An intelligent charge-discharge monitoring system suitable for deep-sea self-contained pressure battery

Abstract

The application relates to the technical field of deep-sea batteries, and discloses an intelligent charging and discharging monitoring system suitable for a deep-sea self-pressure-bearing battery. The system comprises a sensing module, a judging module, an alarm module, a monitoring module, a control module and a message module. The system realizes real-time collection of battery voltage, current, temperature and pressure information through a distributed high-precision sensor array, dynamic correction of a charging and discharging optimization algorithm of pressure and temperature coupling by using an LSTM neural network, and risk assessment and remote regulation and control in combination with a three-level alarm mechanism and a QPSK underwater acoustic communication protocol. The system reduces dormant power consumption through an energy consumption optimization module, and improves battery life based on an environmental pressure correction algorithm, thereby solving the problems of insufficient battery monitoring precision and poor adaptability in a deep-sea high-pressure and low-temperature environment, and significantly improving the operation safety and operation and maintenance efficiency of the deep-sea battery.

Description

Technical Field

[0001] This invention belongs to the field of deep-sea battery technology, specifically, it relates to an intelligent charge and discharge monitoring system suitable for deep-sea self-pressure batteries. Background Technology

[0002] With the development of deep-sea exploration technology, deep-sea self-pressure batteries, as energy devices without additional pressure-resistant casings, face unique challenges in charge and discharge monitoring: the high pressure (up to 100 MPa or more), low temperature, and corrosive environment of the deep sea significantly affect battery performance, while existing battery monitoring systems are mostly designed for atmospheric pressure environments, which have the following problems:

[0003] ① The monitoring parameter is singular and does not consider the impact of pressure on the battery;

[0004] ② The algorithm lacks environmental adaptability and cannot dynamically adjust the charging and discharging strategy;

[0005] ③ Deep-sea acoustic communication is unreliable and data transmission is prone to interruption;

[0006] ④ The energy consumption management is crude and cannot meet the needs of long-term deep-sea operations.

[0007] Therefore, there is an urgent need for an intelligent monitoring system specifically designed for deep-sea self-pressure batteries.

[0008] In view of this, the present invention is proposed. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an intelligent charging and discharging monitoring system suitable for deep-sea self-pressure-bearing batteries, thus solving the problems mentioned in the background art.

[0010] To solve the above-mentioned technical problems, the basic concept of the technical solution adopted by the present invention is as follows:

[0011] A smart charge and discharge monitoring system for deep-sea self-pressure-bearing batteries includes:

[0012] The sensing module is used to collect real-time voltage, current, temperature, and environmental pressure information of the deep-sea self-supporting pressure battery through high-precision pressure sensors, temperature sensors, voltage sensors, and current sensors. The pressure sensor has a range of 0-120MPa and an accuracy of 0.1%FS, while the temperature sensor has a response time of ≤0.5s. The sensing module is connected to a linkage unit and a storage unit. When a pressure change rate exceeding 0.5MPa / s is detected, the linkage unit triggers the hardware protection switch of the battery charging and discharging circuit, and the storage unit records the full parameter data for 15 minutes before and after the change in time sequence. The sensing module adopts a distributed sensor array, arranging at least three sets of pressure and temperature composite sensors at different locations in the battery pack, and uses a Kalman filter algorithm to fuse multi-source data to reduce measurement errors.

[0013] The determination module is used to set the charging and discharging safety range and limit threshold, receive status information from the sensing module, and calculate the optimized charging and discharging current; the calculation formula is: Among them, I opt I represents the optimized charge / discharge current. max k is the maximum allowable charge and discharge current of the battery. p P is the pressure influence coefficient, where P is the current ambient pressure and P0 is the standard atmospheric pressure. max The maximum pressure the battery is designed to withstand, kt is the temperature degradation coefficient, T is the current ambient temperature, T0 is the standard reference temperature, and k p and k t The system dynamically corrects the pressure and temperature coupling relationship based on historical operating data in real time. The judgment module and the monitoring module interact via underwater acoustic communication. The monitoring module uses an LSTM neural network model to predict pressure and temperature changes in the next 0.5-2 hours and adaptively adjusts k based on the prediction results. p and k t The step size for taking values;

[0014] The alarm module receives the judgment result from the judgment module and triggers an alarm signal when there is an abnormality. When there is a normal condition, it jumps to the judgment module to refresh and run. The alarm module sets three alarm thresholds. When the charging and discharging current exceeds the safe range by 10%, a level 1 alarm is triggered. When it exceeds 20%, a level 2 alarm is triggered and the backup power supply is switched on. When it exceeds 30%, a level 3 alarm is triggered and the charging and discharging circuit is forcibly cut off.

