Industrial mobile power supply system and control method, device and medium thereof
By combining intelligent battery modules, thermal management modules, and digital twin models, the problem of insufficient thermal management in industrial mobile power systems under extreme environments is solved, achieving stable temperature control and improved system reliability, and supporting the collaborative operation of multiple power supplies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHONGKE RUINENG NEW ENERGY (GUANGDONG) CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-09
Smart Images
Figure CN122178504A_ABST
Abstract
Description
Technical Field
[0001] This application relates to, but is not limited to, the field of intelligent power safety management technology, and in particular to an industrial mobile power supply system and its control method, device, and medium. Background Technology
[0002] Industrial mobile power systems are portable, highly reliable power supply solutions specifically designed for industrial scenarios. They are primarily used to provide stable and continuous power to equipment, tools, emergency systems, or work sites in situations where there is no fixed power grid, the power grid is unstable, or there is a temporary power demand. However, existing industrial mobile power supplies suffer from inadequate thermal management. They rely solely on air cooling or water cooling when high temperatures are required, and their efficiency is low in extreme environments. Furthermore, battery activity decreases at low temperatures, and the lack of heating and insulation mechanisms affects the reliability of industrial mobile power systems operating in extreme environments. Summary of the Invention
[0003] This application provides an industrial mobile power supply system and its control method, device, and medium, which can ensure the temperature control stability of the industrial mobile power supply system and thus improve the reliability of its operation.
[0004] In a first aspect, embodiments of this application provide an industrial mobile power supply system, including: A smart battery module, wherein the number of smart battery modules is at least one; A power conversion module is connected to the intelligent battery module; The thermal management module includes a phase change material, a semiconductor refrigeration chip, a liquid cooling circuit, and a sub-control module, wherein the sub-control module is connected to the phase change material, the semiconductor refrigeration chip, the liquid cooling circuit, and the smart battery module, respectively. Autonomous mobile module; The data acquisition and control module includes an edge computing module and multiple high-precision sensors, wherein any one of the high-precision sensors is used to acquire a first physical parameter of the smart battery module, and the edge computing module is used to preprocess the first physical parameter; The model application module is equipped with a digital twin model and is connected to the edge computing module. The model application module is used to perform power failure early warning and power parameter analysis based on the second physical parameters preprocessed by the edge computing module, and to obtain early warning information and analysis results. The collaborative application module is connected to the terminal device, the edge computing module, and the model application module, respectively. It can send the early warning information to the terminal device and issue system control commands to the edge computing module based on the analysis results, so that the smart battery module, and / or the thermal management module, and / or the autonomous mobility module can perform the operations corresponding to the system control commands.
[0005] In some embodiments, the smart battery module includes: a battery cell, the battery cell being connected to a DC bus via a bidirectional DC / DC converter, wherein the battery cell is a lithium iron phosphate battery cell or a ternary lithium battery cell.
[0006] In some embodiments, the power conversion module includes a bidirectional energy storage converter that integrates a bridgeless totem pole PFC circuit and an LLC resonant converter topology.
[0007] In some embodiments, the autonomous mobility module includes: a differential drive chassis, a lidar, a UWB positioning tag, and an inertial measurement unit.
[0008] Secondly, embodiments of this application provide a control method for an industrial mobile power supply system, applied to the industrial mobile power supply system described in the first aspect, the method comprising: The various first physical parameters of the intelligent battery module are collected in real time by each of the high-precision sensors. The edge computing module preprocesses all the first physical parameters to obtain the second physical parameters, and sends the second physical parameters to the model application module. The model application module performs power failure early warning and power parameter analysis based on the digital twin model and the second physical parameters, obtains early warning information and analysis results, and sends the early warning information and analysis results to the collaborative application module; The collaborative application module sends the early warning information to the terminal device and issues system control commands to the edge computing module based on the analysis results. The system control commands are used to indicate control commands for the smart battery module, the thermal management module, and the autonomous mobility module. The edge computing module controls the smart battery module, and / or the thermal management module, and / or the autonomous mobility module executes the operations corresponding to the system control commands.
