Off-grid super-capacitor elevator energy-saving one-driving-two regulation and control method and system

Abstract

The invention relates to the technical field of elevator energy conservation and off-grid energy storage regulation and control, in particular to an off-grid super-capacitor elevator energy conservation one-driving-two regulation and control method and system. The real-time operation data set comprises operation parameters of the first elevator, operation parameters of the second elevator, voltage and current of the first direct-current bus, voltage and current of the second direct-current bus and charging and discharging states of the composite energy storage array. By preferentially selecting a direct energy transmission path between a regenerative power generation elevator room and an electric operation elevator room, energy loss caused by two times of energy storage conversion is avoided, and the energy utilization efficiency is improved; based on a multi-dimensional working condition identification model and short-term load prediction, power distribution between two paths of direct-current buses and composite energy storage is accurately and dynamically adjusted, and the dynamic coordination capability is enhanced; through a path switching smooth transition mechanism, voltage oscillation and power impact are avoided.

Description

Technical Field

[0001] This invention relates to the field of elevator energy saving and off-grid energy storage control technology, specifically to an off-grid supercapacitor elevator energy saving one-to-two control method and system. Background Technology

[0002] With the continuous development of elevator energy-saving technologies, supercapacitor energy storage has gradually become a key technology for elevator energy saving in off-grid scenarios due to its high power density and rapid charging and discharging characteristics. However, existing elevator energy-saving devices still have shortcomings in terms of multi-elevator collaborative energy saving, efficient energy scheduling, and system stability. In particular, in the energy management strategy of the "one-to-two" mode (i.e., one energy storage device serves two elevators at the same time), there are problems of efficiency loss and insufficient dynamic coordination capabilities.

[0003] A search revealed an elevator energy-saving device with publication number CN109455605B, published on May 1, 2020. This patent stores the electrical energy of the traction motor in regenerative power generation mode using a supercapacitor and releases the energy for elevator use during motor operation. However, this technical solution is only applicable to a single elevator in a "one-to-one" architecture, lacking an efficient coordination mechanism for the dynamic energy flow of the two DC buses in multi-elevator parallel scenarios. When one elevator is generating power while another is simultaneously consuming power, the energy generated must first be stored in the supercapacitor and then released to the consuming side via the same bidirectional DC-DC module, resulting in two energy conversions (charge-discharge), which causes a certain degree of efficiency loss. Furthermore, this solution does not consider the voltage oscillation problem that may be caused by diode voltage difference and relay operation delay between the two DC buses, further reducing the stability and reliability of the system.

[0004] Furthermore, an elevator energy-saving device with publication number CN110048445B, published on May 25, 2021, achieves energy management for different motors by connecting multiple inverters to a DC bus, and transfers energy to or releases energy from the energy storage device when the DC bus voltage is high or low. However, this technical solution also suffers from a single energy path and insufficient scheduling flexibility in the "one-to-two" mode. Specifically, this solution relies solely on the energy storage device as a transfer node, failing to achieve direct energy scheduling between the two DC buses, resulting in a lengthy and inefficient energy transmission path. In addition, this solution lacks a precise power matching mechanism for real-time coupling of multiple elevator operating conditions, and cannot dynamically adjust the power distribution ratio according to load characteristics, thus limiting the energy-saving effect of the system under complex operating conditions.

[0005] The aforementioned problems indicate that existing elevator energy-saving devices suffer from shortcomings in energy management strategies for "one-to-two" elevator scenarios, including efficiency losses, insufficient dynamic coordination capabilities, and poor system stability. Therefore, this invention provides an off-grid supercapacitor elevator energy-saving one-to-two control method and system. It aims to optimize the energy flow path across multiple elevator shafts through an adaptive energy routing mechanism, a hybrid energy storage scheduling strategy, and an operational status prediction algorithm, thereby improving system efficiency and stability and meeting the needs of collaborative energy saving across multiple elevators in off-grid scenarios. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an off-grid supercapacitor elevator energy-saving one-to-two control method and system, which solves the problems of large energy conversion loss, insufficient dynamic coordination capability and poor system stability compared with existing technologies.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an off-grid supercapacitor elevator energy-saving one-to-two control method, comprising:

[0008] S1. Obtain the real-time operation dataset of two off-grid elevators. The real-time operation dataset includes the operation parameters of the first elevator, the operation parameters of the second elevator, the voltage and current of the first DC bus, the voltage and current of the second DC bus, and the charging and discharging status of the composite energy storage array.

[0009] S2. Based on the real-time operation dataset, construct an elevator operating condition identification model based on multi-dimensional features, and use a short-term load forecasting algorithm to predict the future load changes and energy flow requirements of the two off-grid elevators to obtain predicted operating trend information.

[0010] S3. Based on the predicted operating trend information, the energy difference between the two DC buses is evaluated in real time, and based on the energy difference and the preset scheduling strategy, the optimal energy transmission path is adaptively selected by the intelligent power router. The energy transmission path includes the direct energy transmission path between the first DC bus and the second DC bus, and the transmission path through which energy is converted by the bidirectional DC-DC module via the composite energy storage array.

[0011] S4. Based on the selection of the optimal energy transmission path, dynamically adjust the power distribution ratio between the two DC buses and between the two DC buses and the composite energy storage array to achieve intelligent distribution of energy from the power generation side to the power consumption side.

[0012] S5. Cooperative control of the composite energy storage array, the composite energy storage array includes a supercapacitor and at least one auxiliary energy storage unit. Cooperative control includes allocating the energy storage capacity of the supercapacitor and the auxiliary energy storage unit according to a preset capacity allocation strategy, and designing time-segmented energy storage priority call rules to optimize the charging and discharging management of the energy storage unit.

[0013] S6. When the system experiences sudden operating conditions or failures, formulate an emergency power supply plan and implement a multi-level protection mechanism based on the emergency power supply plan. The multi-level protection mechanism includes realizing adaptive reconfiguration of the system under failure conditions and establishing a system health status assessment system.

[0014] Further, step S1 includes:

[0015] S11. Collect the operating speed, position, load, and power supply voltage of the first elevator and the second elevator through a dynamic monitoring network.

[0016] S12. Collect the voltage and current of the first DC bus and the voltage and current of the second DC bus through a dynamic monitoring network;

[0017] S13. Collect real-time power, state of charge, and health status of the composite energy storage array through a dynamic monitoring network.

[0018] Further, step S2 includes:

[0019] S21. Based on the operating parameters, DC bus status and energy storage status in the real-time operating dataset, construct a multi-dimensional feature vector that includes elevator operating mode, load characteristics and energy supply and demand status.

[0020] S22. Use machine learning algorithms to train the elevator operating condition recognition model to identify the regenerative power generation state, electric operation state, standby state and combined operation state of the two elevators.

[0021] S23. Based on the operating condition identification model, combined with historical load data and external environmental factors, the power demand and energy generation of the two elevators in the future time window are predicted by the short-term load prediction algorithm to obtain the predicted operating trend information.

