Lithium battery and super capacitor management system

Abstract

The invention relates to the technical field of energy storage element management, and discloses a lithium battery and super-capacitor management system, which comprises an energy storage module, a coordination module, a power distribution module, a power distribution module, a power distribution module, a power distribution module, a power distribution module and a power distribution module, and is characterized in that the energy storage module comprises a plurality of groups of super-capacitors and a plurality of groups of lithium batteries; the energy prediction module is provided with an energy storage monitoring unit, a hierarchical feedback unit and an automatic tuning unit, the energy storage monitoring unit is used for predicting energy storage data of the multiple groups of super capacitors and the multiple groups of lithium batteries, and a prediction result is output to the hierarchical feedback unit; the hierarchical feedback unit identifies the energy storage level of the energy storage module, the electric quantity switching between the lithium battery and the super capacitor is allocated in advance, and finally the automatic tuning unit optimizes the total power predicted value of the super capacitor and the electric quantity allocation result, so that the electric energy transmission efficiency of the super capacitor and the lithium battery is improved, and the energy consumption is reduced. And the influence of electric energy surplus and capacity overload on the system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage element management and discloses a lithium battery and supercapacitor management system. Background Art

[0002] Existing hybrid systems of lithium batteries and supercapacitors have deficiencies in dynamic power distribution, life management, and response speed. Lithium batteries are easily damaged during high-voltage charging and discharging. Although supercapacitors can quickly respond to instantaneous power demands, their energy density is low, and they cannot meet long-term battery life requirements when used alone. Traditional systems lack refined monitoring of the health status of energy storage units, resulting in a mismatch between energy distribution strategies and actual equipment performance, accelerating aging risks. Early hybrid energy storage systems mostly used energy management methods based on experience or simple optimization, which made it difficult to adapt to random loads and complex working conditions. The high material cost and complex integration process of supercapacitors limit their large-scale application. Traditional hybrid systems have efficiency losses during energy recovery and lack a dynamic braking resistor adjustment mechanism, resulting in energy waste. Summary of the Invention

[0003] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0004] In order to solve the above technical problems, the main purpose of the present invention is to provide a lithium battery and supercapacitor management system, comprising:

[0005] Energy storage module, coordination module and energy prediction module, wherein the energy storage module includes multiple groups of supercapacitors and multiple groups of lithium batteries;

[0006] The coordination module is used to receive the power allocation strategy output by the energy prediction module, and coordinate the power of multiple groups of supercapacitors and multiple groups of lithium batteries according to the power allocation strategy;

[0007] The energy prediction module is equipped with an energy storage monitoring unit, a hierarchical feedback unit and an automatic tuning unit. The energy storage monitoring unit predicts the energy storage data of multiple groups of supercapacitors and multiple groups of lithium batteries, and outputs the prediction results to the hierarchical feedback unit. The hierarchical feedback unit identifies the energy storage level of the energy storage module and allocates the power switching between lithium batteries and supercapacitors in advance. Finally, the automatic tuning unit optimizes the total power prediction value of the supercapacitor and the power allocation result.

[0008] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0009] The multiple groups of lithium batteries include an emergency area, a standby area and a dynamic switching area;

[0010] The emergency zone includes multiple lithium batteries, which are used to dynamically call the emergency zone lithium battery pack for replenishment when the supercapacitor power is insufficient during the switching acceleration phase;

[0011] The dynamic switching area is set between the emergency area and the standby area and is used to buffer the flow of power from the standby area to the emergency area;

[0012] The other end of the standby area is connected to the power grid for receiving power from the power grid.

[0013] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0014] The coordination module is provided with a dynamic adjustment unit for a braking resistor, and the braking resistor includes multiple gears. If the coordination module receives the electric energy allocation overflow output by the energy prediction module, the resistance value of the braking resistor is dynamically adjusted, and the overflow electric energy is consumed by the braking resistor;

[0015] If the total charging energy of multiple supercapacitors is greater than the total capacity of multiple lithium battery groups, the coordination module will dynamically adjust the resistance of the braking resistor to consume the overflowing energy.