[0009] In some embodiments, the model application module performs power fault early warning and power parameter analysis based on the digital twin model and the physical characteristics to obtain early warning information and analysis results, including: The second physical parameters of each type are fused to obtain physical characteristics; The physical characteristics, target load curve, and reference environmental boundary conditions are input into the digital twin model, and simulation results are output. The simulation results include a temperature distribution cloud map and a SOH decay curve under the reference environmental boundary conditions. The temperature distribution cloud map and the SOH decay curve are identified as the analytical results. The temperature distribution cloud map and the SOH decay curve are input into a preset fault prediction model, and the early warning information is output.
[0010] In some embodiments, the second physical parameter includes a voltage sequence, an internal resistance sequence, and an infrared potential image. The various types of the second physical parameters are fused to obtain physical features, including: The first capacity decay feature and the second capacity decay feature are extracted from the voltage sequence and the internal resistance sequence, respectively, using the LSTM model. A CNN model is used to identify temperature anomaly regions in the infrared potential image, and a temperature anomaly region mask carrying temperature anomaly values is obtained. The physical characteristics are obtained by fusing the first capacity decay feature, the second capacity decay feature, and the temperature anomaly region mask.
[0011] Secondly, embodiments of this application provide a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the control method of the industrial mobile power supply system as described in the first aspect.
[0012] Thirdly, embodiments of this application also provide an electronic device, including the control device of the second aspect.
[0013] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for performing the control method of the industrial mobile power supply system as described in the first aspect.
[0014] This application provides an industrial mobile power supply system and its control method, device, and medium. The industrial mobile power supply system includes: a smart battery module, the number of which is at least one; a power conversion module connected to the smart battery module; a thermal management module including a phase change material, a semiconductor refrigeration chip, a liquid cooling circuit, and a sub-control module, the sub-control module being connected to the phase change material, the semiconductor refrigeration chip, the liquid cooling circuit, and the smart battery module respectively; an autonomous mobility module; a data acquisition and control module including an edge computing module and multiple high-precision sensors, wherein any one of the high-precision sensors is used to acquire a first physical parameter of the smart battery module, and the edge computing module is used to preprocess the first physical parameter; and a model application module. The module includes a model application module that deploys a digital twin model and connects to the edge computing module. The model application module performs power fault early warning and power parameter analysis based on the received pre-processed second physical parameters from the edge computing module, obtaining early warning information and analysis results. A collaborative application module connects to the terminal device, the edge computing module, and the model application module, respectively. It can send the early warning information to the terminal device and issue system control commands to the edge computing module based on the analysis results, causing the smart battery module, and / or the thermal management module, and / or the autonomous mobile module to execute the operations corresponding to the system control commands. According to the solution provided in this application embodiment, real-time acquisition of physical characteristics, combined with dynamic adjustment of semiconductor cooling chip power and other operations using a digital twin model, upgrades traditional independent thermal control to system-level temperature collaboration, ensuring stable temperature control of the industrial mobile power system and thus improving operational reliability. Attached Figure Description
[0015] Figure 1 This is a system architecture diagram of an industrial mobile power supply system provided in one embodiment of this application; Figure 2 This is a flowchart of the steps of a control method for an industrial mobile power supply system provided in one embodiment of this application; Figure 3 This is a structural diagram of a control device provided in another embodiment of this application. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0017] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0018] Industrial mobile power systems are portable, highly reliable power supply solutions specifically designed for industrial scenarios. They are primarily used to provide stable and continuous power to equipment, tools, emergency systems, or work sites in situations where there is no fixed power grid, the power grid is unstable, or there is a temporary power demand. However, existing industrial mobile power supplies suffer from inadequate thermal management. They rely solely on air cooling or water cooling when high temperatures are required, and their efficiency is low in extreme environments. Furthermore, battery activity decreases at low temperatures, and the lack of heating and insulation mechanisms affects the reliability of industrial mobile power systems operating in extreme environments.
