Centralized battery charging and replacing cabinet system

Abstract

The invention relates to the technical field of electric energy storage and utilization, and discloses a centralized battery charging and replacing cabinet system comprising a charging module used for charging; the monitoring module is used for monitoring the states of the charging module and the battery compartment; the battery BMS module is used for detecting abnormity and sending the abnormity to the management and scheduling module; the management and scheduling module is used for controlling the charging module to charge the batteries in the plurality of battery compartments at the same time with the maximum required current, performing power-off protection according to abnormal conditions, performing priority ranking on the batteries through a battery state matrix, and replacing the batteries with serious faults; the unmanned industrial vehicle is used for communicating with the management and scheduling module and receiving scheduling; the display and video recording module is used for displaying the fault state and recording the battery replacement process; and the prediction module is used for predicting future battery replacement demand fluctuation and feeding back the fluctuation to the charging module for energy supplement. The system is convenient and fast to operate, safe and reliable, can realize high-efficiency and high-stability electricity storage-charging-discharging, monitors the state of the battery in real time, identifies faults and gives an alarm in time.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage and utilization, and in particular to a centralized charging and swapping cabinet system. Background Art

[0002] Forklifts are common industrial handling vehicles, primarily used for loading, unloading, stacking, and short-distance transportation of goods. Using their front forks to lift and move cargo, they are widely used in warehouses, ports, factories, logistics centers, and other locations. With the implementation of environmental policies and the widespread adoption of environmental awareness, more and more users are opting for electric forklifts.

[0003] However, in the field of construction machinery, especially electric forklifts, there are no industry standards for the voltage levels and dimensions of their batteries, resulting in the inability to quickly and conveniently replace forklift batteries. The transformer capacity in industrial logistics parks is obviously insufficient, making it difficult to meet customers' battery replacement needs. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problems in the prior art of inconvenient battery replacement and insufficient charging capacity caused by the non-standard voltage levels and external dimensions of batteries for electric forklifts, and to provide a centralized charging and battery replacement cabinet system. The centralized charging and battery replacement cabinet system has convenient and fast battery replacement operation, is safe and reliable, and can achieve high-efficiency and high-stability storage-charging-discharging, monitor the battery status in real time, identify faults and promptly issue alarms.

[0005] In order to achieve the above objectives, the present invention provides a centralized charging and swapping cabinet system, which includes: Charging module, used to charge the batteries in each battery compartment; A monitoring module is used to monitor the status of the charging module and multiple battery compartments; The battery BMS module is used to detect abnormalities in each battery compartment and send them to the management and scheduling modules; The management and scheduling module is used to control the charging module to simultaneously charge the batteries in multiple battery slots at the maximum required current, perform power-off protection based on abnormal conditions reported by the battery BMS module, and prioritize batteries through the battery status matrix and replace batteries with serious faults; Unmanned industrial vehicles, used to communicate with the management and scheduling modules and receive scheduling; Display and recording module, used to display fault status and record the battery replacement process; The prediction module is used to predict the future fluctuation range of battery replacement demand based on the ARIMA model and Monte Carlo simulation and provide feedback to the charging module for energy replenishment.

[0006] Preferably, the monitoring module is configured to upload battery and charging module data to the battery cloud for data storage and analysis, calculate the battery state of charge SOC through remote data training and verification of the artificial neural network ANN, and calculate the battery SOH based on the differential voltage DVA and incremental capacity analysis ICA.

[0007] Preferably, each charging module port is configured with an independent physical address.

[0008] Preferably, the display and recording module includes a display screen and a monitoring camera. The battery BMS module and the charging module communicate with the unmanned industrial vehicle through the CAN bus, and can display the fault status of each module in real time through the display screen; the monitoring camera is configured to record the entire battery replacement process.

[0009] Preferably, the battery BMS module is configured to send a CAN message to the management and scheduling module when it detects an abnormality in a battery. The management and scheduling module cuts off the charging function protection of this battery and displays the faulty position on the display screen.

[0010] Preferably, a 40-60mm gap is formed between the left and right walls of each battery compartment and the battery, and a 90-110mm gap is formed between the top wall and the battery. Limit guide blocks are provided on the left and right walls and the top wall. The guide chamfer of the limit guide block forms a slow guide from the position of half the depth to the edge of the battery side shovel charging cabinet, and a reserved gap of 1 / 2 of the size of the automatic brush block adjustment is formed between the limit guide block and the battery.