[0016] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0017] The energy storage monitoring unit is provided with a high-frequency ripple monitoring model and an equivalent series resistance identification model;

[0018] The high-frequency ripple monitoring model obtains the supercapacitor charging and discharging ripple characteristics through a wide-band voltage sensor, decomposes the supercapacitor charging and discharging ripple energy distribution characteristics through wavelet packets, generates the voltage waveform frequency band energy entropy value, and extracts the ripple feature fingerprint from the voltage waveform frequency band energy entropy value;

[0019] The equivalent series resistance identification model injects a pseudo-random binary sequence excitation signal with controllable amplitude, and simultaneously iteratively calculates the equivalent series resistance of the supercapacitor in real time through an adaptive forgetting factor.

[0020] The equivalent series resistance and ripple characteristic fingerprint of the supercapacitor are received through a dual-channel deep residual network, and the supercapacitor resistance and output voltage ripple characteristics are captured through dual channels;

[0021] The first channel extracts the spatial features of the ripple feature fingerprint and the equivalent series resistance time series through a three-level convolutional layer. The second channel captures the temporal dependency through a gated recurrent unit, outputs the fused feature matrix through a cross-attention mechanism, and outputs the health status prediction value and confidence.

[0022] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0023] The energy storage monitoring unit also includes a lithium battery partition monitoring system, which includes a domain-specific collaborative perception and a three-dimensional evaluation model;

[0024] The domain-specific collaborative sensing includes configuring a voltage sampling circuit and an integrated contact thermal film in the emergency zone to monitor sudden changes in the lug temperature, and a bidirectional power flow monitoring module in the dynamic switching zone to detect bidirectional power impacts in the dynamic switching zone.

[0025] The three-dimensional evaluation model predicts the state of the lithium battery pack through dynamic internal resistance cloud map, battery thermal characteristics and power flow adaptability.

[0026] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0027] The hierarchical feedback unit is configured with a dual-path intelligent distribution interface in the emergency area, which is connected to the first supercapacitor group and the second supercapacitor group respectively. A bidirectional DC / DC converter cluster is deployed in the dynamic switching area for bidirectional power regulation, and a three-level power bus architecture is established, wherein the first-level bus emergency area is directly connected to the supercapacitor group, the second-level bus dynamic switching area is interconnected with the emergency area, and the third-level bus standby area is connected to the power grid.

[0028] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0029] receiving a plurality of supercapacitor total power prediction values ​​through a hierarchical feedback unit, and simultaneously correcting the plurality of supercapacitor total power prediction values ​​through an automatic tuning unit;

[0030] The corrected predicted value of the total supercapacitor power is input into the automatic tuning unit, and the automatic tuning unit outputs an electric energy allocation strategy.

[0031] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0032] The power allocation strategy includes:

[0033] If the total power prediction value of multiple supercapacitors is greater than the emergency zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone;

[0034] If the total power prediction value of multiple supercapacitors is less than or equal to the emergency zone, the emergency zone will allocate power to multiple supercapacitors;

[0035] If the total power prediction value of multiple supercapacitors is greater than the sum of the power of the emergency zone and the dynamic switching zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone, and the bidirectional DC / DC converter cluster is switched to control the backup zone to flow power to the dynamic switching zone.

[0036] The power gap in the reserve area is filled when the power grid electricity price is the lowest;

[0037] The hierarchical feedback unit is also provided with an energy storage redundancy circuit. If the total power prediction value of multiple supercapacitors is greater than the sum of the electric energy of the emergency zone and the dynamic switching zone, the electric energy of the emergency zone flows into the energy storage redundancy circuit, and the electric energy of the standby zone is switched to flow to the dynamic switching zone, and the electric energy of the dynamic switching zone flows to the emergency zone.

[0038] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0039] The automatic tuning unit receives the load curve, the real-time working condition and the predicted value of the supercapacitor health status, and modifies the actual power allocation strategy;

[0040] The automatic tuning unit includes a basic layer, a working condition correction layer and an emergency correction layer. The basic layer uses a neural learning network to learn load cycle changes. The working condition correction layer constructs a multi-dimensional influencing factor matrix and dynamically corrects the actual power demand prediction values ​​of multiple supercapacitors. The emergency correction layer outputs dynamic braking resistance conversion to the coordination module, and the coordination module adjusts the braking resistance.

