A modular energy storage system

Abstract

The application discloses a modular energy storage system, and belongs to the technical field of energy storage.The modular energy storage system comprises a state acquisition module, a cost prediction module, a weight calculation module, a power distribution module and a parameter correction module.The state acquisition module is used for determining the core temperature and the equivalent direct-current internal resistance of each energy storage unit as current state parameters.The cost prediction module is used for determining the predicted core temperature of each energy storage unit under a preset scheduling power instruction based on the current state parameters, 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 a total power demand, and generating the final output power of each energy storage unit according to the health scheduling weight.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.The application provides a decision basis with physical meaning for subsequent optimization scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, in particular to a modular energy storage system. BACKGROUND

[0002] In new energy photovoltaic storage and charging integrated power station and other applications, the energy storage system needs to frequently respond to large power and random charging demand. The traditional control strategy takes the equalization of the state of charge of each module as the goal, but ignores the inherent differences in the health state of each module. Due to differences in manufacturing process and operating environment, the health state of each energy storage module will gradually differentiate. For modules with low health state and high internal resistance, using the same scheduling strategy as healthy modules will cause them to generate heat far exceeding the normal level when responding to large power demand, sharply accelerating their electrochemical aging and further increasing internal resistance. This vicious positive feedback of aging-heat generation-reduction eventually leads to the premature failure of some modules and may trigger a system avalanche aging, severely shortening the overall service life.

[0003] To solve this problem, the present application proposes an active management method that takes the module health state into closed-loop control.

[0004] The above information disclosed in the above BACKGROUND section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a modular energy storage system to solve the problems raised in the above BACKGROUND.

[0006] The technical solution of the present application is:

[0007] The state acquisition module is used to determine the core temperature and equivalent DC internal resistance of each energy storage unit as the current state parameters;

[0008] The cost prediction module is used to determine the predicted core temperature of each energy storage unit under the preset scheduling power instruction based on the current state parameters, and further determine the predicted aging cost;

[0009] 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;

[0010] The power distribution 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;

[0011] 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.

[0012] Preferably, the cost prediction module is configured to determine the predicted core temperature by invoking a thermodynamic energy conservation equation based on the lumped parameter method, based on the current state parameters, the ambient temperature, and the equivalent heat capacity and the equivalent heat dissipation coefficient of the unit.

[0013] Preferably, the cost prediction module is further configured to calculate the predicted aging cost by invoking an aging cost quantification model based on the Arrhenius equation, based on the predicted core temperature.

[0014] Preferably, the weight calculation module determines the health scheduling weight, including:

[0015] The preset standard reference power is taken as the preset scheduling power instruction to determine the benchmark aging cost of each energy storage unit.

[0016] The benchmark aging cost is calculated and normalized to determine the health scheduling weight.

[0017] Preferably, the power distribution module generates the final output power, including:

[0018] Based on the total power demand and the health scheduling weight, a preliminary power pre-distribution is performed to obtain the initial target power of each energy storage unit.

[0019] Preferably, the power distribution module generates the final output power, further including:

[0020] The initial target power is taken as the preset scheduling power instruction to determine the corresponding predicted core temperature.

[0021] According to the preset internal resistance threshold and the temperature threshold, all energy storage units are divided into a health set and a limited set.

[0022] Preferably, the power distribution module generates the final output power, further including:

[0023] For the energy storage units in the limited set, the output power is determined as a preset safety value.

[0024] The remaining total power is calculated, and the remaining total power is distributed to the energy storage units in the health set according to the relative proportion of the health scheduling weight to determine the output power.

[0025] Preferably, the parameter correction module is configured to:

[0026] When the actual measured core temperature is higher than the predicted core temperature, the equivalent DC internal resistance is adjusted according to a preset learning rate;

[0027] When the actual measured core temperature is lower than the predicted core temperature, the equivalent DC internal resistance is adjusted.

[0028] The equivalent direct current internal resistance is maintained unchanged when the actual core temperature is equal to the predicted core temperature.

[0029] The application improves a modular energy storage system, which has the following improvements and advantages compared with the prior art.