[0022] Furthermore, the adaptive selection of the optimal energy transmission path in step S3 includes a direct energy transmission path between the first DC bus and the second DC bus, and a transmission path through which energy is converted via the bidirectional DC-DC module and the composite energy storage array, including:

[0023] S31. When one elevator is in regenerative power generation mode and another elevator is in electric operation mode at the same time, the direct energy transmission path shall be selected first to directly transmit the regenerative energy of the power generation elevator to the DC bus of the power consumption elevator.

[0024] S32. When the energy generated on the power generation side is insufficient to meet the demand on the power consumption side, or when both elevators are in power generation mode, or when both elevators are in power consumption mode, the transmission path of energy conversion through the bidirectional DC-DC module and the composite energy storage array is selected.

[0025] S33. Establish a smooth transition mechanism between the direct energy transmission path and the transmission path through which energy is converted via a composite energy storage array using a bidirectional DC-DC module, in order to avoid voltage oscillations or power surges during the switching process.

[0026] Furthermore, step S4 includes:

[0027] S41. Calculate the power deficit or surplus of each elevator based on the predicted operating trend information and real-time load characteristics.

[0028] S42. Based on the calculation results, adjust the conduction state and duty cycle of the power electronic switches in the intelligent power router to precisely control the direction and magnitude of energy flow in the direct energy transmission path;

[0029] S43. Adjust the working mode and conversion ratio of the bidirectional DC-DC module to control the charging and discharging power of the composite energy storage array and ensure that energy flows efficiently to the demand side or storage.

[0030] Furthermore, step S5 includes:

[0031] S51. Based on the instantaneous power demand and long-term energy storage demand of the elevator system, establish a capacity allocation strategy for supercapacitors and auxiliary energy storage units. Supercapacitors should prioritize responding to high-power instantaneous charging and discharging demands, while auxiliary energy storage units should undertake long-term energy storage tasks.

[0032] S52. Design a time-segmented energy storage priority calling rule based on grid electricity price, energy storage unit state of charge and elevator operation prediction, and call different types of energy storage units for charging and discharging according to priority in different time periods.

[0033] Furthermore, the execution of a multi-level protection mechanism in step S6 includes implementing adaptive system reconfiguration under fault conditions and establishing a system health status assessment system, including:

[0034] S61. Monitor DC bus voltage fluctuations, current overload, and abnormal temperature fault parameters of energy storage unit.

[0035] S62. When a fault is detected, the appropriate control strategy is selected from the preset preventive control strategy library according to the fault type and severity, and the intelligent power router, bidirectional DC-DC module and composite energy storage array are adjusted to achieve adaptive reconfiguration of the system operation mode.

[0036] S63. Regularly assess the health status of each energy storage unit in the composite energy storage array, and adjust its operating parameters or perform maintenance based on the assessment results to ensure long-term stable operation of the system.

[0037] This invention also provides an off-grid supercapacitor elevator energy-saving one-to-two control system, applied to any of the above-described off-grid supercapacitor elevator energy-saving one-to-two control methods, comprising:

[0038] The dynamic monitoring network is used to acquire real-time operation datasets of two off-grid elevators. The real-time operation datasets include the operation parameters of the first elevator, the operation parameters of the second elevator, the voltage and current of the first DC bus, the voltage and current of the second DC bus, and the charging and discharging status of the composite energy storage array.

[0039] The distributed energy management unit is used to build an elevator condition identification model based on multi-dimensional features based on real-time operation datasets, and to use short-term load forecasting algorithms to predict future load changes and energy flow demands of two off-grid elevators, thereby obtaining predicted operation trend information.

[0040] The intelligent power router is used to assess the energy difference between the two DC buses in real time based on predicted operating trend information, and adaptively select the optimal energy transmission path based on the energy difference and preset scheduling strategy. The energy transmission path includes the direct energy transmission path between the first DC bus and the second DC bus, as well as the transmission path through which energy is converted via a composite energy storage array through a bidirectional DC-DC module. Based on the selection result of the optimal energy transmission path, the router dynamically adjusts the power distribution ratio between the two DC buses and between the two DC buses and the composite energy storage array to achieve intelligent distribution of energy from the generation side to the consumption side.

[0041] A composite energy storage array, including a supercapacitor and at least one auxiliary energy storage unit, is used for coordinated control. The coordinated control includes allocating the energy storage capacity of the supercapacitor and the auxiliary energy storage unit according to a preset capacity allocation strategy, and designing time-segmented energy storage priority call rules to optimize the charging and discharging management of the energy storage unit.

[0042] The safety protection module is used to formulate an emergency power supply plan when the system experiences sudden operating conditions or failures, and to implement a multi-level protection mechanism based on the emergency power supply plan. The multi-level protection mechanism includes realizing adaptive reconfiguration of the system in the event of a failure, and establishing a system health status assessment system.

[0043] Furthermore, the dynamic monitoring network is used for:

[0044] The operating speed, position, load, and power supply voltage of the first elevator, and the operating speed, position, load, and power supply voltage of the second elevator were collected.

[0045] Collect the voltage and current of the first DC bus and the voltage and current of the second DC bus;

[0046] Collect real-time power, state of charge, and health status of the composite energy storage array;

[0047] The distributed energy management unit is also used for:

[0048] Based on the operating parameters, DC bus status and energy storage status in the real-time operating dataset, a multi-dimensional feature vector is constructed that includes elevator operating mode, load characteristics and energy supply and demand status.

[0049] Machine learning algorithms are used to train an elevator operating condition recognition model to identify the regenerative power generation status, electric operation status, standby status, and combined operation status of two elevators.

[0050] Furthermore, the intelligent power router is also used for:

[0051] When one elevator is in regenerative power generation mode and another elevator is in electric operation mode at the same time, the direct energy transmission path is selected first to directly transmit the regenerative energy of the power generation elevator to the DC bus of the power consumption elevator.

[0052] When the energy generated is insufficient to meet the demand of the power consumption side, or when both elevators are generating or consuming power, the transmission path of energy conversion through the bidirectional DC-DC module and the composite energy storage array is selected.

[0053] A smooth transition mechanism is established between the direct energy transmission path and the transmission path that converts energy through a composite energy storage array via a bidirectional DC-DC module, in order to avoid voltage oscillations or power surges during the switching process.

[0054] The composite energy storage array is also used for:

[0055] Based on the instantaneous power demand and long-term energy storage demand of the elevator system, a capacity allocation strategy for supercapacitors and auxiliary energy storage units is established. Supercapacitors prioritize responding to high-power instantaneous charging and discharging demands, while auxiliary energy storage units undertake long-term energy storage tasks.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] This invention improves energy utilization efficiency by prioritizing the direct energy transfer path between regenerative power generation and electrically operated elevators, avoiding energy losses from two conversions via energy storage. It enhances dynamic coordination by relying on a multi-dimensional operating condition identification model and short-term load forecasting to precisely and dynamically adjust the power distribution between the two DC buses and the composite energy storage. A smooth transition mechanism for path switching avoids voltage oscillations and power surges. The composite energy storage array allocates supercapacitor and auxiliary energy storage capacity according to instantaneous / long-term needs, optimizing energy storage management through time-segmented dispatch rules. Furthermore, multi-layered protection, including fault adaptive reconfiguration and energy storage health assessment, ensures system stability. Ultimately, this improves the efficiency and reliability of off-grid dual-elevator collaborative energy saving, meeting the requirements of off-grid scenarios. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method of the present invention;

[0059] Figure 2 This is a logic diagram for selecting the energy transmission path in this invention;

[0060] Figure 3 This is a flowchart of the collaborative control process for the composite energy storage array of the present invention;

[0061] Figure 4 This is a system structure diagram of the present invention. Detailed Implementation

[0062] 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.

