Renewable energy recycling battery energy storage system based on traction machine

Abstract

The invention relates to the technical field of power systems and energy recycling, in particular to a renewable energy recycling battery energy storage system based on a traction machine. Renewable energy generated by an elevator traction machine is stored based on the energy storage unit and the direct current conversion unit, and power is supplied when an elevator needs power; a fuzzy control algorithm and a model prediction algorithm are updated through multi-source data, collected by a multi-source sensor, at historical moments, and a renewable energy recovery strategy and an energy utilization strategy are adjusted; the control unit transmits a control instruction converted from the renewable energy recovery strategy and the energy utilization strategy to the direct current conversion unit for switching circuits and adjusting charging and discharging power, and calculates a battery energy storage characterization value based on the adjusted energy throughput efficiency and power response characterization value of the system; and the analysis unit determines the battery energy storage state based on the battery energy storage characterization value, and adjusts corresponding parameters based on the battery energy storage state. The energy utilization rate is improved.

Description

Technical Field

[0001] This invention relates to the field of power systems and energy recovery and utilization technology, and in particular to a battery energy storage system for renewable energy recovery and utilization based on a traction machine. Background Technology

[0002] Car elevators, as a special lifting device used for vertical vehicle transportation, are widely used in automated parking garages, car dealerships, repair centers, car exhibition halls, and ship vehicle cabins. Compared with traditional passenger elevators, car elevators have the characteristics of large load capacity, large car size, relatively low operating speed but frequent start and stop.

[0003] Car elevators involve significant kinetic and potential energy conversion during operation. When the car is fully loaded or empty and descends, the traction machine generates electricity. However, most existing systems lack efficient energy feedback devices or are only equipped with simple energy-consuming braking units. The generated regenerated electricity cannot be effectively fed back to the power grid and is instead consumed as heat in the machine room through the braking resistor. Therefore, there are significant deficiencies in energy utilization, especially the extremely low energy recovery efficiency, resulting in enormous energy waste and indirectly exacerbating the greenhouse effect.

[0004] Chinese Patent Publication No. CN114940427A discloses a device for recovering and utilizing renewable energy in elevator operation: including a traction machine, a variable frequency and voltage processing unit, a capacitor, an inverter processing unit, a bidirectional control unit, an electric heater, a water storage heating device, a water temperature sensor, a water level sensor, and a control system; the output end of the traction machine is sequentially connected to the variable frequency and voltage processing unit, the capacitor, the inverter processing unit, the bidirectional control unit, and the electric heater; the electric heater is installed in the water storage chamber of the water storage heating device, and the water storage chamber is also equipped with a water temperature sensor and a water level sensor; the water inlet of the water storage chamber is connected to the water outlet of the water supply pump through a water supply pipe, and the water inlet of the water supply pump is connected to an external water source; the output end of the water storage chamber is connected to the user's hot water pipe.

[0005] It is evident that existing technologies have the following problems: because renewable energy cannot be effectively recycled, a large amount of electrical energy is dissipated as heat, resulting in low energy utilization. Summary of the Invention

[0006] To address this issue, the present invention provides a battery energy storage system for the recovery and utilization of renewable energy based on a traction machine, which overcomes the problem in the prior art where a large amount of electrical energy is dissipated as heat due to the ineffective recovery of renewable energy, resulting in low energy utilization.

[0007] To achieve the above objectives, the present invention provides a battery energy storage system for renewable energy recovery and utilization based on a traction machine, comprising: The energy storage unit is used to prioritize storing the renewable energy generated by the elevator traction machine and to prioritize supplying power to the traction machine when the elevator needs power. A DC-DC converter unit, which is connected to the energy storage unit, is used to charge the energy storage unit after rectification when recovering renewable energy, and to drive the traction machine after inverting the energy storage unit when supplying power. The multi-source data acquisition unit is used to collect elevator operating parameters, traction machine operating parameters, and energy storage unit status parameters using multi-source sensors. An energy management unit, connected to the multi-source data acquisition unit, is used to periodically update the fuzzy control algorithm and model prediction algorithm based on historical multi-source datasets, and to periodically adjust the renewable energy recovery strategy and energy utilization strategy based on the algorithms. The control unit is connected to the DC-DC conversion unit and the energy management unit respectively, and is used to convert the adjusted renewable energy recovery strategy and the energy utilization strategy into control commands, and transmit the control commands to the DC-DC conversion unit to switch circuits and adjust charging and discharging power; A computing unit, connected to the control unit, is used to calculate the energy throughput efficiency and power response characterization values, and to calculate the battery energy storage characterization values ​​based on the energy throughput efficiency and power response characterization values. An analysis unit, connected to the control unit, determines the battery energy storage state based on the battery energy storage characterization value, adjusts the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the battery energy storage state, and adjusts the update cycle of historical multi-source data used for updating the algorithm based on the adjusted battery energy storage state.