[0030] 1. The transition from passive response to forward-looking prediction is realized; the cost prediction module in the system is no longer simply monitoring the current state, but can determine a predicted core temperature after the execution of a power instruction based on the current state parameters and the thermodynamic energy conservation equation; then, the module calls the aging cost quantification model inspired by the Arrhenius equation to convert abstract health damage into a quantifiable predicted aging cost; this design enables the system to predict the short-term thermal effect and long-term aging cost of the power instruction on the unit before it is actually issued, providing a physically meaningful decision basis for subsequent optimization scheduling;

[0031] 2. The asymmetric power distribution principle based on the intrinsic health state is established; the weight calculation module in the system calculates the reference aging cost for each energy storage unit by introducing a standard reference power as a unified evaluation benchmark, and determines the health scheduling weight that can stably reflect the true health level of each unit; the power distribution module then distributes power according to this weight, allowing the units with better health states to bear a larger proportion of power and protecting the sub-healthy units; this changes the technical drawbacks in traditional technology where the potential of healthy units cannot be fully utilized and sub-healthy units are exhausted prematurely;

[0032] 3. A robust power distribution mechanism that takes into account both optimization objectives and safety boundaries is constructed; the power distribution module adopts a two-stage distribution strategy; after preliminary power pre-distribution based on health weights, the system performs a forward-looking safety threshold check, compares the predicted core temperature under the pre-distribution scheme with the preset threshold, and divides all units into a healthy set and a restricted set; for units in the restricted set, their output power is 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 the system pursues long-life goals while never exceeding the safety operating boundaries of any unit, achieving dual protection of system overall performance and individual unit safety;

[0033] 4. The system is endowed with the ability of self-learning and dynamic adaptation; the parameter correction module in the system corrects the key parameters, i.e. the equivalent direct current internal resistance, as the basis for decision-making, through comparing the deviation between the predicted core temperature and the actually measured core temperature after the end of a scheduling period; when the actual temperature is higher than the predicted one, the system will increase the internal resistance value; otherwise, it will decrease; this self-adaptive correction mechanism ensures that the digital model of the system can accurately track the real aging process of each energy storage unit in the entire life cycle, greatly improving the reliability and accuracy of the system in long-term operation, and ensuring the continuous effectiveness of the technical solution of the application. BRIEF DESCRIPTION OF DRAWINGS

[0034] The application will be further explained in conjunction with the accompanying drawings and embodiments:

[0035] Figure 1 is the flow chart of the system of the application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the application more clear and explicit, the application will be further described in detail in conjunction with specific embodiments.

[0037] Embodiment 1

[0038] Please refer to Figure 1 , the application provides a modular energy storage system, comprising:

[0039] a state acquisition module for determining the unit core temperature and the equivalent direct current internal resistance of each energy storage unit as the current state parameters;

[0040] a cost prediction module for determining the predicted core temperature of each energy storage unit under the preset scheduling power instruction based on the current state parameters, and further determining the predicted aging cost;

[0041] a weight calculation module for determining the health scheduling weight of each energy storage unit based on the calculation logic of the cost prediction module;

[0042] a power distribution module for responding to the total power demand and generating the final output power of each energy storage unit according to the health scheduling weight;

[0043] a parameter correction module for correcting the equivalent direct current internal resistance based on the deviation between the predicted core temperature and the actually measured core temperature;

[0044] The embodiment provides a modular energy storage system, which aims to solve the problem that the traditional energy storage system ignores the health state differences of each module, leading to premature failure of part of the modules and shortening the overall life of the system; the system realizes early identification and active intervention of the aging trend through the intelligent health management closed loop of state evaluation-cost prediction-asymmetric scheduling-closed loop correction.

[0045] The system comprises a state acquisition module, a cost prediction module, a weight calculation module, a power distribution module, and a parameter correction module.

[0046] The state acquisition module aims to provide real-time and accurate basic data for subsequent health state evaluation and scheduling decisions. In this embodiment, the module is implemented by deploying a sensor network composed of battery modules and their corresponding power conversion systems in each independent energy storage unit. The network collects the current, voltage, and temperature of key temperature measurement points of each unit i in real time. Based on these raw data, the state acquisition module further constructs and maintains a digital twin model describing the core state of each unit, and determines two key current state parameters: the unit core temperature and the equivalent DC internal resistance .

