An elevator energy storage device control system, method, and related devices

Abstract

This application discloses a control system, method, and related devices for an elevator energy storage device, relating to the field of elevator energy management technology. The system includes an intelligent control layer, a data sensing layer, and an energy storage execution layer. The data sensing layer includes elevator operation sensors, a smart meter, and a battery management unit. The energy storage execution layer includes an energy storage device and a power command execution mechanism. The controller in the intelligent control layer accurately predicts the net power sequence for future time windows based on elevator operating state parameters. Combined with the SOC of the energy storage device, it predicts the SOC change trajectory of the energy storage device in future time windows, transforming the charging and discharging feedback control from a traditional passive threshold response to an active preventative adjustment. This effectively avoids SOC overshoot and severe fluctuations caused by sudden changes in elevator operating conditions, improving the foresight and stability of the energy storage device's SOC control. It also ensures that the generation of charging and discharging commands better matches the actual energy flow changes during elevator operation, guaranteeing that the energy storage system always operates within a safe and efficient range.

Description

Technical Field

[0001] This application relates to the field of elevator energy management technology, and in particular to a control system, method and related devices for an elevator energy storage device. Background Technology

[0002] With the development of elevator energy-saving technology, utilizing energy storage devices such as lithium batteries and supercapacitors to recover regenerative electrical energy generated during elevator operation has become the mainstream energy-saving solution in the industry. However, elevator operating conditions are highly random and intermittent. The instantaneous changes in load, frequent switching of operating direction, and nonlinear characteristics of speed curves lead to strong unpredictability in the energy inflow (charging) and outflow (discharging) of the energy storage system, which places higher demands on the state of charge (SOC) control of the energy storage system.

[0003] Currently, the industry generally adopts a simple charge and discharge control strategy based on fixed thresholds. This involves setting upper and lower limits for the State of Charge (SOC) of the energy storage unit. When the SOC exceeds the upper limit (e.g., 80%), charging stops; when it falls below the lower limit (e.g., 20%), discharging stops or the system draws power from the grid. While this control method achieves basic SOC management, it has many inherent flaws in practical applications and cannot adapt to the complex operating conditions of elevators. Summary of the Invention

[0004] In view of the above problems, this application provides an elevator energy storage device control system, method and related devices, the specific solution of which is as follows:

[0005] The first aspect of this application provides an elevator energy storage device control system, including: an intelligent control layer, a data sensing layer, and an energy storage execution layer;

[0006] The intelligent control layer includes a controller;

[0007] The data sensing layer includes an elevator operation sensor, a smart meter, and a battery management unit. The elevator operation sensor collects elevator operation status parameters, the smart meter measures the bidirectional power flow between the grid and the energy storage device, and the battery management unit monitors the SOC of the energy storage device.

[0008] The energy storage execution layer includes an energy storage device and a power command execution mechanism;

[0009] The controller is communicatively connected to both the data sensing layer and the energy storage execution layer.

[0010] The controller is configured to: acquire elevator operating status parameters collected by the elevator operating sensor, call a pre-trained power prediction model, predict the net power sequence of a future time window based on the elevator operating status parameters, determine the SOC change trajectory of the energy storage device in the future time window based on the net power sequence of the future time window and the SOC of the energy storage device, and send a power command to the power command execution mechanism based on the SOC change trajectory of the energy storage device.

[0011] The power command execution mechanism performs charging and discharging control on the energy storage device according to the power command.

[0012] In one possible implementation, the controller is specifically configured to: determine the charge / discharge state and power of the energy storage device at each time point in the future time window based on the net power sequence of the future time window; and determine the SOC change trajectory of the energy storage device in the future time window based on the charge / discharge state and power of the energy storage device at each time point in the future time window and the SOC of the energy storage device.

[0013] In one possible implementation, the controller is specifically configured to: determine the SOC deviation and SOC deviation rate of the energy storage device based on the SOC change trajectory of the energy storage device; adjust the proportional coefficient and integral coefficient of the PI controller based on the SOC deviation and SOC deviation rate of the energy storage device and a preset expert rule base; and call the PI controller to output a basic charging and discharging power command based on the SOC target value.

[0014] In one possible implementation, the controller is further configured to adjust the base charge / discharge power command based on the real-time electricity price, with the goal of minimizing the cycle operating cost, to obtain a target charge / discharge power command.

[0015] In one possible implementation, the controller is further configured to: acquire the output current, terminal voltage, and SOC of each module in the energy storage device; determine the current distribution deviation of each module based on the equivalent circuit model of the energy storage device and the output current and terminal voltage of each module in the energy storage device; determine the SOC deviation of each module based on the SOC of each module and the average SOC of each module; and output a balancing current command to adjust the output of each module based on the current distribution deviation and SOC deviation of each module.

