Modular energy storage system

Abstract

The invention relates to a modular energy storage system, which belongs to the technical field of energy storage and comprises a state acquisition module used for determining unit core temperature and equivalent direct-current internal resistance of each energy storage unit as current state parameters; the cost prediction module is used for determining a predicted core temperature of each energy storage unit under a preset scheduling power instruction based on the current state parameter and further determining a predicted aging cost; the weight calculation module is used for determining the health scheduling weight of each energy storage unit based on the calculation logic of the cost prediction module; the power distribution module is used for responding to the total power demand and generating the final output power of each energy storage unit according to the health scheduling weight; and the parameter correction module is used for correcting the equivalent direct-current internal resistance based on the deviation between the predicted core temperature and the actually measured core temperature, and a decision-making basis with physical significance is provided for subsequent optimal scheduling.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, specifically a modular energy storage system. Background Technology

[0002] In applications such as integrated photovoltaic-storage-charging power stations, energy storage systems need to frequently respond to high-power, random charging demands. Traditional control strategies aim to balance the state of charge of each module, but ignore the inherent differences in the health status of each module. Due to differences in manufacturing processes and operating environments, the health status of each energy storage module will gradually diverge. For modules with low health status and high internal resistance, using the same scheduling strategy as healthy modules will cause them to generate heat far exceeding normal levels when responding to high-power demands, drastically accelerating their electrochemical aging and further increasing their internal resistance. This vicious positive feedback loop of aging-heat generation-derating ultimately leads to the premature failure of some modules and may trigger avalanche aging of the system, severely shortening its overall service life.

[0003] To address this issue, this invention proposes an active management method that incorporates module health status into closed-loop control.

[0004] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a modular energy storage system to solve the problems mentioned in the background art.

[0006] The technical solution of the present invention includes: The status acquisition module is used to determine the core temperature and equivalent DC internal resistance of each energy storage unit as the current status parameters. The cost prediction module is used to determine the predicted core temperature of each energy storage unit under the preset scheduling power command based on the current state parameters, and then determine the predicted aging cost. The weight calculation module is used to determine the health scheduling weight of each energy storage unit based on the calculation logic of the cost prediction module. The power allocation module is used to respond to the total power demand and generate the final output power of each energy storage unit according to the health scheduling weight; The parameter correction module is used to correct the equivalent DC internal resistance based on the deviation between the predicted core temperature and the actual measured core temperature.

[0007] Preferably, the cost prediction module is used to determine the predicted core temperature by calling the thermodynamic energy conservation equation based on the lumped parameter method, based on the current state parameters, ambient temperature, and the unit's equivalent heat capacity and equivalent heat dissipation coefficient.

[0008] Preferably, the cost prediction module is further used to calculate the predicted aging cost based on the predicted core temperature by calling an aging cost quantification model that draws on the idea of ​​the Arrhenius equation.

[0009] Preferably, the weight calculation module determines the health scheduling weight, including: The preset standard reference power is used as the preset dispatch power command to determine the baseline aging cost of each energy storage unit; The health scheduling weights are determined by calculating and normalizing the baseline aging cost.

[0010] Preferably, the power distribution module generates the final output power, including: Based on the total power demand and health scheduling weights, preliminary power pre-allocation is carried out to obtain the initial target power of each energy storage unit.

[0011] Preferably, the power distribution module generates the final output power, further comprising: The initial target power is used as the preset scheduling power command to determine the corresponding predicted core temperature; Based on preset internal resistance and temperature thresholds, all energy storage units are divided into healthy sets and restricted sets.

[0012] Preferably, the power distribution module generates the final output power, further comprising: For energy storage units in a constrained set, the output power is determined to be a preset safety value; Calculate the remaining total power and allocate it to the energy storage units in the healthy set according to the relative proportion of the health scheduling weight to determine the output power.

