Energy Co-management Methods for Containerized Battery Integrated Systems

Abstract

This invention discloses an energy collaborative management method for a containerized battery integrated system, relating to the field of containerized energy storage equipment technology. This method uses a sensor array deployed inside the battery compartment, combined with the equivalent heat capacity Ct calibrated at the factory, to acquire raw data during battery compartment operation. After data processing, energy datasets and thermal state datasets are formed. Based on the energy datasets, an effective energy transfer index Pli and a transfer matrix Pli(K,L) are constructed for energy migration state assessment. When the energy migration flow meets the standard, a thermal response energy index Qth is calculated based on the thermal state dataset for thermal accumulation state assessment. If a thermal accumulation trend exists, an energy flow collaborative decision index Efw is constructed for collaborative decision assessment. The analysis results are written into a time-series database for archiving, and data drift is judged by the periodic differential rate of change, automatically triggering a parameter self-correction mechanism. This method achieves dynamic coordinated control of energy transfer and heat load between battery compartments.

Description

Technical Field

[0001] This invention relates to the field of containerized energy storage equipment technology, specifically to an energy collaborative management method for containerized battery integrated systems. Background Technology

[0002] With the popularization of new energy ships and shore-based energy storage systems, containerized battery integrated systems are gradually becoming the core unit of modular energy storage and mobile energy supply. These systems achieve centralized storage and flexible allocation of electrical energy through a containerized structure composed of multiple independent battery compartments. However, during system operation, energy migration between compartments is limited by factors such as conductor impedance, temperature rise characteristics, and uneven current distribution, often leading to decreased energy transfer efficiency and concentrated heat load. Traditional energy management methods only focus on power allocation on the electrical side, neglecting the synergistic relationship between thermal behavior and energy transfer. Therefore, this paper proposes an energy collaborative management method that achieves coupled analysis of energy flow and heat flow in the same spatiotemporal domain. By calculating the energy transfer channels, heat load status, and collaborative steady-state indices between battery compartments in real time, the method dynamically coordinates the energy migration and thermal response of the battery compartments as a whole.

[0003] Existing battery compartment energy transfer control is mostly based on power or voltage balancing strategies, such as automatically performing energy diversion or current limiting control when a voltage difference between compartments is detected. However, it lacks the ability to identify internal heat accumulation, uneven heat distribution, and cross-compartment energy coupling effects. Due to the lack of an integrated electrical and thermal analysis model, it is impossible to accurately assess the sustainability of energy transfer when faced with temperature gradients, differences in insulation performance, and heat conduction delays between different compartments. This often leads to the hidden danger of "stable surface energy migration flow while continuously increasing internal heat accumulation." Furthermore, traditional methods do not introduce a real-time analysis mechanism for equivalent energy transfer impedance. This means that when the impedance of the current path changes (such as aging of connector contacts or local temperature rise of conductors), the system still distributes energy according to the default path, easily causing poor energy flow or thermal offset in some compartments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an energy collaborative management method for containerized battery integrated systems, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an energy collaborative management method for a containerized battery integrated system, comprising the following steps:

[0006] S1. By using a sensor array arranged inside the battery compartment and extracting the equivalent heat capacity Ct from the data storage system, the raw data of the battery compartment during operation is obtained in real time. After data processing, an energy dataset and a thermal state dataset are formed.

[0007] S2: The energy dataset performs effective transmission analysis on the energy transmission channels and constructs a transmission matrix to calculate the mean of the effective transmission analysis results of all channels for energy migration status assessment. When the assessment shows that the energy migration flow meets the standard, S3 is triggered.

[0008] S3. Perform thermal load state analysis on the battery compartment based on the thermal state dataset, and evaluate the thermal accumulation state based on the analysis results. If the evaluation indicates that there is a thermal accumulation trend, trigger S4.

[0009] S4. Fit the effective transmission analysis results and the thermal load state analysis results, perform a collaborative steady-state analysis on the battery compartment with heat accumulation, and make a collaborative decision evaluation based on the analysis results.

[0010] S5. At the end of the control cycle, all analysis results are automatically written into the time series database for structured archiving, and parameter calibration is performed by judging data drift through the period difference change rate.

[0011] Preferably, S1 includes S11;

[0012] S11. By using sensor groups arranged inside each battery compartment and extracting the equivalent heat capacity Ct of the battery compartment material in the data storage system via the communication bus, the raw data of the battery compartment during operation is obtained in real time. The equivalent heat capacity Ct is a fixed constant obtained by heating experiment calibration during the factory stage.

[0013] The sensor group includes a voltage sensor, a temperature sensor array, and a heat flow meter;

[0014] The voltage sensor is used to fix the transition conductor section between the bus connection terminal inside the battery compartment and the DC bus outside the compartment to collect the terminal voltage U of the battery compartment in real time.

[0015] The temperature sensor array is used to install on the positive busbar of the main output circuit, that is, between the battery module busbar and the DC output terminal outside the compartment, to collect the battery compartment temperature T in real time.

[0016] The heat flux meter is used to embed one on the outside of the insulation layer between adjacent battery compartments to collect the heat flux density rq of the battery compartment sidewall in real time.

[0017] Preferably, S1 further includes S12;

[0018] S12. Edge computing nodes receive raw data from each battery compartment in real time via a communication bus, process the data, and form energy datasets and thermal state datasets.

[0019] The data processing is used to perform anomaly detection, filtering, and time alignment on the raw data before impedance analysis.

[0020] The anomaly detection, filtering operation, and time alignment are used to remove and compensate for abrupt changes, packet loss, and out-of-limit signals in the original data, as well as to unify the original data with the same time reference; the impedance analysis is used to compare the temperature change ΔT at the energy input end and the heat flux density rq to obtain the equivalent energy transmission impedance R.