[0008] Furthermore, the analysis unit is also used to calculate the absolute value of the difference between the energy throughput efficiency and the preset efficiency when the battery energy storage state is unqualified; the analysis unit is also used to adjust the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the difference between the absolute value and the preset threshold when the absolute value is greater than the preset threshold; wherein, the battery energy storage state is determined to be unqualified when the battery energy storage characterization value is less than the preset characterization value.

[0009] Furthermore, the analysis unit is also used to reduce the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the difference between the absolute value and the preset threshold, and the reduction in the update cycle is proportional to the difference.

[0010] Furthermore, if the battery energy storage state is unqualified after adjusting the update cycle of the fuzzy control algorithm and the model prediction algorithm, the current multi-source dataset is obtained; the analysis unit is also used to calculate the cosine similarity between the current multi-source dataset and the historical multi-source dataset used for updating the algorithm in multiple dimensions to obtain a similarity score; the analysis unit is also used to adjust the update cycle of the historical multi-source dataset used for updating the algorithm based on the ratio of the similarity score to the preset similarity score if the similarity score is less than the preset similarity score.

[0011] Furthermore, the analysis unit is also used to reduce the update cycle of the historical multi-source dataset used for the update algorithm based on the ratio of the similarity score to the preset similarity, and the reduction in the update cycle is inversely proportional to the ratio.

[0012] Furthermore, the analysis unit also includes a cross-sensor verification module, which is used to locate abnormal data sources through traction machine operating parameters; the analysis unit is also used to repeatedly adjust the update cycle of the historical multi-source dataset used to update the algorithm at least once when the battery energy storage state is unqualified after adjusting the update cycle of the historical multi-source dataset used to update the algorithm, until the number of adjustments is less than a preset number and the battery energy storage state is qualified, or the number of adjustments is equal to the preset number, and then the adjustment stops; the analysis unit is also used to calculate the difference between adjacent sampling points of the sensor using the cross-sensor verification module when the battery energy storage state is unqualified after the adjustment stops; the analysis unit is also used to adjust the cutoff frequency of the filter connected to the output terminal of the multi-source sensor based on the ratio of the number of times the difference is greater than the preset difference within a preset time period.

[0013] Furthermore, the analysis unit is also used to reduce the cutoff frequency of the filter connected to the output terminal of the multi-source sensor based on the ratio of the number of times to the preset number of times, and the reduction of the cutoff frequency is proportional to the ratio.

[0014] Furthermore, the analysis unit is also used to acquire the voltage at the output terminal of the traction machine at multiple historical moments when the battery energy storage state is unqualified after adjusting the cutoff frequency of the filter; the analysis unit is also used to calculate the variance of multiple voltages, and when the variance is greater than the preset variance, adjust the calibration cycle of the multi-source sensor based on the ratio of the variance to the preset variance.

[0015] Furthermore, the analysis unit is also used to reduce the calibration cycle of the multi-source sensor based on the ratio of the variance to the preset variance, and the reduction in the calibration cycle is proportional to the ratio.

[0016] Furthermore, the analysis unit is also used to calculate the difference between the preset characterization value and the battery energy storage characterization value when the battery energy storage state is unqualified after adjusting the calibration cycle of the multi-source sensor; the analysis unit is also used to issue an online self-test notification for the DC-DC conversion unit when the difference is greater than the preset difference.