[0019] To address the aforementioned problems, this application provides an industrial mobile power supply system and its control method, apparatus, and medium. The industrial mobile power supply system includes: a smart battery module, with at least one smart battery module; a power conversion module connected to the smart battery module; a thermal management module including a phase change material, a semiconductor refrigeration chip, a liquid cooling circuit, and a sub-control module, the sub-control module being connected to the phase change material, the semiconductor refrigeration chip, the liquid cooling circuit, and the smart battery module respectively; an autonomous mobility module; a data acquisition and control module including an edge computing module and multiple high-precision sensors, wherein any one of the high-precision sensors is used to acquire a first physical parameter of the smart battery module, and the edge computing module is used to preprocess the first physical parameter; and a model application module. The module includes a model application module that deploys a digital twin model and connects to the edge computing module. The model application module performs power fault early warning and power parameter analysis based on the received pre-processed second physical parameters from the edge computing module, obtaining early warning information and analysis results. A collaborative application module connects to the terminal device, the edge computing module, and the model application module, respectively. It can send the early warning information to the terminal device and issue system control commands to the edge computing module based on the analysis results, causing the smart battery module, and / or the thermal management module, and / or the autonomous mobile module to execute the operations corresponding to the system control commands. According to the solution provided in this application embodiment, real-time acquisition of physical parameters, combined with dynamic adjustment of semiconductor cooling chip power and other operations using a digital twin model, upgrades traditional independent thermal control to system-level temperature collaboration, ensuring stable temperature control of the industrial mobile power system and thus improving operational reliability.
[0020] The embodiments of this application will be further described below with reference to the accompanying drawings.
[0021] refer to Figure 1 , Figure 1 This is a system architecture diagram of an industrial mobile power supply system provided in one embodiment of this application. This embodiment of the application provides an industrial mobile power supply system, including: Intelligent battery module, the number of which is at least 1; The power conversion module is connected to the smart battery module; The thermal management module includes a phase change material, a semiconductor refrigeration chip, a liquid cooling circuit, and a sub-control module. The sub-control module is connected to the phase change material, the semiconductor refrigeration chip, the liquid cooling circuit, and the smart battery module, respectively. Autonomous mobile module; The data acquisition and control module includes an edge computing module and multiple high-precision sensors. Each high-precision sensor is used to acquire the first physical parameter of the smart battery module, and the edge computing module is used to preprocess the first physical parameter. The model application module is equipped with a digital twin model and is connected to the edge computing module. The model application module is used to perform power failure early warning and power parameter analysis based on the second physical parameters preprocessed by the edge computing module, and to obtain early warning information and analysis results. The collaborative application module is connected to the terminal device, the edge computing module, and the model application module, respectively. It can send early warning information to the terminal device and issue system control commands based on the analysis results, so that the smart battery module, and / or the thermal management module, and / or the autonomous movement module can execute the operations corresponding to the system control commands.
[0022] Understandably, reference Figure 1 This embodiment acquires the first physical parameters of various types of intelligent battery modules in real time and dynamically adjusts the power of semiconductor cooling chips using a digital twin model, upgrading traditional independent thermal control to system-level temperature coordination. This ensures stable temperature control of the industrial mobile power supply system and improves operational reliability.
[0023] It should be noted that the liquid cooling circuit of the thermal management module in this embodiment includes a micro-pump and a radiator. Specifically, based on the hardware foundation of the thermal management module of this application, it is possible to achieve the following in high-temperature environments: the cold end of the thermoelectric cooler (TEC) cools the battery, while the heat at the hot end is carried away by the liquid cooling circuit; the phase change material (PCM) absorbs the peak heat generated by instantaneous high power. In low-temperature environments, the TEC reverses its polarity to heat the battery, while the PCM phase changes to release heat for insulation. Furthermore, the power of the TEC and the pumping speed of the micro-pump in the thermal management module of this embodiment can be dynamically adjusted by a digital twin model based on the predicted load and ambient temperature of the smart battery module, thus realizing a system-level temperature control coordination strategy and effectively ensuring the temperature stability of the industrial mobile power supply system.