[0047] The unit core temperature refers to the internal temperature that best reflects the electrochemical reaction activity of the battery cell, measured by multi-point temperature measurement data and thermal model estimation, in degrees Celsius, °C, and serves as a key input for thermal safety evaluation and aging rate calculation.

[0048] The equivalent DC internal resistance is an equivalent resistance parameter that comprehensively reflects the aging degree and internal heat generation capacity of the battery cell, with units of ohms Ω, and serves to quantify the heat generation potential and health state degradation of the unit under current load. The initial value of this parameter is obtained through offline calibration, and is dynamically updated by the parameter correction module during operation.

[0049] The cost prediction module aims to convert the long-term and abstract health damage caused by the power instruction to be executed to the energy storage unit into a quantifiable and comparable cost indicator. In this embodiment, the module receives the current state parameters output by the state acquisition module , and based on a preset scheduling power instruction , performs step-by-step calculations:

[0050] determines the predicted core temperature of the unit after executing the power instruction .

[0051] Based on the predicted temperature, a dimensionless predicted aging cost is further determined.

[0052] The weight calculation module aims to generate a weight coefficient for guiding 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 reference by inputting the standardized reference power, and determines the health scheduling weight of each energy storage unit based on the reciprocal of this cost. A higher weight value represents that the unit is in a healthier state and is suitable for bearing a larger proportion of power.

[0053] The power allocation module aims to respond to the total power demand PT issued by the outside and execute an asymmetric power allocation strategy according to the health scheduling weight generated by the weight calculation module , and finally generate the final output power for each energy storage unit. The core technical idea of this module is to let the units in better health state bear more power, and protect the use of units in sub-health state.

[0054] The parameter correction module aims to ensure the accuracy of the system 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 the end of a scheduling period, the module compares the deviation between the predicted core temperature calculated by the cost prediction module and the actual measured core temperature by the state acquisition module , and performs closed-loop feedback correction on the key model parameter equivalent DC internal resistance .

[0055] This correction mechanism is mainly used to correct the deviation of the equivalent DC internal resistance caused by short-term thermal effects to ensure the instant accuracy of the model prediction. For long-term and nonlinear aging processes, it is recommended to conduct regular comprehensive online or offline tests to ensure that the model parameters can continuously track the true health state of the battery.

[0056] This embodiment constitutes a complete closed-loop intelligent health management system through the cooperative work of the above-mentioned modules.

[0057] It should be noted that this model is mainly designed for system-level optimization and scheduling, so it is simplified in the fine modeling of single battery cells. For example, it mainly focuses on the impact of power stress and temperature stress on aging, and does not explicitly include factors such as battery state of charge and cycle number in the model, which may need further correction in some special application scenarios.

[0058] The traditional one-size-fits-all balancing control strategy is abandoned, and the health state differences of each energy storage unit are accurately identified and quantified, and the differences are taken as the core basis of power scheduling; this makes the potential of healthy units fully utilized, while effectively protecting sub-healthy units, avoiding the vicious cycle of aging-heat production-reduction, and finally significantly delaying the occurrence of system avalanche aging, greatly extending the overall service life and economic benefits of the energy storage system.

[0059] Embodiment 2

[0060] The cost prediction module is configured to determine a predicted core temperature by invoking a thermodynamic energy conservation equation based on the lumped parameter method, based on the current state parameters, the ambient temperature, and the equivalent heat capacity and the equivalent heat dissipation coefficient of the unit;

[0061] This method regards the energy storage unit as a whole and assumes that the internal temperature is uniform. Although this simplifies the complex internal heat conduction process, it can effectively capture the key trends of thermal effects in most application scenarios;

[0062] The cost prediction module is further configured to calculate a predicted aging cost by invoking an aging cost quantification model inspired by the Arrhenius equation, based on the predicted core temperature;

[0063] This model abstracts the main aging driving factors as power stress and temperature stress, and ignores the complex coupling effects of other secondary factors such as state of charge. This simplification aims to balance the calculation efficiency and prediction accuracy to meet the real-time requirements of online decision-making;