[0021] The energy dataset includes terminal voltage U and equivalent energy transfer impedance R;

[0022] The thermal state dataset includes the equivalent heat capacity Ct and the battery compartment temperature T.

[0023] Preferably, S2 includes S21;

[0024] S21. The edge nodes perform effective transmission analysis on the energy transmission channels between each electromagnetic compartment of the ship based on the energy dataset, and construct an effective energy transmission index Pli, which represents the instantaneous effective capability of the energy migration channel and is used to quantify the energy mobility and accessibility between compartments.

[0025] Preferably, S2 further includes S22 and S23;

[0026] S22. Construct the transfer matrix Pli(K, L) of all compartments based on the effective energy transfer index Pli;

[0027] S23. Calculate the average value of the effective energy transfer index Pli of all inter-cabin channels based on the transfer matrix Pli(K,L), and combine it with the allowable fluctuation range within the standard range. Set the difference between the average value and the fluctuation range as the energy flow trigger boundary threshold Eb, and set the sum of the average value and the fluctuation range as the energy flow trigger critical threshold Ec. Then compare it with the effective energy transfer index Pli acquired in real time, and evaluate the energy migration status based on the comparison results. The specific evaluation scheme is as follows.

[0028] When the effective energy transfer index Pli < the energy flow trigger boundary threshold Eb, it indicates that the energy migration flow is not smooth. At this time, the backup battery compartment is activated to participate in energy compensation, and iterative analysis is performed through S2. If the energy migration flow is still not smooth after three iterations, the energy migration direction between battery compartments is redistributed.

[0029] When the energy flow triggering boundary threshold Eb ≤ effective energy transfer index Pli ≤ energy flow triggering critical threshold Ec, it indicates that the energy migration flow meets the standard, and the heat load behavior analysis is triggered at this time.

[0030] When the effective energy transfer index Pli > the energy flow trigger threshold Ec, it indicates that the energy migration flow is active and there is excessive energy migration. At this time, a current limiting command is automatically issued to reduce the current output limit of the current battery compartment by 15% and mark it as a migration limiting channel. Then, iterative analysis is performed through S2 until the energy migration flow meets the standard.

[0031] Preferably, S3 includes S31;

[0032] S31. When the energy migration state assessment indicates that the energy migration flow meets the standard, perform thermal load state analysis on the battery compartment that meets the standard based on the thermal state dataset, and construct the thermal response energy index Qth, which represents the amount of energy change caused by temperature change inside the battery compartment, and is used to describe the rate of change of internal thermal energy in the battery compartment.

[0033] Preferably, S3 further includes S32;

[0034] S32. Calculate the average thermal response energy index Qth when there is no thermal accumulation trend in energy migration within one year using statistical methods, and preset the thermal energy response threshold Rp based on the average value. Then compare it with the real-time acquired thermal response energy index Qth, and evaluate the thermal accumulation state based on the comparison results. The specific evaluation scheme is as follows.

[0035] When the thermal response energy index Qth < thermal response threshold Rp, it indicates that there is no thermal accumulation trend in energy migration, and the current battery energy migration strategy is maintained.

[0036] When the thermal response energy index Qth is greater than or equal to the thermal response threshold Rp, it indicates that there is a tendency for thermal accumulation in energy migration, and at this time, cooperative steady-state analysis is triggered.

[0037] Preferably, S4 includes S41;

[0038] S41. When the thermal accumulation state assessment indicates that there is a thermal accumulation trend in energy migration, the effective energy transfer index Pli and the thermal response energy index Qth are fitted together to perform a collaborative steady-state analysis on the battery compartment with thermal accumulation, and an energy flow collaborative decision index Efw is constructed to analyze the state of thermal balance and energy flow coordination in the current cycle, reflecting the dynamic matching degree between energy transfer efficiency and heat load distribution.

[0039] Preferably, S4 further includes S42;

[0040] S42. Calculate the mean value of the energy flow collaborative decision index Efw when energy migration and heat distribution are in a coordinated state within one year using statistical methods, and preset the collaborative steady-state judgment threshold Xe based on the mean value. Then compare it with the real-time energy flow collaborative decision index Efw, and conduct collaborative decision evaluation based on the comparison results. The specific evaluation scheme is as follows.

[0041] When the energy flow collaborative decision index Efw < collaborative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a mismatch state. At this time, the backup compartment and cryogenic compartment are activated to add power supply, and the number of cross-compartment energy transfer paths is increased by 10%, the output power of the battery compartment with heat load is reduced by 15%, and the cooling fan speed is increased by 15%. Iterative analysis is performed through S2. If the mismatch state is still present after three iterations, maintenance personnel are immediately notified to carry out maintenance and repair.

[0042] When the energy flow cooperative decision index Efw ≥ cooperative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a coordinated state, and the current energy allocation command is maintained.

[0043] Preferably, S5 includes S51 and S52;

[0044] S51. At the end of each control cycle, the acquired effective energy transfer index Pli, thermal response energy index Qth, and energy flow collaborative decision index Efw are automatically written into the time series database for structured archiving, and an index is created according to the cycle number and battery compartment identifier.

[0045] S52. Call the energy flow collaborative decision index Efw for two consecutive cycles in the time series database and calculate the differential change rate. When the differential change rate is detected to be decreasing continuously, it is determined that there is a drift between the energy dataset and the thermal state dataset between the current battery compartments. At this time, the battery integration system enters the self-calibration preparation state, marks the current cycle as the parameter calibration trigger cycle, and starts the self-calibration module to perform data calibration on the energy dataset and the thermal state dataset.