[0011] Preferably, each battery compartment is provided with a photoelectric sensor for determining whether the limit switch is pressed when the battery reaches a designated position and the electric push of the active side charging mechanism is extended.

[0012] Preferably, the battery state matrix includes SOC, SOH and temperature, and the management and scheduling module is configured to be able to dynamically prioritize according to SOC threshold classification, SOH correction coefficient and temperature compensation mechanism, wherein, The SOC threshold classification includes three levels: SOH>90%, 50%≤SOH≤90%, and SOH<50%; For batteries with SOH < 95%, the available capacity is calculated according to formula (1): Actual SOC = displayed SOC × SOH / 100%, (1).

[0013] Preferably, the centralized charging and swapping cabinet system also includes an AI central control screen, which is used to coordinate the scheduling of photovoltaic charging and State Grid charging through the energy storage management system EMS.

[0014] Preferably, the fluctuation range of future battery swapping demand predicted based on the ARIMA model and Monte Carlo simulation includes: Collect historical electricity price data and preprocess the data. Use the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs to determine the order prediction and confidence interval calculation of the ARIMA model. Use the ARIMA model to predict future electricity prices. Determine whether the number of batteries in the current inventory that meet the replacement conditions meets demand. If not, the AI ​​central control screen will issue a fast charging request to the charging module.

[0015] According to the above technical solution, the centralized charging and swapping cabinet system uses a charging module to charge the batteries in each battery slot. A monitoring module monitors the status of the charging module and multiple battery slots. The battery BMS module detects anomalies in each battery slot and sends them to the management and scheduling module. The management and scheduling module then controls the charging module to simultaneously charge the batteries in multiple slots at the maximum required current. Power outages are implemented based on anomalies reported by the battery BMS module. A battery status matrix is ​​used to prioritize batteries and replace severely faulty ones. An unmanned industrial vehicle, the core component of the handling process, communicates with the management and scheduling module and receives dispatches. A display and recording module displays fault conditions and records the battery swapping process. To further enhance the system's intelligence, a prediction module uses an ARIMA model and Monte Carlo simulation to predict future fluctuations in battery swapping demand and provide feedback to the charging module for energy replenishment. During operation, depleted batteries from electric forklifts can be shoveled to an empty slot in the centralized swapping cabinet using an automated guided vehicle (AGV). The fully charged batteries are then transferred to the forklifts by the AGV. This makes the entire battery swapping process quick and convenient, effectively solving the problem of insufficient charging capacity for customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 1 is a schematic structural diagram of a centralized charging and swapping cabinet system according to an embodiment of the present invention; Figure 2 This is a usage status diagram of a centralized charging and swapping cabinet system according to an embodiment of the present invention; Figure 3 This is a logical framework diagram of a centralized charging and swapping cabinet system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the results of ARIMA time series prediction performed by a prediction module of a centralized charging and swapping cabinet system in an embodiment provided by the present invention. DETAILED DESCRIPTION

[0017] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0018] See also Figures 1 to 3The present invention provides a centralized charging and swapping cabinet system, which includes: Charging module, used to charge the batteries in each battery compartment; A monitoring module is used to monitor the status of the charging module and multiple battery compartments; The battery BMS module is used to detect abnormalities in each battery compartment and send them to the management and scheduling modules; The management and scheduling module is used to control the charging module to simultaneously charge the batteries in multiple battery slots at the maximum required current, perform power-off protection based on abnormal conditions reported by the battery BMS module, and prioritize batteries through the battery status matrix and replace batteries with serious faults; Unmanned industrial vehicles, used to communicate with the management and scheduling modules and receive scheduling; Display and recording module, used to display fault status and record the battery replacement process; The prediction module is used to predict the future fluctuation range of battery replacement demand based on the ARIMA model and Monte Carlo simulation and provide feedback to the charging module for energy replenishment.