[0064] This embodiment is a specific and optimized implementation of the cost prediction module in Embodiment 1; this implementation abstracts complex electrochemical and thermodynamic processes into two logically progressive mathematical models, thereby achieving accurate prediction of aging cost;

[0065] To determine the predicted core temperature , the cost prediction module invokes a thermodynamic energy conservation equation based on the lumped parameter method according to the scheme; the physical meaning of this equation is that the rate of change of the temperature of the energy storage unit depends on the difference between its internal heat generation power and the heat dissipation power to the environment; the expression is:

[0066]

[0067] To ensure the rigor and implementability of this formula, the parameters are defined as follows:

[0068] : predicted core temperature, main output of the model, unit: Celsius;

[0069] : current core temperature, sourced from real-time output of state acquisition module, unit: Celsius;

[0070] : time step, refers to the duration of power instruction, is a system parameter preset according to system control period, unit: second;

[0071] : equivalent heat capacity of unit i, reflects the ability of unit to store heat, unit: Joule / Celsius;

[0072] : preset dispatch power instruction, i.e. hypothetical power value to be evaluated, sourced from external input or previous calculation step, unit: Watt;

[0073] : current terminal voltage, sourced from real-time acquisition value of state acquisition module, unit: Volt;

[0074] : equivalent DC internal resistance, sourced from current evaluation value of state acquisition module, unit: Ohm;

[0075] : equivalent heat dissipation coefficient of unit i, reflects the ability of unit to dissipate heat to the environment, unit: Watt / Celsius;

[0076] : ambient temperature, sourced from real-time acquisition value of sensor deployed in the system environment, unit: Celsius;

[0077] After the predicted core temperature is determined , the cost prediction module further calculates the predicted aging cost according to the above scheme by calling an aging cost quantification model inspired by the Arrhenius equation. The model aims to quantify the physical and chemical fact that the aging rate is exponentially related to temperature and electrochemical stress, especially for modules with higher internal resistance, the aging cost paid for applying high power will grow exponentially; the expression is:

[0078]

[0079] To ensure the rigor and implementability of the formula, the parameters are defined as follows:

[0080] : predicted aging cost, is the main output of the model, is a dimensionless relative value;

[0081] : preset dispatch power instruction, same as the input in the previous step, unit: Watt;

[0082] Unit rated power, used to make the dispatch power dimensionless, is a factory-calibrated parameter of the equipment, and the unit is watts;

[0083] The predicted core temperature is derived from the calculation output of the aforementioned thermodynamic energy conservation equation, and the unit is degrees Celsius.

[0084] 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;

[0085] 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;

[0086] 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.

[0087] Equivalent DC internal resistance, derived from the current evaluation value of the status acquisition module, in ohms;

[0088] 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;

[0089] : Sensitivity adjustment parameter, the key exponential coefficient, used to amplify the effect of internal resistance differences, with dimensions of 1 / Ω;

[0090] 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 curve with the solution of the thermodynamic equation, the values of and are identified; for calibrating the aging model parameters , and , multi-condition accelerated aging experiments are needed;

[0091] At least three groups, for example, 3 groups or more representative energy storage unit samples, are selected and placed in different constant ambient temperatures, for example , , and different constant charge-discharge rates, for example , , for long-term cycling; record the number of cycles required for the capacity to decay to 80% of the initial capacity, based on these multiple experimental data points, the optimal parameter values can be obtained by regression analysis;

[0092] This process involves selecting multiple groups of samples, under different constant ambient temperatures and different constant charge-discharge rates for long-term cycling, and recording the number of cycles required for the capacity to decay to a certain threshold ; based on multiple experimental data points , , , by least squares and other regression analysis methods, the optimal , and values are obtained, so that the difference between the cumulative damage predicted by the aging cost model and the actual experimental results is minimized;

[0093] Through this two-stage modeling method, the embodiment realizes the transition from passive monitoring to active prediction; the temperature prediction model in the first step enables the system to prospectively assess the thermal effect risk before the power command is actually executed; the aging cost model in the second step further deeply relates the short-term thermal effect to the long-term health decline, especially through the design of the exponential term , which greatly amplifies the punishment cost of sub-healthy modules and high internal resistance power, providing direct, accurate and physically meaningful decision basis for subsequent implementation of truly asymmetric and health-aware scheduling.