[0046] This invention provides a method for coordinated energy management of a containerized battery integrated system. It offers the following advantages:

[0047] (1) Method S1 achieves synchronous acquisition of the energy state and thermal state inside each battery compartment by using voltage sensors, temperature sensor arrays, and heat flow meters arranged inside each battery compartment, along with the factory-calibrated equivalent heat capacity Ct. After anomaly detection, filtering, time alignment, and impedance analysis of the raw data by edge computing nodes, an energy dataset containing terminal voltage U and equivalent energy transfer impedance R, and a thermal state dataset containing compartment temperature T and equivalent heat capacity Ct are formed. This data structuring method enables the system to simultaneously grasp the characteristics of electrical energy migration and the laws of heat conduction, and establishes a physical connection between electrical energy flow and heat energy flow through the special parameter of equivalent energy transfer impedance R, overcoming the problem that traditional battery monitoring systems only monitor single parameters of current and voltage and ignore heat accumulation.

[0048] (2) The method S2 and S3 construct a joint judgment system for energy migration and thermal load based on energy datasets and thermal state datasets. S2 uses the effective energy transfer index Pli calculated from the energy dataset to evaluate the smoothness of energy transfer channels between compartments in real time, and reflects the energy migration topology of the entire battery pack in the form of a transfer matrix Pli(K,L). By calculating the mean and fluctuation range, the energy flow trigger boundary threshold Eb and the energy flow trigger critical threshold Ec are determined, realizing the dynamic identification and control of energy flow status. In the thermal analysis stage, S3 calculates the thermal response energy index Qth based on the thermal state dataset, and determines the thermal response judgment threshold Rp by comparing it with the historical mean, thereby distinguishing whether there is a trend of heat accumulation in the battery compartment. The interactive judgment of energy migration status and thermal response status ensures that the containerized battery integration system can maintain high efficiency in energy transfer efficiency and balance in thermal safety, so that the energy flow and heat flow between battery compartments are mutually constrained and dynamically coordinated.

[0049] (3) In method S4, the energy transfer and thermal response results are fitted to form the energy flow collaborative decision index Efw, which is used to measure the degree of coordination between energy migration and thermal distribution in the current cycle. By statistically analyzing the average value of the energy flow collaborative decision index Efw over a period of time, a collaborative steady-state threshold Xe is established to evaluate the real-time operating status. When the energy flow collaborative decision index Efw is lower than the collaborative steady-state threshold Xe, the backup compartment and cryogenic compartment are actively triggered to provide energy, and the cross-compartment energy transfer path, output power and cooling fan speed are adjusted to achieve dynamic rebalancing of energy distribution and thermal environment. In S5, after each control cycle, the analysis results are automatically written into the time series database for structured archiving, and potential parameter drift is detected by the differential change rate of adjacent cycles. When a trend deviation is found in the data, the self-correction stage is automatically entered. Through this periodic archiving and differential calibration mechanism, a self-learning parameter optimization and adaptive adjustment capability is formed, enabling the battery pack to maintain a stable energy coordination relationship and thermal balance state during long-term operation. Attached Figure Description

[0050] Figure 1 This is a schematic diagram illustrating the steps of the energy collaborative management method for the containerized battery integrated system of the present invention;

[0051] Figure 2 This is a logical block diagram of the energy collaborative management method steps of the containerized battery integrated system of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0053] Example 1: Please refer to Figure 1 This invention provides an energy collaborative management method for a containerized battery integrated system. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps:

[0054] S1. By using a sensor array arranged inside the battery compartment and extracting the equivalent heat capacity Ct from the data storage system, the raw data of the battery compartment during operation is obtained in real time. After data processing, an energy dataset and a thermal state dataset are formed.

[0055] S2: The energy dataset performs effective transmission analysis on the energy transmission channels and constructs a transmission matrix to calculate the mean of the effective transmission analysis results of all channels for energy migration status assessment. When the assessment shows that the energy migration flow meets the standard, S3 is triggered.

[0056] S3. Perform thermal load state analysis on the battery compartment based on the thermal state dataset, and evaluate the thermal accumulation state based on the analysis results. If the evaluation indicates that there is a thermal accumulation trend, trigger S4.

[0057] S4. Fit the effective transmission analysis results and the thermal load state analysis results, perform a collaborative steady-state analysis on the battery compartment with heat accumulation, and make a collaborative decision evaluation based on the analysis results.

[0058] S5. At the end of the control cycle, all analysis results are automatically written into the time series database for structured archiving, and parameter calibration is performed by judging data drift through the period difference change rate.