[0013] Preferably, the parameter correction module is used for: When the actual measured core temperature is higher than the predicted core temperature, the equivalent DC internal resistance is increased according to the preset learning rate. When the actual measured core temperature is lower than the predicted core temperature, the equivalent DC internal resistance is reduced. When the actual measured core temperature equals the predicted core temperature, the equivalent DC internal resistance remains unchanged.

[0014] This invention provides a modular energy storage system with the following improvements and advantages compared to the prior art: 1. This system achieves a shift from passive response to proactive prediction. The cost prediction module no longer simply monitors the current state but uses the thermodynamic energy conservation equation to determine the predicted core temperature after a power command is executed, based on current state parameters. Furthermore, this module utilizes an aging cost quantification model inspired by the Arrhenius equation, transforming abstract health damage into a quantifiable predicted aging cost. This design enables the system to anticipate the short-term thermal effects and long-term aging costs on the unit before the power command is actually issued, providing a physically meaningful basis for subsequent optimized scheduling. 2. An asymmetric power allocation principle based on intrinsic health status has been established. The weight calculation module in the system uses a standard reference power as a unified evaluation benchmark to calculate the benchmark aging cost for each energy storage unit and determine the health scheduling weight that can stably reflect its true health level. The power allocation module then allocates power according to this weight, allowing units with better health status to bear a larger proportion of power, while protecting units with sub-health status. This changes the technical drawbacks of traditional technologies, such as the inability to fully utilize the potential of healthy units and the premature depletion of sub-health units. 3. A robust power allocation mechanism that balances optimization objectives and safety boundaries was constructed. The power allocation module adopts a two-stage allocation strategy. After initial power pre-allocation based on health weights, the system performs a forward-looking safety threshold check, comparing the predicted core temperature under the pre-allocation scheme with the preset threshold, and dividing all units into healthy sets and restricted sets. For units in the restricted set, their output power will be forcibly limited to a preset safety value to avoid overheating or overstress risks. The remaining total power demand is dynamically borne by units in the healthy set according to their relative health weights. This mechanism ensures that while pursuing the goal of long lifespan, the system never exceeds the safe operating boundary of any unit, achieving dual protection for the overall system performance and the safety of individual units. 4. The system is endowed with self-learning and dynamic adaptation capabilities. The parameter correction module in the system compares the deviation between the predicted core temperature and the actual measured core temperature after a scheduling cycle, and performs closed-loop feedback correction on the key parameter that serves as the basis for decision-making, namely the equivalent DC internal resistance. When the actual temperature is higher than the prediction, the system will increase the internal resistance value; otherwise, it will decrease it. This adaptive correction mechanism ensures that the digital model of the system can accurately track the real aging process of each energy storage unit throughout its entire life cycle, greatly improving the reliability of the system's long-term operation and the accuracy of decision-making, and ensuring the continued effectiveness of the technical solution of this invention. Attached Figure Description