[0015] The monitoring module predicts potential abnormal trends based on environmental pressure change rate, temperature rise rate, and historical charge / discharge data, generating risk coefficient assessment results. When calculating the risk coefficient, the monitoring module incorporates a battery cycle life loss factor, calculated using the following formula: Where R is the real-time risk coefficient, R0 is the baseline risk value, λ is the environmental impact weighting coefficient, and ΔP and ΔT are the real-time fluctuations of pressure and temperature, respectively.

[0016] The control module receives the risk factor assessment results. When the risk factor exceeds the threshold, it controls the charging and discharging equipment to perform current derating or pause operations through the underwater acoustic communication protocol. The control module is interconnected with the storage unit, which pre-stores multiple sets of charging and discharging strategy templates. The control module automatically matches and executes the corresponding strategy template based on the risk factor and the remaining battery capacity.

[0017] The message module generates encrypted messages from battery status information, judgment results, alarm logs, and control records, and feeds them back to the deep-sea operation platform via a QPSK-modulated underwater acoustic communication network. The message module employs an underwater acoustic communication protocol with forward error correction coding (FEC), controlling the bit error rate to within 10%. -7 The following features support real-time data transmission and resume capability at depths of up to 1500 meters in the deep sea.

[0018] The energy consumption optimization module dynamically adjusts the sampling frequency of the monitoring module based on the battery self-discharge rate and ambient temperature collected by the sensing module. In the battery dormant state, the sampling interval is extended to 5-10 times that in the normal state.

[0019] The human-machine interaction module receives parameter configuration commands from the maintenance terminal via underwater acoustic communication, and supports remote adjustment of charging and discharging safety range, risk coefficient threshold and alarm level.

[0020] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all of the following advantages at the same time:

[0021] 1. Pressure measurement error is reduced by using three sets of distributed pressure and temperature composite sensors and Kalman filtering algorithm; when the pressure change rate is >0.5MPa / s, the linkage unit cuts off the charging and discharging circuit within 100ms and records the full parameter data for 5 minutes before and 10 minutes after the change at a sampling rate of 100Hz, providing a complete data chain for fault root cause analysis.

[0022] 2. The decision-making module incorporates an LSTM neural network and gradient descent method to dynamically correct the pressure influence coefficient k. p and temperature decay coefficient k t Improve the accuracy of charging and discharging current optimization compared to static algorithms; when the pressure is >0.8Pmax, automatically activate the high-pressure correction mode to adapt to nonlinear pressure changes in the deep sea;

[0023] 3. The control module pre-stores scenario-based strategies such as high-voltage current limiting and low-temperature preheating. When the battery is in hibernation, the sampling frequency of the sensing module is reduced and non-core sensors are powered off, thereby reducing power consumption and supporting the continuous operation of deep-sea equipment. The risk assessment introduces the battery cycle life factor λ to quantify the risk coefficient R and improve the accuracy of early warning.

[0024] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings described below are merely some embodiments. Those skilled in the art can obtain other drawings based on these drawings without any creative effort. In the drawings:

[0026] Figure 1 This is a panoramic view of the module interaction of the present invention.

[0027] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0028] The invention will now be described in further detail with reference to the accompanying drawings.

[0029] Please see Figure 1 As shown, this embodiment provides an intelligent charge and discharge monitoring system suitable for deep-sea self-pressure-bearing batteries, including:

[0030] The sensing module is used to collect real-time voltage, current, temperature, and environmental pressure information of the deep-sea self-supporting pressure battery through high-precision pressure sensors, temperature sensors, voltage sensors, and current sensors. The pressure sensor range covers 0-120MPa with an accuracy of 0.1%FS, and the temperature sensor response time is ≤0.5s. The sensing module is connected to a linkage unit and a storage unit. When a pressure change rate exceeding 0.5MPa / s is detected, the linkage unit triggers the hardware protection switch of the battery charging and discharging circuit, and the storage unit records the full parameter data for 15 minutes before and after the change in time sequence. The full parameter data includes... Voltage, current, temperature, and pressure; the sensing module adopts a distributed sensor array, with at least three sets of pressure and temperature composite sensors (pressure range 0-120MPa, accuracy 0.1%FS, temperature sensor response time 0.3s) arranged at the top, middle, and bottom of the battery pack. The multi-source data is fused through a Kalman filter algorithm, reducing the error to within 0.5%. When the pressure change rate exceeds 0.5MPa / s, the linkage unit cuts off the charging and discharging circuit within 100ms, and the storage unit records the full parameter data (voltage, current, temperature, and pressure) for 5 minutes before and 10 minutes after the change at a sampling rate of 100Hz.