[0059] In this embodiment, S1 achieves synchronous acquisition and calculation of electrical and thermophysical characteristics by arranging multiple types of sensor groups inside the battery compartment and combining them with the pre-calibrated equivalent heat capacity Ct in the data storage system. After data processing, a unified structure of energy dataset and thermal state dataset is established, enabling subsequent energy transmission and thermal behavior analysis to have a common data foundation. This fundamentally solves the problem of incomplete state assessment and delayed response caused by the "separation of thermal and electrical data" in traditional monitoring methods, providing data support for constructing closed-loop energy control logic. S2 uses the energy dataset to perform effective transmission analysis on the energy transmission channels between each electromagnetic compartment of the ship. By calculating the effective energy transmission index Pli, a transmission matrix Pli(K, L) of the effective energy transmission index Pli of all compartments is constructed based on the effective energy transmission index Pli to perform energy migration state assessment, realizing the quantitative assessment of energy flow. S3 When the energy migration state assessment shows that the energy migration flow meets the standard, the thermal state dataset is used to perform thermal load state analysis on the battery compartment with the energy migration flow meeting the standard to determine whether there is a heat accumulation trend. This joint analysis mode of "energy flow and heat flow" can identify risks in a timely manner at the initial stage of energy migration anomalies or heat accumulation, and form a graded response logic through the setting of dynamic thresholds. Compared with the existing technology that only performs unidirectional compensation when energy is abnormal, this method achieves synchronous monitoring of energy flow balance and thermal safety at the multi-compartment linkage level, effectively reducing local overheating caused by uneven energy distribution. In S4, the effective transmission analysis results are fitted with the heat load state analysis results, and a collaborative steady-state analysis is performed on the battery compartment with heat accumulation. An energy flow collaborative decision index Efw is constructed to comprehensively determine the energy distribution and thermal balance state, and dynamic control is performed based on the collaborative steady-state threshold Xe. When a mismatch trend in energy migration and heat distribution is detected, the cross-compartment energy transmission path can be actively adjusted, the output power of the high-temperature compartment can be reduced, and the fan speed of the cooling compartment can be increased to achieve intelligent energy and heat load redistribution. S5 automatically writes the key evaluation results into the time series database at the end of each control cycle, judges data drift through the cycle difference change rate and performs parameter self-correction, forming a self-learning and adaptive mechanism. Compared with traditional static monitoring or manual adjustment methods, this method achieves data-driven continuous optimization control, which not only improves the operational stability and energy utilization of the battery system, but also maintains thermal-electric synergistic balance during long-term operation, giving the containerized battery system higher safety, intelligence and service life.

[0060] Example 2: Please refer to Figure 1 and Figure 2 Specifically: S1 includes S11;

[0061] S11. By using sensor groups arranged inside each battery compartment and extracting the equivalent heat capacity Ct of the battery compartment material in the data storage system via the communication bus, the raw data of the battery compartment during operation is obtained in real time. The equivalent heat capacity Ct is a fixed constant obtained by heating experiment calibration during the factory stage.

[0062] The sensor group includes a voltage sensor, a temperature sensor array, and a heat flow meter;

[0063] The voltage sensor is used to fix the transition conductor section between the bus connection terminal inside the battery compartment and the DC bus outside the compartment to collect the terminal voltage U of the battery compartment in real time.

[0064] The temperature sensor array is used to install on the positive busbar of the main output circuit, that is, between the battery module busbar and the DC output terminal outside the compartment, to collect the battery compartment temperature T in real time.

[0065] The heat flux meter is used to embed one on the outside of the insulation layer between adjacent battery compartments to collect the heat flux density rq of the battery compartment sidewall in real time.

[0066] S1 further includes S12;

[0067] S12. Edge computing nodes receive raw data from each battery compartment in real time via a communication bus, process the data, and form energy datasets and thermal state datasets.

[0068] The data processing is used to perform anomaly detection, filtering, and time alignment on the raw data before impedance analysis.

[0069] The anomaly detection, filtering operation, and time alignment are used to remove and compensate for abrupt changes, packet loss, and out-of-limit signals in the original data, as well as to unify the original data with the same time reference; the impedance analysis is used to compare the temperature change ΔT at the energy input end and the heat flux density rq to obtain the equivalent energy transmission impedance R.

[0070] The energy dataset includes terminal voltage U and equivalent energy transfer impedance R;

[0071] The thermal state dataset includes the equivalent heat capacity Ct and the battery compartment temperature T.

[0072] In this embodiment, S11 involves arranging voltage sensors, temperature sensor arrays, and heat flux meters inside each battery compartment. Combined with the communication bus, the equivalent heat capacity Ct, calibrated during the heating test at the factory stage, is extracted to obtain raw data during battery compartment operation in real time, forming a multi-dimensional data acquisition system. This enables synchronous monitoring of the electrical and thermophysical states of the battery compartments during operation. S12 utilizes edge computing nodes to process the raw data collected from each compartment in real time, sequentially performing anomaly detection, filtering operations, and time alignment to eliminate abrupt changes, packet loss, and out-of-limit signals, ensuring data continuity and time consistency. Based on this, impedance analysis is performed, calculating the equivalent energy transfer impedance R by the ratio of temperature change ΔT to heat flux density rq, establishing the coupling relationship between energy flow and heat transfer. Finally, an energy dataset containing the terminal voltage U and the equivalent energy transfer impedance R, and a thermal state dataset containing the equivalent heat capacity Ct and the compartment temperature T are formed, constituting the basic data source for subsequent energy migration analysis and heat load assessment. This step not only accurately characterizes the energy transfer characteristics and heat flow distribution between battery compartments, but also achieves integrated collaboration in data acquisition, processing, and modeling, enhancing the real-time response capability to changes in energy flow and thermal state, and providing a high-precision, low-latency technical foundation for collaborative energy management and thermal safety control.

[0073] Example 3: Please refer to Figure 1 and Figure 2 Specifically: S2 includes S21;

[0074] S21. Edge nodes perform effective transmission analysis on the energy transmission channels between each electromagnetic compartment of the ship based on the energy dataset, and construct an effective energy transmission index Pli, which represents the instantaneous effective capacity of the energy migration channel. This index is used to quantify the energy mobility and pathway unobstructedness between compartments. Specifically: In the formula, Pli(i,t) represents the effective energy transfer index when the i-th battery compartment transfers energy to the j-th battery compartment, U(i,t) and U(j,t) represent the terminal voltages of the i-th and j-th battery compartments at time t, respectively, and R(i,j) represents the equivalent energy transfer impedance between the i-th and j-th battery compartments.