[0015] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1This is a flowchart of the system of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] Example 1 Please see Figure 1 This invention provides a modular energy storage system, comprising: The status acquisition module is used to determine the core temperature and equivalent DC internal resistance of each energy storage unit as the current status parameters. The cost prediction module is used to determine the predicted core temperature of each energy storage unit under the preset scheduling power command based on the current state parameters, and then determine the predicted aging cost. The weight calculation module is used to determine the health scheduling weight of each energy storage unit based on the calculation logic of the cost prediction module. The power allocation module is used to respond to the total power demand and generate the final output power of each energy storage unit according to the health scheduling weight; The parameter correction module is used to correct the equivalent DC internal resistance based on the deviation between the predicted core temperature and the actual measured core temperature. This embodiment provides a modular energy storage system designed to address the problem that traditional energy storage systems fail prematurely and shorten the overall lifespan of the system due to neglecting the differences in the health status of each module. The system achieves early identification and proactive intervention of aging trends by constructing an intelligent health management closed loop of status assessment, cost prediction, asymmetric scheduling, and closed-loop correction. The system includes: a status acquisition module, a cost prediction module, a weight calculation module, a power allocation module, and a parameter correction module; The status acquisition module aims to provide real-time and accurate basic data for subsequent health status assessment and scheduling decisions. In this embodiment, this module is deployed in each independent energy storage unit, forming an internal sensor network composed of battery modules and their corresponding power conversion systems. This network collects the current, voltage, and temperature of key measurement points of each unit i in real time. Based on this raw data, the status acquisition module further constructs and maintains a digital twin model describing the core state of each unit, and determines two key current state parameters: unit core temperature. With equivalent DC internal resistance ; Unit core temperature It refers to the internal temperature that best reflects the activity of the electrochemical reaction of the battery cell, which is estimated by multi-point temperature measurement data and thermal model. The unit is Celsius, ∘C. Its function is to serve as a key input for thermal safety assessment and aging rate calculation. Equivalent DC internal resistance It refers to an equivalent resistance parameter that comprehensively reflects the aging degree of the battery cell and its internal heat generation capacity. The unit is ohms (Ω). Its function is to quantify the heat generation potential of the cell when subjected to current load and the degree of decline in its health status. The initial value of this parameter is obtained through offline calibration and is dynamically updated by the parameter correction module during operation. The cost prediction module aims to transform the long-term, abstract health damage caused to the energy storage unit by the power command to be executed into quantifiable and comparable cost indicators. In this embodiment, the module receives the current state parameters output by the state acquisition module. , And based on a preset scheduling power command The calculation is performed step by step: Determine the predicted core temperature of the cell after executing the power command. ; Based on this predicted temperature, a dimensionless predicted aging cost is further determined. ; The weighting calculation module aims to generate weighting coefficients to guide power allocation based on the current health level of each energy storage unit. In this embodiment, the module calls the calculation logic of the cost prediction module, calculates the aging cost of each unit under the same benchmark by inputting a standardized reference power, and performs normalization processing based on the reciprocal of this cost to determine the health scheduling weight of each energy storage unit. A higher weight value indicates that the unit is in a healthier state and is suitable for undertaking a larger proportion of power. The power allocation module is designed to respond to the total power demand (PT) issued externally and to calculate the healthy scheduling weights based on the weights generated by the weight calculation module. It implements an asymmetric power allocation strategy, ultimately generating the final output power for each energy storage unit. The core technical idea of ​​this module is to allow units in better health to handle more power, while protecting units in a sub-healthy state. The parameter correction module aims to ensure the accuracy of the system's digital twin model and prevent the model parameters from deviating from the actual physical state of the energy storage unit due to long-term operation. In this embodiment, after a scheduling cycle, this module compares the predicted core temperature calculated by the cost prediction module with the predicted core temperature. The core temperature was actually measured by the status acquisition module. The deviation between them affects the equivalent DC internal resistance of the key model parameter. Perform closed-loop feedback correction; This correction mechanism is mainly used to correct the equivalent DC internal resistance deviation caused by short-term thermal effects, so as to ensure the real-time accuracy of the model prediction. For long-term, nonlinear aging processes, it is recommended to conduct comprehensive online or offline tests regularly to ensure that the model parameters can continuously track the real health status of the battery. This embodiment, through the collaborative work of the above modules, constitutes a complete closed-loop intelligent health management system; It should be noted that this model is designed primarily for system-level optimization and scheduling, thus simplifying the detailed modeling at the individual cell level. For example, it mainly focuses on the impact of power stress and temperature stress on aging, without explicitly incorporating factors such as cell state of charge and cycle count into the model. This may require further correction in some special application scenarios. Instead of the traditional one-size-fits-all balanced control strategy, it accurately identifies and quantifies the differences in the health status of each energy storage unit, and uses these differences as the core basis for power dispatch. This allows the potential of healthy units to be fully utilized, while effectively protecting sub-healthy units, avoiding the vicious cycle of aging-heat generation-degradation, and ultimately significantly delaying the occurrence of avalanche aging of the system, greatly extending the overall service life and economic benefits of the energy storage system.