[0063] Please see Figure 1-3 This invention provides an energy-saving one-to-two control method for off-grid supercapacitor elevators, comprising:

[0064] S1. Obtain the real-time operation dataset of two off-grid elevators. The real-time operation dataset includes the operation parameters of the first elevator, the operation parameters of the second elevator, the voltage and current of the first DC bus, the voltage and current of the second DC bus, and the charging and discharging status of the composite energy storage array.

[0065] S2. Based on the real-time operation dataset, construct an elevator operating condition identification model based on multi-dimensional features, and use a short-term load forecasting algorithm to predict the future load changes and energy flow demand of two off-grid elevators to obtain predicted operating trend information.

[0066] S3. Based on the predicted operating trend information, the energy difference between the two DC buses is evaluated in real time. Based on the energy difference and the preset scheduling strategy, the optimal energy transmission path is adaptively selected through the intelligent power router. The energy transmission path includes the direct energy transmission path between the first DC bus and the second DC bus, as well as the transmission path through which energy is converted via a bidirectional DC-DC module and a composite energy storage array.

[0067] S4. Based on the selection of the optimal energy transmission path, dynamically adjust the power distribution ratio between the two DC buses and between the two DC buses and the composite energy storage array to achieve intelligent distribution of energy from the power generation side to the power consumption side.

[0068] S5. Cooperative control of the composite energy storage array, the composite energy storage array includes a supercapacitor and at least one auxiliary energy storage unit. Cooperative control includes allocating the energy storage capacity of the supercapacitor and the auxiliary energy storage unit according to a preset capacity allocation strategy, and designing time-segmented energy storage priority call rules to optimize the charging and discharging management of the energy storage unit.

[0069] S6. When the system experiences sudden operating conditions or failures, formulate an emergency power supply plan and implement a multi-level protection mechanism based on the emergency power supply plan. The multi-level protection mechanism includes realizing adaptive reconfiguration of the system under failure conditions and establishing a system health status assessment system.

[0070] Specifically, in a two-elevator collaborative operation system in an off-grid scenario, the specific steps for implementing the off-grid supercapacitor elevator energy-saving one-to-two control method are as follows:

[0071] Phase S1 data acquisition is accomplished through a dynamic monitoring network. Hall speed sensors are installed at the traction machine shaft ends of the first and second elevators, strain gauge load sensors are arranged on the top of the car, and voltage sensors and Hall current sensors are installed at the DC bus inlet and outlet of each elevator. Power sensors and temperature sensors are connected in series in the charging and discharging circuit of the composite energy storage array. All sensors transmit data to the distributed energy management unit via industrial Ethernet at a frequency of 10kHz, forming a real-time dataset containing elevator operating parameters, bus electrical parameters, and energy storage status.

[0072] Phase S2 involves constructing the operating condition identification model and prediction algorithm. Six basic parameters—elevator operating speed, load, bus voltage, bus current, energy storage power, and state of charge—are extracted from the real-time dataset. These parameters are expanded through feature engineering into a 12-dimensional feature vector including operating acceleration and power fluctuation coefficient. The operating condition identification model is trained using a random forest algorithm, dividing the sample data into a 70% training set and a 30% test set. Optimal model structure with 100 decision trees is obtained through 5-fold cross-validation, enabling the identification of four operating conditions: regenerative power generation, electric power generation, etc. Short-term load forecasting uses the ARIMA(3,1,2) model, with the following formula: ;

[0073] in This represents the power sequence after differencing at time t, where c is a constant term. These are autoregressive parameters. It is a moving average parameter. This is the white noise error term. The model takes historical load data from the previous 24 hours as input and outputs the power demand forecast for the next 5 minutes.

[0074] Phase S3 involves energy difference assessment and path selection. The energy difference calculation uses per-unit value normalization. ;

[0075] In the formula and These are the real-time energy values ​​of the two DC buses, respectively. and For the corresponding rated energy value, This represents the normalized energy difference. When one elevator is in regenerative braking mode while the other is in electric operation mode... When the smart power router connects to the direct energy transmission path; when If two elevators are operating under the same conditions, the system switches to the transmission path via the composite energy storage array. A duty cycle gradient algorithm is used during path switching. ;

[0076] in Let t be the duty cycle of the switch. and These are the initial and target duty cycles, respectively, and k is the adjustment coefficient. This mechanism enables a shock-free transition.

[0077] Phase S4 dynamically adjusts power allocation. Power deficit is calculated based on predicted trends. When selecting the direct path, adjust the on-time of the IGBTs in the smart power router to increase the transmission power. Precisely match the missing amount, among which Bus voltage D represents the duty cycle, and D is the current transfer factor. When using a combined energy storage path, the conversion ratio of the bidirectional DC-DC module is set to... This ensures that efficiency is maximized when energy flows in both directions.

[0078] Phase S5 implements a combined energy storage and synergistic control system. A combined energy storage array is formed by supercapacitors and lithium iron phosphate batteries, with capacity allocation based on: ;

[0079] in and The power distribution is between the supercapacitor and the battery, respectively. The total power requirement is represented by k, which is a dynamic coefficient. When the instantaneous power fluctuation exceeds 20% of the rated power, k is set to 0.8 to leverage the high power advantage of the supercapacitor; during steady-state operation, k is set to 0.2 to extend battery life. The time-segmented dispatch rule prioritizes supercapacitor discharge during weekday morning peak hours, while excess energy is stored in the battery during off-peak hours at night.

[0080] Phase S6 implements a multi-level protection mechanism. It monitors bus voltage fluctuations in real time. Current overload factor and energy storage temperature When any parameter exceeds a threshold, the corresponding scheme is called from the control strategy library. For example, when the voltage fluctuation rate exceeds 5%, the bus voltage stabilization algorithm is triggered; when the temperature exceeds 60℃, the energy storage power is automatically reduced. Health status assessment uses: ;

[0081] in For the current capacity, The system operates at its initial capacity and issues a maintenance warning when the State of Health (SOH) falls below 80%. Through these steps, the system reduces energy conversion steps, enhances dynamic coordination capabilities, and improves operational stability.

[0082] In this embodiment, step S1 includes:

[0083] S11. Collect the operating speed, position, load, and power supply voltage of the first elevator and the second elevator through a dynamic monitoring network.

[0084] S12. Collect the voltage and current of the first DC bus and the voltage and current of the second DC bus through a dynamic monitoring network;

[0085] S13. Collect real-time power, state of charge, and health status of the composite energy storage array through a dynamic monitoring network.

