Internal Short Circuit Detection Method, Device, Equipment and Medium for Yacht Energy Storage Battery

Abstract

The present invention relates to the technical field of energy storage batteries, and discloses a method, device, equipment and medium for detecting internal short circuits in yacht energy storage batteries. The method includes: sampling the voltage and current of a yacht energy storage battery pack to obtain original sampling data; performing parameter identification to obtain RC network dynamic response data; performing sliding window Fourier transform and wavelet packet decomposition to obtain target feature vectors; based on the target feature vectors, performing voltage mutation feature judgment, current mutation feature judgment and impedance spectrum feature judgment to obtain multi-level fault judgment results; starting a three-loop nested control structure and inputting it into a PWM modulation unit to obtain a control compensation signal; inputting the control compensation signal into a current sharing controller to adjust the PWM duty ratio of parallel branches to obtain a current sharing control signal. The present invention improves the reliability of fault detection, realizes the coordinated control of state protection, power balance and current tracking, and effectively solves the current distribution problem of parallel branches.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage batteries, and particularly to a method, device, equipment and medium for detecting internal short circuits in yacht energy storage batteries. Background Art

[0002] The yacht energy storage battery system is the core component of yacht electric propulsion, and its safety and reliability directly affect the navigation safety of the yacht. With the development of the electrification trend of yachts, the series-parallel structure of large-capacity energy storage battery packs is becoming increasingly complex, and the risk of internal short circuit faults has increased significantly. Traditional short circuit detection methods mainly rely on the judgment of voltage and current thresholds, but in the marine environment, due to the influence of factors such as temperature and humidity, false judgments and missed judgments are likely to occur. In addition, the frequent start-stop transitions and changes in navigation conditions of the yacht energy storage system make it difficult to accurately extract the short circuit fault characteristics.

[0003] When the yacht energy storage battery pack operates in the marine environment for a long time, its internal materials are prone to aging and corrosion, resulting in an increased risk of local short circuits. Currently, the short circuit detection technology generally uses a single frequency domain feature analysis method, such as fast Fourier transform or continuous wavelet transform, but the detection accuracy of these methods varies greatly under different working conditions. Especially when the yacht is sailing at high speed, the dynamic response characteristics of the battery pack change violently, and traditional detection methods are difficult to meet the real-time monitoring requirements. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for detecting internal short circuits in yacht energy storage batteries. The present invention improves the reliability of fault detection, realizes the coordinated control of state protection, power balance and current tracking, and effectively solves the current distribution problem of parallel branches.

[0005] In a first aspect, the present invention provides a method for detecting internal short circuits in yacht energy storage batteries, and the method for detecting internal short circuits in yacht energy storage batteries includes:

[0006] Sampling the voltage and current of the yacht energy storage battery pack to obtain original sampling data;

[0007] Inputting the original sampling data into a three-stage RC parallel network model for parameter identification to obtain RC network dynamic response data;

[0008] Performing sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain a target feature vector;

[0009] Based on the target feature vector, performing voltage mutation feature judgment, current mutation feature judgment and impedance spectrum feature judgment to obtain a multi-level fault judgment result;

[0010] Start the three - loop nested control structure according to the multi - level fault judgment result, input the calculation results of current tracking control, power balance control, and status protection control into the PWM modulation unit to obtain a control compensation signal;

[0011] Input the control compensation signal into the current sharing controller to adjust the PWM duty cycle of the parallel branches to obtain a current sharing control signal.

[0012] In a second aspect, the present invention provides a device for detecting internal short - circuits in a yacht energy storage battery. The device for detecting internal short - circuits in a yacht energy storage battery includes:

[0013] A sampling module for sampling the voltage and current of the yacht energy storage battery pack to obtain original sampling data;

[0014] A parameter identification module for inputting the original sampling data into a three - stage RC parallel network model for parameter identification to obtain RC network dynamic response data;

[0015] A decomposition module for performing sliding - window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain a target feature vector;

[0016] A judgment module for performing voltage mutation feature judgment, current mutation feature judgment, and impedance spectrum feature judgment based on the target feature vector to obtain a multi - level fault judgment result;

[0017] A control compensation module for starting a three - loop nested control structure according to the multi - level fault judgment result, inputting the calculation results of current tracking control, power balance control, and status protection control into the PWM modulation unit to obtain a control compensation signal;

[0018] A current sharing control module for inputting the control compensation signal into the current sharing controller to adjust the PWM duty cycle of the parallel branches to obtain a current sharing control signal.