[0018] Example 2 The cost prediction module is used to determine the predicted core temperature by calling the thermodynamic energy conservation equation based on the lumped parameter method, based on the current state parameters, ambient temperature, and the unit's equivalent heat capacity and equivalent heat dissipation coefficient. This method treats the energy storage unit as a whole, assuming a uniform internal temperature. While this simplifies the complex internal heat conduction process, it effectively captures key trends in thermal effects in most application scenarios. The cost prediction module is also used to calculate the predicted aging cost based on the predicted core temperature and by calling an aging cost quantification model that draws on the idea of ​​the Arrhenius equation. This model abstracts the main aging drivers into power stress and temperature stress, while neglecting the complex coupling effects of other secondary factors such as state of charge. This simplification aims to strike a balance between computational efficiency and prediction accuracy to meet the real-time requirements of online decision-making. This embodiment is a specific and optimized implementation of the cost prediction module in Embodiment 1; this implementation abstracts the complex electrochemical and thermodynamic processes into two logically progressive mathematical models, thereby achieving accurate prediction of aging costs; To achieve prediction of core temperature To determine the value, the cost prediction module, based on the scheme of claim 2, invokes the thermodynamic energy conservation equation based on the lumped parameter method; the physical meaning of this equation is that the rate of change of the energy storage unit's temperature depends on the difference between its internal heat generation power and the heat dissipation power to the environment; the expression is: ; To ensure the rigor and feasibility of this formula, the parameters are defined as follows: The predicted core temperature is the main output of the model, expressed in degrees Celsius. Current core temperature, sourced from the real-time output of the status acquisition module, in degrees Celsius; Time step refers to the duration of the power command, which is a system parameter preset according to the system control cycle, and is measured in seconds. The equivalent heat capacity of unit i reflects the unit's ability to store heat, and the unit is joules per degree Celsius. : Preset scheduling power command, i.e., the assumed power value to be evaluated, which is derived from external input or previous calculation steps, and is in watts; : Current terminal voltage, sourced from real-time data collected by the status acquisition module, in volts; Equivalent DC internal resistance, derived from the current evaluation value of the status acquisition module, in ohms; The equivalent heat dissipation coefficient of unit i reflects the unit's ability to dissipate heat to the environment, and is expressed in watts per degree Celsius. Ambient temperature, sourced from real-time data collected by sensors deployed in the system environment, in degrees Celsius; After determining the predicted core temperature Subsequently, the cost prediction module, based on the above scheme, invokes an aging cost quantification model that draws on the ideas of the Arrhenius equation to calculate and predict aging costs. This model aims to quantify the core physicochemical fact: the aging rate is exponentially related to temperature and electrochemical stress, especially for modules with higher internal resistance, where the aging cost of applying high power increases exponentially; the expression is: ; To ensure the rigor and feasibility of this formula, the parameters are defined as follows: Predicting aging costs is the main output of the model, and it is a dimensionless relative value. : Preset scheduling power command, the same as the input in the previous step, in watts; Unit rated power, used to make the dispatch power dimensionless, is a factory-calibrated parameter of the equipment, and the unit is watts; The predicted core temperature is derived from the calculation output of the aforementioned thermodynamic energy conservation equation, and the unit is degrees Celsius. Reference safe temperature, preset ideal operating temperature benchmark, set according to industry standards or the battery's optimal operating range, such as 25°C, its function is to quantify the aging effects caused by temperature rise; The dimensions are This is used to adjust the weight of the power stress term; while The dimensions are , representing the contribution of temperature stress to aging; The dimensions here are set to ensure Item and The terms are all dimensionless, allowing them to be directly added; therefore, It is dimensionless itself and is used to adjust the weight of the dimensionless power-stress term; while The dimensions are This is used to offset the dimension of the temperature square term, thus making the term dimensionless and ensuring the dimensional consistency of the entire model. Equivalent DC internal resistance, derived from the current evaluation value of the status acquisition module, in ohms; The initial internal resistance of the unit at the time of manufacture is the factory-calibrated parameter of the equipment, and the unit is ohms; : Sensitivity adjustment parameter, the key exponential coefficient, used to amplify the effect of internal resistance differences, with dimensions of 1 / Ω; To ensure reproducibility for those skilled in the art, the calibration parameters in the above model are... , , , and The acquisition method is further explained; these parameters were all obtained through offline calibration experiments, the process of which was independent of the online operation of the system; for calibrating thermal model parameters and Thermal characteristic experiments are required, such as placing the energy storage unit at a specific ambient temperature and applying different constant currents. Record the curve of its temperature change over time. By fitting the experimental curves to the solutions of the thermodynamic equations, the following can be identified: and The value; for calibrating aging model parameters. , and Accelerated aging tests under multiple operating conditions are required; At least three groups, for example, three or more representative energy storage unit samples, can be selected and placed in different constant ambient temperatures, for example... , , and different constant charge / discharge rates, for example , , The system is subjected to long-term cycling; the number of cycles required for its capacity to decay to 80% of its initial capacity is recorded. Based on these multiple sets of experimental data points, the optimal parameter values ​​can be obtained through regression analysis. This process involves selecting multiple groups of samples and subjecting them to different constant ambient temperatures. and different constant charge / discharge rates Under certain conditions, long-term cycling is performed, and the number of cycles required for its capacity to decay to a specific threshold is recorded. Based on multiple groups ( , , The experimental data points were fitted using regression analysis techniques such as least squares to obtain the optimal values. , and The value minimizes the difference between the cumulative damage predicted by the aging cost model and the actual experimental results; This two-stage modeling approach enables a shift from passive monitoring to proactive prediction. The first stage, the temperature prediction model, allows the system to proactively assess the thermal effect risk before the power command is actually executed. The second stage, the aging cost model, further links this short-term thermal effect to long-term health decline, particularly through exponential terms. The design significantly amplifies the penalty cost of imposing high power on sub-health modules with high internal resistance, providing a direct, accurate, and physically meaningful decision-making basis for the subsequent realization of truly asymmetric, health-aware scheduling.