[0024] In some embodiments, the smart battery module includes: a battery cell connected to a DC bus via a bidirectional DC / DC converter, wherein the battery cell is a lithium iron phosphate battery cell or a ternary lithium battery cell.
[0025] It should be noted that the intelligent battery module in this embodiment supports hot-swapping, enabling flexible capacity expansion. Based on the hardware structure of the intelligent battery module in this embodiment, a bidirectional DC / DC converter is responsible for precisely managing the battery's charging and discharging process. During charging, it converts the high voltage of the external power supply into a voltage suitable for the battery; during discharging, it boosts the battery voltage to the operating voltage required by the system and monitors the voltage, current, and temperature of each cell in real time.
[0026] It should be noted that the number of smart battery modules in this embodiment is at least 1. When there are multiple smart battery modules, this application is in a multi-power supply collaboration scenario.
[0027] In some embodiments, the power conversion module includes a bidirectional energy storage converter that integrates a bridgeless totem pole PFC circuit and an LLC resonant converter topology.
[0028] It should be noted that the power conversion module in this embodiment also provides AC (220V / 380V) and DC (12V / 24V / 800V) output interfaces. In charging (AC->DC) mode, the PFC circuit rectifies the AC power into high-voltage DC power, and the LLC converter efficiently steps it down to the DC power required for battery charging. In discharging (DC->AC) mode, the process is reversed, inverting the DC power provided by the battery into a pure, stable sinusoidal AC power output. This device reuse design reduces components and improves overall energy efficiency.
[0029] In some embodiments, the autonomous mobility module includes: a differential drive chassis, a lidar, a UWB positioning tag, and an inertial measurement unit.
[0030] It is understood that the autonomous mobile module in this embodiment uses LiDAR to achieve Simultaneous Localization and Mapping (SLAM), enabling the platform to perceive its environment and navigate autonomously. UWB positioning tags are used for precise positioning (error <10cm) and "automatic following" operation when multiple power sources are working together. The autonomous mobile module is designed to bear a weight of up to 500kg, adapting to complex industrial environments. The UWB positioning tags also enable power sources to autonomously follow personnel movement.
[0031] It should be noted that the data acquisition and control module in this embodiment is responsible for data acquisition and command issuance. Various high-precision sensors form a multimodal sensor network, capable of acquiring primary physical parameters such as voltage, current, temperature, and vibration. The output ripple spectrum (0-10MHz) is analyzed using Fast Fourier Transform (FFT) to provide early warning of capacitor aging. The edge computing module, with a high-performance MCU (Microcontroller Unit) at its core, integrates a BMS and a real-time operating system. It is responsible for running lightweight algorithms (LSTM models), performing local real-time data processing, preliminary diagnosis, and rapid control (short-circuit protection), and uploading the pre-processed data (i.e., the secondary physical parameters) to the cloud-based model application module via a 4G / 5G network.
[0032] It should be noted that the model application module in this embodiment corresponds to a cloud device, which specifically deploys a data hub, a digital twin model (in this embodiment, this includes an electrochemical-thermal coupling model and a mechanical stress model), and prediction and optimization algorithms. Specifically, the data hub can receive and integrate real-time data, product lifecycle data (PLM), failure mode library (FMEA), and historical operation and maintenance records from edge computing units to build a complete digital thread; the electrochemical-thermal coupling model is based on an extended single-particle model, which can simulate the lithium-ion concentration and electrochemical reaction heat generation inside the battery, and couple with the macroscopic thermal model to simulate battery behavior with high fidelity; the mechanical stress model can combine the vibration data and path planning of the mobile platform to predict the mechanical stress experienced by the power supply during movement; the prediction and optimization algorithms include predictive maintenance model and model predictive control (MPC). The predictive maintenance model uses algorithms such as long short-term memory network (LSTM) to analyze historical data and predict the remaining useful life (RUL) of key components (such as electrolytic capacitors) to achieve early warning of faults (e.g., warnings for periods more than 200 hours in advance); model predictive control (MPC) uses a real-time simulation engine (simulation step size up to 1μs) to predict the load change trend in the future (e.g., 10ms) and optimize the current control strategy (e.g., PWM parameters) based on this, thereby improving conversion efficiency and extending battery life. In other words, the model application module can output early warning information and analysis results on the health status of the smart battery module.