[0019] In a third aspect of the present invention, a computer device is provided, including: a memory and at least one processor. Instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the computer device executes the above - mentioned method for detecting internal short - circuits in a yacht energy storage battery.

[0020] In a fourth aspect of the present invention, a computer - readable storage medium is provided. Instructions are stored in the computer - readable storage medium, and when it runs on a computer, it causes the computer to execute the above - mentioned method for detecting internal short - circuits in a yacht energy storage battery.

[0021] In the technical solution provided by the present invention, by adopting a three-stage RC parallel network model and combining with the correction of marine environmental parameters, the dynamic characteristics of the yacht energy storage battery under different working conditions are accurately described; through the combined application of sliding window Fourier transform and wavelet packet decomposition, multi-scale feature extraction is realized, and the integrity of feature information is enhanced; a three-stage judgment mechanism based on voltage mutation, current mutation and impedance spectrum is designed, and hysteresis comparison processing is introduced to improve the reliability of fault detection; a three-ring nested control structure is proposed to realize the coordinated control of state protection, power balance and current tracking, ensuring the stability of the system output; based on the master-slave current sharing control strategy, the current distribution problem of parallel branches is effectively solved, and the dynamic response ability of the system is improved. Through the coordinated cooperation of multi-level protection and control strategies, the safety and reliability of the system are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic diagram of the steps of the method for detecting internal short circuit of the yacht energy storage battery in the embodiment of the present invention;

[0024] Figure 2 It is a schematic diagram of the structure of the device for detecting internal short circuit of the yacht energy storage battery in the embodiment of the present invention;

[0025] Figure 3 It is a schematic block diagram of the structure of the computer device in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The embodiments of the present invention provide a method, device, equipment and medium for detecting internal short circuit of a yacht energy storage battery. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Please refer to Figure 1 , an embodiment of the method for detecting internal short circuit of a yacht energy storage battery in the embodiments of the present invention includes:

[0028] Step S1: Sample the voltage and current of the yacht energy storage battery pack to obtain the original sampling data;

[0029] It can be understood that the execution subject of the present invention can be a device for detecting internal short circuit of a yacht energy storage battery, or a terminal or a server. Specifically, it is not limited here. In the embodiments of the present invention, the server is taken as the execution subject for illustration.

[0030] Specifically, high-precision sampling is performed on the voltage and current of the yacht energy storage battery pack. Among them, the sampling channels of the voltage acquisition unit are differentially amplified to improve the anti-interference ability of the signal and ensure the accuracy of the signal. During this process, a high-precision instrumentation amplifier is used to amplify the voltage signal of the sampling channel, and the input signal is corrected through a precision resistor network to ensure that the sampled voltage signal is not affected by external interference sources. After the amplification process, a voltage acquisition gain coefficient is obtained, which is used to compensate for the errors in the sampling circuit, making the sampling data more accurate. The voltage acquisition gain coefficient is input into the voltage sampling circuit to perform high-precision sampling on the voltages of multiple series-connected battery cells in the yacht energy storage battery pack. The sampling process uses a 16-bit precision ADC to ensure that the voltage measurement results have sufficient resolution and accuracy. At the same time, the output signal of the Hall sensor in the current acquisition unit is subjected to operational amplification processing. A high-gain operational amplifier is used to enhance the signal, and high-frequency noise is eliminated through a filter to obtain a current acquisition gain coefficient. The current acquisition gain coefficient is input into the current sampling circuit to sample the currents of two parallel branches of the yacht energy storage battery pack. This sampling process also uses a 16-bit precision ADC to ensure the high precision and high resolution of the current measurement. When sampling the current signal, temperature compensation and signal filtering are performed to obtain current sampling data. The sampling data is subjected to ADC conversion. The sampled voltage and current data are converted into digital data through a 12-bit ADC, converting the analog signal into a data format that can be calculated and analyzed by a digital signal processor (DSP), enabling subsequent short-circuit detection algorithms to process and analyze the data. To ensure the stability and real-time performance of data transmission, the converted digital sampling data is transmitted to the digital signal processor through the CAN bus. During the data transmission process, the CAN bus supports multi-node communication and allows the system to maintain high-reliability data transmission in a complex electrical environment, avoiding data damage or loss caused by transmission errors. After receiving the data, the digital signal processor performs verification processing on the sampling data to ensure the integrity and accuracy of the data. The verification process uses methods such as CRC (Cyclic Redundancy Check) or parity check to verify whether errors have occurred during data transmission and perform data retransmission if necessary to improve the reliability of the data. The data after verification processing is used as the original sampling data for subsequent short-circuit detection algorithm analysis.