[0041] As a preferred solution of the lithium battery and supercapacitor management system of the present invention, wherein:

[0042] The multidimensional influencing factor matrix includes voltage fluctuation and supercapacitor health status prediction values;

[0043] The direction of electric energy flow is adjusted by setting a composite power switch, wherein the composite power switch includes a main channel, a redundant channel and node monitoring.

[0044] Beneficial effects of the present invention:

[0045] Through the partitioned lithium battery and multi-level power bus architecture, precise coordination between supercapacitors and lithium batteries is achieved. The emergency zone quickly replenishes the instantaneous high power demand of the supercapacitor, the dynamic switching zone buffers the energy flow, and the standby zone connects to the grid to achieve peak shaving and valley filling. The introduction of a dynamic adjustment unit for the brake resistor automatically adjusts the resistance level according to the overflow of power to reduce energy loss.

[0046] A high-frequency ripple monitoring model and an equivalent series resistance identification model, combined with a dual-channel deep residual network, enable real-time prediction of supercapacitor health status, preventing system failures due to aging. The lithium battery partition monitoring system uses multi-dimensional data such as dynamic internal resistance cloud maps and thermal signature analysis to provide early warning of battery pack anomalies, extending overall lifespan.

[0047] The hierarchical feedback unit is combined with a three-level power bus architecture to dynamically adjust the flow of power. When the total power prediction value of the supercapacitor exceeds the capacity of the emergency zone, the backup zone grid is automatically activated for energy replenishment, and the charging and discharging timing is optimized to match the time-of-use electricity price, reducing operating costs. The automatic tuning unit uses a neural learning network and a multi-dimensional influencing factor matrix to analyze voltage fluctuations and capacitor health status, and to correct the power prediction value in real time to adapt to complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0049] Figure 1 This is a structural diagram of a lithium battery and supercapacitor management system of the present invention;

[0050] Figure 2 This is a collaborative working diagram of a lithium battery and supercapacitor management system of the present invention;

[0051] Figure 3 This is a flow chart of multi-load operation of a lithium battery and supercapacitor management system of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0055] like Figure 1 As shown, a lithium battery and supercapacitor management system includes:

[0056] Energy storage module, coordination module and energy forecasting module;

[0057] The energy storage module includes multiple groups of supercapacitors and multiple groups of lithium batteries;

[0058] The multiple groups of lithium batteries include an emergency area, a standby area and a dynamic switching area;

[0059] The emergency zone includes multiple lithium batteries, which are used to dynamically call the emergency zone lithium battery pack for replenishment when the supercapacitor power is insufficient during the switching acceleration phase;

[0060] The dynamic switching area is set between the emergency area and the standby area and is used to buffer the flow of power from the standby area to the emergency area;

[0061] The other end of the standby area is connected to the power grid for receiving power from the power grid.

[0062] A specific implementation method of an energy storage module includes:

[0063] The supercapacitor array adopts a modular parallel design. Each group of supercapacitors is equipped with an independent voltage-equalizing circuit and temperature monitoring node to form a distributed management network. The charging and discharging ripple is captured in real time through a wide-band voltage sensor, and the ripple feature fingerprint is extracted by combining signal decomposition technology for health status assessment.

[0064] Furthermore, the emergency zone is composed of a high-rate lithium battery pack, which is directly connected to the supercapacitor array for instantaneous high-power charging.

[0065] The dynamic switching area is located between the emergency area and the backup area, and a bidirectional DC / DC converter is deployed to achieve buffering and bidirectional transmission of energy flow.

[0066] The standby area is connected to the power grid, equipped with intelligent charging and discharging interfaces, and performs grid energy dispatch according to the time-of-use electricity price strategy.

[0067] The intelligent charging and discharging interface supports bidirectional energy flow, integrates power quality monitoring function, and adjusts grid-connected parameters in real time to match grid demand.

[0068] The coordination module is used to receive the power allocation strategy output by the energy prediction module, and coordinate the power of multiple groups of supercapacitors and multiple groups of lithium batteries according to the power allocation strategy;

[0069] The coordination module is provided with a dynamic adjustment unit for a braking resistor, and the braking resistor includes multiple gears. If the coordination module receives the electric energy allocation overflow output by the energy prediction module, the resistance value of the braking resistor is dynamically adjusted, and the overflow electric energy is consumed by the braking resistor;

[0070] If the total charging energy of multiple supercapacitors is greater than the total capacity of multiple lithium battery groups, the coordination module will dynamically adjust the resistance of the braking resistor to consume the overflowing energy.