[0019] The weight calculation module determines the health scheduling weights, including: The preset standard reference power is used as the preset dispatch power command to determine the baseline aging cost of each energy storage unit; The health scheduling weights are determined by calculating and normalizing based on the baseline aging cost. This embodiment is a specific implementation of the weight calculation module in Embodiment 1. Its core lies in establishing a fair and standardized health level assessment benchmark; this module determines the health scheduling weights. The process includes: The preset standard reference power As a preset scheduling power command The cost is input into the cost prediction module, thereby calculating the baseline aging cost for each energy storage unit i in the system. ; Standard reference power This refers to a fixed, medium-sized power value. The technical consideration behind this setting is to provide a unified standard of measurement, ensuring that all units are compared horizontally under the same virtual load conditions, thereby effectively eliminating the interference of the current actual load size. In specific implementation, this value can be taken as 30% of the rated power. In practical applications, this value can be taken as 30% to 50% of the rated power, depending on the characteristics of the specific energy storage unit. Based on the calculated baseline aging cost The final health scheduling weight is determined by taking the reciprocal and normalizing it. The calculation formula is as follows: ; in, : Health scheduling weight of energy storage unit i; Energy storage unit i at standard reference power Benchmark aging cost; Energy storage unit j at standard reference power Benchmark aging cost; The total number of energy storage units; : A preset standard reference power, used to provide a unified unit of measurement; The underlying logic of this formula is that the lower the baseline aging cost of a unit, the better its health status; therefore, its reciprocal... The larger the sum of the values ​​of all units, the greater the weights; by dividing by the sum of the values ​​of all units, the weights are normalized, ensuring that the sum of all weights is 1. This embodiment solves the technical challenge of fairly comparing the health status of each unit under different times and operating conditions by introducing the concept of standard reference power. It transforms the dynamic and complex health assessment problem into a cost calculation problem under a standardized static benchmark, so that the calculated health scheduling weight can more purely and stably reflect the inherent health differences of each unit, providing a solid foundation for the rationality and accuracy of subsequent power allocation.