[0031] Step S2: Input the original sampling data into a three-stage RC parallel network model for parameter identification to obtain RC network dynamic response data;

[0032] Specifically, consider the impact of the marine environment on battery performance. Due to the large temperature and humidity fluctuations in the marine environment, these factors will affect the internal resistance, polarization characteristics, and charge-discharge efficiency of the battery. Therefore, the marine environment parameters are used to correct the original sampling data to ensure the calculation accuracy of the RC model. In this process, the original sampling data is corrected based on the temperature coefficient and humidity coefficient. By establishing relationship models such as temperature-impedance and humidity-leakage current, the voltage and current sampling data of the battery are corrected to obtain the environment-corrected data. The correction process uses non-linear interpolation or data fitting methods to ensure that the corrected data can accurately reflect the actual working state of the battery. The environment-corrected data is input into the three-stage RC parallel network model, which is used to characterize the dynamic response characteristics of the battery. Among them, different branches of the three-stage RC network correspond to different yacht working conditions. Therefore, appropriate time constants are set according to different working modes. The time constant of the fast charge-discharge response branch is set to the first target value, which is used to describe the short-time high-power charge-discharge process, such as the acceleration or sudden load change of the yacht. The time constant of the start-stop transition branch is set to the second target value, which is used to describe the energy change characteristics when the yacht starts or stops, usually involving large current changes. The time constant of the sailing steady-state branch is set to the third target value, which reflects the steady-state charge-discharge characteristics of the battery during the stable sailing of the yacht. The RC branch parameters of the yacht working conditions are discretized to facilitate subsequent numerical calculations and real-time control. In the discretization process, the state-space expression method is used, and the capacitor voltage and temperature state of the three-stage RC parallel network model are used as state variables to establish an extended discrete state equation. This equation can describe the dynamic behavior of the battery and perform numerical calculations at different time steps, enabling the system to adapt to different sampling periods and calculation accuracy requirements. Since the dynamic response of the battery is greatly affected by temperature, humidity, and load changes, these environmental factors are introduced into the discrete state equation and an adaptive algorithm is used to correct them. To improve the adaptability of the model, the least squares method is used to calculate the response data of the battery pack under different temperature, humidity, and load conditions according to the extended discrete state equation to obtain the environment-adaptive parameter matrix. The least squares calculation solves the model parameters through the optimal fitting method, so that the prediction results of the model can be closest to the actual observed data to the greatest extent. This calculation uses the recursive least squares or Kalman filter method to ensure the stability and convergence of parameter estimation. The environment-adaptive parameter matrix enables the RC network model to dynamically adjust its own parameters to adapt to different marine environment conditions. Multivariable correlation calculations are performed on the environment-adaptive parameter matrix to establish the mapping relationship between the RC parameters and temperature, humidity, and state of charge (SOC). This mapping relationship is used to describe the change law of the RC parameters under different environmental conditions. For example, an increase in temperature leads to a decrease in the internal resistance of the battery, while an increase in humidity leads to an increase in the leakage current of the battery.Construct an RC parameter correction model through multivariate regression or machine learning methods, and train it using an environment-adaptive parameter matrix to obtain more accurate RC parameter correction values. Update the RC parameter correction values to the extended discrete state equation for numerical calculation to obtain RC network dynamic response data. Euler's method or the Runge-Kutta method is used for numerical calculation to ensure the stability and accuracy of the calculation. During the calculation process, the corrected RC parameters are used to calculate the transient and steady-state responses of the battery and are used in subsequent short-circuit detection algorithms.