[0071] Among them, the function of the coordination module is equivalent to that of the control module, which coordinates the power of multiple groups of supercapacitors and multiple groups of lithium batteries by receiving the power allocation strategy.

[0072] The coordination module establishes a communication link with the energy prediction module, receives the power allocation strategy in real time, then analyzes the logical rules in the power allocation strategy, generates specific control signals, and adjusts the energy flow between the supercapacitor and the lithium battery in real time through the power switch matrix and DC / DC converter cluster according to the requirements of the power allocation strategy.

[0073] Furthermore, the coordination module adjusts the dynamic braking resistor to set a stepped resistance level, such as high, medium and low. Each level corresponds to a different energy overflow threshold range. Based on the overflowed electrical energy, it is judged whether the total amount of supercapacitor charging exceeds the maximum energy storage capacity of the lithium battery. At the same time, the lithium battery cannot absorb excess electrical energy. The electrical energy overflow signal is monitored and the overflow type is identified, including strategy overflow or capacity overflow. The braking resistor level is automatically switched according to the overflow level. For example, the low level consumes a small amount of overflow, and the high level handles severe overflow. The overflow level can include mild, moderate and severe. The braking resistor is controlled to connect to the circuit and the overflowed electrical energy is converted into heat energy consumption to avoid system overload.

[0074] The energy prediction module is equipped with an energy storage monitoring unit, a hierarchical feedback unit and an automatic tuning unit. The energy storage monitoring unit predicts the energy storage data of multiple groups of supercapacitors and multiple groups of lithium batteries, and outputs the prediction results to the hierarchical feedback unit. The hierarchical feedback unit identifies the energy storage level of the energy storage module and allocates the power switching between lithium batteries and supercapacitors in advance. Finally, the automatic tuning unit optimizes the total power prediction value of the supercapacitor and the power allocation result.

[0075] The energy storage monitoring unit is provided with a high-frequency ripple monitoring model and an equivalent series resistance identification model;

[0076] The high-frequency ripple monitoring model obtains the supercapacitor charging and discharging ripple characteristics through a wide-band voltage sensor with a high-frequency sampling rate, decomposes the supercapacitor charging and discharging ripple energy distribution characteristics through wavelet packets, generates the voltage waveform frequency band energy entropy value, and extracts the ripple feature fingerprint from the voltage waveform frequency band energy entropy value;

[0077] The equivalent series resistance identification model injects a pseudo-random binary sequence excitation signal with controllable amplitude, and simultaneously iteratively calculates the equivalent series resistance of the supercapacitor in real time through an adaptive forgetting factor.

[0078] The equivalent series resistance and ripple characteristic fingerprint of the supercapacitor are received through a dual-channel deep residual network, and the supercapacitor resistance and output voltage ripple characteristics are captured through dual channels;

[0079] The first channel extracts the spatial features of the ripple feature fingerprint and the equivalent series resistance time series through a three-level convolutional layer. The second channel captures the temporal dependency through a gated recurrent unit, outputs the fused feature matrix through a cross-attention mechanism, and outputs the health status prediction value and confidence.

[0080] For example, a wideband voltage sensor with a 100kHz sampling rate is used to capture the supercapacitor charging and discharging ripple characteristics, and wavelet packet decomposition technology is used to extract the energy distribution in the 0.1-10MHz frequency band, significantly improving the detection accuracy of high-frequency noise. Traditional ripple detection relies heavily on low-pass filtering or simple peak detection. However, this application uses wideband coverage and wavelet packet decomposition to accurately separate high-frequency ripple components, avoiding signal omissions caused by frequency band limitations in traditional methods.

[0081] Compared with the filter circuit with a fixed cutoff frequency, wavelet packet decomposition can flexibly adjust the frequency band energy analysis range according to the actual working conditions, thereby enhancing the system's adaptability to complex working conditions.