[0016] In one possible implementation, the controller is further configured to: periodically acquire system operating performance indicators, which include at least one of: SOC fluctuation variance, energy recovery efficiency, and power saving rate; and optimize the power prediction model and the expert rule base based on the system operating performance indicators.

[0017] In one possible implementation, the elevator energy storage device control system further includes a human-machine interface layer;

[0018] The human-machine interface layer is communicatively connected to the controller and is configured to provide a human-machine interface and display target display information output by the controller. The target display information includes at least one of the following: system status information and elevator energy consumption report.

[0019] A second aspect of this application provides a method for controlling an elevator energy storage device, comprising:

[0020] Obtain elevator operating status parameters;

[0021] The pre-trained power prediction model is invoked to predict the net power sequence for future time windows based on the elevator operating state parameters.

[0022] Based on the net power sequence and the SOC of the energy storage device in the future time window, determine the SOC change trajectory of the energy storage device in the future time window;

[0023] Based on the SOC change trajectory of the energy storage device, charge and discharge feedback control is performed on the energy storage device.

[0024] In one possible implementation, determining the SOC change trajectory of the energy storage device within the future time window, based on the net power sequence and the SOC of the energy storage device, includes:

[0025] The charging and discharging state and power of the energy storage device at each time point in the future time window are determined based on the net power sequence of the future time window.

[0026] Based on the charging and discharging state and power of the energy storage device at each point in the future time window, and the SOC of the energy storage device, the SOC change trajectory of the energy storage device in the future time window is determined.

[0027] In one possible implementation, the step of performing charge / discharge feedback control on the energy storage device based on the SOC change trajectory of the energy storage device includes:

[0028] The SOC deviation and SOC deviation rate of the energy storage device are determined based on the SOC change trajectory of the energy storage device.

[0029] Based on the SOC deviation and SOC deviation change rate of the energy storage device, and using a preset expert rule base, the proportional coefficient and integral coefficient of the PI controller are adjusted.

[0030] The PI controller is invoked to output a basic charge / discharge power command based on the SOC target value.

[0031] In one possible implementation, after invoking the PI controller to output a basic charge / discharge power command based on the SOC target value, the method further includes:

[0032] Based on the real-time electricity price, and with the goal of minimizing the cycle operating cost, the basic charge and discharge power command is adjusted to obtain the target charge and discharge power command.

[0033] In one possible implementation, the elevator energy storage device control method further includes:

[0034] Obtain the output current, terminal voltage, and SOC of each module in the energy storage device;

[0035] Based on the equivalent circuit model of the energy storage device, the current distribution deviation of each module is determined according to the output current and terminal voltage of each module in the energy storage device.

[0036] The SOC deviation of each module is determined based on the SOC of each module and the average SOC of each module.

[0037] Based on the current distribution deviation and SOC deviation of each module, a balanced current command is output to adjust the output of each module.

[0038] In one possible implementation, the elevator energy storage device control method further includes:

[0039] The system operating performance indicators are periodically acquired, and the system operating performance indicators include at least one of the following: SOC fluctuation variance, energy recovery efficiency, and power saving rate;

[0040] The power prediction model and the expert rule base are optimized based on the system's operating performance indicators.

[0041] A third aspect of this application provides a controller, comprising: at least one processor and a memory connected to the processor, wherein:

[0042] The memory is used to store computer programs;

[0043] The processor is used to execute the computer program so that the controller can implement the elevator energy storage device control method of the first aspect or any implementation thereof.

[0044] The fourth aspect of this application provides a computer program product including computer-readable instructions that, when executed on a controller, cause the controller to implement the elevator energy storage device control method of the first aspect or any implementation thereof.

[0045] The fifth aspect of this application provides a computer storage medium carrying one or more computer programs, which, when executed by a controller, enable the controller to implement the elevator energy storage device control method described in the first aspect or any implementation thereof.

