a health assessment of the batteries is essential. Therefore, this step uses real-time voltage and current data to calculate changes in internal resistance and records long-term temperature history. These two key parameters, reflecting the battery's internal chemical aging and external stress accumulation, are compared and modeled with the battery's rated baseline values. This quantitatively assesses the health status of each battery, providing a core basis for subsequent differentiated and protective balancing decisions.

[0056] Specifically, based on the real-time current and real-time voltage of each battery cell, the estimated real-time internal resistance of each battery cell is obtained through the internal resistance model formula; the internal resistance model formula is a well-known technique and will not be elaborated here.

[0057] The temperature aging coefficient was obtained by fitting the battery accelerated aging test data.

[0058] The battery health status of each cell is obtained based on its real-time estimated internal resistance, historical average temperature, reference temperature, temperature aging coefficient, and rated internal resistance. The specific formula for the battery health status of each cell is as follows:

[0059]

[0060] In the formula, This indicates the battery's rated internal resistance. Indicates the first Real-time estimation of the internal resistance of the battery. Indicates the temperature aging coefficient. Indicates the first Historical average temperature of batteries Indicates reference temperature. This represents an exponential function with the natural constant as its base. Indicates the first The battery's health status.

[0061] in, This represents the difference (temperature deviation) between the historical average temperature of each battery and the reference temperature. When the historical average temperature of the battery is much higher than the reference temperature, that is, the larger the difference, the more severe the high-temperature environment the battery has experienced, the stronger the aging effect caused by high temperature, and the worse the health of the battery. When the historical average temperature of the battery is close to or lower than the reference temperature, that is, the smaller the difference, the weaker or insignificant the accelerating effect of temperature on aging, and the better its health. This indicates the relative strength of the aging acceleration effect caused by temperature deviation. Therefore, the temperature deviation is converted into "the dimensionless or normalized influence intensity on the aging rate" through the temperature aging coefficient; that is, by converting the linearly increasing temperature aging effect into a nonlinear accelerated penalty on health. The internal resistance health factor represents the internal resistance health factor. The higher the real-time estimated internal resistance of each battery cell (the more aged it is), the lower the internal resistance health factor is, which directly reflects the result of the internal electrochemical aging of the battery.

[0062] Thus, the battery health status of each battery is obtained through the above method.

[0063] Step S003: Based on the rated time constant of the battery and the state health of each battery, obtain the predicted time constant of each battery; based on the real-time current and real-time voltage of the battery, obtain the current theoretical open-circuit voltage of each battery; based on the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery, and the preset predicted time span, obtain the predicted future voltage value of each battery.

[0064] It should be noted that, in order to accurately distinguish between the inconsistency of the actual state of charge of the battery and the transient voltage disturbances caused by instantaneous current or polarization effects under dynamic operating conditions, and thus avoid the malfunctions caused by the direct response of traditional equalization methods to instantaneous voltage, it is necessary to predict the future voltage trend. Therefore, this step establishes a battery dynamic model that includes a relaxation time constant, and uses the collected real-time terminal voltage, the estimated open-circuit voltage, and the set prediction time window to calculate the stable voltage value of the battery in the short term. This serves as the basis for judging whether equalization is needed and the urgency of equalization, thereby achieving a "penetration" of the battery's true state and forward-looking decision-making.

[0065] Specifically, the predicted time constant for each battery is obtained based on the battery's rated time constant and the battery's state of health. The predicted time constant for each battery is expressed by the following formula:

[0066]

[0067] In the formula, This indicates the battery's rated time constant (from manufacturer data). Indicates the first Battery health status of the battery. Indicates the first Predicted time constant of the battery.

[0068] Based on the real-time current and real-time voltage of the battery, the estimated state of charge (SOC) value of the battery is obtained by combining the ampere-hour integration method with the voltage correction method, or by using Kalman filtering. Based on the estimated state of charge value of the battery, the current theoretical open circuit voltage (OCV) of each battery is obtained through the OCV-SOC curve.