[0031] The determination module is used to set the charging and discharging safety range and limit threshold, receive status information from the sensing module, and calculate the optimized charging and discharging current; the calculation formula is: Among them, I opt I represents the optimized charge / discharge current. max k is the maximum allowable charge and discharge current of the battery. p P is the pressure influence coefficient, where P is the current ambient pressure and P0 is the standard atmospheric pressure. max k is the maximum stress that the battery design can withstand. t Here, T is the temperature decay coefficient, T0 is the current ambient temperature, and k is the standard reference temperature. p and k t The system dynamically corrects the pressure and temperature coupling relationship based on historical operating data in real time. The judgment module and the monitoring module interact via underwater acoustic communication. The monitoring module uses an LSTM neural network model to predict pressure and temperature changes in the next 0.5-2 hours and adaptively adjusts k based on the prediction results. p and k t The value retrieval step size; the determination module executes the following steps every 30 seconds:

[0032] Receive the LSTM model output from the monitoring module (predicted pressure and temperature values ​​for the next hour, with a prediction error of ≤3%).

[0033] Based on historical 72-hour operating data, the gradient descent method is used to dynamically correct k. p and k t Correction step size adaptive adjustment (when pressure change rate > 1 MPa / h, step size × 1.5);

[0034] When the measured pressure P exceeds 0.8Pmax, the high pressure correction mode is automatically activated, and the value of kp is increased by 20%.

[0035] The alarm module receives the judgment result from the judgment module and triggers an alarm signal when there is an abnormality. When there is a normal condition, it jumps to the judgment module to refresh and run. The alarm module sets three alarm thresholds. When the charging and discharging current exceeds the safe range by 10%, a level 1 alarm is triggered. When it exceeds 20%, a level 2 alarm is triggered and the backup power supply is switched on. When it exceeds 30%, a level 3 alarm is triggered and the charging and discharging circuit is forcibly cut off.

[0036] Level 1 alarm (current exceeds safe range by 10%): The alarm module drives the underwater audible and visual alarm to flash, and the message module sends a warning message (priority P1);

[0037] Level 2 alarm (over 20%): Initiate backup power switching and simultaneously send an emergency message (priority P2) containing pressure-temperature curves to the maintenance terminal;

[0038] Level 3 alarm (over 30%): The control module forcibly disconnects the main circuit, and the message module sends a shutdown message with the highest priority (P3), along with data from the 15 minutes prior to the fault.

[0039] The monitoring module predicts potential abnormal trends based on environmental pressure change rate, temperature rise rate, and historical charge / discharge data, generating risk coefficient assessment results. When calculating the risk coefficient, the monitoring module incorporates a battery cycle life loss factor, calculated using the following formula: Where R is the real-time risk coefficient, R0 is the baseline risk value, λ is the environmental impact weighting coefficient, and ΔP and ΔT are the real-time fluctuations of pressure and temperature, respectively; the monitoring module evaluates the risk coefficient according to the following process:

[0040] Calculate the pressure fluctuation ΔP = |PP avg | Temperature rise rate Where P represents the real-time input data of the monitoring module, P avg The monitoring module calculates the average pressure based on historical data on a rolling basis, v T For the rate of temperature rise, T t The temperature value collected at the current moment, T t-10min The temperature value was collected 10 minutes ago;

[0041] Retrieve battery cycle life data and determine the environmental impact weighting coefficient λ = 0.01 × number of cycles / 1000;

[0042] Substitute into the formula A Level 1 warning is generated when R ≥ 0.8Rmax; ΔT is the value of v calculated from the temperature rise rate. T ;

[0043] The control module receives the risk factor assessment results. When the risk factor exceeds the threshold, it controls the charging and discharging equipment to perform current derating or pause operations through the underwater acoustic communication protocol. The control module is interconnected with the storage unit, which pre-stores multiple sets of charging and discharging strategy templates. The control module automatically matches and executes the corresponding strategy template based on the risk factor and the remaining battery capacity.