[0094] The weight calculation module determines the health scheduling weight, including:

[0095] The preset standard reference power is taken as the preset scheduling power command to determine the reference aging cost of each energy storage unit;

[0096] The health scheduling weights are determined by calculating and normalizing based on the baseline aging cost.

[0097] 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:

[0098] 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. ;

[0099] 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.

[0100] 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:

[0101]

[0102] 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;

[0103] 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.

[0104] The embodiment introduces the concept of standard reference power, solves the technical problem of how to compare the health states of each unit at different times and under different conditions, converts the dynamic and complex health evaluation problem into a cost calculation problem under the standardized static reference, and makes the calculated health scheduling weight more pure and stable to reflect the internal health differences of each unit, thereby providing a solid foundation for the rationality and accuracy of subsequent power distribution.

[0105] Embodiment 3

[0106] The power distribution module generates the final output power, including:

[0107] Based on the total power demand and the health scheduling weight, a preliminary power pre-distribution is performed to obtain an initial target power of each energy storage unit;

[0108] The power distribution module generates the final output power, and further includes:

[0109] The initial target power is taken as a preset scheduling power instruction to determine the corresponding predicted core temperature;

[0110] According to the preset internal resistance threshold and temperature threshold, all energy storage units are divided into a health set and a limited set;

[0111] The power distribution module generates the final output power, and further includes:

[0112] For the energy storage units in the limited set, the output power is determined as a preset safety value;

[0113] The remaining total power is calculated, and the remaining total power is distributed to the energy storage units in the health set according to the relative proportion of the health scheduling weight to determine the output power;

[0114] The embodiment is a specific, safe and efficient implementation of the power distribution module in embodiment 1; instead of simply performing one-time distribution according to the weight proportion, a rigorous two-stage algorithm is adopted to ensure that the distribution result realizes health optimization while meeting the preset safety boundary;

[0115] According to the above, in the first stage, the module performs preliminary power pre-distribution; based on the total power demand input from the outside and the health scheduling weight determined by the weight calculation module , an initial target power is calculated for each energy storage unit :

[0116]

[0117] Among them, : the initial target power of the energy storage unit i; : total power demand of external input; : health scheduling weight of energy storage unit i;

[0118] This step embodies the core idea of asymmetric scheduling, that is, healthy units, Higher is initially assigned a higher power responsibility;

[0119] According to the above, the system enters the second phase of the safety threshold check link;

[0120] The initial target power of each unit As the preset scheduling power instruction Pd, the temperature prediction model in the cost prediction module is called again to determine the corresponding predicted core temperature of each unit after executing the pre-allocation scheme ;

[0121] According to the preset internal resistance threshold And the temperature threshold , all N energy storage units are divided into two sets:

[0122] The setting logic of the internal resistance threshold And the temperature threshold , according to the official safety operation specification of the energy storage unit and a large amount of experimental data statistical analysis, set the boundary that can cover most normal working conditions and leave enough safety margin;

[0123] For example, the critical internal resistance value and the critical temperature at which the battery occurs thermal runaway can be determined through thermal abuse experiments such as external short circuit, heating, etc., and then based on engineering safety margin, for example 80%-85% to set And . Or, you can refer to the test conditions and limits specified in the published battery safety standards, such as UN / DOT38.3, IEC62619, etc., and the highest resistance and temperature values observed in the test as a reference basis for setting thresholds;

[0124] For example, Can be set to 80% of the critical internal resistance value that ensures that heat production does not lead to thermal runaway, Can be set to 85% of the temperature at which the battery material begins to exhibit irreversible side reactions;

[0125] Healthy set SH: all units k satisfy And ;

[0126] Restricted set SR: at least one unit j satisfies Or ;

[0127] According to the above, the system performs power redistribution to generate the final output power ;

[0128] For all energy storage units j in the restricted set SR, their output power is forced to be limited to a preset safety value :

[0129]