[0069] Based on the real-time voltage of each battery cell, the current theoretical open-circuit voltage of each battery cell, the predicted time constant of each battery cell, and the preset prediction time span, the predicted future voltage value of each battery cell is obtained; the specific formula for the predicted future voltage value of each battery cell is as follows:

[0070]

[0071] In the formula, Indicates the first Real-time voltage of the battery. Indicates the first The current theoretical open-circuit voltage of the battery, Indicates the first The predicted time constant of the battery. Indicates the preset prediction time span. Indicates the first Predicted future voltage of the battery Represents the natural constant.

[0072] in, This represents the ratio of the preset prediction time span to the prediction time constant for each battery cell. A large ratio indicates that the given relaxation time is sufficiently long, meaning that the prediction time... The more complete the internal relaxation, the more the transient disturbance has dissipated, thus requiring a significant degree of correction and adjustment; conversely, the smaller the ratio, the more likely the problem is that the forecast time is too short. The less sufficient the internal relaxation, the more significant the transient disturbance effect remains. Therefore, it is unclear whether a large degree of correction is needed, i.e., no correction or only a very small correction. This indicates the current voltage deviation, where the current voltage deviation is the total amount of interference that needs to be adjusted; This represents the relaxation decay factor. The larger the relaxation decay factor (the closer it is to 1), the faster the prediction time. The less sufficient the internal relaxation, the lower the predicted future voltage value. It is closer to the current voltage affected by interference. The smaller the relaxation decay factor (closer to 0), the less significant the transient disturbance remains, indicating that the voltage disturbance has hardly subsided within the prediction time, and the future voltage will still be close to the current disturbed value. Therefore, no correction is made, which is equivalent to trusting the current measured voltage. The more fully the internal relaxation, the more the transient disturbance has dissipated, and the greater the "correction" to the current voltage deviation. It is assumed that the voltage disturbance has basically subsided within the prediction time. Therefore, it is necessary to obtain the adjusted voltage by adjusting the current voltage deviation through the relaxation attenuation factor, which is the future voltage prediction value.

[0073] Thus, the predicted future voltage value of each battery cell is obtained using the above method.

[0074] Step S004: Based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery, obtain the predicted balance urgency of each battery.

[0075] It should be noted that, in order to achieve priority protection and precise resource allocation for high-risk batteries in a resource-constrained balancing system, and to avoid the problem of insufficient protection for aging batteries by traditional "egalitarian" balancing strategies, it is necessary to quantify and prioritize the urgency of balancing. Therefore, this step integrates the predicted value reflecting future voltage risk with the health assessment reflecting the inherent vulnerability of the battery. This means that batteries with poorer health will have their same future voltage deviation amplified into a higher urgency score, thereby generating a comprehensive priority instruction that considers both "future risk level" and "battery strength," guiding the subsequent balancing system to intelligently allocate balancing current.

[0076] Specifically, the predicted urgency of balancing for each battery is obtained based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery; wherein, the predicted urgency of balancing for each battery is expressed by the following formula:

[0077]

[0078] In the formula, Indicates the first Predicted future voltage of the battery This represents the average of the predicted future voltage values ​​for all batteries. Indicates the first Battery health status of the battery. Indicates the first The urgency of predicting battery equalization It is the absolute value symbol.

[0079] in, This represents the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries. A larger difference indicates a greater deviation of each battery from the average, suggesting a higher probability of anomalies in that battery, and thus a greater urgency for predictive balance. Conversely, a smaller difference indicates a smaller deviation, suggesting a lower probability of anomalies in that battery, and thus a lower urgency for predictive balance. Similarly, a higher battery health status for each battery reduces the likelihood of aging-related damage, and thus a lower urgency for predictive balance.

[0080] Thus, the predicted urgency of balancing each battery is obtained through the above method.

[0081] Step S005: Based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency, obtain the dynamic equalization activation energy of each battery cell; based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum allowable equalization current of the system, obtain the equalization current of each battery cell.

[0082] It should be noted that, in order to ensure the balancing effect while avoiding the problems of abrupt response, increased system load, and electromagnetic interference caused by traditional fixed current or switching control, and to achieve smooth, efficient, and system-condition-coordinated intelligent balancing execution, adaptive decision-making for the balancing current is necessary. Therefore, this step introduces the concept of "energy barrier" from physics, constructing a dynamic "activation energy" model between the urgency (PEU) representing the balancing demand and the total current representing the system load. An exponential function is used to transform the competitive relationship between the two into a current command that continuously and non-linearly varies between 0 and its maximum value: when the urgency is high and the system is idle, the current approaches its maximum for rapid balancing; when the urgency is low or the system is busy, the current is exponentially suppressed to reduce the burden, thereby achieving a dynamic optimal match between the balancing strength and the system state.