[0019] In this embodiment, the monitoring module is configured to upload battery and charging module data to the battery cloud for data storage and analysis. The monitoring module calculates the battery's state of charge (SOC) through remote data training and validation of an artificial neural network (ANN). It also calculates the battery's state of health (SOH) based on differential voltage (DVA) and incremental capacity analysis (ICA). Specifically, the monitoring module monitors the status of the charging module and each storage unit, accurately collecting data on the battery's SOC, charging voltage, and current. The monitoring module then uploads the battery and charging module data to the battery manufacturer's cloud platform for data storage and analysis. The battery cloud leverages cloud computing and IoT technologies to collect and analyze battery data to improve battery safety, performance, and cost-effectiveness. This battery cloud can be applied to electric forklifts (EVs) and energy storage systems (ESSs), using remote data training and validation of an artificial neural network (ANN) to calculate the battery's state of charge (SOC). The battery cloud's data includes data from battery manufacturing, assembly, vehicle operation, and charging. To support the big data platform, Hadoop or a time series database (TSDB) can be used to apply machine learning to analyze battery data and improve battery performance. The battery cloud also displays battery data in real time for data analysts or operators to monitor. When calculating battery SOH based on differential voltage analysis (DVA) and incremental capacity analysis (ICA), the battery's cycling data at different temperatures is analyzed, the charge cycles are extracted, and DVA and ICA curves are calculated. Multiple features are extracted from these curves to analyze and estimate the battery's SOH. This method achieves an SOH estimation accuracy of ±5% across various operating temperatures.

[0020] In this embodiment, to accurately locate and manage each storage location, each charging module port is preferably assigned a separate physical address. For example, the storage rack is designed with two rows and three layers, and the storage locations are numbered: C11, C12, C13, C21, C22, and C23, where the first digit is the row number and the second is the layer number.

[0021] In this embodiment, preferably, the display and recording module includes a display screen and a monitoring camera. The battery BMS module and the charging module communicate with the unmanned industrial vehicle through the CAN bus, and can display the fault status of each module in real time through the display screen; the monitoring camera is configured to be able to record the entire battery replacement process.

[0022] In this embodiment, in order to enable the centralized charging and swapping cabinet system to have the function of automatic power-off protection, it is preferred to set the battery BMS module to be able to send a CAN message to the management and scheduling module when it detects an abnormality in a certain battery. The management and scheduling module will cut off the charging function protection of this battery and display the faulty position on the display screen.

[0023] When designing the battery compartments of the centralized charging and swapping cabinet system, in order to facilitate battery placement and replacement operations, it is preferred that a 40-60mm gap is formed between the left and right walls of each battery compartment and the battery, and a 90-110mm gap is formed between the top wall and the battery. Limiting guide blocks are provided on the left and right walls and the top wall. The guiding chamfers of the limiting guide blocks form a slow guide from the position of half the depth to the edge of the battery side shovel charging cabinet, and a reserved gap of 1 / 2 of the size of the automatic brush block adjustment is formed between the limiting guide block and the battery.

[0024] To safely monitor the contact closure between the brush and brush block in each battery compartment, a photoelectric sensor is preferably installed in each battery compartment. This sensor determines whether the limit switch is engaged when the active charging mechanism electrically extends and the battery reaches the designated position. When the battery reaches the designated position, the charging brush block electrically extends, compressing the battery brush plate. This generates a compression signal, changing the normally closed position to normally open. The system display can detect this compression signal. Furthermore, the charging contact pressure can be adjusted by adjusting the distance from the front limit switch to the brush block.

[0025] In this embodiment, the battery state matrix includes SOC, SOH and temperature, and the management and scheduling module is configured to dynamically prioritize according to SOC threshold classification, SOH correction coefficient and temperature compensation mechanism, wherein: SOC threshold grading includes high (SOH>90%), medium (50%≤SOH≤90%), and low (SOH<50%). High SOC batteries are prioritized to meet emergency battery replacement needs. Batteries in the medium SOC range can be used in special circumstances. To ensure customer satisfaction, batteries in the low SOC range are not allowed to be used. For batteries with SOH < 95%, the available capacity is calculated according to formula (1): Actual SOC = displayed SOC × SOH / 100%, (1) This will prevent the remaining power of low-health batteries from being overestimated.