[0130] The determination principle of the preset safety value is to ensure that at this power, the unit's heat generation power is much lower than its heat dissipation power, so that its temperature can actively drop; this value can be set as a fixed value much smaller than the rated power, or as 10% or 0.1 of the unit's rated power , or even zero in extreme cases, the purpose is to forcibly protect the units that have triggered or are about to trigger safety risks;

[0131] Calculate the total remaining power that all healthy units need to bear :

[0132]

[0133] This part of the limited power will be transferred to the units in the healthy set;

[0134] The total remaining power will be allocated to the units in the healthy set SH according to the relative proportion of their health scheduling weights ; for all energy storage units k in the healthy set SH, the final output power is:

[0135]

[0136] wherein : the final output power of unit k in the healthy set ; : the total remaining power, i.e. the total power demand minus the sum of the powers of the units in the restricted set; : the health scheduling weight of unit k in the healthy set ; : the healthy set, where all units meet the internal resistance and temperature threshold; : the health scheduling weight of all units l in the healthy set ;

[0137] The normalization denominator here is the sum of the weights of all units in the healthy set, which ensures that the remaining power is only redistributed among the healthy units according to their relative health;

[0138] The above contents jointly define a robust power allocation process; it not only realizes the preliminary optimized allocation based on the health state, but more importantly, introduces a forward-looking safety check and reallocation mechanism; this mechanism ensures that even in the case of high total demand power, any unit in a sub-health state will not be allocated power exceeding its safe bearing capacity, effectively avoiding the risk of overheating or over-stress; by limiting the output of sub-health units, the pressure is transferred to healthy units, thereby realizing the dual protection of the safety and long-term stability of the entire system under the premise of meeting the total power demand of the system, producing a synergistic technical effect.

[0139] The parameter correction module is configured to:

[0140] When the actual measured core temperature is higher than the predicted core temperature, the equivalent DC internal resistance is adjusted according to the preset learning rate;

[0141] When the actual measured core temperature is lower than the predicted core temperature, the equivalent DC internal resistance is adjusted;

[0142] When the actual measured core temperature is equal to the predicted core temperature, the equivalent DC internal resistance is maintained unchanged;

[0143] The embodiment is a specific implementation of the parameter correction module in Embodiment 1, and the core is to use actual operation data to adaptively correct the key parameters of the digital twin model in a closed loop;

[0144] After the dispatching cycle is completed, the module obtains two key data: the predicted core temperature calculated by the cost prediction module before dispatching , and the core temperature actually measured by the state acquisition module after the cycle ends ; based on the deviation of the two, the module updates the equivalent DC internal resistance according to the following rules :

[0145]

[0146] In this formula:

[0147] and are the internal resistance values of unit i at k+1 and k time points, respectively;

[0148] : learning rate, which is a small normal number used to control the correction step, with a unit of 1 / °C, and its value range is usually between and , and the specific value needs to be determined through system simulation and actual debugging; a suitable learning rate can ensure that the correction process can timely reflect the state change, and will not produce violent oscillation due to single measurement noise;

[0149] The formula embodies a proportional feedback correction mechanism based on temperature error, i.e. by multiplying the deviation between actual measured temperature and model predicted temperature with a small learning rate to proportionally correct the equivalent DC internal resistance, so as to ensure that the model predicted temperature trend is consistent with the actual situation;

[0150] To ensure the robustness of the calculation, in actual implementation, a small positive lower limit can be set for the reference aging cost , for example , to avoid the value tending to zero leading to unstable weight calculation;

[0151] The correction logic is as follows:

[0152] It should be pointed out that the correction formula mainly corrects the internal resistance change caused by short-term thermal effect; in order to more comprehensively reflect the long-term and nonlinear aging process, online DC internal resistance test can be performed periodically, for example, once every 100 cycle periods, during system operation, and the measured value is used to forcibly calibrate the in the model, so as to ensure the long-term accuracy of the parameter;

[0153] According to the above content, when the actual measured core temperature is higher than the predicted core temperature , it indicates that the actual heat generation of the unit is more than expected, which usually indicates that the equivalent DC internal resistance of the unit has increased; at this time, is positive, and the formula will automatically increase the value of ;