[0075] S2 also includes S22 and S23;

[0076] S22. Construct the transfer matrix Pli(K, L) of the effective energy transfer index Pli for all compartments, as follows;

[0077] ;

[0078] S23. Calculate the average value of the effective energy transfer index Pli of all inter-cabin channels based on the transfer matrix Pli(K,L), and combine it with the allowable fluctuation range within the standard range. Set the difference between the average value and the fluctuation range as the energy flow trigger boundary threshold Eb, and set the sum of the average value and the fluctuation range as the energy flow trigger critical threshold Ec. Then compare it with the effective energy transfer index Pli acquired in real time, and evaluate the energy migration status based on the comparison results. The specific evaluation scheme is as follows.

[0079] When the effective energy transfer index Pli < the energy flow trigger boundary threshold Eb, it indicates that the energy migration flow is not smooth. At this time, the backup battery compartment is activated to participate in energy compensation, and iterative analysis is performed through S2. If the energy migration flow is still not smooth after three iterations, the energy migration direction between battery compartments is redistributed.

[0080] When the energy flow triggering boundary threshold Eb ≤ effective energy transfer index Pli ≤ energy flow triggering critical threshold Ec, it indicates that the energy migration flow meets the standard, and the heat load behavior analysis is triggered at this time.

[0081] When the effective energy transfer index Pli > the energy flow trigger threshold Ec, it indicates that the energy migration flow is active and there is excessive energy migration. At this time, a current limiting command is automatically issued to reduce the current output limit of the current battery compartment by 15% and mark it as a migration limiting channel. Then, iterative analysis is performed through S2 until the energy migration flow meets the standard.

[0082] In this embodiment, S21 uses the energy dataset to calculate the effective energy transfer index Pli of each channel, quantifying the smoothness and migration direction of energy flow between compartments. This formula originates from Joule's law in physics and the principle of square integral of energy flux density function in mathematics. In electrical theory, the power P driven by potential difference and transferred through a resistive channel per unit time satisfies the following condition: where U is the voltage difference across the channel and R is the equivalent resistance of the channel. This is the power form of Joule's law. When multiple energy units exist and exchange energy with each other through electrical connection channels, the instantaneous energy flow between the i-th and j-th battery compartments can be equivalently regarded as a variable resistive load system, whose effective transfer power is... Based on this physical relationship, this application introduces a dynamic term of time t and the inter-cell identifier (i,j) to quantify the inter-cell energy transfer capability in real time, and redefines it as the effective energy transfer index Pli(i,j) to describe the inter-cell energy mobility. Here, U(i,t)-U(j,t) represents the potential difference between the i-th and j-th battery cells at time t, which is the fundamental driving force for energy transfer. The square root originates from the physical characteristic of Joule's law that power is proportional to the square of voltage. This ensures the positive definiteness of the energy flow intensity on the one hand, and makes the energy change exhibit a quadratic response to the change in voltage difference on the other hand, conforming to the nonlinear relationship between power and potential difference. R(i,j) represents the equivalent energy transfer impedance of the energy channel between the i-th and j-th cells. The overall ratio has the physical meaning of "potential difference energy flow density per unit impedance," which is a quantitative expression for the smoothness of inter-cell energy flow. This formula is the power form of standard Joule's law; therefore, the dimension of the effective energy transfer index Pli(i,j) is watts (W). S22 constructs a full-cabin-level transmission matrix Pli(K,L) to achieve a holistic analysis of the energy distribution relationship across multiple channels. S23 calculates the average effective energy transfer index Pli of all inter-cabin channels based on the transmission matrix Pli(K,L). Combining the matrix mean and fluctuation range, it sets an energy flow trigger boundary threshold Eb and an energy flow trigger critical threshold Ec to finely classify the energy migration state. When the effective energy transfer index Pli is detected to be lower than the energy flow trigger boundary threshold Eb, a backup cabin is automatically invoked for energy compensation. When the effective energy transfer index Pli is between the energy flow trigger boundary threshold Eb and the energy flow trigger critical threshold Ec, the energy flow state is considered stable, and subsequent thermal load analysis is triggered. When the effective energy transfer index Pli exceeds the energy flow trigger critical threshold Ec, it is determined to be excessive energy flow migration, and current limiting and marking operations are immediately executed to prevent overheating and power imbalance. Through this dynamic threshold control and matrix-based evaluation method, adaptive balanced energy flow distribution can be achieved in complex multi-cabin environments, reducing energy loss and local heat accumulation caused by uneven energy flow, and improving the overall energy transfer efficiency and operational stability of the battery integrated system.

[0083] Example 4: Please refer to Figure 1 and Figure 2 Specifically: S3 includes S31;

[0084] S31. When the energy migration state assessment indicates that the energy migration flow meets the standard, a thermal load state analysis is performed on the battery compartment whose energy migration flow meets the standard based on the thermal state dataset, and a thermal response energy index Qth is constructed to represent the amount of energy change caused by temperature changes inside the battery compartment, used to describe the rate of change of internal thermal energy in the battery compartment, specifically: Qth(i,t) represents the thermal response energy index of the i-th battery compartment at time t, and Ct(i) represents the equivalent thermal melting of the i-th battery compartment. Let dt represent the rate of temperature change of the i-th battery compartment at time t, and dt represent the change in the time variable t in the reciprocal.

[0085] S3 further includes S32;

[0086] S32. Calculate the average thermal response energy index Qth when there is no thermal accumulation trend in energy migration within one year using statistical methods, and preset the thermal energy response threshold Rp based on the average value. Then compare it with the real-time acquired thermal response energy index Qth, and evaluate the thermal accumulation state based on the comparison results. The specific evaluation scheme is as follows.

[0087] When the thermal response energy index Qth < thermal response threshold Rp, it indicates that there is no thermal accumulation trend in energy migration, and the current battery energy migration strategy is maintained.