[0020] Example 3 The power distribution module generates the final output power, including: Based on the total power demand and health scheduling weights, preliminary power pre-allocation is carried out to obtain the initial target power of each energy storage unit; The power distribution module generates the final output power and also includes: The initial target power is used as the preset scheduling power command to determine the corresponding predicted core temperature; Based on preset internal resistance and temperature thresholds, all energy storage units are divided into healthy sets and restricted sets; The power distribution module generates the final output power and also includes: For energy storage units in a constrained set, the output power is determined to be a preset safety value; Calculate the remaining total power and allocate it to the energy storage units in the healthy set according to the relative proportion of the health scheduling weight to determine the output power; This embodiment is a specific, safe and efficient implementation of the power allocation module in Embodiment 1; instead of simply allocating power according to weight ratios in one go, it adopts a rigorous two-stage algorithm to ensure that the allocation result achieves healthy optimization while meeting the preset safety boundaries. Based on the above, in the first stage, the module performs preliminary power pre-allocation; based on the total power demand from external input. Health scheduling weights determined by the weight calculation module An initial target power is calculated for each energy storage unit. : ; in, : The initial target power of energy storage unit i; Total power requirement from external input; : Health scheduling weight of energy storage unit i; This step embodies the core idea of ​​asymmetric scheduling, namely, healthy units. The higher level was initially assigned a higher power responsibility; Based on the above, the system enters the second stage of the security threshold check process; The initial target power of each unit As the preset scheduling power instruction Pd, the temperature prediction model in the cost prediction module is invoked again to determine the predicted core temperature of each unit after the pre-allocation scheme is executed. ; Based on the preset internal resistance threshold and temperature threshold Divide all N energy storage units into two sets: Internal resistance threshold With temperature threshold The setting logic is based on the official safety operation specifications of the energy storage unit and the statistical analysis of a large amount of experimental data, setting a boundary that can cover most normal operating conditions and leave sufficient safety margin. For example, thermal abuse tests, such as external short circuits and heating, can be used to determine the critical internal resistance and critical temperature at which a battery will experience thermal runaway. Then, based on an engineering safety margin, such as 80%-85%, the critical resistance can be set in reverse. and Alternatively, one can refer to the test conditions and limitations specified in published battery safety standards, such as UN / DOT38.3 and IEC62619, and use the highest internal resistance and temperature values ​​observed during testing as a reference for setting thresholds; For example, It can be set to 80% of the critical internal resistance value to ensure that heat generation does not lead to thermal runaway. It can be set to 85% of the temperature at which irreversible side reactions begin to occur in the battery materials; Healthy set SH: where all elements k satisfy and ; A restricted set SR: in which at least one element j satisfies or ; Based on the above, the system performs power redistribution to generate the final output power. ; For all energy storage units j in the restricted set SR, their output power is forcibly limited to a preset safety value. : ; Preset safety value The principle for determining this value is to ensure that, at this power level, the heat generation power of the unit is much lower than its heat dissipation power, thereby allowing its temperature to actively decrease; this value can be set to a fixed value much smaller than the rated power, or it can be set to the unit's rated power. 10% or 0.1% In extreme cases, it can even be set to zero, with the aim of providing mandatory protection for units that have triggered or are on the verge of security risks. Calculate the remaining total power that all healthy units need to handle. : ; This portion of the restricted power will be transferred to the units in the healthy set; Remaining total power Based on the health scheduling weights of each unit in the health set SH The relative proportions are allocated to them; for all energy storage units k in the healthy set SH, the final output power is: ; in, Health Collection The final output power of the middle unit k; Remaining total power, which is the total power demand minus the sum of the power of the units in the restricted set; Health Collection Health scheduling weight of unit k; : A healthy set in which all units satisfy the internal resistance and temperature threshold; Health Collection Health scheduling weights for all units l in the middle; The normalized denominator here is the sum of the weights of all units within the healthy set, which ensures that the remaining power is redistributed only among healthy units according to their relative health. These three claims together define a robust power allocation process; it not only achieves preliminary optimized allocation based on health status, but more importantly, introduces a forward-looking safety verification and redistribution mechanism; this mechanism ensures that even when the total power demand is high, any unit in a sub-healthy state will not be allocated power exceeding its safe tolerance, effectively avoiding the risk of overheating or overstress; by limiting the output of sub-healthy units, the pressure is transferred to healthy units, thereby achieving dual protection for the safety and long-term stability of the entire system while meeting the total power demand of the system, resulting in a synergistic gain effect.