[0154] According to the above content, when the actual measured core temperature Tm, i is lower than the predicted core temperature , it indicates that the actual heat generation of the unit is less than expected, is negative, and the formula will accordingly decrease the value of ;

[0155] According to the above content, in the ideal case, when the actual measured core temperature is equal to the predicted core temperature , the deviation is zero, will remain unchanged, which indicates that the current model parameters are accurate;

[0156] The corrected internal resistance value will be used in the temperature prediction and aging cost calculation of the next scheduling period, thereby forming a complete feedback closed loop;

[0157] To ensure that the corrected internal resistance value always conforms to the physical common sense, the module will perform boundary check on the calculation result to ensure that the corrected internal resistance value Always not less than its initial internal resistance value ; in addition, in order to prevent non-physical correction caused by too large temperature measurement or prediction error, the deviation value An upper limit is set, for example, when its absolute value exceeds a certain threshold, then stop correction or adopt a more conservative correction strategy;

[0158] The present embodiment introduces such a feedback correction mechanism based on actual measurement error, giving the system the ability to learn and adapt; ensures that the equivalent DC internal resistance The parameter can dynamically track the real aging process of the battery, rather than a static parameter; this greatly improves the long-term fidelity of the digital twin model, so that the prediction and scheduling decisions of the entire intelligent health management system can always be based on the model closest to the real situation, thereby ensuring the continuous effectiveness and reliability of the technical solution of the present application throughout the life cycle.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A modular energy storage system, characterized by, The method comprises the following steps: The state acquisition module is used to determine the core temperature and the equivalent DC internal resistance of each energy storage unit as the current state parameters; The cost prediction module is used to determine the predicted core temperature of each energy storage unit under the preset scheduling power instruction based on the current state parameters, and further determine the predicted aging cost; Computing a predicted aging cost ; the expression is: Each parameter is defined as follows: : predicted aging cost, a dimensionless relative value; : preset scheduling power instruction, unit: watt; : unit power rating in watts; : Predicted core temperature in degrees Celsius; : reference safety temperature; : equivalent direct current internal resistance; : initial internal resistance at factory shipment of the unit; : sensitivity adjustment parameter for amplifying the effect of the difference in internal resistance, dimensionless; the dimension of is used to adjust the weight of the power stress term; while the dimension of is the contribution of temperature stress to aging. 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; Health scheduling weights ; The calculation formula is as follows: wherein, : health schedule weight of energy storage unit i; : baseline aging cost of energy storage unit i at standard reference power ; : baseline aging cost of energy storage unit j at standard reference power ; : total number of energy storage units; : preset standard reference power for providing a uniform metric; 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 of claim 1, wherein, 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, the environmental temperature, and the equivalent heat capacity and the equivalent heat dissipation coefficient of the unit.

3. The modular energy storage system of claim 2, wherein, The cost prediction module is also used to calculate the predicted aging cost by calling the aging cost quantification model based on the idea of Arrhenius equation based on the predicted core temperature.

4. The modular energy storage system of claim 1, wherein, The weight calculation module determines the health scheduling weight, including: The standard reference power is used as the preset scheduling power instruction to determine the benchmark aging cost of each energy storage unit; The benchmark aging cost is calculated and normalized to determine the health scheduling weight.

5. The modular energy storage system of claim 1, wherein, The power allocation module generates the final output power, including: Based on the total power demand and the health scheduling weight, the initial target power of each energy storage unit is obtained through preliminary power pre-allocation.

6. The modular energy storage system of claim 5, wherein, The power allocation module generates the final output power, including: The initial target power is used as the preset scheduling power instruction to determine the corresponding predicted core temperature; According to the preset internal resistance threshold and temperature threshold, all energy storage units are divided into a health set and a limited set.

7. The modular energy storage system of claim 6, wherein, The power allocation module generates the final output power, including: For the energy storage units in the limited set, the output power is determined as a preset safety value; The remaining total power is calculated, and the remaining total power is allocated to the energy storage units in the health set according to the relative proportion of the health scheduling weight to determine the output power.

8. The modular energy storage system of claim 1, wherein, The parameter correction module is used to: 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 is equal to the predicted core temperature, the equivalent DC internal resistance remains unchanged.

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