[0033] It should be noted that the collaborative application module of this embodiment can achieve the following functions: (1) Multi-machine self-organizing network and collaborative control: The power supplies form a self-organizing network through LoRa or 5G-NR communication. Using a power scheduling algorithm based on consistent hashing, when multiple power supplies are connected in parallel, the master node can be automatically elected and the basic load and peak load can be dynamically allocated to achieve accurate power sharing (error <3%) and SOC (power) balance of the cluster. (2) Predictive maintenance and health management: The fault prediction results provided by the digital twin model enable the operation and maintenance to change from "post-event maintenance" to "pre-event warning", significantly reducing unexpected downtime. The system can optimize the charging and discharging strategy based on the battery health status (SOH) to extend the overall life. (3) Intelligent human-machine interaction is realized through communication connection with terminal equipment: Through mobile applications or augmented reality (AR) glasses, operation and maintenance personnel can view the virtual status panel, heat distribution map, etc. of the power supply cluster, remotely set parameters and diagnose faults, and improve maintenance efficiency.
[0034] refer to Figure 2 , Figure 2 This is a flowchart illustrating the steps of a control method for an industrial mobile power supply system according to an embodiment of this application. This application provides a control method for an industrial mobile power supply system, which is applied to the industrial mobile battery system described above. The method includes, but is not limited to, the following steps: Step S10: Collect the first physical parameters of the smart battery module in real time using various high-precision sensors.
[0035] It is understood that this embodiment uses high-precision sensors to collect various first physical parameters of the smart battery module in real time. These first physical parameters include directly measurable parameters (voltage, current, power, frequency, internal resistance, ambient temperature and humidity, air pressure, light intensity, etc.) and derived operating parameters (SOC, SOH, SOF, charge and discharge efficiency, heat accumulation rate, etc.), which can provide an effective data foundation for subsequent system regulation and command issuance.
[0036] In step S20, the edge computing module preprocesses all the first physical parameters to obtain the second physical parameters, and sends the second physical parameters to the model application module.
[0037] Understandably, the edge computing module in the data acquisition and control module preprocesses all the first physical parameters to obtain the second physical parameters, and then transmits them to the model application module in the cloud via the 4G / 5G network to drive the digital twin model to update in real time and accurately map the current state of the physical entity.
[0038] In step S30, the model application module performs power fault early warning and power parameter analysis based on the digital twin model and the second physical parameters, obtains early warning information and analysis results, and sends the early warning information and analysis results to the collaborative application module.
[0039] Specifically, in some embodiments, Figure 2 Step S30 includes, but is not limited to, the following steps: Step S31: The second physical parameters of each type are fused to obtain physical characteristics; Step S32: Input the physical characteristics, target load curve and reference environmental boundary conditions into the digital twin model and output the simulation results, including the temperature distribution cloud map and SOH decay curve under the reference environmental boundary conditions. Step S33: Determine the temperature distribution cloud map and SOH decay curve as the analysis results; Step S34: Input the temperature distribution cloud map and SOH decay curve into the preset fault prediction model and output the early warning information.
[0040] It should be noted that the target load curve in this embodiment is the battery load curve within a future time period, which can be 10 minutes. Those skilled in the art can determine this based on the actual situation.
[0041] It should be noted that the reference environmental boundary condition for this embodiment is an environment of -20℃.