[0017] Compared with existing technologies, the advantages of this invention are as follows: This invention stores regenerative energy generated by the elevator traction machine based on an energy storage unit and a DC-DC converter, and provides power when the elevator requires it; it updates the fuzzy control algorithm and model prediction algorithm with multi-source data collected from historical moments by multi-source sensors, and adjusts the regenerative energy recovery strategy and energy utilization strategy; the control unit transmits control commands converted from the regenerative energy recovery strategy and energy utilization strategy to the DC-DC converter to switch circuits and adjust charging and discharging power, and calculates the battery energy storage characterization value based on the adjusted system's energy throughput efficiency and power response characterization value; the analysis unit determines the battery energy storage state based on the battery energy storage characterization value and adjusts the corresponding parameters based on the battery energy storage state. This invention improves the utilization rate of regenerative energy.

[0018] Furthermore, this invention determines the cause of battery energy storage failure based on the absolute value of the difference between energy throughput efficiency and preset efficiency. This allows for a more accurate determination of the cause of battery energy storage failure, enabling more effective subsequent adjustments and further improving energy utilization.

[0019] Furthermore, this invention reduces the update cycle of the fuzzy control algorithm and model prediction algorithm based on the difference between the absolute value and the preset threshold, which enables the control strategy to be more closely related to the actual elevator operating conditions, thereby enabling the energy storage unit to charge and discharge more effectively, and further improving the energy-saving effect and the energy utilization rate.

[0020] Furthermore, this invention determines the cause of unqualified battery energy storage status based on the similarity score obtained by calculating the cosine similarity between the current multi-source dataset and the historical multi-source dataset used to update the algorithm in multiple dimensions. This can more accurately determine whether the unqualified battery energy storage status is due to the failure of the learning data, thereby enabling more effective subsequent adjustments and further improving energy utilization.

[0021] Furthermore, this invention reduces the update cycle of historical multi-source datasets used to update the algorithm by lowering the ratio of similarity score to preset similarity, which can update multi-source data more effectively, making the algorithm more accurate and thus further improving energy utilization.

[0022] Furthermore, this invention determines the cause of unqualified battery energy storage state by calculating the difference between adjacent sampling points of the sensor based on the cross-sensor verification module. It can determine whether the noise surge is caused by sensor interference, thereby enabling more effective subsequent adjustments and further improving energy utilization.

[0023] Furthermore, the present invention reduces the cutoff frequency of the filter connected to the output terminal of the multi-source sensor based on the ratio of the number of times to the preset number of times, which can more effectively cut off noise data, thereby making the acquired multi-source data more accurate and further improving the energy utilization rate.

[0024] Furthermore, this invention determines the cause of unqualified battery energy storage state based on the variance of the voltage at the output terminal of the traction machine at multiple historical moments. This allows for a more accurate determination of whether the unqualified battery energy storage state is caused by zero drift or damage to the sensor, thereby enabling more effective subsequent adjustments and further improving energy utilization.

[0025] Furthermore, this invention reduces the calibration cycle of multi-source sensors based on the ratio of variance to preset variance, which can more effectively adjust the calibration cycle of the sensors, thereby making the acquired multi-source data more accurate, further improving the accuracy of the control strategy, and thus further improving the energy utilization rate.

[0026] Furthermore, the present invention determines the reason for the battery energy storage state failure based on the difference between the preset characterization value and the battery energy storage characterization value, which enables more effective subsequent adjustments and further improves energy utilization. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structure of a battery energy storage system for renewable energy recovery and utilization based on a traction machine, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the steps of a battery energy storage method for renewable energy recovery and utilization based on a traction machine, according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the steps of determining the energy storage characterization result based on the comparison between the battery energy storage characterization value and the preset characterization value in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the steps of determining the battery energy storage state based on adjusting the update cycle of the historical multi-source dataset used for updating the algorithm, according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0029] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0030] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0031] Please see Figure 1 As shown, it is a structural schematic diagram of a battery energy storage system for renewable energy recovery and utilization based on a traction machine according to an embodiment of the present invention.

[0032] The system includes an energy storage unit, a DC-DC conversion unit, a multi-source data acquisition unit, an energy management unit, a control unit, a computing unit, and an analysis unit.