[0046] By employing the above technical solution, this application provides an elevator energy storage device control system. The controller accurately predicts the net power sequence of future time windows based on elevator operating status parameters, and combines this with the SOC of the energy storage device to predict the SOC change trajectory of the energy storage device in future time windows. This transforms the charging and discharging feedback control from a traditional passive threshold response to an active preventive adjustment, effectively avoiding SOC overshoot and severe fluctuations caused by sudden changes in elevator operating conditions. This improves the foresight and stability of the energy storage device's SOC control, while also making the generation of charging and discharging commands more closely match the energy flow changes during actual elevator operation, ensuring that the energy storage system always operates within a safe and efficient range. Attached Figure Description

[0047] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0048] Figure 1 A schematic diagram of an elevator energy storage device control system provided in this application embodiment;

[0049] Figure 2 A schematic diagram of another elevator energy storage device control system provided in this application embodiment;

[0050] Figure 3 A flowchart illustrating an elevator energy storage device control method provided in this application embodiment;

[0051] Figure 4 A schematic flowchart of a charge-discharge feedback control provided in an embodiment of this application;

[0052] Figure 5 A schematic diagram of an active balancing control provided for an embodiment of this application;

[0053] Figure 6 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Detailed Implementation

[0054] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0055] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0056] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0057] This application provides an elevator energy storage device control system. Please refer to [link / reference]. Figure 1 The control system of the elevator energy storage device includes an intelligent control layer, a data sensing layer, and an energy storage execution layer.

[0058] The intelligent control layer includes controllers.

[0059] The data sensing layer includes elevator operation sensors, smart meters, and a battery management unit.

[0060] Elevator operation sensors are used to collect elevator operation status parameters. These sensors include various types, such as pressure sensors, and each sensor collects one or more of these parameters. The elevator operation status parameters include: load, direction of travel, and speed. In some embodiments, the elevator operation status parameters also include: acceleration and car position. The load is the elevator car load, which can be collected by a pressure sensor at the bottom of the car; the direction of travel includes ascending and descending, which can be determined by the status of the elevator limit switches; speed, acceleration, and car position can be obtained from the elevator encoder.

[0061] Smart meters are used for high-precision metering of bidirectional power flow between the grid and energy storage devices.

[0062] The battery management unit is used to monitor the state of charge (SOC) of the energy storage device. In some embodiments, the battery management unit can also monitor the voltage, temperature and other state parameters of the energy storage device.

[0063] The energy storage execution layer includes energy storage devices and a power command execution mechanism. The energy storage devices can be lithium battery modules, supercapacitor modules, or an array of hybrid lithium battery and supercapacitor modules. The power command execution mechanism executes control commands issued by the controller, including power electronic converters (such as bidirectional DC-DC or PWM rectifiers). In some embodiments, the power command execution mechanism also includes an active balancing circuit for achieving energy transfer between modules.

[0064] The controller communicates with both the data sensing layer and the energy storage execution layer.

[0065] The controller is configured to: acquire elevator operating status parameters collected by elevator operation sensors, call a pre-trained power prediction model, predict the net power sequence for the future time window based on the elevator operating status parameters, determine the SOC change trajectory of the energy storage device for the future time window based on the net power sequence for the future time window and the SOC of the energy storage device, and send a power command to the power command execution mechanism based on the SOC change trajectory of the energy storage device.

[0066] The power command execution mechanism performs charging and discharging control on the energy storage device according to the power command.

[0067] Specifically, the elevator operating status parameters are input into the power prediction model to obtain the net power sequence for future time windows output by the model. A positive net power value in the net power sequence indicates that the energy storage device is in a charging state, while a negative net power value indicates that the energy storage device is in a discharging state. The net power value Pi at each time point is converted into the energy change within the corresponding time step, and then combined with the charging and discharging efficiency to calculate the actual effective energy change. Finally, the SOC value at each time point is derived, thus obtaining the SOC change trajectory.

[0068] Based on the SOC change trajectory of the energy storage device within a future time window, potential SOC overshoot risks (exceeding the upper limit of SOC safety) and SOC undershoot risks (below the lower limit of SOC safety) can be identified. Therefore, the charging and discharging of the energy storage device can be controlled in conjunction with the SOC change trajectory to avoid SOC overshoot and SOC undershoot, thus ensuring the safe operation of the energy storage device.

[0069] This embodiment provides an elevator energy storage device control system. The controller accurately predicts the net power sequence of future time windows based on elevator operating status parameters, and combines this with the SOC of the energy storage device to predict the SOC change trajectory of the energy storage device in the future time window. This transforms the charging and discharging feedback control from a traditional passive threshold response to an active preventive adjustment, effectively avoiding SOC overshoot and severe fluctuations caused by sudden changes in elevator operating conditions. This improves the foresight and stability of the energy storage device's SOC control, while also making the generation of charging and discharging commands more closely match the energy flow changes during actual elevator operation, ensuring that the energy storage system always operates within a safe and efficient range.

[0070] In one possible implementation, the controller is specifically configured to: determine the charge / discharge state and power of the energy storage device at each time point in the future time window based on the net power sequence of the future time window; and determine the SOC change trajectory of the energy storage device in the future time window based on the charge / discharge state and power of the energy storage device at each time point in the future time window and the SOC of the energy storage device.