[0088] When the thermal response energy index Qth is greater than or equal to the thermal response threshold Rp, it indicates that there is a tendency for thermal accumulation in energy migration, and at this time, cooperative steady-state analysis is triggered.

[0089] In this embodiment, after the energy migration state assessment indicates that the energy migration flow meets the standard, S31 performs a thermal load state analysis on the battery compartments whose energy migration flow meets the standard based on the thermal state dataset, calculating the thermal response energy index Qth for each battery compartment. This index reflects the rate of energy change caused by temperature changes in the battery compartment, considering the equivalent heat capacity Ct of the compartment and the real-time temperature change rate, describing the accumulation and release characteristics of thermal energy over time. This formula is derived from the combination of the classical thermodynamic energy balance equation and the principle of differential rate of change, and is a quantitative expression of the rate of change of thermal energy inside the battery compartment. In thermodynamics, the change of thermal energy of an object can be represented by Q=C×ΔT, where Q is heat, C is the heat capacity of the object, and ΔT is the temperature change. This is the classical formula for calculating heat under steady conditions, describing the static process of heat absorption or release. During the operation of the battery compartment, the temperature change is not instantaneous, but a dynamic process that changes continuously over time. Therefore, mathematically, the finite temperature difference ΔT is replaced by the form of a rate of change over time. This represents the rate of change of the system's thermal energy per unit time, i.e., the rate of heat transfer in the form of power. It is the basic form of the transient heat balance equation and the theoretical basis of this formula. The equivalent heat capacity Ct in this formula has dimensions of Joules per Kelvin (J / K), and the rate of temperature change... The unit of measurement is Kelvin (K / s), while the unit of measurement in this formula is Joules (J / s), or Watts (W). S32 obtains the historical average of the thermal response energy index Qth under no-heat-accumulation conditions during the entire year's operation by statistically analyzing the thermal response energy index Qth. This average is then used to set a thermal response threshold Rp for real-time judgment of whether the battery compartment is in a heat-accumulation trend. When the thermal response energy index Qth ≥ the thermal response threshold Rp, collaborative steady-state analysis is immediately triggered, initiating cross-compartment thermal regulation and energy redistribution mechanisms. If the thermal response energy index Qth < the thermal response threshold Rp, the existing energy migration strategy remains unchanged. This method achieves a transition from "energy transfer compliance" to "thermal behavior safety" monitoring, forming a thermal safety control link centered on real-time thermal response. Compared to traditional methods relying on fixed temperature thresholds or single-point temperature rise detection, this scheme can dynamically capture heat accumulation trends, identify potential overheating hazards in advance, suppress thermal imbalance within the compartment, and improve the thermal stability and overall synergy of the battery integrated system.

[0090] Example 5: Please refer to Figure 1 and Figure 2 Specifically: S4 includes S41;

[0091] S41. When the thermal accumulation state assessment indicates a trend of thermal accumulation in energy migration, the effective energy transfer index Pli is fitted with the thermal response energy index Qth to perform a coordinated steady-state analysis on the battery compartment with thermal accumulation. An energy flow coordination decision index Efw is then constructed to analyze the state of thermal balance and energy flow coordination in the current cycle, reflecting the dynamic matching degree between energy transfer efficiency and heat load distribution. Specifically: In the formula, N represents the number of battery compartments. This represents the average thermal response energy index of all battery compartments.

[0092] S4 also includes S42;

[0093] S42. Calculate the mean value of the energy flow collaborative decision index Efw when energy migration and heat distribution are in a coordinated state within one year using statistical methods, and preset the collaborative steady-state judgment threshold Xe based on the mean value. Then compare it with the real-time energy flow collaborative decision index Efw, and conduct collaborative decision evaluation based on the comparison results. The specific evaluation scheme is as follows.

[0094] When the energy flow collaborative decision index Efw < collaborative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a mismatch state. At this time, the backup compartment and cryogenic compartment are activated to add power supply, and the number of cross-compartment energy transfer paths is increased by 10%, the output power of the battery compartment with heat load is reduced by 15%, and the cooling fan speed is increased by 15%. Iterative analysis is performed through S2. If the mismatch state is still present after three iterations, maintenance personnel are immediately notified to carry out maintenance and repair.

[0095] When the energy flow cooperative decision index Efw ≥ cooperative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a coordinated state, and the current energy allocation command is maintained.

[0096] In this embodiment, when there is a tendency for heat accumulation during energy migration, S41 fits the effective energy transfer index Pli with the thermal response energy index Qth to construct the energy flow collaborative decision index Efw, quantifying the dynamic matching degree between the energy transfer efficiency and heat load distribution of the battery pack group in the current cycle. This formula originates from the combination of energy flow network theory and the feedback principle of thermodynamic non-equilibrium systems, and belongs to the energy-thermal collaborative balance index formula. Its mathematical origin can be traced back to the effective power summation formula of the power transmission network. The stability factor expression in the nonlinear thermal feedback model Where η represents the thermal hysteresis effect or hysteresis ratio inside the battery compartment, which in this scheme is equivalent to By coupling the two, we obtain an "energy flow summation formula with a thermal feedback term". Finally, the energy flow collaborative decision index Efw is obtained. In this formula... Since the effective energy transfer index Pli(i,j) is dimensionless, its dimension is watts (W), therefore the dimension of this formula is watts (W). S42 establishes an annual operational statistical model, extracting the average value of the energy flow collaborative decision index Efw under the coordinated state of energy migration and heat distribution, forming a collaborative steady-state threshold Xe, used to determine the energy-thermal coupling state of the battery module group in real time. When the energy flow collaborative decision index Efw is lower than the collaborative steady-state threshold Xe, control commands are automatically executed, activating the backup module and cryogenic module to participate in energy supply, while dynamically adjusting the cross-module energy transfer path, output power, and cooling fan speed to achieve energy flow redistribution and rapid thermal environment correction; when the energy flow collaborative decision index Efw is greater than or equal to the collaborative steady-state threshold Xe, the current energy strategy remains unchanged to maintain stable energy-thermal collaborative operation. In this way, the system achieves adaptive balance of energy transfer process and dynamic coordination of heat load without changing the hardware structure. This reduces the risk of local heat accumulation in the battery compartment and maintains stable energy transfer efficiency. It enables the battery integrated system to have self-sensing, self-regulating and self-steady-state capabilities during long-term operation, thereby improving the thermal safety and energy management accuracy of the battery integrated system.