[0086] Specifically, in step S11, the dynamic monitoring network consists of a three-layer acquisition structure. The strain gauge load sensor installed on the top of the elevator car employs a full-bridge circuit design, covering a measurement range from 0 to 120% of the rated load. The output signal is converted into a digital quantity by a 24-bit AD converter. The speed sensor uses a non-contact Hall element, installed on the shaft end of the traction machine's tachogenerator, generating 1024 pulse signals per revolution, and calculating the running speed using a counting method. Position information is acquired through the encoder signal of the elevator control system, with a sampling interval of 10ms. Power supply voltage monitoring uses a differential amplifier circuit to measure the effective value of the 380V AC input.

[0087] Step S12 is for DC bus monitoring. The voltage sensor uses a combination of a voltage divider resistor network and an isolation amplifier, with a measurement range of 0 to 800V and an accuracy of 0.5%. The current sensor uses the closed-loop Hall effect principle and can measure DC current of ±500A with a linearity error of less than 0.1%. The output signals of both types of sensors are transmitted to the data acquisition card through shielded cables to ensure measurement accuracy in the strong electromagnetic environment of the elevator.

[0088] In step S13, the real-time power of the composite energy storage array is measured by a high-precision shunt connected in series in the main circuit, and the voltage drop is obtained using a differential amplifier to calculate the real-time power. State of charge monitoring employs current integration combined with open-circuit voltage calibration, performing calibration hourly to correct accumulated errors. Health status is monitored by collecting parameters such as cell voltage, temperature, and cycle count through the built-in battery management system, which are then evaluated using a dedicated algorithm and uploaded to the monitoring network. All collected data is synchronized via timestamps to ensure temporal consistency of the dataset.

[0089] In this embodiment, step S2 includes:

[0090] S21. Based on the operating parameters, DC bus status and energy storage status in the real-time operating dataset, construct a multi-dimensional feature vector that includes elevator operating mode, load characteristics and energy supply and demand status.

[0091] S22. Use machine learning algorithms to train an elevator operating condition recognition model to identify the regenerative power generation status, electric operation status, standby status and combined operation status of the two elevators.

[0092] S23. Based on the operating condition identification model, combined with historical load data and external environmental factors, the power demand and energy generation of the two elevators in the future time window are predicted by the short-term load prediction algorithm to obtain the predicted operating trend information.

[0093] Specifically, in step S21, when constructing the multi-dimensional feature vector, the following feature parameters are extracted from the real-time data: elevator operating speed and its first derivative, the ratio of car load to rated load, the deviation rate of DC bus voltage from rated voltage, the effective value of bus current, the charging and discharging power and state of charge of the energy storage array, and the power change between adjacent sampling periods. These features are normalized to form a 12-dimensional feature vector, where the speed derivative is calculated using the five-point difference method, and the power change is smoothed using the sliding window averaging method.

[0094] The training process for the operating condition identification model in step S22 is as follows: 12 hours of typical workday operating data are selected as samples, encompassing complete operating conditions including different floor combinations, load distributions, and operating modes. The sample data is labeled with four categories: regenerative power generation, electric operation, standby, and combined operation. A random forest algorithm is used for training. The model's input layer is a 12-dimensional feature vector, and the output layer is the probability distribution of the four operating conditions. By adjusting the number and depth parameters of the decision trees, the accuracy on the validation set reaches over 95%. The trained model is deployed in the edge computing module of the distributed energy management unit, achieving an operating condition identification response within 50ms.

[0095] Step S23's short-term load forecasting uses the operating condition identification results as one of the input conditions, combining historical load data and external factors for prediction. Historical data uses the load curves for the same period over the past 7 days, and external factors include date type and time period characteristics. The forecasting algorithm employs a rolling forecasting mechanism, updating the forecast results every 5 minutes, with the forecast time window set to the next 30 minutes. The output of the forecasting model is the power demand curves and energy generation curves for two elevators, with the curves spaced at 1-minute intervals, providing accurate timing data for energy dispatching. By controlling the forecasting error to within 8%, the foresight of energy dispatching is effectively improved.

[0096] In this embodiment, the adaptive selection of the optimal energy transmission path in step S3 includes a direct energy transmission path between the first DC bus and the second DC bus, and a transmission path through which energy is converted via a bidirectional DC-DC module and a composite energy storage array, including:

[0097] S31. When one elevator is in regenerative power generation mode and another elevator is in electric operation mode at the same time, the direct energy transmission path shall be selected first to directly transmit the regenerative energy of the power generation elevator to the DC bus of the power consumption elevator.

[0098] S32. When the energy generated on the power generation side is insufficient to meet the demand on the power consumption side, or when both elevators are in power generation mode, or when both elevators are in power consumption mode, the transmission path of energy conversion through the bidirectional DC-DC module and the composite energy storage array is selected.

[0099] S33. Establish a smooth transition mechanism between the direct energy transmission path and the transmission path through which energy is converted via a composite energy storage array using a bidirectional DC-DC module, in order to avoid voltage oscillations or power surges during the switching process.

[0100] Specifically, the direct energy transfer path in step S31 adopts a bidirectional DC-DC converter topology based on IGBTs. When the first elevator is in regenerative power generation mode and the second elevator is in electric operation mode, the controller of the intelligent power router receives the operating condition identification signal and closes the fast relay of the corresponding path within 10ms, simultaneously triggering the IGBT drive signal. During energy transfer, a dual closed-loop control strategy of voltage outer loop and current inner loop is adopted. The outer loop reference voltage is set to the rated voltage of the power consumption side bus, and the inner loop current reference value is dynamically adjusted according to power demand, ensuring that the regenerative energy from the power generation side is directly absorbed by the power consumption side, reducing energy conversion stages.

[0101] The composite energy storage transmission path in step S32 is activated in the following scenarios: when the regenerative energy on the power generation side can only meet less than 70% of the power demand on the consumption side, the shortfall is supplemented by the composite energy storage array; when both elevators are in regenerative power generation mode, excess energy is stored in the energy storage array through the bidirectional DC-DC module; when both elevators are in electric operation mode, the energy storage array supplies power to both buses through the DC-DC module. The bidirectional DC-DC module adopts a phase-shifted full-bridge topology, and the conversion efficiency remains stable within the load range of 85% to 96%, ensuring minimal energy conversion loss.

[0102] The smooth transition mechanism of step S33 is achieved through soft-switching technology. During path switching, the transmission power of the current path is first reduced exponentially, while the transmission power of the target path is increased according to the same curve, keeping the sum of the two powers constant. During the switching process, bus voltage fluctuations are compensated by an additional PI regulator, whose proportional coefficient and integral time constant are dynamically adjusted according to the voltage deviation. For example, when switching from a direct path to an energy storage path, the switching time is controlled within 50ms, and the bus voltage fluctuation amplitude is controlled within ±3% of the rated value, effectively avoiding the voltage oscillation problem caused by traditional hard switching.

[0103] In this embodiment, step S4 includes:

[0104] S41. Calculate the power deficit or surplus of each elevator based on the predicted operating trend information and real-time load characteristics.