[0071] Specifically, SOC is the ratio of the current stored energy to the rated energy of an energy storage device. Therefore, it is necessary to first convert the net power value Pi at each time point into the energy change within the corresponding time step, and then calculate the actual effective energy change in combination with the charging and discharging efficiency, and finally deduce the SOC value at each time point. The core calculation formula for a single time step is divided into the following three operating conditions:

[0072] 1. Charging condition (Pi>0):

[0073] The actual charging energy within the i-th time step: E ch,i =Pi×Δt×η ch ;

[0074] Change in SOC at the i-th time step: ΔSOC ch,i =E ch,i / C rated ×100%.

[0075] 2. Discharge condition (Pi<0):

[0076] First, take the absolute value of the discharge power |Pi|, then the actual discharge energy within the i-th time step: E dis,i =∣Pi∣×Δt / η dis .

[0077] The change in SOC discharge at the i-th time step: ΔSOC dis,i =−E dis,i / C rated ×100% (the negative sign indicates a decrease in SOC).

[0078] 3. No energy interaction (Pi=0):

[0079] ΔSOCi=0, SOC remains unchanged.

[0080] Where Δt is the time interval between two adjacent time points, and η ch For charging efficiency, η dis For discharge efficiency, C rated This refers to the rated capacity of the energy storage device.

[0081] Using the initial SOC0 at the start time as a benchmark, iterative calculations are performed on each prediction time point in chronological order to obtain the predicted SOC value SOCi for each time point. All SOCi values ​​are sorted by time to form the SOC change trend trajectory.

[0082] Based on the SOC change trajectory of energy storage devices within a future time window, potential SOC overshoot risk (exceeding the SOC safety upper limit) and SOC undershoot risk (below the SOC safety lower limit) can be identified. Furthermore, risk levels (such as low, medium, and high) can be classified accordingly.

[0083] In one possible implementation, the controller is specifically configured to: determine the SOC deviation and SOC deviation rate of the energy storage device based on the SOC change trajectory of the energy storage device; adjust the proportional coefficient and integral coefficient of the PI controller based on the SOC deviation and SOC deviation rate of the energy storage device and a preset expert rule base; and call the PI controller to output a basic charging and discharging power command based on the SOC target value.

[0084] Wherein, SOC deviation = SOC target value - SOC actual value, and SOC change rate = SOC deviation / Δt.

[0085] The fuzzy inference rules in the preset expert rule base adjust the proportional coefficient Kp and integral coefficient Ki of the PI controller based on the SOC deviation and SOC deviation change rate of the energy storage device.

[0086] For example, the fuzzy inference rules in the preset expert rule base include: If the SOC is significantly lower than the target SOC and rapidly decreasing, increase Kp to speed up the response and increase Ki to eliminate steady-state error; if the SOC matches the target SOC and remains unchanged, do not adjust the PI parameter; if the SOC is significantly higher than the target SOC and rapidly increasing, decrease Kp to slow down the response and decrease Ki to avoid overshoot; if the SOC is slightly lower than the target SOC and slowly decreasing, slightly increase Kp / Ki; if the SOC is slightly higher than the target SOC and slowly increasing, slightly decrease Kp / Ki. If the current SOC is higher than the target SOC and is still slowly increasing (continuous charging under heavy load), decreasing Kp can slow down the response speed of charge / discharge control, avoiding sudden command changes caused by rapidly reducing charging power.

[0087] The PI controller outputs a basic charge / discharge power command based on the target SOC value and the actual SOC value.

[0088] For example, the power calculation formula is as follows:

[0089] Pref(k)=Kp×e(k)+Ki×e(k) ×Ts;

[0090] Where Pref(k) is the charge / discharge power value in the basic charge / discharge power command of step k, e(k) is the SOC deviation of step k, and Ts is the time interval between two adjacent time points, i.e., the time step.

[0091] In one possible implementation, the controller is further configured to adjust the base charge / discharge power command based on real-time electricity prices, with the goal of minimizing cycle operating costs, to obtain a target charge / discharge power command. For example, a rolling optimization mathematical model is constructed with the objective function of minimizing cycle operating electricity costs or maximizing revenue, and constraints such as the upper and lower limits of SOC safety and the power limit of the power electronic converter, to optimize the matching of the time window with the control cycle T_c. The upper and lower limits of SOC safety are set to prevent overcharging or over-discharging of the energy storage device. Numerical optimization algorithms (such as linear programming and dynamic programming) are used to solve the rolling optimization mathematical model, and the generated base charge / discharge power command is modified in terms of amplitude or timing, such as appropriately increasing the charging power during periods of low electricity prices and appropriately increasing the discharging power during periods of high electricity prices, thereby generating the target charge / discharge power command. The target charge and discharge power command takes into account safety, SOC smoothness, and economy. Under the premise of ensuring the safe operation of the energy storage system, the charge and discharge power command is optimized for economy, guiding the SOC to charge during off-peak hours and discharge during peak hours. This not only saves energy but also further reduces electricity costs, maximizes the return on investment of the energy storage system, and achieves a synergistic improvement in technical performance and economic benefits.