[0097] Example 6: Please refer to Figure 1 and Figure 2 Specifically: S5 includes S51 and S52;

[0098] S51. At the end of each control cycle, the acquired effective energy transfer index Pli, thermal response energy index Qth, and energy flow collaborative decision index Efw are automatically written into the time series database for structured archiving, and an index is created according to the cycle number and battery compartment identifier.

[0099] S52. Call the energy flow collaborative decision index Efw for two consecutive cycles in the time series database and calculate the differential change rate. When the differential change rate is detected to be decreasing continuously, it is determined that there is a drift between the energy dataset and the thermal state dataset between the current battery compartments. At this time, the battery integration system enters the self-calibration preparation state, marks the current cycle as the parameter calibration trigger cycle, and starts the self-calibration module to perform data calibration on the energy dataset and the thermal state dataset.

[0100] In this embodiment, at the end of each control cycle, S51 automatically writes the key indicators—effective energy transfer index Pli, thermal response energy index Qth, and energy flow collaborative decision index Efw—into the time-series database and establishes an index using the cycle number and battery compartment identifier to form a high-time-precision operational data archive. S52 calls the energy flow collaborative decision index Efw from two consecutive cycles to calculate the differential rate of change. When a continuous decrease in this rate of change is detected, it is determined that the energy and thermal state data between battery compartments have drifted, and self-correction is automatically triggered to synchronously correct the energy dataset and thermal state dataset. This achieves continuous monitoring of the operational status and dynamic parameter self-adjustment, performing self-correction in the early stages of data deviation or model error accumulation, ensuring the long-term accuracy and stability of energy transfer and thermal distribution analysis results. Compared to traditional systems that require manual intervention to adjust parameters, this method achieves automated data consistency maintenance at the algorithm level, enabling the containerized battery system to maintain thermal-electrical synergistic balance during long-term operation, reducing energy loss and thermal imbalance risks, extending the safe operating cycle of the battery integration system, and improving overall operational reliability.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An energy collaborative management method for a containerized battery integrated system, characterized in that: Includes the following steps: S1. By using a sensor array arranged inside the battery compartment and extracting the equivalent heat capacity Ct from the data storage system, the raw data of the battery compartment during operation is obtained in real time. After data processing, an energy dataset and a thermal state dataset are formed. S2: The energy dataset performs effective transmission analysis on the energy transmission channels and constructs a transmission matrix to calculate the mean of the effective transmission analysis results of all channels for energy migration status assessment. When the assessment shows that the energy migration flow meets the standard, S3 is triggered. S3. Perform thermal load state analysis on the battery compartment based on the thermal state dataset, and evaluate the thermal accumulation state based on the analysis results. If the evaluation indicates that there is a thermal accumulation trend, trigger S4. S4. Fit the effective transmission analysis results and the thermal load state analysis results, perform a collaborative steady-state analysis on the battery compartment with heat accumulation, and make a collaborative decision evaluation based on the analysis results. S5. At the end of the control cycle, all analysis results are automatically written into the time series database for structured archiving, and parameter calibration is performed by judging data drift through the period difference change rate.

2. The energy collaborative management method for a containerized battery integrated system according to claim 1, characterized in that: S1 includes S11; S11. By using sensor groups arranged inside each battery compartment and extracting the equivalent heat capacity Ct of the battery compartment material in the data storage system via the communication bus, the raw data of the battery compartment during operation is obtained in real time. The equivalent heat capacity Ct is a fixed constant obtained by heating experiment calibration during the factory stage. The sensor group includes a voltage sensor, a temperature sensor array, and a heat flow meter; The voltage sensor is used to fix the transition conductor section between the bus connection terminal inside the battery compartment and the DC bus outside the compartment to collect the terminal voltage U of the battery compartment in real time. The temperature sensor array is used to install on the positive busbar of the main output circuit, that is, between the battery module busbar and the DC output terminal outside the compartment, to collect the battery compartment temperature T in real time. The heat flux meter is used to embed one on the outside of the insulation layer between adjacent battery compartments to collect the heat flux density rq of the battery compartment sidewall in real time.

3. The energy collaborative management method for a containerized battery integrated system according to claim 2, characterized in that: S1 further includes S12; S12. Edge computing nodes receive raw data from each battery compartment in real time via a communication bus, process the data, and form energy datasets and thermal state datasets. The data processing is used to perform anomaly detection, filtering, and time alignment on the raw data before impedance analysis. The anomaly detection, filtering operation, and time alignment are used to remove and compensate for abrupt changes, packet loss, and out-of-limit signals in the original data, as well as to unify the original data with the same time reference; the impedance analysis is used to compare the temperature change ΔT at the energy input end and the heat flux density rq to obtain the equivalent energy transmission impedance R. The energy dataset includes terminal voltage U and equivalent energy transfer impedance R; The thermal state dataset includes the equivalent heat capacity Ct and the battery compartment temperature T.