[0105] S42. Based on the calculation results, adjust the conduction state and duty cycle of the power electronic switches in the intelligent power router to precisely control the direction and magnitude of energy flow in the direct energy transmission path;

[0106] S43. Adjust the working mode and conversion ratio of the bidirectional DC-DC module to control the charging and discharging power of the composite energy storage array and ensure that energy flows efficiently to the demand side or storage.

[0107] Specifically, the power deficit or surplus calculation in step S41 is based on real-time monitoring data and predicted trends, and the calculation formula is as follows: ;

[0108] in Let represent the power difference of the i-th elevator, where a positive value indicates a shortage and a negative value indicates a surplus. and These represent the elevator's required power and supplied power, respectively. During calculation, power values ​​at different times must be uniformly converted to the same time base to ensure dimensional consistency. For the combined operation of two elevators, the total power difference is the algebraic sum of the two elevators, providing a basis for power allocation.

[0109] Step S42 achieves power control by adjusting the switching state of the intelligent power router. The IGBT in the direct path uses PWM control, and its duty cycle is determined by the following formula:

[0110] in For the target transmission power, This refers to the voltage of the high-voltage side bus. The maximum allowable current is set. The controller dynamically adjusts the duty cycle based on real-time power feedback, with an adjustment period of 100μs, ensuring that the steady-state error of the transmitted power is less than 2%. Simultaneously, overcurrent and short-circuit protection are achieved by monitoring the on-state voltage drop of the switching transistor.

[0111] The bidirectional DC-DC module in step S43 operates under logic signal control. In charging mode, power flows from the bus to the energy storage array, and vice versa in discharging mode. The conversion ratio is dynamically adjusted based on the bus voltage and the energy storage voltage. ;

[0112] in For the target side voltage, This represents the source-side voltage. The module's output power is controlled via a current loop, with the current reference value calculated based on power requirements and the target voltage. During charge / discharge switching, dead-time control is employed to prevent shoot-through between upper and lower bridge arms. The dead-time is set to 5μs to ensure safe and reliable module operation. Through these controls, the accuracy of energy distribution across different paths is effectively guaranteed.

[0113] In this embodiment, step S5 includes:

[0114] S51. Based on the instantaneous power demand and long-term energy storage demand of the elevator system, establish a capacity allocation strategy for supercapacitors and auxiliary energy storage units. Supercapacitors should prioritize responding to high-power instantaneous charging and discharging demands, while auxiliary energy storage units should undertake long-term energy storage tasks.

[0115] S52. Design a time-segmented energy storage priority calling rule based on grid electricity price, energy storage unit state of charge and elevator operation prediction, and call different types of energy storage units for charging and discharging according to priority in different time periods.

[0116] Specifically, the capacity allocation strategy in step S51 is based on the time characteristics of power demand. The supercapacitor's capacity design satisfies: ;

[0117] in This refers to the capacitance of the supercapacitor. For the instantaneous energy that needs to be stored, and These are the highest and lowest operating voltages, respectively. The auxiliary energy storage unit uses lithium iron phosphate batteries, and its capacity is calculated based on the average daily energy demand. ;

[0118] In the formula For battery capacity, To store energy on average per day, Where is the battery operating voltage, and DOD is the depth of discharge factor. In practical applications, supercapacitors prioritize responding to instantaneous power fluctuations within 10 seconds, while batteries handle continuous power demands exceeding 1 minute, forming a complementary energy storage system.

[0119] The time-segmented energy storage priority rules in step S52 are divided according to daily time periods. 7:00-9:00 and 17:00-19:00 on weekdays are designated as peak periods, during which the supercapacitor's charging and discharging has the highest priority, ensuring rapid response to frequent elevator start-stop demands. During the off-peak period from 12:00-14:00, the priority is adjusted to battery priority, utilizing load gaps to charge the battery. During the off-peak period from 22:00 to 6:00 the next day, supplementary charging is only performed when the energy storage's state of charge is below 20%. Priority adjustments are achieved through weighting coefficients, which are optimized and determined based on historical data to maximize the cycle life of the energy storage units.

[0120] In this embodiment, the execution of a multi-level protection mechanism in step S6 includes implementing adaptive system reconfiguration under fault conditions and establishing a system health status assessment system, including:

[0121] S61. Monitor fault parameters such as voltage fluctuations, current overloads, and abnormal temperatures of the energy storage unit on the DC bus.

[0122] S62. When a fault is detected, the appropriate control strategy is selected from the preset preventive control strategy library according to the fault type and severity, and the intelligent power router, bidirectional DC-DC module and composite energy storage array are adjusted to achieve adaptive reconfiguration of the system operation mode.

[0123] S63. Regularly assess the health status of each energy storage unit in the composite energy storage array, and adjust its operating parameters or perform maintenance based on the assessment results to ensure long-term stable operation of the system.

[0124] Specifically, the fault parameter monitoring in step S61 employs a multi-level sampling mechanism. The DC bus voltage is monitored for fluctuations through high-frequency sampling at 1000 times per second, and the voltage fluctuation rate is calculated using a sliding window algorithm. ;

[0125] in For sampling voltage, The value is the average value within the window, and N is the window length. Current overload monitoring uses a threshold comparison method, setting 1.2 times the rated current as the warning value and 1.5 times as the protection action value. The energy storage unit temperature is measured by the built-in NTC thermometer with a sampling interval of 1 second, while simultaneously monitoring the temperature change rate. An alarm is triggered when the change rate exceeds 5℃ / minute.

[0126] The adaptive reconfiguration mechanism in step S62 includes a three-level response strategy. For minor faults, such as voltage fluctuations exceeding 5% but less than 10%, the system compensates by adjusting the transmission power of the power router. For moderate faults, such as abnormal temperature in a single energy storage unit, the system automatically disconnects that unit and transfers its load to other units. For severe faults, such as bus short circuits or continuous current overload, the main contactor is immediately triggered to disconnect, and the backup power supply is activated to maintain emergency elevator operation. The reconfiguration strategy is determined through fault tree analysis, with each fault type corresponding to a preset processing flow, ensuring a response time of less than 100ms.

[0127] The health status assessment in step S63 is performed periodically, and the health status of the supercapacitor is assessed through the capacity decay rate. ;

[0128] The health of a battery is determined by a combination of capacity decay and internal resistance increase. ;

[0129] The evaluation cycle is set to once a week. When the State of Health (SOH) is below 80%, the system automatically adjusts the charging and discharging parameters, such as reducing the maximum charging and discharging current; when the SOH is below 70%, a maintenance prompt is issued. Through continuous monitoring of the health status and parameter adjustment, the long-term operational reliability of the system is effectively guaranteed.

[0130] Please see Figure 4The present invention also provides an off-grid supercapacitor elevator energy-saving one-to-two control system, comprising:

[0131] The dynamic monitoring network is used to acquire real-time operation datasets of two off-grid elevators. The real-time operation datasets include the operation parameters of the first elevator, the operation parameters of the second elevator, the voltage and current of the first DC bus, the voltage and current of the second DC bus, and the charging and discharging status of the composite energy storage array.