[0033] The energy storage unit is used to prioritize storing the renewable energy generated by the elevator traction machine and to prioritize supplying power to the traction machine when the elevator needs power. The DC-DC converter is connected to the energy storage unit. It is used to charge the energy storage unit after rectification when recovering renewable energy, and to drive the traction machine after inverting the power supply from the energy storage unit. The multi-source data acquisition unit is used to collect elevator operating parameters, traction machine operating parameters, and energy storage unit status parameters using multi-source sensors. The energy management unit is connected to the multi-source data acquisition unit, and is used to periodically update the fuzzy control algorithm and model prediction algorithm based on historical multi-source datasets, and to periodically adjust the renewable energy recovery strategy and energy utilization strategy based on the algorithm. The control unit is connected to the DC-DC conversion unit and the energy management unit respectively. It is used to convert the adjusted renewable energy recovery strategy and the energy utilization strategy into control commands and transmit the control commands to the DC-DC conversion unit to switch circuits and adjust charging and discharging power. The computing unit is connected to the control unit and is used to calculate the energy throughput efficiency and power response characterization values, and to calculate the battery energy storage characterization values ​​based on the energy throughput efficiency and power response characterization values. The analysis unit is connected to the control unit and is used to determine the battery energy storage state based on the battery energy storage characterization value, adjust the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the battery energy storage state, and adjust the update cycle of the historical multi-source data used for updating the algorithm based on the adjusted battery energy storage state.

[0034] Specifically, the energy storage unit uses high-power-density power batteries to prioritize the storage of regenerative energy generated by the traction motor during braking and other operating conditions, and to prioritize powering the traction motor when it needs to operate, such as during acceleration or constant-speed driving. This unit has rapid charging and discharging capabilities, matching the high-power characteristics of instantaneous regenerative energy generation, ensuring efficient storage and rapid release of regenerative energy for the traction machine.

[0035] Specifically, the DC-DC converter is a bidirectional DC-DC converter built based on SiC MOSFET (silicon carbide metal-oxide semiconductor field-effect transistor) technology. Its function is to achieve efficient conversion of different voltage levels, ensure rapid rectification and charging of energy storage units during renewable energy recovery, and stable inversion and drive of power traction motors when energy storage units are supplying power. This can significantly reduce conversion losses and improve conversion efficiency.

[0036] Specifically, the system collects multi-source data such as elevator load, speed, and battery level in real time. Fuzzy control converts this data into fuzzy language such as "light / heavy" and "high / low," matching it against a preset rule base. For example, if the load is "heavy" and the elevator is "going downwards," then the recovery intensity is "high." After calculation, a precise strategy weight coefficient is output (e.g., "recovery urgency = 0.8"). This strategy weight signal is then used as input to the model prediction algorithm and incorporated into its optimization objective function. The model prediction algorithm is configured to construct an optimization problem within each control cycle, with the total system operating cost J over a finite time domain as the objective. The calculation function for the total operating cost J includes a term adjusted by the strategy weight coefficient α. Simultaneously, based on the system's internal dynamic model, such as a recurrent neural network, the system predicts the state evolution under different control sequences. Under the condition of satisfying battery power and state of charge constraints, the optimization problem is solved to obtain the optimal charging and discharging power control sequence. Finally, the control quantity corresponding to the current moment in this sequence is output and sent to the control unit for execution. This process is existing technology and will not be elaborated further.

[0037] Specifically, the energy throughput rate is the ratio of the energy actually released by the energy storage unit to drive the traction machine to the total energy recovered and stored from the traction machine; the power response characterization value is the root mean square error between the command power curve and the actual power curve over a period of time, calculated using real-time data and normalized to a fraction of 0-1; the battery energy storage characterization value is the result of a weighted sum of the energy throughput efficiency and the power response characterization value.

[0038] Please see Figure 2 The diagram shown is a flowchart of the steps of the battery energy storage method for renewable energy recovery and utilization based on a traction machine according to an embodiment of the present invention.