[0083] Specifically, the activation energy corresponding to the charge transfer resistance obtained by electrochemical impedance spectroscopy (EIS) is used as the theoretical reference equilibrium activation energy.

[0084] Based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency, the dynamic equalization activation energy of each battery cell is obtained; the specific formula for the dynamic equalization activation energy of each battery cell is as follows:

[0085]

[0086] In the formula, This represents the theoretical reference equilibrium activation energy. This indicates the total current of the battery pack. This represents a reference value for the total current. Indicates the first The urgency of predicting battery equalization A reference value indicating the urgency of predicting equilibrium. Indicates the first The dynamic equilibrium activation energy of the battery. It is the absolute value symbol.

[0087] in, This represents the system load term and the total system current. The larger the current, the "busier" the system, which is equivalent to increasing the "activation energy" of the balancing process, making it more difficult to occur (i.e., the balancing current should be reduced). In other words, the larger the total current of the system, the stronger the logical suppression or weakening of the balancing action, and the easier it is for the system to achieve the action of "reducing or pausing energy transfer." This is because the algorithm will actively and automatically output a smaller balancing current, thus making the balancing action physically weaker or stopping, which is exactly the desired "suppression" effect of the system. Conversely, the smaller the total current of the system, the weaker the logical suppression or weakening of the balancing action, and the easier (or more inclined) it is to achieve the action of "strengthening or initiating energy transfer." The algorithm will automatically output a larger balancing current, making the balancing action physically stronger and easier to occur. This represents the equilibrium demand term and the urgency of equilibrium. The larger the value, the stronger the equilibrium requirement, which is equivalent to lowering the "activation energy" of the equilibrium process, making it easier to occur (i.e., the equilibrium current should be increased). This term is in the denominator, making... The calculation decreases. When the predicted urgency of equalization for each battery cell is much lower than the reference value, the urgency is not significant, and this term has little impact on the denominator, making it difficult to initiate equalization. When the predicted urgency of equalization for each battery cell is close to or greater than the reference value, the urgency is significant, and this term significantly increases the denominator, thereby effectively reducing the dynamic activation energy and promoting equalization. Specifically, the total current of the battery pack and the predicted urgency of equalization for each battery cell are calculated by comparing them with the reference value to obtain two relative significance levels. This is mainly to unify the dimensions, ensure the physical rationality of the formula, and adjust the theoretical reference equalization activation energy through the relationship between the two relative significance levels. The adjusted value is the dynamic equalization activation energy of each battery cell. Adding 1 to the denominator prevents it from being zero.

[0088] The equalization current of each battery is obtained based on its dynamic equalization activation energy, energy reference value, and the maximum allowable equalization current of the system. The specific formula for the equalization current of each battery is as follows:

[0089]

[0090] In the formula, Indicates the first The dynamic equilibrium activation energy of the battery. Indicates the energy reference value. Indicates the maximum allowable equalization current of the system. Indicates the first The equalization current of the battery, This represents an exponential function with the natural constant as its base.

[0091] The design of this formula is based on the fact that the reaction rate decreases exponentially with increasing activation energy in the Arrhenius equation. This represents the ratio (relative difficulty) of the dynamic equilibrium activation energy of each battery cell to the energy reference value. A higher relative difficulty means less sustained effort is required, resulting in a lower equilibrium current for each battery cell; conversely, a lower relative difficulty allows for easier application of more effort, leading to a higher equilibrium current for each battery cell. Therefore, the maximum allowable equilibrium current of the system is adjusted using an exponential function with a negative correlation mapping, yielding the adjusted value, which is the equilibrium current for each battery cell.

[0092] Thus, the balanced current of each battery is obtained through the above method.

[0093] To verify the effectiveness of this solution, the following experimental data will be used for illustration.

[0094] Two experimental groups were set up: a control group for the traditional method and an experimental group for this protocol.