[0033] Step S3: Perform sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain the target feature vector;

[0034] Specifically, sliding window processing is performed on the RC network dynamic response data and the original sampling data to eliminate the non-stationarity of the data in the time domain. During the sliding window processing, an overlapping window technique is adopted to avoid signal distortion problems caused by window boundary effects. The selection of the window length needs to be optimized in combination with the dynamic characteristics of the yacht energy storage battery so that it can capture short-term transient characteristics and retain long-term trend information. After completing the sliding window processing, the data is subjected to spectral leakage suppression processing to reduce the spectral expansion problem caused by the finite sampling time and obtain de-spectral leakage data. By applying a window function, such as a Hanning window or a Kaiser window, the high-frequency interference caused by spectral leakage is suppressed, making the spectral characteristics of the signal clearer. The de-spectral leakage data is input into a second-order Butterworth low-pass filter. Due to its maximum flat amplitude-frequency response characteristic, the Butterworth filter can effectively remove high-frequency noise without introducing significant phase distortion, enabling the filtered signal to retain the original dynamic characteristics. In this step, the cut-off frequency of the second-order filter is optimized and set according to the operating frequency range of the energy storage battery, and is set at the upper limit of the short-circuit characteristic frequency to ensure that key fault information is not filtered out. The signal after low-pass filtering is the preprocessed signal data. The fast Fourier transform operation is performed on the preprocessed signal data to transform the signal from the time domain to the frequency domain and extract the frequency characteristics of the signal. The fast Fourier transform (FFT) can efficiently calculate the spectral information of the signal. Especially when a battery short-circuit fault occurs, due to the change in the equivalent resistance, the dynamic response signal of the battery will change significantly within a specific frequency range. The frequency-domain characteristic data of the battery short-circuit fault is obtained through FFT analysis. During the FFT calculation process, to avoid the influence of aliasing, it is ensured that the sampling frequency satisfies the Nyquist sampling theorem, and amplitude normalization is performed after the spectral calculation to improve the stability of feature extraction. The phase difference between adjacent characteristic frequency points is calculated based on the frequency-domain characteristic data to obtain the phase difference characteristic data. During the calculation process, the phase information of the frequency-domain signal is extracted, and then the differential method is used to calculate the phase change amount between adjacent frequency points, and the phase mutation threshold is set in combination with the short-circuit characteristic frequency range, so as to screen out the phase difference characteristic data related to the short-circuit fault. At the same time, wavelet packet decomposition is performed on the preprocessed signal data to extract the time-frequency characteristics of the signal. Wavelet packet decomposition is a multi-scale analysis method that decomposes the signal into different frequency bands to obtain more detailed energy distribution information. In the specific implementation process, a suitable wavelet basis function, such as the Daubechies wavelet, is selected to ensure the stability and calculation efficiency of the decomposition. Then the signal is decomposed into multiple layers, and the energy distribution of each frequency band is calculated to obtain the wavelet energy characteristic data. Vector mapping is performed on the frequency-domain characteristic data, the phase difference characteristic data, and the wavelet energy characteristic data to unify different types of characteristic data.During the vector mapping process, different features are normalized to eliminate the magnitude differences between different features, and the standardization method is used to adjust the data distribution to make it meet the normal distribution assumption, thereby improving the effect of subsequent data fusion. Feature selection is performed on the normalized feature data to remove redundant information and improve the effectiveness of the feature data. Vector fusion is performed on the mapped feature data to form the final target feature vector.

[0035] Step S4: Based on the target feature vector, perform voltage mutation feature judgment, current mutation feature judgment, and impedance spectrum feature judgment to obtain a multi-level fault judgment result;