4. The energy collaborative management method for a containerized battery integrated system according to claim 3, characterized in that: S2 includes S21; S21. The edge nodes perform effective transmission analysis on the energy transmission channels between each electromagnetic compartment of the ship based on the energy dataset, and construct an effective energy transmission index Pli, which represents the instantaneous effective capability of the energy migration channel and is used to quantify the energy mobility and accessibility between compartments.

5. The energy collaborative management method for a containerized battery integrated system according to claim 4, characterized in that: S2 also includes S22 and S23; S22. Construct the transfer matrix Pli(K, L) of all compartments based on the effective energy transfer index Pli; S23. Calculate the average value of the effective energy transfer index Pli of all inter-cabin channels based on the transfer matrix Pli(K,L), and combine it with the allowable fluctuation range within the standard range. Set the difference between the average value and the fluctuation range as the energy flow trigger boundary threshold Eb, and set the sum of the average value and the fluctuation range as the energy flow trigger critical threshold Ec. Then compare it with the effective energy transfer index Pli acquired in real time, and evaluate the energy migration status based on the comparison results. The specific evaluation scheme is as follows. When the effective energy transfer index Pli < the energy flow trigger boundary threshold Eb, it indicates that the energy migration flow is not smooth. At this time, the backup battery compartment is activated to participate in energy compensation, and iterative analysis is performed through S2. If the energy migration flow is still not smooth after three iterations, the energy migration direction between battery compartments is redistributed. When the energy flow triggering boundary threshold Eb ≤ effective energy transfer index Pli ≤ energy flow triggering critical threshold Ec, it means that the energy migration flow meets the standard, and the heat load behavior analysis is triggered at this time. When the effective energy transfer index Pli > the energy flow trigger threshold Ec, it indicates that the energy migration flow is active and there is excessive energy migration. At this time, a current limiting command is automatically issued to reduce the current output limit of the current battery compartment by 15% and mark it as a migration limiting channel. Then, iterative analysis is performed through S2 until the energy migration flow meets the standard.

6. The energy collaborative management method for a containerized battery integrated system according to claim 5, characterized in that: S3 includes S31; S31. When the energy migration state assessment indicates that the energy migration flow meets the standard, perform thermal load state analysis on the battery compartment that meets the standard based on the thermal state dataset, and construct the thermal response energy index Qth, which represents the amount of energy change caused by temperature change inside the battery compartment, and is used to describe the rate of change of internal thermal energy in the battery compartment.

7. The energy collaborative management method for a containerized battery integrated system according to claim 6, characterized in that: S3 further includes S32; S32. Calculate the average thermal response energy index Qth when there is no thermal accumulation trend in energy migration within one year using statistical methods, and preset the thermal energy response threshold Rp based on the average value. Then compare it with the real-time acquired thermal response energy index Qth, and evaluate the thermal accumulation state based on the comparison results. The specific evaluation scheme is as follows. When the thermal response energy index Qth < thermal response threshold Rp, it indicates that there is no thermal accumulation trend in energy migration, and the current battery energy migration strategy is maintained. When the thermal response energy index Qth is greater than or equal to the thermal response threshold Rp, it indicates that there is a tendency for thermal accumulation in energy migration, and at this time, cooperative steady-state analysis is triggered.

8. The energy collaborative management method for a containerized battery integrated system according to claim 7, characterized in that: S4 includes S41; S41. When the thermal accumulation state assessment indicates that there is a thermal accumulation trend in energy migration, the effective energy transfer index Pli and the thermal response energy index Qth are fitted together to perform a collaborative steady-state analysis on the battery compartment with thermal accumulation, and an energy flow collaborative decision index Efw is constructed to analyze the state of thermal balance and energy flow coordination in the current cycle, reflecting the dynamic matching degree between energy transfer efficiency and heat load distribution.

9. The energy collaborative management method for a containerized battery integrated system according to claim 8, characterized in that: S4 also includes S42; S42. Calculate the mean value of the energy flow collaborative decision index Efw when energy migration and heat distribution are in a coordinated state within one year using statistical methods, and preset the collaborative steady-state judgment threshold Xe based on the mean value. Then compare it with the real-time energy flow collaborative decision index Efw, and conduct collaborative decision evaluation based on the comparison results. The specific evaluation scheme is as follows. When the energy flow collaborative decision index Efw < collaborative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a mismatch state. At this time, the backup compartment and cryogenic compartment are activated to add power supply, and the number of cross-compartment energy transfer paths is increased by 10%, the output power of the battery compartment with heat load is reduced by 15%, and the cooling fan speed is increased by 15%. Iterative analysis is performed through S2. If the mismatch state is still present after three iterations, maintenance personnel are immediately notified to carry out maintenance and repair. When the energy flow cooperative decision index Efw ≥ cooperative steady-state threshold Xe, it indicates that energy migration and heat distribution are in a coordinated state, and the current energy allocation command is maintained.

10. The energy collaborative management method for a containerized battery integrated system according to claim 1, characterized in that: S5 includes S51 and S52; S51. At the end of each control cycle, the acquired effective energy transfer index Pli, thermal response energy index Qth, and energy flow collaborative decision index Efw are automatically written into the time series database for structured archiving, and an index is created according to the cycle number and battery compartment identifier. S52. Call the energy flow collaborative decision index Efw for two consecutive cycles in the time series database and calculate the differential change rate. When the differential change rate is detected to be decreasing continuously, it is determined that there is a drift between the energy dataset and the thermal state dataset between the current battery compartments. At this time, the battery integration system enters the self-calibration preparation state, marks the current cycle as the parameter calibration trigger cycle, and starts the self-calibration module to perform data calibration on the energy dataset and the thermal state dataset.

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