[0082] The energy storage monitoring unit also includes a lithium battery partition monitoring system, which includes a domain-specific collaborative perception and a three-dimensional evaluation model;

[0083] The domain-specific collaborative sensing includes configuring a voltage sampling circuit and an integrated contact thermal film in the emergency zone to monitor sudden changes in the lug temperature, and a bidirectional power flow monitoring module in the dynamic switching zone to detect bidirectional power impacts in the dynamic switching zone.

[0084] The three-dimensional evaluation model predicts the state of the lithium battery pack through dynamic internal resistance cloud map, battery thermal characteristics and power flow adaptability.

[0085] The hierarchical feedback unit is configured with a dual-path intelligent distribution interface in the emergency area, which is connected to the first supercapacitor group and the second supercapacitor group respectively. A bidirectional DC / DC converter cluster is deployed in the dynamic switching area for bidirectional power regulation, establishing a three-level power bus architecture, wherein the first-level bus emergency area is directly connected to the supercapacitor group, the second-level bus dynamic switching area is interconnected with the emergency area, and the third-level bus standby area is connected to the power grid.

[0086] receiving a plurality of supercapacitor total power prediction values ​​through a hierarchical feedback unit, and simultaneously correcting the plurality of supercapacitor total power prediction values ​​through an automatic tuning unit;

[0087] The corrected predicted value of the total supercapacitor power is input into the automatic tuning unit, and the automatic tuning unit outputs an electric energy allocation strategy.

[0088] The power allocation strategy includes:

[0089] If the total power prediction value of multiple supercapacitors is greater than the emergency zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone;

[0090] If the total power prediction value of multiple supercapacitors is less than or equal to the emergency zone, the emergency zone will allocate power to multiple supercapacitors;

[0091] If the total power prediction value of multiple supercapacitors is greater than the sum of the power of the emergency zone and the dynamic switching zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone, and the bidirectional DC / DC converter cluster is switched to control the backup zone to flow power to the dynamic switching zone.

[0092] The power gap in the standby area is filled when the power grid electricity price is the lowest.

[0093] The hierarchical feedback unit is also provided with an energy storage redundancy circuit. If the total power prediction value of multiple supercapacitors is greater than the sum of the electric energy of the emergency zone and the dynamic switching zone, the electric energy of the emergency zone flows into the energy storage redundancy circuit, and the electric energy of the standby zone is switched to flow to the dynamic switching zone, and the electric energy of the dynamic switching zone flows to the emergency zone.

[0094] Table 1 shows the regional energy switching comparison table;

[0095] Table 1 Regional energy switching comparison table

[0096] Emergency area → Supercapacitor Power forecast value ≤ available capacity of emergency area Activate the main channel IGBT and block the bidirectional DC / DC Dynamic switching area → emergency area Power forecast value > emergency area capacity Start bidirectional DC / DC boost mode Spare area → dynamic switching area Low electricity prices Synchronous control of the grid-side PCS and DC / DC step-down module Energy storage redundancy circuit activated Total power forecast value > the sum of emergency area capacity and dynamic switching area capacity Cut into redundant channels in stages and start the forced air cooling system

[0097] Furthermore, the automatic tuning unit receives the load curve, the real-time working condition and the predicted value of the supercapacitor health status, and modifies the actual power allocation strategy;

[0098] The automatic tuning unit includes a basic layer, a working condition correction layer and an emergency correction layer. The basic layer uses a neural learning network to learn load cycle changes. The working condition correction layer constructs a multi-dimensional influencing factor matrix and dynamically corrects the actual power demand prediction values ​​of multiple supercapacitors. The emergency correction layer outputs dynamic braking resistance conversion to the coordination module, and the coordination module adjusts the braking resistance.