[0039] The specific steps for recycling and utilizing battery energy storage based on traction machines for renewable energy are as follows: S1 prioritizes storing the renewable energy generated by the elevator traction machine through the energy storage unit, and prioritizes supplying power to the traction machine when the elevator needs power. S2, the energy storage unit is rectified and charged by the DC-DC converter connected to the energy storage unit when recovering renewable energy, and the energy storage unit is inverted and driven by the traction machine when supplying power. S3 uses a multi-source data acquisition unit to collect elevator operating parameters, traction machine operating parameters, and energy storage unit status parameters from multiple source sensors. S4, the energy management unit connected to the multi-source data acquisition unit periodically updates the fuzzy control algorithm and model prediction algorithm based on the historical multi-source dataset, and periodically adjusts the renewable energy recovery strategy and energy utilization strategy based on the algorithm. S5, the adjusted renewable energy recovery strategy and energy utilization strategy are converted into control commands by the control unit connected to the DC conversion unit and the energy management unit respectively, and the control commands are transmitted to the DC conversion unit to switch circuits and adjust charging and discharging power; S6, calculate the energy throughput efficiency and power response characterization values ​​through the computing unit connected to the control unit, and calculate the battery energy storage characterization value based on the energy throughput efficiency and power response characterization values; S7, the analysis unit connected to the control unit determines the battery energy storage state based on the battery energy storage characterization value, adjusts the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the battery energy storage state, and adjusts the update cycle of the historical multi-source data used to update the algorithm based on the adjusted battery energy storage state.

[0040] Please see Figure 3 The diagram shown is a flowchart illustrating the steps for determining the energy storage characterization result based on the comparison between the battery energy storage characterization value and the preset characterization value in an embodiment of the present invention.

[0041] Specifically, based on the hardware performance limits of the battery pack and power converter, such as maximum charge and discharge current, instantaneous power carrying capacity, cycle life decay characteristics, and the fault tolerance requirements of the actual elevator operating environment, as well as the regenerative energy distribution, load cycle mode, and battery health decay data obtained from historical operation statistics and analysis, the corresponding preset or critical parameters are set accordingly.

[0042] Specifically, if the preset characterization value L0 = 0.83, the comparison process between the battery characterization value L and the preset characterization value L0 is as follows: If the battery characterization value L is greater than or equal to the preset characterization value L0, then the battery energy storage state is determined to be qualified. If the battery characterization value L is less than the preset characterization value L0, then the battery energy storage state is determined to be unqualified.

[0043] Specifically, when the battery energy storage state is unqualified, the absolute value of the difference between the energy throughput efficiency and the preset efficiency is calculated. If the absolute value is greater than the preset threshold, it indicates that the parameters in the fuzzy control algorithm and the model prediction algorithm cannot adapt to the actual operation of the elevator, resulting in a serious disconnect between the control strategy and the actual elevator operating conditions. This leads to the energy storage unit not being fully charged or effectively discharged, resulting in poor energy-saving performance. Therefore, the update cycle of the fuzzy control algorithm and the model prediction algorithm is adjusted based on the difference between the absolute value and the preset threshold. The preset difference between the absolute value and the preset threshold is P0 = 0.1. The comparison process between the absolute value and the preset threshold P and the preset difference P0 is as follows: If the difference P between the absolute value and the preset threshold is less than or equal to the preset difference P0, the update cycle of the fuzzy control algorithm and the model prediction algorithm will be adjusted to 0.91 times the original update cycle, where the adjusted value will be rounded up. If the difference P between the absolute value and the preset threshold is greater than the preset difference P0, the update cycle of the fuzzy control algorithm and the model prediction algorithm will be adjusted to 0.77 times the original update cycle, and the adjusted value will be rounded up.

[0044] Specifically, when the battery energy storage state is unqualified after adjusting the update cycle of the fuzzy control algorithm and the model prediction algorithm, the current multi-source dataset is obtained; the cosine similarity between the current multi-source dataset and the historical multivariate dataset used to update the algorithm is calculated on the key dimensions to obtain the similarity score. The key dimension refers to the feature vector extracted from the dataset to characterize the energy mode of elevator operation. The feature vector includes statistics of one or more of the following parameters: elevator car load, running direction distribution, average running speed, battery state of charge, and battery temperature.

[0045] Specifically, if the similarity score is less than the preset similarity, it indicates that the learning data is invalid, meaning that the multi-source data used for periodic parameter updates is not representative or contains a large number of abnormal conditions. Therefore, the update cycle of the historical multi-source dataset used for updating the algorithm is adjusted based on the ratio of the similarity score to the preset similarity. The preset ratio of the similarity score to the preset similarity is Q0 = 0.92. The comparison process between the similarity score and the preset similarity ratio Q and the preset ratio Q0 is as follows: If the ratio Q of the similarity score to the preset similarity is less than or equal to the preset ratio Q0, the update cycle of the historical multi-source dataset used to update the algorithm will be adjusted to 0.59 times the original update cycle, where the adjusted value will be rounded up. If the ratio Q of the similarity score to the preset similarity score is greater than the preset ratio Q0, the update cycle of the historical multi-source dataset used to update the algorithm will be adjusted to 0.72 times the original update cycle, where the adjusted value will be rounded up.