[0042] Understandably, after receiving physical characteristics, target load curves, and reference environmental boundary conditions, the digital twin model in the model application module inputs these characteristics, these boundary conditions, and outputs simulation results. These simulation results include temperature distribution cloud maps and SOH decay curves under the reference environmental boundary conditions. The temperature distribution cloud maps and SOH decay curves are then used as analysis results. Finally, the temperature distribution cloud maps and SOH decay curves are input into a preset fault prediction model, which outputs early warning information. For example, in some embodiments, a coupled simulation (step size 1μs) is run on the model application module in the cloud. For instance, the load curve for the next 10 minutes is input to simulate the voltage, temperature distribution, and stress changes of the smart battery module in a -20℃ environment. Then, deep reinforcement learning (PPO algorithm) is used to train the fault prediction model. Historical temperatures, cycle counts, and internal resistance changes are input into the fault prediction model, which outputs the fault probability for the next 200 hours. For example, if the lithium plating risk is >90%, a corresponding early warning message is generated. For example: a) Under high load, the maximum temperature difference of the battery cells exceeds 15℃ (preset risk threshold); b) It is predicted that the capacity will drop to 80% of the initial value after 1000 cycles; c) A sudden increase of 10% in the internal resistance of a battery cell is detected in advance (indicating a loose connection). Based on these three output results, it can be concluded that "the current heat dissipation design cannot support continuous 50kW output, and TEC auxiliary cooling needs to be activated" or "the SOC difference between battery cells is >5%, and active balancing is required," thus providing an effective data basis for the subsequent issuance of system control commands.
[0043] Specifically, in some embodiments, the second physical parameter includes a voltage sequence, an internal resistance sequence, and an infrared potential image. The edge computing module in step S31 performs data preprocessing on all physical parameters to obtain physical features, including but not limited to the following steps: Step S311: Use an LSTM model to extract the first capacity decay feature and the second capacity decay feature from the voltage sequence and the internal resistance sequence, respectively. Step S312: Use a CNN model to identify the temperature anomaly region in the infrared potential image and obtain a temperature anomaly region mask carrying the temperature anomaly value. Step S313: Perform data fusion on the first capacity attenuation feature, the second capacity attenuation feature, and the temperature anomaly region mask to obtain the physical feature.
[0044] Understandably, after receiving the second physical parameter, this embodiment also performs data fusion and feature extraction on the second physical parameter to provide an effective data foundation for ensuring the accuracy of subsequent early warning information and analysis results. Specifically, this embodiment uses an LSTM model to process time-series data, extracting a first capacity decay feature and a second capacity decay feature from the voltage sequence and the internal resistance sequence, respectively. Then, a CNN model is used to identify temperature anomaly regions in the infrared potential image to obtain a temperature anomaly region mask carrying temperature anomaly values. The first capacity decay feature, the second capacity decay feature, and the temperature anomaly region mask are fused to obtain the physical feature.
[0045] In step S40, the collaborative application module sends the early warning information to the terminal device and issues system control commands to the edge computing module based on the analysis results. The system control commands are used to indicate control commands for the smart battery module, thermal management module and autonomous mobility module.
[0046] In step S50, the edge computing module controls the smart battery module, and / or the thermal management module, and / or the autonomous mobility module to execute the operations corresponding to the system control commands.
[0047] It is understandable that after receiving the warning information and analysis results from the model application module, the collaborative application module sends the warning information to the terminal device, generates the optimal control strategy (such as adjusting the output power, starting thermal management, and planning the movement path) based on the analysis results, and generates the corresponding system control instructions and sends them to the edge computing module. The system control instructions involve processing strategies corresponding to various scenarios. For example, they include: (1) When it is determined that the current scenario is a high temperature and high load, the digital twin model input data is "ambient temperature 40℃, load demand 50kW for 10min", and the output analysis result is that the cell temperature is predicted to rise to 52℃, which exceeds the preset threshold. At this time, the optimized system control instruction is "reduce the output to 40kW, start TEC (power 300W), and increase the liquid cooling pump speed to 90%"; (2) When it is determined that the current scenario is a multi-machine collaboration, the digital twin model input data is "the SOH decay curve of smart battery module A indicates 95%, and the SOH decay curve of smart battery module B indicates 85%". The output analysis result is to balance the loss and extend the life of the cluster. At this time, the optimized system control command is generated as "Smart battery module A is allocated 70% of the load (35kW), smart battery module B is allocated 30% of the load (15kW), and the rotation cycle is 1 hour". That is, when coordinating power supply, the power supply with higher health is used first to extend the overall life of the cluster. (3) When it is determined that the current scenario is low temperature start-up, the digital twin model input data is "Ambient -20℃, SOC difference between cells = 30%". The output analysis result is that the battery activity is insufficient and the voltage may collapse. At this time, the optimized system control command is generated as "Trigger TEC heating mode (power 150W), and allow discharge after the cell temperature is >5℃".