[0026] In the above temperature compensation mechanism, in summer (ambient temperature > 30°C), batteries with low temperature and small temperature difference are given priority. In winter (ambient temperature < 0°C), battery packs with high temperature and small temperature difference are given priority. This can balance the contradiction between high temperature life degradation and low temperature performance degradation.

[0027] Furthermore, it can be managed based on health levels. When SOH > 90%, it can be used normally, with priority assigned to high-load battery swaps. When SOH is 80% < SOH ≤ 90%, the depth of charge and discharge (DOD ≤ 70%) is limited to avoid frequent fast charging. When SOH ≤ 80%, it is downgraded to a backup battery and can be set to be used only during peak hours or in emergencies.

[0028] In this embodiment, the centralized charging and swapping cabinet system preferably also includes an AI central control screen, which is used to coordinate the scheduling of photovoltaic charging and State Grid charging through the energy storage management system EMS. Specifically, the AI ​​central control screen can realize charging cut-off control. If the AI ​​central control screen calculates that the battery pack currently in the charging state needs to be replaced for the forklift, the AI ​​central control screen will send a stop charging instruction to the energy storage management, and return the stop charging flag after stopping charging, so that the AI ​​central control system can issue a battery replacement operation instruction. The AI ​​central control screen can also perform dynamic inventory management. By setting a safety inventory threshold (such as the number of fully charged batteries accounting for 30% of the total number of batteries), when the inventory is lower than the threshold, the AI ​​central control screen will send a fast charging request to the charging module, triggering the dual-path energy replenishment of the State Grid + energy storage, and quickly replenishing the number of fully charged batteries.

[0029] Furthermore, the centralized charging and swapping cabinet system uses the ARIMA model and Monte Carlo simulation to predict the fluctuation range of future battery swapping demand, including: Collect historical electricity price data and pre-process the data. Use the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs to determine the order (p, d, q) of the ARIMA model for prediction and confidence interval calculation. Use the ARIMA model to predict future electricity prices. Determine whether the number of batteries in the current inventory that meet the replacement conditions meets the demand. If not, the AI ​​central control screen will issue a fast charging request to the charging module (see Figure 4 ), triggering the above-mentioned dual-path energy replenishment of State Grid + energy storage, quickly replenishing the number of fully charged batteries and greatly improving efficiency.

[0030] In addition, when a single battery pack has an irrecoverable serious fault (such as the battery dynamic and static voltage difference is too large, the BMS detects that the maximum and minimum voltage difference of any single battery exceeds 500mv, the battery undervoltage is lower than 2.3-2.8V, the overvoltage exceeds 3.6-3.8V, etc.), the specific judgment conditions are shown in Table 1: Table 1

[0031] At this time, the AI ​​central control screen sends instructions to the AGV car to move the battery to the faulty battery area and call a replacement battery from the spare battery library to replenish the batteries in the library.

[0032] Through the above technical solution, the centralized charging and swapping cabinet system uses a charging module to charge the batteries in each battery slot, uses a monitoring module to monitor the status of the charging module and multiple battery slots, detects the abnormality of each battery slot through the battery BMS module and sends it to the management and scheduling module, and then the management and scheduling module controls the charging module to charge the batteries in multiple battery slots at the maximum required current at the same time, performs power-off protection according to the abnormal conditions fed back by the battery BMS module, and prioritizes the batteries through the battery status matrix and replaces batteries with serious faults. As the core component of transportation, an unmanned industrial vehicle is used to communicate with the management and scheduling module and accept its scheduling. At the same time, the display and recording module displays the fault status and records the battery swap process. In order to further enhance the intelligence of the system, a prediction module is used based on the ARIMA model and Monte Carlo simulation to predict the future fluctuation range of battery swap demand and feedback to the charging module for energy replenishment. During use, the depleted battery on the electric forklift can be shoveled to the empty space in the centralized battery exchange cabinet by AGV, and then the fully charged battery can be moved to the forklift by AGV. The entire battery exchange process is quick and convenient, and effectively solves the problem of insufficient charging capacity for customers. It is easy and fast to operate, safe and reliable, and can achieve high-efficiency and high-stability storage-charging-discharging, real-time monitoring of battery status, and timely alarm when faults are identified.