[0092] In one possible implementation, the controller is further configured to: acquire the output current, terminal voltage, and SOC of each module in the energy storage device through the battery management unit; determine the current distribution deviation of each module based on the equivalent circuit model of the energy storage device and the output current and terminal voltage of each module; determine the SOC deviation of each module based on the SOC of each module and the average SOC of each module; and output a balancing current command based on the current distribution deviation and SOC deviation of each module to adjust the output of each module. For example, a two-layer decision algorithm is used to prioritize compensating for the current distribution deviation, then correcting the SOC deviation, generating a balancing current command, and actively adjusting the output of each module through an active balancing circuit to achieve fast and accurate SOC balancing.

[0093] In one possible implementation, the controller is also configured to: periodically acquire system operating performance indicators, including at least one of: SOC fluctuation variance, energy recovery efficiency, and energy saving rate; optimize the power prediction model and expert rule base based on the system operating performance indicators to achieve self-improvement and iterative upgrade of the system, making the control strategy more suitable for the actual operating conditions of the elevator.

[0094] For example, if the SOC fluctuation variance is greater than the preset fluctuation threshold, the proportional coefficient Kp of the PI controller is increased to improve the response speed; if the energy recovery efficiency is less than the preset efficiency threshold, the charging and discharging power range is adjusted to increase the proportion of the high-efficiency zone.

[0095] In one possible implementation, please refer to Figure 2 The elevator energy storage device control system also includes a human-machine interface (HMI) layer. This HMI layer is communicatively connected to the controller and configured to provide a user interface and display target information output by the controller. The target information includes at least one of the following: system status information and elevator energy consumption reports. The system status information includes the SOC curve, energy-saving data, and alarm information. Authorized personnel can also set control parameters and adjust operating strategies through the HMI interface.

[0096] This application also provides a control method for an elevator energy storage device. The elevator energy storage device control method of this application embodiment will be described in detail below with reference to the accompanying drawings.

[0097] Reference Figure 3 , Figure 3 This is a flowchart illustrating a control method for an elevator energy storage device provided in an embodiment of this application, as shown below. Figure 3 As shown in the figure, the elevator energy storage device control method provided in this application embodiment may include steps 301 to 304, which are described in detail below.

[0098] 301: Obtain elevator operating status parameters.

[0099] Elevator operating status parameters include: load, direction of travel, and speed. In some embodiments, elevator operating status parameters also include: acceleration, car position, etc.

[0100] The load is the elevator car load, which can be collected by the pressure sensor at the bottom of the car; the running direction includes going up and down, which can be determined by the status of the elevator limit switch; the speed, acceleration and car position can be obtained from the elevator encoder.

[0101] The collected elevator operating status parameters are preprocessed to output standardized, noise-free elevator operating status parameters with unified timestamps for subsequent processing. Preprocessing includes: filtering and denoising, outlier removal, and timestamp unification. Filtering and denoising can employ methods such as Kalman filtering and moving average filtering to eliminate data noise caused by sensor jitter and power grid harmonics. Outlier removal can be based on the 3σ principle to identify and remove abnormal data exceeding a reasonable range (such as invalid data where the load suddenly drops to 0 or is more than twice the rated value). Timestamp unification specifically involves time alignment of asynchronous data collected from different acquisition devices to ensure matching of multi-source data at the same time point.

[0102] 302: Call the pre-trained power prediction model to predict the net power sequence for future time windows based on elevator operating status parameters.

[0103] Since elevator operating conditions are highly random and nonlinear, and accurate prediction of short-term net power is required, machine learning regression prediction models, such as gradient boosting regression trees (GBRT / XGBoost / LightGBM) or long short-term memory networks (LSTM), can be used. Hybrid algorithm model architectures can also be used. This embodiment does not impose specific limitations.

[0104] A training sample set is pre-constructed, which includes elevator operating status parameters and a label set. The label set is the net power value of the energy storage device that is synchronized with the elevator operating status parameters in time. The net power value is positive when the energy storage device is charging and negative when the energy storage device is discharging.

[0105] The training sample set is divided into a training set, a validation set, and a test set. With the goal of minimizing the error between the predicted net power value and the actual net power value, the selected algorithm model is trained offline iteratively. The model hyperparameters are tuned through the validation set, and the prediction accuracy and generalization ability of the model are verified through the test set, thus obtaining the power prediction model.