[0048] Furthermore, after generating system control commands, the system control commands are sent to the edge computing module, thereby driving the actuators (such as switches, TECs, and mobile chassis) of the physical entity layer of the industrial mobile power system to perform the operations corresponding to the system control commands, thereby optimizing the operation of the power system.
[0049] It should be noted that the steps for issuing and parsing system control commands in this embodiment are as follows: The cloud (i.e., the model application module) issues system control commands (JSON format) to the edge computing module (ARM Cortex-M7 MCU) via the MQTT / HTTPS protocol. Example command: {"TEC_power": 200, "pump_speed": 80, "output_power":40}. The edge module parses the commands and schedules tasks through the real-time operating system (FreeRTOS) to ensure a response latency of <10ms. The actuators are as follows: The power conversion module adjusts the duty cycle of the bidirectional DC / DC converter through PWM information signals to precisely control the output power (error <3%), adjusting the output voltage from 400V to 380V; the thermal management module achieves heating / cooling switching by driving the H-bridge circuit (polarity reversible) of the TEC; at the same time, it adjusts the speed of the liquid cooling pump (0-5000 RPM) through a PID algorithm; the automatic movement module generates obstacle avoidance paths based on LiDAR and UWB data and path planning algorithms (such as A*), and sends speed commands (left wheel / right wheel speed difference) to the differential drive motor through the CAN bus.
[0050] In summary, the industrial mobile power supply system proposed in this application is based on intelligent collaboration using digital twins. In terms of thermal management, it employs phase change materials (PCM) to absorb peak heat and a thermoelectric cooler (TEC) for bidirectional temperature control (-20℃~60℃), achieving a 40% improvement in energy efficiency compared to existing solutions that rely on passive cooling or single liquid cooling. Regarding mobility, it integrates LiDAR SLAM and UWB positioning, supporting path planning and obstacle avoidance, and has a load-bearing capacity of ≥500kg, fundamentally different from mechanical rope pulling or manual towing. For status monitoring, while previous systems used mechanical power indicators or basic sensor temperature monitoring, this application's system utilizes multiphysics (electric-thermal-life) simulation to predict battery SOH (state of health) in real time, achieving a fault warning accuracy rate >95%. Furthermore, it supports automatic networking of multiple power supplies, effectively improving the reliability and functional scalability of industrial mobile power supply operation. In other words, this application's solution, through deep hardware and software integration, solves the reliability, mobility, and collaboration bottlenecks of industrial mobile power supplies in extreme environments, providing a standardized solution for emergency power supply in smart grids.
[0051] like Figure 3 As shown, Figure 3 This is a structural diagram of a control device provided in one embodiment of this application. The present invention also provides a control device 300, comprising: The processor 310 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 320 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 320 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310 to execute the control method of the industrial mobile power supply system of the embodiments of this application. Input / output interface 330 is used to realize information input and output; The communication interface 340 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 350 transmits information between various components of the device (e.g., processor 310, memory 320, input / output interface 330, and communication interface 340); The processor 310, memory 320, input / output interface 330 and communication interface 340 are connected to each other within the device via bus 350.
[0052] In addition, this application also provides an industrial mobile power supply system, including the control device 300 of the above embodiments.
[0053] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described control method for an industrial mobile power supply system.
[0054] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and 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.
[0055] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
[0056] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.