[0095] 1. Objective of the experiment.

[0096] Under simulated real-world conditions, the superiority of the proposed state prediction equalization control method (based on BSH, FVP, PEU, and dynamic energy barrier decision-making) over the traditional voltage equalization method in improving equalization accuracy and extending battery life was verified.

[0097] 2. Experiment setup.

[0098] Test subjects: Two identical 4-cell 18650 lithium-ion battery packs connected in series (Battery pack A - control group, Battery pack B - test group).

[0099] Initial state: Both battery packs were screened, with identical initial capacity and internal resistance (capacity: 2500mAh ± 10mAh; internal resistance: 30mΩ ± 2mΩ). Key setting: In battery pack B, the second battery was artificially replaced with a slightly aged battery (capacity decayed to 2400mAh, internal resistance increased to 35mΩ) to simulate inconsistencies that may occur in actual use.

[0100] Environment: Incubator, with the temperature set to vary periodically between 25°C and 45°C (simulating day / night temperature difference).

[0101] Cyclic operation: 500 charge-discharge cycles. Each cycle includes: 1C constant current charging to 16.8V, resting for 5 minutes, and 1C constant current discharging to 12.0V. Key settings: A 5-second 2C high-current pulse is randomly inserted during the charge-discharge process to simulate real-world conditions such as vehicle acceleration.

[0102] Equilibrium methods:

[0103] Battery pack A (control group): The traditional voltage balancing method is used. When the voltage difference between any battery and the average voltage of the pack exceeds 25mV, a fixed discharge current of 150mA is started to balance the high-voltage batteries.

[0104] Battery pack B (experimental group): The state prediction equalization control method of this invention is used to calculate BSH, FVP, and PEU in real time, and the equalization current is determined based on the dynamic energy barrier formula. Parameter settings are as follows: , Among them, BSH is the battery state health, FVP is the predicted future voltage of the battery, and PEU is the predicted urgency of battery equalization.

[0105] 3. Evaluation indicators.

[0106] Equalization accuracy: The maximum voltage deviation of the battery pack throughout the entire test cycle. The smaller the value, the higher the precision of the equalization control and the better the battery consistency is maintained.

[0107] Lifetime retention: The percentage of total usable capacity retained by the battery pack after 500 cycles. The higher this value, the more effective the method is in extending battery life.

[0108] Aging battery protection: Capacity retention of the aged battery (second cell) after 500 cycles. This value directly reflects the method's ability to protect the weakest cell in the group.

[0109] 4. Experimental results and data analysis.

[0110]

[0111] Interpretation of Results:

[0112] Regarding balancing accuracy: The method of this invention reduces the maximum voltage deviation of the battery pack by 62.5%, demonstrating extremely superior balancing accuracy. This is because:

[0113] When a high-current pulse (2C) occurs, traditional methods will immediately detect a falsely high voltage due to the ohmic voltage drop, thus falsely triggering equalization. However, this invention determines the voltage relaxation trend by predicting the battery's future voltage, identifying it as temporary polarization rather than a true state of charge (SOC) inconsistency, thereby avoiding false equalization.

[0114] This invention accurately identifies the second battery as an aging battery with higher internal resistance using (BSH) and assigns it a higher balancing weight in (PEU), thereby enabling focused and timely balancing to prevent further voltage deterioration. Here, BSH represents the battery's state of health, and PEU represents the predicted balancing urgency.

[0115] Regarding the extension of overall battery life: This invention retains 6.7% more capacity than the original battery pack. The fundamental reason lies in the precise balancing process, which avoids long-term overcharging / over-discharging of some batteries (especially aging batteries), thus slowing down the overall degradation rate. The dynamic energy barrier plays a crucial role: it automatically reduces the balancing current during high-current operation (such as 2C pulses) to prevent system overload; and increases the balancing current during idle or low-current operation, achieving efficient balancing. This intelligent current scheduling is impossible to achieve with traditional methods.

[0116] Regarding the protection of aging batteries: This is the most prominent advantage of this invention. The protection effect on aging batteries is improved by 8.7 percentage points, significantly higher than the overall improvement level. This directly proves the success of the model construction corresponding to the battery's state health and the urgency of predicting equilibrium.