[0033] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Within the scope of the technical concept of the present invention, the technical solution of the present invention may be subjected to a variety of simple modifications, including combining the various specific technical features in any suitable manner. To avoid unnecessary repetition, the present invention will not further describe various possible combinations. However, these simple modifications and combinations should also be regarded as disclosed in the present invention and fall within the scope of protection of the present invention.

Claims

1. A centralized charging and swapping cabinet system, characterized in that: The centralized charging and swapping cabinet system includes: Charging module, used to charge the batteries in each battery compartment; A monitoring module is used to monitor the status of the charging module and multiple battery compartments; The battery BMS module is used to detect abnormalities in each battery compartment and send them to the management and scheduling modules; The management and scheduling module is used to control the charging module to simultaneously charge the batteries in multiple battery slots at the maximum required current, perform power-off protection based on abnormal conditions reported by the battery BMS module, and prioritize batteries through the battery status matrix and replace batteries with serious faults; Unmanned industrial vehicles, used to communicate with the management and scheduling modules and receive scheduling; Display and recording module, used to display fault status and record the battery replacement process; The prediction module is used to predict the future fluctuation range of battery replacement demand based on the ARIMA model and Monte Carlo simulation and provide feedback to the charging module for energy replenishment.

2. The centralized charging and swapping cabinet system according to claim 1, characterized in that: The monitoring module is configured to upload battery and charging module data to the battery cloud for data storage and analysis, calculate the battery state of charge (SOC) through remote data training and verification of the artificial neural network (ANN), and calculate the battery state of charge (SOH) based on differential voltage (DVA) and incremental capacity analysis (ICA).

3. The centralized charging and swapping cabinet system according to claim 1, characterized in that: Each charging module port is configured with an independent physical address.

4. The centralized charging and swapping cabinet system according to claim 1, characterized in that: The display and recording module includes a display screen and a monitoring camera. The battery BMS module and the charging module communicate with the unmanned industrial vehicle through the CAN bus, and can display the fault status of each module in real time through the display screen; the monitoring camera is set to be able to record the entire battery replacement process.

5. The centralized charging and swapping cabinet system according to claim 4, characterized in that: The battery BMS module is configured to send a CAN message to the management and scheduling module when it detects an abnormality in a battery. The management and scheduling module will cut off the charging function of the battery for protection and display the faulty position on the display screen.

6. The centralized charging and swapping cabinet system according to claim 1, characterized in that: There is a 40-60mm gap between the left and right walls of each battery compartment and the battery, and a 90-110mm gap between the top wall and the battery. Limit guide blocks are provided on the left and right walls and the top wall. The guide chamfer of the limit guide block forms a slow guide from the position of half the depth to the edge of the battery side shovel charging cabinet, and a reserved gap of 1 / 2 of the size for automatic brush block adjustment is formed between the limit guide block and the battery.

7. The centralized charging and swapping cabinet system according to claim 1, characterized in that: Each battery compartment is equipped with a photoelectric sensor to determine whether the limit switch is pressed when the battery reaches the specified position and the electric push of the active side charging mechanism is extended.

8. The centralized charging and swapping cabinet system according to claim 1, characterized in that: The battery state matrix includes SOC, SOH and temperature. The management and scheduling module is set to dynamically prioritize according to SOC threshold classification, SOH correction coefficient and temperature compensation mechanism, among which, The SOC threshold classification includes three levels: SOH>90%, 50%≤SOH≤90%, and SOH<50%; For batteries with SOH < 95%, the available capacity is calculated according to formula (1): Actual SOC = displayed SOC × SOH / 100%, (1).

9. The centralized charging and swapping cabinet system according to claim 1, characterized in that: The centralized charging and swapping cabinet system also includes an AI central control screen, which is used to coordinate the scheduling of photovoltaic charging and State Grid charging through the energy storage management system EMS.

10. The centralized charging and swapping cabinet system according to claim 9, characterized in that: Based on the ARIMA model and Monte Carlo simulation, the fluctuation range of future battery swap demand is predicted to include: Collect historical electricity price data and preprocess the data. Use the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs to determine the order prediction and confidence interval calculation of the ARIMA model. Use the ARIMA model to predict future electricity prices. Determine whether the number of batteries in the current inventory that meet the replacement conditions meets demand. If not, the AI ​​central control screen will issue a fast charging request to the charging module.

Images