[0106] In practical applications, elevator operating status parameters are input into the power prediction model to obtain the net power sequence of the future time window output by the power prediction model.

[0107] 303: Based on the net power sequence and the SOC of the energy storage device in the future time window, determine the SOC change trajectory of the energy storage device in the future time window.

[0108] In one possible implementation, the state of charge / discharge (SOC) and power of the energy storage device at each time point within the future time window are determined based on the net power sequence of the future time window. A positive net power value in the net power sequence indicates that the energy storage device is in a charging state, while a negative net power value indicates that the energy storage device is in a discharging state. Then, based on the SOC and power of the energy storage device at each time point within the future time window, and the SOC of the energy storage device, the trend of SOC change of the energy storage device within the future time window is determined.

[0109] 304: Based on the SOC change trajectory of the energy storage device, perform charge and discharge feedback control on the energy storage device.

[0110] By combining the SOC change trajectory of the energy storage device, dynamic charge and discharge feedback control of the energy storage device is performed through fuzzy inference and PI control.

[0111] In one possible implementation, please refer to Figure 4The flowchart of charge / discharge feedback control shown below, step 304 specifically includes the following steps 401-403:

[0112] 401: Determine the SOC deviation and SOC deviation rate of the energy storage device based on the SOC change trajectory of the energy storage device;

[0113] SOC deviation = SOC target value - SOC actual value;

[0114] SOC change rate = SOC deviation / Δt.

[0115] 402: Based on the SOC deviation and SOC deviation change rate of the energy storage device, adjust the proportional coefficient and integral coefficient of the PI controller according to the preset expert rule base;

[0116] The fuzzy inference rules in the preset expert rule base adjust the proportional coefficient Kp and integral coefficient Ki of the PI controller based on the SOC deviation and SOC deviation change rate of the energy storage device.

[0117] For example, the fuzzy inference rules of the preset expert rule base include: if the SOC is much lower than the target SOC and decreases rapidly, increase Kp to speed up the response and increase Ki to eliminate steady-state error; if the SOC matches the target SOC and remains unchanged, do not adjust the PI parameter; if the SOC is much higher than the target SOC and increases rapidly, decrease Kp to slow down the response and decrease Ki to avoid overshoot; if the SOC is slightly lower than the target SOC and decreases slowly, slightly increase Kp / Ki; if the SOC is slightly higher than the target SOC and increases slowly, slightly decrease Kp / Ki.

[0118] The current SOC is higher than the target SOC and is still slowly increasing (continuous charging under heavy load). Reducing Kp can slow down the response speed of charge and discharge control and avoid sudden changes in commands caused by rapidly reducing charging power. Reducing Ki can reduce the integral effect and prevent SOC overshoot caused by integral saturation (avoiding SOC from continuing to rise beyond the 80% safety limit), which is fully adapted to the current operating condition requirement of "gently limiting charging power".

[0119] 403: Call the PI controller to output the basic charge and discharge power command based on the SOC target value.

[0120] The PI controller outputs a basic charge / discharge power command based on the target SOC value and the actual SOC value.

[0121] For example, the power calculation formula is as follows:

[0122] Pref(k)= Kp×e(k)+Ki×e(k) ×Ts;

[0123] Where Pref(k) is the charge / discharge power value in the basic charge / discharge power command of step k, e(k) is the SOC deviation of step k, and Ts is the time interval between two adjacent time points, i.e., the time step.

[0124] This embodiment uses fuzzy logic to transform expert experience into online adjustment rules for PI parameters, enabling the controller to adapt to the elevator's random and nonlinear operating conditions in real time. Ultimately, it generates smooth, overshoot-free charging and discharging power basic commands that closely match the actual energy flow, providing a core basis for subsequent economic scheduling optimization and command execution.

[0125] In one possible implementation, following step 403 above, the basic charge / discharge power command is adjusted based on the real-time electricity price to minimize the cycle operating cost, resulting in a target charge / discharge power command. Specifically, a rolling optimization mathematical model is constructed with the objective function of minimizing cycle operating electricity costs or maximizing revenue, and constraints such as the upper and lower limits of SOC safety and the power limit of the power electronic converter. This model optimizes the matching of the time window and the control cycle T_c. The upper and lower limits of SOC safety are set to prevent overcharging or over-discharging of the energy storage device. Numerical optimization algorithms (such as linear programming and dynamic programming) are used to solve the rolling optimization mathematical model, and the generated basic charge / discharge power command is modified in terms of amplitude or timing. For example, the charging power is appropriately increased during periods of low electricity prices, and the discharging power is appropriately increased during periods of high electricity prices, thereby generating the target charge / discharge power command. The target charge and discharge power command takes into account safety, SOC smoothness, and economy. Under the premise of ensuring the safe operation of the energy storage system, the charge and discharge power command is optimized for economy, guiding the SOC to charge during off-peak hours and discharge during peak hours. This not only saves energy but also further reduces electricity costs, maximizes the return on investment of the energy storage system, and achieves a synergistic improvement in technical performance and economic benefits.