[0099] The multidimensional influencing factor matrix includes voltage fluctuation and supercapacitor health status prediction values;

[0100] By quantifying the degree of influence of different dimensional parameters on the system, a dynamic weight allocation mechanism is established to achieve accurate correction of the predicted value. Voltage data is used as a basis to form a multi-dimensional coupling relationship with the health status of supercapacitors, environmental parameters, etc., and the voltage fluctuation is decomposed into instantaneous fluctuations, periodic trends and abnormal mutations. The differences between dimensions are eliminated through standardization. The mapping relationship between each dimensional parameter and the power demand is established, and finally the contribution of each factor is adjusted according to the real-time system. The multi-dimensional influencing factor matrix The specific expression of the construction logic is:

[0101]

[0102] Among them, V(t) is the voltage state, including the voltage waveform band energy entropy value Vi(t), is the periodic trend component Vp(t), and is obtained by moving average decomposition, and the ripple energy distribution characteristic Va(t);

[0103] SOH(t) is the predicted health status of the supercapacitor, which is calculated based on the internal resistance ratio or capacity decay model;

[0104] Specifically, the capacity decay model is used to evaluate the capacity decay degree of the supercapacitor and then calculate the predicted value of the supercapacitor health status. Its input parameters come from the refined monitoring data of the supercapacitor by the energy storage monitoring unit, including:

[0105] The equivalent series resistance is obtained through the equivalent series resistance identification model. The model injects a pseudo-random binary sequence excitation signal with controllable amplitude and combines it with an adaptive forgetting factor to iteratively calculate the equivalent series resistance in real time. If the capacity of the supercapacitor decays, the equivalent series resistance increases.

[0106] The ripple characteristic fingerprint is extracted through a high-frequency ripple monitoring model, and the charge and discharge ripple characteristics are obtained through a wide-band voltage sensor. The voltage waveform band energy entropy value is generated through wavelet packet decomposition to form a ripple characteristic fingerprint. Capacity decay will cause changes in the charge and discharge ripple characteristics of the supercapacitor.

[0107] E(t) is the environmental parameter, including standardized values ​​such as temperature and humidity;

[0108] is the dynamic weight coefficient, where k = 1, 2, ..., 4. The dynamic weight distribution is scored through multi-dimensional states and corrected by the LSTM network.

[0109] The multi-dimensional influencing factor matrix outputs a correction amount for correcting the predicted value of the actual power demand of each supercapacitor. The correction amount includes a single correction amount and a total correction amount, wherein the single correction amount is the product of a quantitative parameter, an adjustment coefficient and a parameter weight, wherein the quantitative parameter includes the predicted values ​​of instantaneous fluctuations, periodic trends and abnormal mutations; the adjustment coefficient is set by a technician in this field according to actual needs and the rated parameters of the supercapacitor.

[0110] The total correction amount is equal to the sum of the products of all individual correction amounts and the corresponding supercapacitor health status prediction value.

[0111] The direction of electric energy flow is adjusted by setting a composite power switch, wherein the composite power switch includes a main channel, a redundant channel and node monitoring.

[0112] The redundant channels adopt three-stage control: rapid approach stage, fine adjustment stage, and steady-state locking stage;

[0113] Node monitoring is used to suppress voltage shocks, configure distributed buffer inductors, and dynamically adjust the voltage change rate and maximum allowable voltage according to the real-time voltage difference through the adaptive slope control algorithm. Value, where Indicates the rate of change of current.

[0114] like Figure 3 As shown, multiple sets of supercapacitors receive and store electrical energy, providing energy support for the operation of multiple loads, such as multiple elevators;

[0115] When the system experiences energy overflow, the total energy charged by the supercapacitor exceeds the capacity of the lithium battery, or the energy prediction module determines overflow, the coordination module's dynamic braking resistor adjustment unit starts to consume excess energy by adjusting the braking resistor value;

[0116] During the process, energy allocation of multiple lithium battery emergency zones, dynamic switching zones, and standby zones is combined to achieve energy balance and stable supply during multi-load operation.

[0117] like Figure 2 As shown, the first supercapacitor and the second supercapacitor respectively power the first elevator and the second elevator. The two are connected to the functional module containing the emergency area, the dynamic switching area, and the standby area through lines. The functional module is also connected to the braking resistor. The first switch and the second switch are respectively used for energy storage redundancy circuit access and circuit on-off control, realizing functions such as power distribution, state switching and redundant energy storage, and ensuring elevator power supply and operation control.

[0118] Furthermore, the first supercapacitor supplies power to the first elevator through the first switch, and the second supercapacitor supplies power to the second elevator through the second switch, thereby realizing independent energy supply for the loads.