[0046] Please see Figure 4 As shown, it is a flowchart of the steps for determining the battery energy storage state based on adjusting the update cycle of the historical multi-source dataset used for updating the algorithm, according to an embodiment of the present invention.

[0047] Specifically, if the battery energy storage status is unqualified after adjusting the update cycle of the historical multi-source dataset used to update the algorithm, the update cycle of the historical multi-source data used to update the algorithm is repeatedly adjusted at least once until the number of adjustments is less than a preset number and the battery energy storage status is qualified, or the number of adjustments is equal to the preset number, at which point the adjustment stops. If the battery energy storage status is still unqualified after stopping the adjustment, the difference between adjacent sampling points of the sensor is calculated using the cross-sensor verification module. If the number of times the difference is greater than the preset difference within a preset time period is greater than the preset number, it indicates that the sensor is being interfered with, and its output noise surges. Then, the cutoff frequency of the filter connected to the output terminal of the multi-source sensor is adjusted based on the ratio of the number of times to the preset number, where the preset ratio R0 = 2. The comparison process between the ratio R0 and the preset number is as follows: If the ratio R of the number of times to the preset number of times is less than or equal to the preset ratio R0, the cutoff frequency of the filter connected to the output terminal of the multi-source sensor is adjusted to 0.87 times the original cutoff frequency, where the adjusted value is rounded up. If the ratio R of the number of times to the preset number of times is greater than the preset ratio R0, the cutoff frequency of the filter connected to the output terminal of the multi-source sensor will be adjusted to 0.61 times the original cutoff frequency, and the adjusted value will be rounded up.

[0048] Specifically, when the battery energy storage state is unqualified after adjusting the filter cutoff frequency, the voltage at the output terminal of the traction machine at multiple historical moments is acquired. The variance of the multiple voltages is calculated. If the variance is greater than the preset variance, it indicates that the key sensor has experienced zero drift, temperature drift, or damage. The calibration cycle of the multi-source sensor is then adjusted based on the ratio of the variance to the preset variance. The preset ratio of the variance to the preset variance is T0 = 1.8. The comparison process based on the ratio T0 of the variance to the preset variance is as follows: If the ratio T of the variance to the preset variance is less than or equal to the preset ratio T0, the calibration period of the multi-source sensor will be adjusted to 0.84 times the original calibration period, where the adjusted value will be rounded up. If the ratio T of the variance to the preset variance is greater than the preset ratio T0, the calibration period of the multi-source sensor will be adjusted to 0.65 times the original calibration period, and the adjusted value will be rounded up.

[0049] Specifically, if the battery energy storage state fails to meet the requirements after adjusting the calibration cycle of the multi-source sensors, the difference between the preset characteristic value and the battery energy storage characteristic value is calculated. If the difference is greater than the preset difference, it indicates that the DC-DC converter's performance has deteriorated or malfunctioned, resulting in the actual charging and discharging power failing to accurately follow the command and an abnormally increased loss during the conversion process. In this case, an online self-test notification for the DC-DC converter is issued. The online self-test includes checking the aging degree of power devices such as SiC MOSFETs, whether the drive circuit is normal, the performance of passive components such as filter capacitors, and the thermal resistance value of the devices.

[0050] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery energy storage system for renewable energy recovery and utilization based on a traction machine, characterized in that, include: The energy storage unit is used to prioritize storing the renewable energy generated by the elevator traction machine and to prioritize supplying power to the traction machine when the elevator needs power. A DC-DC converter unit, which is connected to the energy storage unit, is used to charge the energy storage unit after rectification when recovering renewable energy, and to drive the traction machine after inverting the energy storage unit when supplying power. The multi-source data acquisition unit is used to collect elevator operating parameters, traction machine operating parameters, and energy storage unit status parameters using multi-source sensors. An energy management unit, connected to the multi-source data acquisition unit, is used to periodically update the fuzzy control algorithm and model prediction algorithm based on historical multi-source datasets, and to periodically adjust the renewable energy recovery strategy and energy utilization strategy based on the algorithms. The control unit is connected to the DC-DC conversion unit and the energy management unit respectively, and is used to convert the adjusted renewable energy recovery strategy and the energy utilization strategy into control commands, and transmit the control commands to the DC-DC conversion unit to switch circuits and adjust charging and discharging power; A computing unit, connected to the control unit, is used to calculate the energy throughput efficiency and power response characterization values, and to calculate the battery energy storage characterization values ​​based on the energy throughput efficiency and power response characterization values. An analysis unit, connected to the control unit, is used to determine the battery energy storage state based on the battery energy storage characterization value, adjust the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the battery energy storage state, and adjust the update cycle of historical multi-source data used for updating the algorithm based on the adjusted battery energy storage state.

2. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 1, characterized in that, The analysis unit is also used to calculate the absolute value of the difference between the energy throughput efficiency and the preset efficiency when the battery energy storage state is unqualified. The analysis unit is also used to adjust the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the difference between the absolute value and the preset threshold when the absolute value is greater than the preset threshold. Specifically, if the battery energy storage characterization value is less than the preset characterization value, the battery energy storage state is determined to be unqualified.

3. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 2, characterized in that, The analysis unit is also used to reduce the update cycle of the fuzzy control algorithm and the model prediction algorithm based on the difference between the absolute value and the preset threshold, and the reduction in the update cycle is proportional to the difference.

4. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 3, characterized in that, If the battery energy storage state is unqualified after adjusting the update cycle of the fuzzy control algorithm and the model prediction algorithm, obtain the current multi-source dataset; The analysis unit is also used to calculate the cosine similarity between the current multi-source dataset and the historical multi-source dataset used to update the algorithm in multiple dimensions to obtain a similarity score; The analysis unit is also used to adjust the update cycle of the historical multi-source dataset used for the update algorithm based on the ratio of the similarity score to the preset similarity score when the similarity score is less than the preset similarity score.

5. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 4, characterized in that, The analysis unit is also used to reduce the update cycle of the historical multi-source dataset used for the update algorithm based on the ratio of the similarity score to the preset similarity, and the reduction in the update cycle is inversely proportional to the ratio.

6. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 5, characterized in that, The analysis unit also includes a cross-sensor verification module, which is used to locate abnormal data sources through traction machine operating parameters; The analysis unit is also used to repeatedly adjust the update cycle of the historical multi-source dataset used to update the algorithm at least once if the battery energy storage state is not qualified after adjusting the update cycle of the historical multi-source dataset used to update the algorithm, until the number of adjustments is less than the preset number and the battery energy storage state is qualified or the number of adjustments is equal to the preset number and then the adjustment stops. The analysis unit is also used to calculate the difference between adjacent sampling points of the sensor using the cross-sensor verification module when the battery energy storage state is unqualified after the adjustment is stopped. The analysis unit is also used to adjust the cutoff frequency of the filter connected to the output terminal of the multi-source sensor based on the ratio of the number of times the difference is greater than the preset difference within a preset time period to the preset number of times.

7. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 6, characterized in that, The analysis unit is also used to reduce the cutoff frequency of the filter connected to the output terminal of the multi-source sensor based on the ratio of the number of times to the preset number of times, and the reduction of the cutoff frequency is proportional to the ratio.

8. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 7, characterized in that, The analysis unit is also used to obtain the voltage at the output of the traction machine at multiple historical moments when the battery energy storage state is unqualified after adjusting the cutoff frequency of the filter. The analysis unit is also used to calculate the variance of multiple voltages, and when the variance is greater than a preset variance, adjust the calibration cycle of the multi-source sensor based on the ratio of the variance to the preset variance.

9. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 8, characterized in that, The analysis unit is also used to reduce the calibration cycle of the multi-source sensor based on the ratio of the variance to the preset variance, and the reduction in the calibration cycle is proportional to the ratio.

10. The battery energy storage system for renewable energy recovery and utilization based on a traction machine according to claim 9, characterized in that, The analysis unit is also used to calculate the difference between the preset characterization value and the battery energy storage characterization value when the battery energy storage state is unqualified after adjusting the calibration cycle of the multi-source sensor. The analysis unit is also used to issue an online self-test notification for the DC-DC converter unit when the difference is greater than a preset difference.

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