[0126] In one possible implementation, please refer to Figure 5 The flowchart of the active balancing control shown includes the following steps 501-504:

[0127] 501: Obtain the output current, terminal voltage, and SOC of each module in the energy storage device;

[0128] The output current, terminal voltage, and state of charge (SOC) of each module in the energy storage device are obtained through the battery management system (BMS).

[0129] 502: Based on the equivalent circuit model of the energy storage device, determine the current distribution deviation of each module according to the output current and terminal voltage of each module in the energy storage device;

[0130] An equivalent circuit model incorporating the differences in line impedance caused by variations in physical wiring length and connector resistance of distributed energy storage devices is established in advance.

[0131] Based on the output current and terminal voltage of each module in the energy storage device, the current distribution deviation caused by the difference in line impedance is calculated.

[0132] 503: Determine the SOC deviation of each module based on the SOC of each module and the average SOC of each module;

[0133] The average SOC of each module is calculated as SOC_avg;

[0134] The SOC deviation of each module is ΔSOC_i = SOC_i - SOC_avg.

[0135] 504: Based on the current distribution deviation and SOC deviation of each module, output a balanced current command to adjust the output of each module.

[0136] Specifically, a two-layer decision-making algorithm is used to first compensate for current distribution deviation, then correct SOC deviation, generate a balanced current command, and actively adjust the output of each module to achieve fast and accurate SOC balancing.

[0137] In one possible implementation, system performance indicators can be acquired periodically. These indicators include at least one of the following: SOC fluctuation variance, energy recovery efficiency, and energy saving rate. The power prediction model and expert rule base can be optimized based on these performance indicators to achieve self-improvement and iterative upgrades of the system, making the control strategy more suitable for the actual operating conditions of the elevator.

[0138] This application also provides a controller in its embodiments. (See reference...) Figure 6 As shown, it illustrates a structural schematic diagram suitable for implementing the controller in the embodiments of this application. Figure 6 The controller shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0139] like Figure 6 As shown, the controller may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. When the controller is powered on, RAM 603 also stores various programs and data required for controller operation. The processing device 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0140] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 608 including, for example, memory card, hard disk, etc.; and communication devices 609. Communication device 609 allows the controller to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 A controller with various devices is shown; however, it should be understood that implementation or possession of all the devices shown is not required. More or fewer devices may be implemented alternatively.

[0141] This application also provides a computer program product including computer-readable instructions, which, when executed on a controller, cause the controller to implement any of the elevator energy storage device control methods provided in this application.

[0142] This application also provides a computer-readable storage medium that carries one or more computer programs. When the one or more computer programs are executed by a controller, the controller can implement any of the elevator energy storage device control methods provided in this application.

[0143] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0145] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0146] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A control system for an elevator energy storage device, characterized in that, include: Intelligent control layer, data perception layer, and energy storage execution layer; The intelligent control layer includes a controller; The data sensing layer includes an elevator operation sensor, a smart meter, and a battery management unit. The elevator operation sensor collects elevator operation status parameters, the smart meter measures the bidirectional power flow between the grid and the energy storage device, and the battery management unit monitors the SOC of the energy storage device. The energy storage execution layer includes an energy storage device and a power command execution mechanism; The controller is communicatively connected to both the data sensing layer and the energy storage execution layer. The controller is configured to: acquire elevator operating status parameters collected by the elevator operating sensor, call a pre-trained power prediction model, predict the net power sequence of a future time window based on the elevator operating status parameters, determine the SOC change trajectory of the energy storage device in the future time window based on the net power sequence of the future time window and the SOC of the energy storage device, and send a power command to the power command execution mechanism based on the SOC change trajectory of the energy storage device. The power command execution mechanism performs charging and discharging control on the energy storage device according to the power command.

2. The elevator energy storage device control system of claim 1, wherein, The controller is specifically configured to: determine the charging / discharging state and power of the energy storage device at each time point in the future time window based on the net power sequence of the future time window; and determine the SOC change trajectory of the energy storage device in the future time window based on the charging / discharging state and power of the energy storage device at each time point in the future time window and the SOC of the energy storage device.