[0132] The distributed energy management unit is used to build an elevator condition identification model based on multi-dimensional features based on real-time operation datasets, and to use short-term load forecasting algorithms to predict future load changes and energy flow demands of two off-grid elevators, thereby obtaining predicted operation trend information.

[0133] The intelligent power router is used to assess the energy difference between the two DC buses in real time based on predicted operating trend information, and adaptively select the optimal energy transmission path based on the energy difference and preset scheduling strategy. The energy transmission path includes the direct energy transmission path between the first DC bus and the second DC bus, as well as the transmission path through which energy is converted via a composite energy storage array through a bidirectional DC-DC module. Based on the selection result of the optimal energy transmission path, the router dynamically adjusts the power distribution ratio between the two DC buses and between the two DC buses and the composite energy storage array to achieve intelligent distribution of energy from the generation side to the consumption side.

[0134] A composite energy storage array, including a supercapacitor and at least one auxiliary energy storage unit, is used for coordinated control. The coordinated control includes allocating the energy storage capacity of the supercapacitor and the auxiliary energy storage unit according to a preset capacity allocation strategy, and designing time-segmented energy storage priority call rules to optimize the charging and discharging management of the energy storage unit.

[0135] The safety protection module is used to formulate an emergency power supply plan when the system experiences sudden operating conditions or failures, and to implement a multi-level protection mechanism based on the emergency power supply plan. The multi-level protection mechanism includes realizing adaptive reconfiguration of the system in the event of a failure, and establishing a system health status assessment system.

[0136] Specifically, the dynamic monitoring network adopts a distributed architecture, consisting of sensor nodes installed in the elevator machine room and shaft, a data aggregation unit, and communication links. The sensor nodes include speed, load, voltage, current, and temperature sensors, and feature a modular design for easy installation and maintenance. The data aggregation unit uses an industrial-grade microprocessor, supporting multi-protocol data acquisition and edge computing preprocessing. Data is transmitted to the central controller via dual communication links—Ethernet and wireless backup—ensuring real-time performance and reliability.

[0137] The distributed energy management unit, centered on an embedded processor, integrates data storage, model training, and predictive analytics. The unit has a built-in 128GB solid-state drive for storing historical operational data and utilizes a GPU-accelerated module to run machine learning algorithms, enabling localized processing of operating condition identification and load forecasting. The unit supports OTA (Over-The-Air) remote upgrades, allowing for model parameter optimization based on application scenarios. It also features data encryption and access control to ensure the security of operational data.

[0138] The intelligent power router adopts a power electronic conversion topology, including two independent DC bus interfaces and a composite energy storage connection interface. Internally, the router integrates a 32-bit DSP controller and an FPGA logic unit. The DSP is responsible for executing the power control algorithm, while the FPGA generates high-speed switching signals. Together, they achieve microsecond-level path switching and power regulation. The router supports both CAN and Ethernet communication interfaces, and can receive control commands from the energy management unit in real time and provide feedback on its operating status.

[0139] The composite energy storage array consists of supercapacitor modules and auxiliary energy storage units, both connected to a common DC bus via their respective bidirectional DC-DC converters. The supercapacitor modules employ a series-parallel configuration, with each module having a capacity of 50F and an operating voltage range of 200-450V. The auxiliary energy storage units utilize lithium iron phosphate battery packs with a rated voltage of 384V and a capacity of 100Ah. The array is equipped with a dedicated battery management system to provide charge / discharge protection, equalization control, and state monitoring functions.

[0140] The safety protection module incorporates a two-tiered mechanism of hardware and software protection. At the hardware level, it is equipped with fast-acting fuses, DC circuit breakers, and surge protectors to prevent damage from overcurrent and overvoltage. At the software level, it runs fault diagnosis algorithms and emergency control strategies, proactively identifying potential faults and triggering preventative protection measures by monitoring system status parameters in real time. The module also integrates a human-machine interface that displays system operating status and fault alarm information, supporting remote fault diagnosis and maintenance.

[0141] In this embodiment, the dynamic monitoring network is used for:

[0142] The operating speed, position, load, and power supply voltage of the first elevator, and the operating speed, position, load, and power supply voltage of the second elevator were collected.

[0143] Collect the voltage and current of the first DC bus and the voltage and current of the second DC bus;

[0144] Collect real-time power, state of charge, and health status of the composite energy storage array;

[0145] The distributed energy management unit is also used for:

[0146] Based on the operating parameters, DC bus status and energy storage status in the real-time operating dataset, a multi-dimensional feature vector is constructed that includes elevator operating mode, load characteristics and energy supply and demand status.

[0147] Machine learning algorithms are used to train an elevator operating condition recognition model to identify the regenerative power generation status, electric operation status, standby status, and combined operation status of two elevators.

[0148] Specifically, the dynamic monitoring network collects elevator parameters covering key indicators of operational status. Speed ​​acquisition is achieved through traction machine encoder signals, generating 2048 pulses per revolution, which, after quadrupling the frequency, achieves a speed resolution of 0.01 m / s. Position information is determined by combining floor sensors and encoder pulse counting, achieving a floor positioning accuracy of ±5 mm. Load measurement utilizes strain sensors mounted on the car frame, with a measurement range of 0-1600 kg and an accuracy of ±1%FS. Power supply voltage monitoring targets the three-phase AC voltage of the elevator main circuit, employing a true RMS measurement method to ensure measurement accuracy even with voltage waveform distortion.

[0149] DC bus monitoring employs voltage sensors installed between the positive and negative busbars, utilizing isolation amplification technology to achieve high-low voltage isolation. With a measurement bandwidth of DC-10kHz, it can capture transient fluctuations in bus voltage. Current monitoring uses a through-hole Hall effect sensor, which can be installed without disconnecting the main circuit. It has a measurement range of ±600A and a response time of less than 1μs, accurately capturing the inrush current during elevator start-up and shutdown. Voltage and current signals are synchronously sampled to calculate active and reactive power, providing data support for energy flow analysis.

[0150] Composite energy storage monitoring includes three types of parameters: real-time power, state of charge (SOC), and state of health. Power monitoring is achieved through a high-precision shunt connected in series in the energy storage circuit, with a measurement accuracy of 0.2%. SOC is calculated using current integration combined with an open-circuit voltage correction algorithm, and is automatically calibrated hourly. State of health assessment is achieved by monitoring the internal resistance, capacity decay, and cycle count of the energy storage unit, with a data storage period of 1 minute, allowing for the tracking of long-term health trends. All monitoring data is timestamped to ensure time synchronization of multi-source data.

[0151] The feature vector construction module of the distributed energy management unit extracts effective features from the raw data and performs dimensionless processing on physical quantities such as velocity and load to form a standardized feature space. The machine learning training module supports various algorithms such as random forest and neural networks, and adopts an incremental learning mechanism to optimize model performance based on new operating data. The operating condition identification results are output as digital signals with an update period of 100ms, providing real-time operating condition information for energy dispatch.