[0021] The parameter correction module is used for: When the actual measured core temperature is higher than the predicted core temperature, the equivalent DC internal resistance is increased according to the preset learning rate. When the actual measured core temperature is lower than the predicted core temperature, the equivalent DC internal resistance is reduced. When the actual measured core temperature equals the predicted core temperature, the equivalent DC internal resistance remains constant. This embodiment is a specific implementation of the parameter correction module in Embodiment 1. The core is to use actual running data to perform adaptive closed-loop correction on the key parameters of the digital twin model. After the scheduling cycle is completed, this module will obtain two key data points: the predicted core temperature calculated by the cost prediction module before scheduling. And the core temperature actually measured by the status acquisition module at the end of the cycle. Based on the deviation between these two, the module updates the equivalent DC internal resistance according to the following rules. : ; In this formula: and These are the internal resistance values ​​of unit i at times k+1 and k, respectively; The learning rate is a small positive constant used to control the step size for corrections. Its unit is 1 / ∘C, and its value typically ranges from 1 / ∘C. arrive The specific values ​​need to be determined through system simulation and actual debugging; an appropriate learning rate can ensure that the correction process can reflect state changes in a timely manner without causing violent oscillations due to noise from a single measurement. This formula embodies a proportional feedback correction mechanism based on temperature error, which involves multiplying the deviation between the actual measured temperature and the model's predicted temperature by a small learning rate. This is done to proportionally correct the equivalent DC internal resistance, thereby ensuring that the temperature change trend predicted by the model matches the actual situation. To ensure the robustness of the calculation, in practical implementation, a baseline aging cost can be used. Set a very small positive lower bound, for example This is to prevent the value from approaching zero, which could lead to instability in the weight calculation. The correction logic is as follows: It should be noted that this correction formula mainly corrects for changes in internal resistance caused by short-term thermal effects. To more comprehensively reflect the long-term, nonlinear aging process, an online DC internal resistance test can be performed periodically during system operation, for example, every 100 cycles, and the measured values ​​can be used to adjust the model. Mandatory calibration is performed to ensure the long-term accuracy of this parameter; Based on the above, when the actual core temperature is measured... Higher than predicted core temperature This indicates that the actual heat generation of the unit is greater than the model predicts, which usually points to its equivalent DC internal resistance. It has increased in size; at this point, If the value is positive, the formula will automatically increase. The value; Based on the above, when the actual measured core temperature Tm,i is lower than the predicted core temperature... This indicates that the actual heat production of the unit is less than expected. If the value is negative, the formula will be adjusted accordingly. The value; Based on the above, under ideal conditions, when the actual core temperature is measured... Equal to predicting core temperature At that time, the deviation is zero. This will remain unchanged, indicating that the current model parameters are accurate; This corrected internal resistance value It will be used in the temperature prediction and aging cost calculation of the next scheduling cycle, thus forming a complete feedback loop; To ensure that the corrected internal resistance value always conforms to physical principles, this module will perform boundary checks on the calculation results to ensure that the corrected internal resistance value... Its internal resistance is always not less than its initial internal resistance value. Furthermore, to prevent non-physical corrections caused by excessive temperature measurement or prediction errors, deviation values ​​can be adjusted. Set an upper limit, for example, when its absolute value exceeds a certain threshold, stop the correction or adopt a more conservative correction strategy; This embodiment, by introducing a feedback correction mechanism based on actual measurement errors, endows the system with the ability to learn and adapt; ensuring the equivalent DC internal resistance, which is the cornerstone of the entire decision-making system. The parameters can dynamically track the actual aging process of the battery, rather than being static parameters that remain unchanged. This greatly improves the long-term fidelity of the digital twin model, enabling the prediction and scheduling decisions of the entire intelligent health management system to always be based on the model that is closest to the real situation, thereby ensuring the continuous effectiveness and reliability of the technical solution of this invention throughout its entire life cycle.