[0117] The second battery was identified as having significantly deteriorated health based on its state of health. According to this diagnosis, the battery's predicted equalization urgency was assigned a higher equalization weight factor, thus increasing its priority in equalization decisions. Throughout the test cycle, this aged battery, receiving higher-priority equalization intervention, had its capacity decay rate effectively controlled, preventing premature performance degradation from limiting the overall battery pack's usable capacity and cycle life.

[0118] This concludes the embodiment.

[0119] like Figure 2 As shown, a second aspect of the present invention is to provide a state prediction and equalization control system for a hybrid energy storage system, comprising:

[0120] Data acquisition module 101: used to acquire the real-time voltage, real-time current of each cell in the battery pack of the hybrid energy storage system, and the total current of the battery pack;

[0121] Health assessment module 102: used to obtain the real-time estimated internal resistance of each battery based on the real-time current and real-time voltage of each battery; to obtain the temperature aging coefficient by fitting the battery accelerated aging test data; and to obtain the battery health status of each battery based on the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery.

[0122] Voltage prediction module 103: used to obtain the predicted time constant of each battery based on the rated time constant of the battery and the state health of each battery; to obtain the current theoretical open circuit voltage of each battery based on the real-time current and real-time voltage of the battery; and to obtain the future voltage prediction value of each battery based on the real-time voltage of each battery, the current theoretical open circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span.

[0123] Urgency Analysis Module 104: Used to obtain the predicted equilibrium urgency of each battery based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery.

[0124] The equalization current decision module 105 is used to obtain the dynamic equalization activation energy of each battery cell based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency; and to obtain the equalization current of each battery cell based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum equalization current allowed by the system.

[0125] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a state prediction and equalization control method for a hybrid energy storage system.

[0126] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a state prediction and equalization control method for a hybrid energy storage system.

[0127] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0128] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A state prediction and equilibrium control method for a hybrid energy storage system, characterized in that, include: Obtain the real-time voltage and current of each cell in the battery pack of the hybrid energy storage system, as well as the total current of the battery pack. Based on the real-time current and real-time voltage of each battery, the estimated real-time internal resistance of each battery is obtained; the temperature aging coefficient is obtained by fitting the battery accelerated aging test data; and the battery health status of each battery is obtained based on the estimated real-time internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery. Based on the battery's rated time constant and the battery's state of health, the predicted time constant of each battery is obtained; based on the battery's real-time current and real-time voltage, the current theoretical open-circuit voltage of each battery is obtained; based on the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span, the future voltage prediction value of each battery is obtained. The forecast urgency for each battery is determined by the difference between the predicted future voltage of each battery and the mean of the predicted future voltage of all batteries, and the state health of each battery. Based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency, the dynamic equalization activation energy of each battery cell is obtained; based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum allowable equalization current of the system, the equalization current of each battery cell is obtained. The dynamic equilibrium activation energy of each battery is obtained based on the theoretical reference equilibrium activation energy, the total current of the battery pack, the reference value of the total current, the predicted equilibrium urgency of each battery cell, and the reference value of the predicted equilibrium urgency. The dynamic equilibrium activation energy of each battery cell is specifically expressed by the following formula: In the formula, This represents the theoretical reference equilibrium activation energy. This indicates the total current of the battery pack. This represents a reference value for the total current. Indicates the first The urgency of predicting battery equalization A reference value indicating the urgency of predicting equilibrium. Indicates the first The dynamic equilibrium activation energy of the battery. It is the absolute value symbol; Among them, the activation energy corresponding to the charge transfer resistance obtained by electrochemical impedance spectroscopy is used as the theoretical reference equilibrium activation energy; The equalization current of each battery is obtained based on the dynamic equalization activation energy, energy reference value, and maximum allowable equalization current of each battery. The equalization current of each battery is specifically expressed by the following formula: In the formula, Indicates the first The dynamic equilibrium activation energy of the battery. Indicates the energy reference value. Indicates the maximum allowable equalization current of the system. Indicates the first The equalization current of the battery, This represents an exponential function with the natural constant as its base.