[0152] In this embodiment, the intelligent power router is also used for:

[0153] When one elevator is in regenerative power generation mode and another elevator is in electric operation mode at the same time, the direct energy transmission path is selected first to directly transmit the regenerative energy of the power generation elevator to the DC bus of the power consumption elevator.

[0154] When the energy generated is insufficient to meet the demand of the power consumption side, or when both elevators are generating or consuming power, the transmission path of energy conversion through the bidirectional DC-DC module and the composite energy storage array is selected.

[0155] A smooth transition mechanism is established between the direct energy transmission path and the transmission path that converts energy through a composite energy storage array via a bidirectional DC-DC module, in order to avoid voltage oscillations or power surges during the switching process.

[0156] Composite energy storage arrays are also used for:

[0157] Based on the instantaneous power demand and long-term energy storage demand of the elevator system, a capacity allocation strategy for supercapacitors and auxiliary energy storage units is established. Supercapacitors prioritize responding to high-power instantaneous charging and discharging demands, while auxiliary energy storage units undertake long-term energy storage tasks.

[0158] Specifically, the direct energy transmission path of the intelligent power router is designed as a bidirectional controllable topology. When the first elevator is in regenerative power generation mode and the second elevator is in electric operation mode, the contactors and IGBT modules inside the router work together to form a low-impedance path from the power generation bus to the power consumption bus. Before the path is connected, the voltage difference between the two buses is detected. The main switch is only allowed to close when the voltage difference is less than 10% of the rated voltage to avoid inrush current. During energy transmission, closed-loop control maintains stable power transmission, with a power adjustment range of 0-50kW and a response time of less than 50ms.

[0159] The composite energy storage transmission path is automatically activated under specific operating conditions. When the power generation side can only meet less than 70% of the power consumption side's demand, the router controls the energy storage array to discharge and supplement the difference in power. When both elevators are in power generation mode, causing the bus voltage to rise, the energy storage charging process is initiated to absorb excess energy. When both elevators are simultaneously in electric operation mode, the energy storage array discharges to support bus voltage stability. The energy storage charging and discharging power is precisely controlled through PWM duty cycle, with a minimum adjustment step of 100W, ensuring smooth power changes.

[0160] The smooth transition mechanism employs a power ramp control strategy. During path switching, the transmitted power changes according to a preset ramp, which is automatically adjusted based on the current power level. A smaller ramp is used at higher power levels to reduce impact. During the switching process, the bus voltage is regulated through an additional compensation algorithm. When a voltage fluctuation exceeds a threshold, the compensation amount is automatically increased to suppress the fluctuation. In practical applications, the entire process of switching from the direct path to the energy storage path is completed within 100ms, and the bus voltage fluctuation can be controlled within 5%.

[0161] The capacity allocation of the composite energy storage array is optimized based on the elevator's operating characteristics. The supercapacitor is designed to meet peak power demands within 30 seconds and employs a modular design for easy expansion. Each module includes voltage balancing and temperature monitoring functions. The auxiliary energy storage unit is configured with a capacity 1.5 times the average daily energy consumption and uses long-cycle-life lithium iron phosphate batteries that support deep discharge to 20% without affecting cycle life. The two energy storage units work collaboratively through energy management algorithms: the supercapacitor handles instantaneous power fluctuations, while the battery provides continuous energy support, forming a highly efficient and complementary energy storage system.

[0162] In summary, this invention improves energy utilization efficiency by prioritizing the direct energy transfer path between regenerative power generation and electric elevator operation, avoiding energy losses through two conversions via energy storage. It enhances dynamic coordination by relying on a multi-dimensional operating condition identification model and short-term load forecasting to precisely and dynamically adjust the power distribution between the two DC buses and the composite energy storage. A smooth transition mechanism for path switching avoids voltage oscillations and power surges. The composite energy storage array allocates supercapacitor and auxiliary energy storage capacity according to instantaneous / long-term needs, optimizing energy storage management through time-segmented call rules. Furthermore, multi-layered protection, including fault adaptive reconfiguration and energy storage health assessment, ensures system stability. Ultimately, this invention improves the efficiency and reliability of off-grid dual-elevator collaborative energy saving, meeting the needs of off-grid scenarios.

[0163] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0164] 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. Off-grid super capacitor elevator energy-saving one-drag-two regulation method, characterized in that, The method comprises the following steps: S1, acquiring real-time operation data sets of two off-grid elevators, the real-time operation data sets comprising operation parameters of a first elevator, operation parameters of a second elevator, voltage and current of a first DC bus, voltage and current of a second DC bus, and charge and discharge states of a composite energy storage array; S2, based on the real-time operation data sets, constructing an elevator working condition recognition model based on multi-dimensional features, and predicting future load changes and energy flow requirements of the two off-grid elevators by using a short-term load prediction algorithm to obtain predicted operation trend information; S3, according to the predicted operation trend information, real-time evaluating the energy difference between the two DC buses, and based on the energy difference and a preset scheduling strategy, selecting an optimal energy transmission path by an intelligent power router, the energy transmission path comprising a direct energy transmission path between the first DC bus and the second DC bus, and a transmission path for energy conversion through the composite energy storage array by a bidirectional DC / DC module; S4, according to the selection result of the optimal energy transmission path, dynamically adjusting the power distribution ratio of energy between the two DC buses and between the two DC buses and the composite energy storage array, so as to realize intelligent distribution of energy from the power generation side to the power consumption side; S5, cooperatively controlling the composite energy storage array, the composite energy storage array comprising a super capacitor and at least one auxiliary energy storage unit, the cooperative control comprising distributing the energy storage capacity of the super capacitor and the auxiliary energy storage unit according to a preset capacity distribution strategy, and designing a time-periodic energy storage priority calling rule to optimize the charge and discharge management of the energy storage unit; S6, when a sudden working condition or fault occurs in the system, formulating an emergency power supply scheme, and executing a multi-level protection mechanism based on the emergency power supply scheme, the multi-level protection mechanism comprising realizing adaptive reconstruction of the system under fault conditions, and establishing a system health state evaluation system.

2. The off-grid supercapacitor elevator energy-saving one-drag-two control method according to claim 1, characterized in that, In the S1 step, the following steps are included: S11, acquiring the running speed, position, load and power voltage of the first elevator, and the running speed, position, load and power voltage of the second elevator through dynamic monitoring network; S12, acquiring the voltage and current of the first DC bus, and the voltage and current of the second DC bus through dynamic monitoring network; S13, acquiring the real-time power, state of charge and health state of the composite energy storage array through dynamic monitoring network.

3. The off-grid supercapacitor elevator energy-saving one-drag-two control method according to claim 2, characterized in that, In the S2 step, the following steps are included: S21, based on the operation parameters, DC bus states and energy storage states in the real-time operation data sets, constructing a multi-dimensional feature vector comprising elevator operation mode, load characteristics and energy supply and demand state; S22, training the elevator working condition recognition model by using a machine learning algorithm to identify the regenerative power generation state, electric operation state, standby state and combined operation state of the two elevators; S23, based on the working condition recognition model, combining historical load data and external environmental factors, predicting the power demand and energy generation of the two elevators within a future time window by using a short-term load prediction algorithm to obtain the predicted operation trend information.