Claims
1. An industrial mobile power supply system, characterized in that, include: A smart battery module, wherein the number of smart battery modules is at least one; A power conversion module is connected to the intelligent battery module; The thermal management module includes a phase change material, a semiconductor refrigeration chip, a liquid cooling circuit, and a sub-control module, wherein the sub-control module is connected to the phase change material, the semiconductor refrigeration chip, the liquid cooling circuit, and the smart battery module, respectively. Autonomous mobile module; The data acquisition and control module includes an edge computing module and multiple high-precision sensors, wherein any one of the high-precision sensors is used to acquire a first physical parameter of the smart battery module, and the edge computing module is used to preprocess the first physical parameter; The model application module is equipped with a digital twin model and is connected to the edge computing module. The model application module is used to perform power failure early warning and power parameter analysis based on the second physical parameters preprocessed by the edge computing module, and to obtain early warning information and analysis results. The collaborative application module is connected to the terminal device, the edge computing module, and the model application module, respectively. It can send the early warning information to the terminal device and issue system control commands to the edge computing module based on the analysis results, so that the smart battery module, and / or the thermal management module, and / or the autonomous mobility module can perform the operations corresponding to the system control commands.
2. The industrial mobile power supply system according to claim 1, characterized in that, The intelligent battery module includes: a battery cell, which is connected to a DC bus via a bidirectional DC / DC converter, wherein the battery cell is a lithium iron phosphate battery cell or a ternary lithium battery cell.
3. The industrial mobile power supply system according to claim 1, characterized in that, The power conversion module includes a bidirectional energy storage converter that integrates a bridgeless totem pole PFC circuit and an LLC resonant converter topology.
4. The industrial mobile power supply system according to claim 1, characterized in that, The autonomous mobility module includes: a differential drive chassis, a lidar, a UWB positioning tag, and an inertial measurement unit.
5. A control method for an industrial mobile power supply system, characterized in that, The method, applied to the industrial mobile power supply system according to any one of claims 1 to 4, comprises: The various first physical parameters of the intelligent battery module are collected in real time by each of the high-precision sensors. The edge computing module preprocesses all the first physical parameters to obtain the second physical parameters, and sends the second physical parameters to the model application module. The model application module performs power failure early warning and power parameter analysis based on the digital twin model and the second physical parameters, obtains early warning information and analysis results, and sends the early warning information and analysis results to the collaborative application module; The collaborative application module sends the early warning information to the terminal device and issues system control commands to the edge computing module based on the analysis results. The system control commands are used to indicate control commands for the smart battery module, the thermal management module, and the autonomous mobility module. The edge computing module controls the smart battery module, and / or the thermal management module, and / or the autonomous mobility module executes the operations corresponding to the system control commands.
6. The control method for the industrial mobile power supply system according to claim 5, characterized in that, The model application module performs power fault early warning and power parameter analysis based on the digital twin model and the physical characteristics, obtaining early warning information and analysis results, including: The second physical parameters of each type are fused to obtain physical characteristics; The physical characteristics, target load curve, and reference environmental boundary conditions are input into the digital twin model, and simulation results are output. The simulation results include a temperature distribution cloud map and a SOH decay curve under the reference environmental boundary conditions. The temperature distribution cloud map and the SOH decay curve are identified as the analytical results. The temperature distribution cloud map and the SOH decay curve are input into a preset fault prediction model, and the early warning information is output.
7. The control method for the industrial mobile power supply system according to claim 6, characterized in that, The second physical parameter includes voltage sequence, internal resistance sequence, and infrared potential image. The various types of the second physical parameter are fused to obtain physical characteristics, including: The first capacity decay feature and the second capacity decay feature are extracted from the voltage sequence and the internal resistance sequence, respectively, using the LSTM model. A CNN model is used to identify temperature anomaly regions in the infrared potential image, and a temperature anomaly region mask carrying temperature anomaly values is obtained. The physical characteristics are obtained by fusing the first capacity decay feature, the second capacity decay feature, and the temperature anomaly region mask.
8. A control device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the control method of the industrial mobile power supply system as described in any one of claims 5 to 7.
9. An industrial mobile power supply system, comprising the control device as described in claim 8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the control method of the industrial mobile power supply system as described in any one of claims 5 to 8.