[0022] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A modular energy storage system, characterized in that, include: The status acquisition module is used to determine the core temperature and equivalent DC internal resistance of each energy storage unit as the current status parameters. The cost prediction module is used to determine the predicted core temperature of each energy storage unit under the preset scheduling power command based on the current state parameters, and then determine the predicted aging cost. The weight calculation module is used to determine the health scheduling weight of each energy storage unit based on the calculation logic of the cost prediction module. The power allocation module is used to respond to the total power demand and generate the final output power of each energy storage unit according to the health scheduling weight; The parameter correction module is used to correct the equivalent DC internal resistance based on the deviation between the predicted core temperature and the actual measured core temperature.

2. The modular energy storage system according to claim 1, characterized in that, The cost prediction module is used to determine the predicted core temperature by calling the thermodynamic energy conservation equation based on the lumped parameter method, based on the current state parameters, ambient temperature, and the unit's equivalent heat capacity and equivalent heat dissipation coefficient.

3. The modular energy storage system according to claim 2, characterized in that, The cost prediction module is also used to calculate and predict aging costs based on the predicted core temperature and by calling an aging cost quantification model that draws on the idea of ​​the Arrhenius equation.

4. The modular energy storage system according to claim 1, characterized in that, The weight calculation module determines the health scheduling weight, including: The preset standard reference power is used as the preset dispatch power command to determine the baseline aging cost of each energy storage unit; The health scheduling weights are determined by calculating and normalizing the baseline aging cost.

5. The modular energy storage system according to claim 1, characterized in that, The power distribution module generates the final output power, including: Based on the total power demand and health scheduling weights, preliminary power pre-allocation is performed to obtain the initial target power of each energy storage unit.

6. The modular energy storage system according to claim 5, characterized in that, The power distribution module generates the final output power and also includes: The initial target power is used as the preset scheduling power command to determine the corresponding predicted core temperature; Based on preset internal resistance and temperature thresholds, all energy storage units are divided into healthy sets and restricted sets.

7. The modular energy storage system according to claim 6, characterized in that, The power distribution module generates the final output power and also includes: For energy storage units in a constrained set, the output power is determined to be a preset safety value; Calculate the remaining total power and allocate it to the energy storage units in the healthy set according to the relative proportion of the health scheduling weight to determine the output power.

8. The modular energy storage system according to claim 1, characterized in that, The parameter correction module is used for: When the actual measured core temperature is higher than the predicted core temperature, the equivalent DC internal resistance is increased according to the preset learning rate. When the actual measured core temperature is lower than the predicted core temperature, the equivalent DC internal resistance is reduced. When the actual measured core temperature equals the predicted core temperature, the equivalent DC internal resistance remains unchanged.

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