4. The off-grid supercapacitor elevator energy-saving one-drag-two control method according to claim 3, characterized in that, The adaptive selection in the S3 step optimizes the energy transmission path, which includes a direct energy transmission path between the first DC bus and the second DC bus, and a transmission path through the composite energy storage array for energy conversion by the bidirectional DC / DC module, including: S31, when one elevator is in a regenerative power generation state and the other elevator is in a motor operation state at the same time, the direct energy transmission path is preferentially selected, and the regenerative energy of the power generation side elevator is directly transmitted to the DC bus of the power consumption side elevator; S32, when the power generation side energy is insufficient to meet the power consumption side demand, or both elevators are in a power generation state, or both elevators are in a power consumption state, the transmission path through the composite energy storage array for energy conversion by the bidirectional DC / DC module is selected; S33, between the direct energy transmission path and the transmission path through the composite energy storage array for energy conversion by the bidirectional DC / DC module, the path switching transition is realized by the way of duty cycle gradual change adjustment and voltage fluctuation compensation, so as to avoid voltage oscillation or power impact in the switching process.

5. The off-grid supercapacitor elevator energy-saving one-drag-two control method according to claim 4, characterized in that, In the S4 step, including: S41, according to the predicted operation trend information and the real-time load characteristics, the power shortage or excess of each elevator is calculated; S42, according to the calculation result, the conduction state and duty cycle of the power electronic switch in the intelligent power router are adjusted to control the flow direction and size of energy on the direct energy transmission path; S43, the working mode and conversion ratio of the bidirectional DC / DC module are adjusted to control the charge and discharge power of the composite energy storage array, so that the energy flows to the demand side or is stored.

6. The off-grid supercapacitor elevator energy-saving one-drag-two control method according to claim 5, characterized in that, In the S5 step, including: S51, according to the instantaneous power demand and continuous energy storage demand of the elevator system, a capacity allocation strategy of super capacitor and auxiliary energy storage unit is established, the super capacitor preferentially responds to the charge and discharge demand corresponding to the instantaneous power fluctuation, and the auxiliary energy storage unit undertakes the energy storage task corresponding to the continuous energy storage demand; S52, design time-of-use energy storage priority calling rules based on power grid price, state of charge of energy storage unit and elevator operation prediction, and in different time periods, different types of energy storage units are called for charging and discharging according to priority.

7. The off-grid supercapacitor elevator energy-saving twin-drive control method according to claim 6, characterized in that, The S6 step executes a multi-level protection mechanism, which includes realizing system adaptive reconstruction in fault condition, and establishing a system health state evaluation system, including: S61, monitor the voltage fluctuation, current overload and abnormal fault parameters of the temperature of the energy storage unit of the DC bus; S62, when a fault is detected, according to the fault type and severity, select the corresponding control strategy from the preset preventive control strategy library, adjust the intelligent power router, bidirectional DC / DC module and composite energy storage array to realize adaptive reconstruction of system operation mode; S63, periodically evaluate the health state of each energy storage unit of the composite energy storage array, and adjust its operation parameters or perform maintenance according to the evaluation result.

8. The off-grid supercapacitor elevator energy-saving one-to-two control system applied to the off-grid supercapacitor elevator energy-saving one-to-two control method of any one of claims 1-7, characterized in that, Including: The dynamic monitoring network is used to obtain real-time operation data sets of the two off-grid elevators, and the real-time operation data sets include operation parameters of the first elevator, operation parameters of the second elevator, voltage and current of the first DC bus, voltage and current of the second DC bus, and charge and discharge states of the composite energy storage array. The distributed energy management unit is used to construct an elevator working condition recognition model based on multi-dimensional features based on the real-time operation data sets, and predict future load changes and energy flow requirements of the two off-grid elevators by using a short-term load prediction algorithm, to obtain predicted operation trend information. The intelligent power router is used to real-time evaluate energy differences between the two DC buses according to the predicted operation trend information, and adaptively select an optimal energy transmission path based on the energy differences and a preset scheduling strategy, the energy transmission path including a direct energy transmission path between the first DC bus and the second DC bus, and a transmission path for energy conversion through the composite energy storage array via the bidirectional DC / DC module, and dynamically adjust power distribution ratios of energy between the two DC buses and between the two DC buses and the composite energy storage array according to a selection result of the optimal energy transmission path, to realize intelligent distribution of power generation side energy to power consumption side. The composite energy storage array includes a super capacitor and at least one auxiliary energy storage unit, and is used for cooperative control, which includes allocating energy storage capacities of the super capacitor and the auxiliary energy storage unit according to a preset capacity allocation strategy, and designing time-periodic energy storage priority calling rules to optimize charge and discharge management of the energy storage units. The safety protection module is used to formulate an emergency power supply scheme when a sudden working condition or a fault occurs in the system, and execute a multi-level protection mechanism based on the emergency power supply scheme, the multi-level protection mechanism including realizing adaptive reconstruction of the system in a fault condition, and establishing a system health state evaluation system.

9. The off-grid supercapacitor elevator energy-saving twin-drive control system according to claim 8, characterized in that, The dynamic monitoring network is used to: collect running speed, position, load and power voltage of the first elevator, and running speed, position, load and power voltage of the second elevator; collect voltage and current of the first DC bus, and voltage and current of the second DC bus; collect real-time power, state of charge and health state of the composite energy storage array; The distributed energy management unit is further used to: construct a multi-dimensional feature vector including elevator operation mode, load characteristics and energy supply and demand state based on operation parameters, DC bus states and energy storage states in the real-time operation data sets; train the elevator working condition recognition model by using a machine learning algorithm to identify regenerative power generation state, electric operation state, standby state and combined operation state of the two elevators.

10. The off-grid supercapacitor elevator energy-saving twin-drive control system according to claim 9, characterized in that, The intelligent power router is further used to: when one elevator is in a regenerative power generation state and the other elevator is in an electric operation state at the same time, preferentially select a direct energy transmission path to transmit regenerative energy of the power generation side elevator to the DC bus of the power consumption side elevator; when power generation side energy is insufficient to meet power consumption side demand, or both elevators are in a power generation state, or both elevators are in a power consumption state, select a transmission path for energy conversion through the composite energy storage array via the bidirectional DC / DC module. In the direct energy transmission path and the transmission path through the composite energy storage array for energy conversion by the bidirectional DCDC module, the path switching transition is realized by the duty ratio gradual adjustment and the voltage fluctuation compensation, so as to avoid the voltage oscillation or power impact in the switching process; The composite energy storage array is also used for: According to the instantaneous power demand and the continuous energy storage demand of the elevator system, a capacity allocation strategy of the super capacitor and the auxiliary energy storage unit is established, the super capacitor preferentially responds to the charging and discharging demand corresponding to the instantaneous power fluctuation, and the auxiliary energy storage unit undertakes the energy storage task corresponding to the continuous energy storage demand.

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