[0044] The control module includes the following sub-processes:

[0045] Strategy template matching:

[0046] High-pressure mode: When the pressure is greater than 0.6Pmax, the "deep-sea high-pressure strategy" is automatically applied, limiting the charging and discharging current to 0.7Imax;

[0047] Low temperature mode: When the temperature is < -20℃, the "low temperature preheating strategy" is activated, and the device is charged at a constant current of 0.3Imax for 1 hour.

[0048] Energy consumption optimization:

[0049] When the battery is in sleep mode (no charging or discharging operation for more than 6 hours), the power optimization module reduces the sampling frequency of the sensing module from 10Hz to 1Hz and shuts down the power supply of non-core sensors, reducing power consumption by 65%.

[0050] Wake-up conditions: Pressure change > 5MPa or receiving a wake-up command from the message module;

[0051] The message module generates encrypted messages from battery status information, judgment results, alarm logs, and control records, and feeds them back to the deep-sea operation platform via a QPSK-modulated underwater acoustic communication network. The message module employs an underwater acoustic communication protocol with forward error correction coding (FEC), controlling the bit error rate to within 10%. -7 The following features support real-time data transmission and resume capability at depths of up to 1500 meters in the deep sea.

[0052] The message module uses the following communication parameters:

[0053] Modulation method: QPSK, carrier frequency 10-15kHz (suitable for deep-sea sonar windows);

[0054] Error correction encoding: Reed-Solomon(255,239), which can correct 8-byte errors;

[0055] Transmission cycle: 1 minute / transmission under normal conditions, 10 seconds / transmission under abnormal conditions;

[0056] Resume interrupted downloads: The sliding window protocol is used, and the timeout for retransmitting lost data packets is set to 5 seconds;

[0057] The energy consumption optimization module dynamically adjusts the sampling frequency of the monitoring module based on the battery self-discharge rate and ambient temperature collected by the sensing module. In the battery dormant state, the sampling interval is extended to 5-10 times that in the normal state.

[0058] The human-machine interaction module receives parameter configuration commands from the maintenance terminal via underwater acoustic communication, supporting remote adjustment of charging and discharging safety range, risk coefficient threshold, and alarm level. The human-machine interaction is implemented as follows: the maintenance terminal sends parameter configuration commands (such as "safety range voltage limit + 0.5V") via underwater acoustic communication. After parsing the commands, the human-machine interaction module completes the safety range update within 5 seconds and returns a confirmation message.

[0059] Beneficial effects:

[0060] 1. Pressure measurement error is reduced by using three sets of distributed pressure and temperature composite sensors and Kalman filtering algorithm; when the pressure change rate is >0.5MPa / s, the linkage unit cuts off the charging and discharging circuit within 100ms and records the full parameter data for 5 minutes before and 10 minutes after the change at a sampling rate of 100Hz, providing a complete data chain for fault root cause analysis.

[0061] 2. The decision-making module incorporates an LSTM neural network and gradient descent method to dynamically correct the pressure influence coefficient k. p and temperature decay coefficient k t Improve the accuracy of charging and discharging current optimization compared to static algorithms; when the pressure is >0.8Pmax, automatically activate the high-pressure correction mode to adapt to nonlinear pressure changes in the deep sea;

[0062] 3. The control module pre-stores scenario-based strategies such as high-voltage current limiting and low-temperature preheating. When the battery is in sleep mode, the sampling frequency of the sensing module is reduced and non-core sensors are powered off, thereby reducing power consumption and supporting the continuous operation of deep-sea equipment. The risk assessment introduces the battery cycle life factor λ to quantify the risk coefficient R and improve the accuracy of early warning.

[0063] 4. The message module uses QPSK modulation and Reed-Solomon (255,239) error correction, achieving a bit error rate of ≤10% in a deep-sea environment at a depth of 1500 meters. -7 The dynamic transmission cycle (1 minute for normal, 10 seconds / 5 seconds for abnormal) balances real-time performance and energy consumption; the alarm module constructs a three-level threshold response: 10% over-limit triggers audible and visual alarms, 20% over-limit switches to backup power, and 30% over-limit forcibly cuts off the circuit, and synchronously uploads data from 15 minutes before the fault, realizing a complete closed loop of early warning, handling, and source tracing.