2. The state prediction and equilibrium control method for a hybrid energy storage system according to claim 1, characterized in that, The battery health status of each battery is obtained based on its real-time estimated internal resistance, historical average temperature, reference temperature, temperature aging coefficient, and rated internal resistance. The battery health status of each battery is specifically expressed by the following formula: In the formula, This indicates the battery's rated internal resistance. Indicates the first Real-time estimation of the internal resistance of the battery. Indicates the temperature aging coefficient. Indicates the first Historical average temperature of batteries Indicates reference temperature. This represents an exponential function with the natural constant as its base. Indicates the first The battery's health status.

3. The state prediction and equilibrium control method for a hybrid energy storage system according to claim 1, characterized in that, The process involves obtaining the predicted time constant for each battery based on its rated time constant and the state health of each battery cell; and obtaining the current theoretical open-circuit voltage of each battery cell based on its real-time current and real-time voltage, including: The predicted time constant for each battery cell is specifically expressed by the following formula: In the formula, This indicates the battery's rated time constant. Indicates the first Battery health status of the battery. Indicates the first Predicted time constant of the battery; The specific process for obtaining the current theoretical open-circuit voltage of each battery cell is as follows: Based on the real-time current and real-time voltage of the battery, the estimated state of charge (SOC) value of the battery is obtained by combining the ampere-hour integration method with the voltage correction method, or by using Kalman filtering. Based on the estimated SOC value of the battery, the current theoretical open-circuit voltage of each battery cell is obtained through the OCV-SOC curve.

4. The state prediction and equilibrium control method for a hybrid energy storage system according to claim 1, characterized in that, The future voltage prediction value of each battery is obtained based on the real-time voltage of each battery, the current theoretical open-circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span. The specific formula for the future voltage prediction value of each battery is as follows: In the formula, Indicates the first Real-time voltage of the battery. Indicates the first The current theoretical open-circuit voltage of the battery. Indicates the first The predicted time constant of the battery. Indicates the preset prediction time span. Indicates the first Predicted future voltage of the battery Represents the natural constant.

5. The state prediction and equilibrium control method for a hybrid energy storage system according to claim 1, characterized in that, The predicted urgency of balancing each battery is obtained based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery. The predicted urgency of balancing each battery is specifically expressed by the following formula: In the formula, Indicates the first Predicted future voltage of the battery This represents the average of the predicted future voltage values ​​for all batteries. Indicates the first Battery health status of the battery. Indicates the first The urgency of predicting battery equalization It is the absolute value symbol.

6. A state predictive equilibrium control system for a hybrid energy storage system, employing the state predictive equilibrium control method for a hybrid energy storage system as described in any one of claims 1-5, characterized in that, include: Data acquisition module: used to acquire the real-time voltage, real-time current of each cell in the battery pack of the hybrid energy storage system, and the total current of the battery pack; Health assessment module: used to obtain the real-time estimated internal resistance of each battery based on the real-time current and real-time voltage of each battery; to obtain the temperature aging coefficient by fitting the battery accelerated aging test data; and to obtain the battery health status of each battery based on the real-time estimated internal resistance of each battery, the historical average temperature of each battery, the reference temperature, the temperature aging coefficient, and the rated internal resistance of the battery. Voltage prediction module: used to obtain the predicted time constant of each battery based on the rated time constant of the battery and the state health of each battery; to obtain the current theoretical open circuit voltage of each battery based on the real-time current and real-time voltage of the battery; and to obtain the future voltage prediction value of each battery based on the real-time voltage of each battery, the current theoretical open circuit voltage of each battery, the predicted time constant of each battery, and the preset prediction time span. Urgency Analysis Module: Used to obtain the predicted equilibrium urgency of each battery based on the difference between the predicted future voltage of each battery and the average predicted future voltage of all batteries, and the battery health status of each battery. The equalization current decision module is used to obtain the dynamic equalization activation energy of each battery cell based on the theoretical reference equalization activation energy, the total current of the battery pack, the reference value of the total current, the predicted equalization urgency of each battery cell, and the reference value of the predicted equalization urgency; and to obtain the equalization current of each battery cell based on the dynamic equalization activation energy of each battery cell, the energy reference value, and the maximum allowable equalization current of the system.

7. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the state prediction and equalization control method for a hybrid energy storage system as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the state prediction and equalization control method for a hybrid energy storage system as described in any one of claims 1-6.

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