[0119] The lithium battery pack consisting of the emergency area, dynamic switching area and backup area is connected to the supercapacitor group through lines; the emergency area is directly connected to the supercapacitor, the dynamic switching area serves as an energy buffer hub, and the backup area is connected to the power grid.

[0120] The system is connected to the energy storage redundant circuit and braking resistor to form a collaborative working network of load, energy, redundancy and braking to ensure energy distribution and emergency switching during elevator operation.

[0121] It is important to note that the configuration and arrangement of the present application, as illustrated in various exemplary embodiments, are exemplary only. Although only two embodiments are described in detail in this disclosure, those reading this disclosure should readily appreciate that numerous modifications are possible without materially departing from the novel teachings and advantages of the subject matter described herein, including, for example, variations in the size, dimensions, structure, shape, and proportions of various components, as well as parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, orientation, and the like. For example, components shown as integrally formed may be constructed from multiple parts or components, the positions of components may be inverted or otherwise altered, and the nature, number, or position of discrete components may be modified or changed. Therefore, all such modifications are intended to be encompassed within the scope of this invention. The order or sequence of any process or method steps may be altered or reordered according to alternative embodiments. Any "means-plus-function" clause is intended to cover structures that perform the functions described herein, and not only structural equivalence but also structural equivalents. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of this invention. Therefore, the present invention is not limited to the specific embodiment, but extends to various modifications that still fall within the scope of the present invention.

[0122] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (i.e., those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention).

[0123] It should be understood that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, the development effort will be a routine task of design, fabrication, and production without undue experimentation.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all of these should be included in the scope of the present invention.

Claims

1. A lithium battery and supercapacitor management system, characterized in that: include: Energy storage module, coordination module and energy prediction module, wherein the energy storage module includes multiple groups of supercapacitors and multiple groups of lithium batteries; The coordination module is used to receive the power allocation strategy output by the energy prediction module, and coordinate the power of multiple groups of supercapacitors and multiple groups of lithium batteries according to the power allocation strategy; The energy prediction module is equipped with an energy storage monitoring unit, a hierarchical feedback unit and an automatic tuning unit. The energy storage monitoring unit predicts the energy storage data of multiple groups of supercapacitors and multiple groups of lithium batteries, and outputs the prediction results to the hierarchical feedback unit. The hierarchical feedback unit identifies the energy storage level of the energy storage module and allocates the power switching between lithium batteries and supercapacitors in advance. Finally, the automatic tuning unit optimizes the total power prediction value of the supercapacitor and the power allocation result.

2. The lithium battery and supercapacitor management system according to claim 1, characterized in that: The multiple groups of lithium batteries include an emergency area, a standby area and a dynamic switching area; The emergency zone includes multiple lithium batteries, which are used to dynamically call the emergency zone lithium battery pack for replenishment when the supercapacitor power is insufficient during the switching acceleration phase; The dynamic switching area is set between the emergency area and the standby area and is used to buffer the flow of power from the standby area to the emergency area; The other end of the standby area is connected to the power grid for receiving power from the power grid.

3. The lithium battery and supercapacitor management system according to claim 1, characterized in that: The coordination module is provided with a dynamic adjustment unit for a braking resistor, and the braking resistor includes multiple gears. If the coordination module receives the electric energy allocation overflow output by the energy prediction module, the resistance value of the braking resistor is dynamically adjusted, and the overflow electric energy is consumed by the braking resistor; If the total charging energy of multiple supercapacitors is greater than the total capacity of multiple lithium battery groups, the coordination module will dynamically adjust the resistance of the braking resistor to consume the overflowing energy.

4. The lithium battery and supercapacitor management system according to claim 1, characterized in that: The energy storage monitoring unit is provided with a high-frequency ripple monitoring model and an equivalent series resistance identification model; The high-frequency ripple monitoring model obtains the supercapacitor charging and discharging ripple characteristics through a wide-band voltage sensor, decomposes the supercapacitor charging and discharging ripple energy distribution characteristics through wavelet packets, generates the voltage waveform frequency band energy entropy value, and extracts the ripple feature fingerprint from the voltage waveform frequency band energy entropy value; The equivalent series resistance identification model injects a pseudo-random binary sequence excitation signal with controllable amplitude, and simultaneously iteratively calculates the equivalent series resistance of the supercapacitor in real time through an adaptive forgetting factor. The equivalent series resistance and ripple characteristic fingerprint of the supercapacitor are received through a dual-channel deep residual network, and the supercapacitor resistance and output voltage ripple characteristics are captured through dual channels; The first channel extracts the spatial features of the ripple feature fingerprint and the equivalent series resistance time series through a three-level convolutional layer. The second channel captures the temporal dependency through a gated recurrent unit, outputs the fused feature matrix through a cross-attention mechanism, and outputs the health status prediction value and confidence.