[0064] This invention is not limited to the embodiments described above. Anyone should understand that structural changes made under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention. Technical aspects, shapes, and structures not described in detail in this invention are all publicly known technologies.

Claims

1. An intelligent charge-discharge monitoring system suitable for deep-sea self-pressurized battery, characterized in that, include: The sensing module is used to collect real-time information on the voltage, current, temperature and environmental pressure of the deep-sea self-pressure-bearing battery through high-precision pressure sensors, temperature sensors, voltage sensors and current sensors. The determination module is used to set the charging and discharging safety range and limit threshold, receive the status information of the sensing module, and calculate the optimized charging and discharging current based on the pressure influence coefficient, temperature decay coefficient and environmental pressure and temperature parameters through a pressure-temperature coupled charging and discharging optimization algorithm. The alarm module receives the judgment result from the judgment module, triggers an alarm signal when there is an abnormality, and jumps to the judgment module to refresh and run when there is no abnormality. The monitoring module is used to predict potential abnormal trends based on the rate of change of environmental pressure, the rate of temperature rise, and historical charging and discharging data, and to generate risk coefficient assessment results. The control module receives the risk coefficient assessment results. When the risk coefficient exceeds the threshold, it controls the charging and discharging equipment to perform current derating or pause operation through the underwater acoustic communication protocol. The message module is used to generate encrypted messages from battery status information, judgment results, alarm logs and control records, and feed them back to the deep-sea operation platform through an underwater acoustic communication network that supports QPSK modulation.

2. The intelligent charge-discharge monitoring system for self-pressurized battery in deep sea according to claim 1, characterized in that, The sensing module is connected to a linkage unit and a storage unit. When the pressure change rate is detected to exceed 0.5 MPa / s, the linkage unit triggers the hardware protection switch of the battery charging and discharging circuit, and the storage unit records the full parameter data for 15 minutes before and after the change in time sequence.

3. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea as claimed in claim 1 wherein, The judgment module and the monitoring module interact via underwater acoustic communication. The monitoring module uses an LSTM neural network model to predict pressure and temperature changes over the next 0.5-2 hours and adaptively adjusts the system based on the prediction results. and The step size for taking values.

4. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea according to claim 1, wherein, The message module employs an underwater acoustic communication protocol with forward error correction coding, and the bit error rate is controlled within 10%. -7 The following features support real-time data transmission and resume capability at depths of up to 1500 meters in the deep sea.

5. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea as claimed in claim 1 wherein, It also includes an energy consumption optimization module, which dynamically adjusts the sampling frequency of the monitoring module based on the battery self-discharge rate and ambient temperature collected by the sensing module, and extends the sampling interval to 5-10 times that of the normal state when the battery is in dormant state.

6. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea according to claim 1, wherein, The alarm module is set with three alarm thresholds. When the charging and discharging current exceeds the safe range by 10%, a level one alarm is triggered. When it exceeds 20%, a level two alarm is triggered and the backup power supply is switched on. When it exceeds 30%, a level three alarm is triggered and the charging and discharging circuit is forcibly cut off.

7. The intelligent charge and discharge monitoring system for deep-sea self-pressure-bearing batteries according to claim 1, characterized in that, The control module is interconnected with the storage unit, which pre-stores multiple sets of charging and discharging strategy templates. The control module automatically matches and executes the corresponding strategy template based on the risk coefficient and the remaining battery capacity.

8. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea according to claim 1, wherein, The sensing module employs a distributed sensor array, with at least three sets of pressure and temperature composite sensors arranged at different locations in the battery pack. The Kalman filter algorithm is used to fuse multi-source data to reduce measurement errors.

9. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea as claimed in claim 1 wherein, It also includes a human-machine interaction module, which receives parameter configuration instructions from the maintenance terminal through underwater acoustic communication, and supports remote adjustment of charging and discharging safety range, risk coefficient threshold and alarm level.

10. The intelligent charge-discharge monitoring system for self-pressurized battery suitable for deep sea as claimed in claim 1 wherein, When calculating the risk coefficient, the monitoring module introduces a battery cycle life loss factor, the formula of which is: in For real-time risk coefficient, As the benchmark risk value, The environmental impact weighting coefficient is λ = 0.01 × number of cycles / 1000. and These are the real-time fluctuations in pressure and temperature, respectively.

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