3. The elevator energy storage device control system according to claim 1, characterized in that, The controller is specifically configured to: determine the SOC deviation and SOC deviation rate of the energy storage device based on the SOC change trajectory of the energy storage device; adjust the proportional coefficient and integral coefficient of the PI controller based on the SOC deviation and SOC deviation rate of the energy storage device and a preset expert rule base; and call the PI controller to output a basic charging and discharging power command based on the SOC target value.

4. The elevator energy storage device control system of claim 3, wherein, The controller is also configured to adjust the basic charge / discharge power command based on the real-time electricity price, with the goal of minimizing the cycle operating cost, to obtain the target charge / discharge power command.

5. An elevator energy storage device control system according to claim 3 or 4, characterized in that The controller is further configured to: acquire the output current, terminal voltage, and SOC of each module in the energy storage device; determine the current distribution deviation of each module based on the equivalent circuit model of the energy storage device and the output current and terminal voltage of each module; determine the SOC deviation of each module based on the SOC of each module and the average SOC of each module; and output a balancing current command to adjust the output of each module based on the current distribution deviation and SOC deviation of each module.

6. The elevator energy storage device control system according to claim 3, characterized in that, The controller is also configured to: periodically acquire system operating performance indicators, including at least one of: SOC fluctuation variance, energy recovery efficiency, and power saving rate; and optimize the power prediction model and the expert rule base based on the system operating performance indicators.

7. The elevator energy storage device control system according to claim 1, characterized in that, The elevator energy storage device control system also includes a human-machine interface layer; The human-machine interface layer is communicatively connected to the controller and is configured to provide a human-machine interface and display target display information output by the controller. The target display information includes at least one of the following: system status information and elevator energy consumption report.

8. A control method for an elevator energy storage device, characterized in that, include: Obtain elevator operating status parameters; The pre-trained power prediction model is invoked to predict the net power sequence for future time windows based on the elevator operating state parameters. Based on the net power sequence and the SOC of the energy storage device in the future time window, determine the SOC change trajectory of the energy storage device in the future time window; Based on the SOC change trajectory of the energy storage device, charge and discharge feedback control is performed on the energy storage device.

9. The elevator energy storage device control method according to claim 8, characterized by, The step of determining the SOC change trajectory of the energy storage device within the future time window based on the net power sequence and the SOC of the energy storage device includes: The charging and discharging state and power of the energy storage device at each time point in the future time window are determined based on the net power sequence of the future time window. Based on the charging and discharging state and power of the energy storage device at each point in the future time window, and the SOC of the energy storage device, the SOC change trajectory of the energy storage device in the future time window is determined.

10. The elevator energy storage device control method according to claim 8, characterized in that, The step of performing charge / discharge feedback control on the energy storage device based on the SOC change trajectory of the energy storage device includes: The SOC deviation and SOC deviation rate of the energy storage device are determined based on the SOC change trajectory of the energy storage device. Based on the SOC deviation and SOC deviation change rate of the energy storage device, and using a preset expert rule base, the proportional coefficient and integral coefficient of the PI controller are adjusted. The PI controller is invoked to output a basic charge / discharge power command based on the SOC target value.

11. The elevator energy storage device control method according to claim 10, characterized by, After invoking the PI controller to output the basic charge / discharge power command based on the SOC target value, the following is also included: Based on the real-time electricity price, and with the goal of minimizing the cycle operating cost, the basic charge and discharge power command is adjusted to obtain the target charge and discharge power command.

12. The elevator energy storage device control method according to claim 10 or 11, characterized by, Also includes: Obtain the output current, terminal voltage, and SOC of each module in the energy storage device; Based on the equivalent circuit model of the energy storage device, the current distribution deviation of each module is determined according to the output current and terminal voltage of each module in the energy storage device. The SOC deviation of each module is determined based on the SOC of each module and the average SOC of each module. Based on the current distribution deviation and SOC deviation of each module, a balanced current command is output to adjust the output of each module.

13. The elevator energy storage device control method according to claim 10, characterized by, Also includes: The system operating performance indicators are periodically acquired, and the system operating performance indicators include at least one of the following: SOC fluctuation variance, energy recovery efficiency, and power saving rate; The power prediction model and the expert rule base are optimized based on the system's operating performance indicators.

14. A controller characterized by comprising: include: It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the controller can implement the elevator energy storage device control method as described in any one of claims 8 to 13.

15. A computer program product, characterised in that, Includes computer-readable instructions that, when executed on a controller, cause the controller to implement the elevator energy storage device control method as described in any one of claims 8 to 13.

16. A computer storage medium, comprising, The storage medium carries one or more computer programs that, when executed by the controller, enable the controller to implement the elevator energy storage device control method as described in any one of claims 8 to 13.

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