5. The lithium battery and supercapacitor management system according to claim 4, characterized in that: The energy storage monitoring unit also includes a lithium battery partition monitoring system, which includes a domain-specific collaborative perception and a three-dimensional evaluation model; The domain-specific collaborative sensing includes configuring a voltage sampling circuit and an integrated contact thermal film in the emergency zone to monitor sudden changes in the lug temperature, and a bidirectional power flow monitoring module in the dynamic switching zone to detect bidirectional power impacts in the dynamic switching zone. The three-dimensional evaluation model predicts the state of the lithium battery pack through dynamic internal resistance cloud map, battery thermal characteristics and power flow adaptability.

6. The lithium battery and supercapacitor management system according to claim 5, characterized in that: The hierarchical feedback unit is configured with a dual-path intelligent distribution interface in the emergency area, which is connected to the first supercapacitor group and the second supercapacitor group respectively. A bidirectional DC / DC converter cluster is deployed in the dynamic switching area for bidirectional power regulation, and a three-level power bus architecture is established, wherein the first-level bus emergency area is directly connected to the supercapacitor group, the second-level bus dynamic switching area is interconnected with the emergency area, and the third-level bus standby area is connected to the power grid.

7. The lithium battery and supercapacitor management system according to claim 6, characterized in that: receiving a plurality of supercapacitor total power prediction values ​​through a hierarchical feedback unit, and simultaneously correcting the plurality of supercapacitor total power prediction values ​​through an automatic tuning unit; The corrected predicted value of the total supercapacitor power is input into the automatic tuning unit, and the automatic tuning unit outputs an electric energy allocation strategy.

8. The lithium battery and supercapacitor management system according to claim 7, characterized in that: The power allocation strategy includes: If the total power prediction value of multiple supercapacitors is greater than the emergency zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone; If the total power prediction value of multiple supercapacitors is less than or equal to the emergency zone, the emergency zone will allocate power to multiple supercapacitors; If the total power prediction value of multiple supercapacitors is greater than the sum of the power of the emergency zone and the dynamic switching zone, the bidirectional DC / DC converter cluster is switched to control the dynamic switching zone to allocate power to the emergency zone, and the bidirectional DC / DC converter cluster is switched to control the backup zone to flow power to the dynamic switching zone. The power gap in the reserve area is filled when the power grid electricity price is the lowest; The hierarchical feedback unit is also provided with an energy storage redundancy circuit. If the total power prediction value of multiple supercapacitors is greater than the sum of the electric energy of the emergency zone and the dynamic switching zone, the electric energy of the emergency zone flows into the energy storage redundancy circuit, and the electric energy of the standby zone is switched to flow to the dynamic switching zone, and the electric energy of the dynamic switching zone flows to the emergency zone.

9. The lithium battery and supercapacitor management system according to claim 8, characterized in that: The automatic tuning unit receives the load curve, the real-time working condition and the predicted value of the supercapacitor health status, and modifies the actual power allocation strategy; The automatic tuning unit includes a basic layer, a working condition correction layer and an emergency correction layer. The basic layer uses a neural learning network to learn load cycle changes. The working condition correction layer constructs a multi-dimensional influencing factor matrix and dynamically corrects the actual power demand prediction values ​​of multiple supercapacitors. The emergency correction layer outputs dynamic braking resistance conversion to the coordination module, and the coordination module adjusts the braking resistance.

10. The lithium battery and supercapacitor management system according to claim 9, characterized in that: The multidimensional influencing factor matrix includes voltage fluctuation and supercapacitor health status prediction values; The direction of electric energy flow is adjusted by setting a composite power switch, wherein the composite power switch includes a main channel, a redundant channel and node monitoring.

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