[0036] Specifically, perform vector demapping on the target feature vector to obtain each feature component data. Use the inverse mapping function to decompose the fused feature vector back into its independent feature components for subsequent analysis and calculation. During the demapping process, perform feature alignment to ensure that the feature component data extracted from the target feature vector is consistent in the time and frequency scales, thus ensuring the accuracy of the analysis. Calculate the voltage drop of the frequency-domain features in the feature component data to determine the voltage mutation feature judgment result. When a short-circuit fault occurs, the reduction of the internal equivalent impedance of the battery will cause a significant drop in the terminal voltage. By calculating the change in the voltage drop of the voltage signal within a specific frequency range, it is judged whether there is a short-circuit fault. The method of calculating the voltage drop uses amplitude difference calculation, that is, perform a subtraction operation on the voltage spectrum amplitudes within a specific time window, and combine it with a set voltage mutation threshold for judgment. If the calculated voltage drop exceeds the set threshold, it is determined that there is a possibility of a short-circuit fault. To improve the accuracy of the calculation, while calculating the voltage drop, perform detrending on the signal to eliminate the influence of the long-term voltage change trend on the calculation result. Calculate the current slope of the frequency-domain features in the feature component data according to the voltage feature judgment result to obtain the current feature judgment result. Since the current signal will show a steep rise characteristic when a short-circuit fault occurs, calculate the transient change rate of the current signal to judge the severity of the short-circuit fault. The method of calculating the current slope uses the method of numerical differentiation, that is, calculate the change rate of the current signal within a short time window and combine it with a current mutation threshold for judgment. If the calculated current slope exceeds the preset threshold, it indicates that a serious short-circuit fault has occurred in the system. Since a short-circuit fault may cause high-frequency oscillation of the current signal, it is necessary to smooth the signal when calculating the current slope to remove the influence of high-frequency noise and improve the stability of the calculation. At the same time, to verify the occurrence of the short-circuit fault, perform complex impedance reconstruction on the phase difference feature and wavelet energy feature in the feature component data to obtain impedance spectrum data. The acquisition of the impedance spectrum data is based on the equivalent circuit model of the battery. By performing complex operations on the phase difference feature and wavelet energy feature, the equivalent impedance parameters of the battery are reconstructed, and this is used to characterize the internal state of the battery. The method of complex impedance reconstruction uses the inverse Fourier transform and polar coordinate conversion methods, that is, convert the phase difference feature into the impedance angle, convert the wavelet energy feature into the impedance amplitude, and obtain the impedance spectrum data through polar coordinate conversion. Since a short-circuit fault will cause a mutation of the equivalent impedance of the battery within a specific frequency range, analyze the impedance spectrum data to confirm the occurrence of the short-circuit fault. Calculate the Euclidean distance of the impedance spectrum data to measure the deviation between the current impedance spectrum data and the impedance spectrum under normal operating conditions, thereby judging the severity of the short-circuit fault to obtain the impedance feature judgment result. Perform hysteresis judgment based on the voltage feature judgment result, current feature judgment result, and impedance feature judgment result to obtain a multi-level fault judgment result.The purpose of hysteresis judgment is to avoid misjudgment caused by short-time noise or transient fluctuations. Therefore, when performing multi-level fault judgment, an appropriate hysteresis threshold is set to ensure the stability of fault judgment.

[0037] Step S5: Start the three-loop nested control structure according to the multi-level fault judgment result, input the calculation results of current tracking control, power balance control, and state protection control into the PWM modulation unit to obtain a control compensation signal;

[0038] Specifically, determine the operating state of the current energy storage battery pack according to the multi-level fault judgment results, and select an appropriate control strategy based on the severity of the short-circuit fault. If the detection results indicate that the operating state of the battery pack is still within the safe range, the system continues to maintain the current control strategy. If a short-circuit fault is detected, the control parameters are immediately adjusted to optimize the power flow to reduce the impact of the short-circuit fault. During the startup process of the three-loop nested control structure, the state protection controller in the outer loop performs the calculation. The main function of this controller is to construct a safe operating boundary based on the key operating parameters of the yacht energy storage battery pack, such as temperature, terminal voltage, and current limit. The construction of the safe operating boundary is achieved by setting threshold ranges. Among them, the temperature limit is used to prevent the battery pack from accelerating aging or causing thermal runaway due to overheating, the voltage limit is used to avoid performance degradation caused by overcharging or over-discharging of the battery, and the current limit is used to suppress the damage of abnormal short-circuit current to the internal structure of the battery. During the outer loop calculation process, the state protection controller monitors the operating state of the battery pack in real time and combines the short-circuit detection results to determine whether to enter the protection mode. If the calculation results indicate that the battery has exceeded the safe operating boundary, the state protection controller will generate a state protection instruction to reduce the output power of the battery pack, or trigger the open-circuit protection mechanism in extreme cases to prevent the fault from deteriorating further. After the state protection instruction is generated, this instruction is transmitted as an input signal to the power balance controller in the three-loop nested control structure, entering the middle loop calculation stage. The power balance controller dynamically adjusts the power output of the battery pack according to the start-stop transition condition and the steady-state navigation condition of the yacht to ensure that the system can still maintain the basic energy supply when a short-circuit fault occurs. Since the operating state of the yacht has large variability, for example, during acceleration, deceleration, and steady-state navigation, the power demand for the battery is not the same. Therefore, the power balance controller needs to perform calculations based on the real-time working conditions and dynamically allocate the power output so that the battery pack can stably meet the load demand. In the specific implementation process, this controller adopts model predictive control or adaptive fuzzy control methods to ensure that the power distribution can remain stable in the complex marine environment while minimizing the impact of short-circuit faults on the system performance. During the calculation process, the power balance controller will combine the state protection instruction to optimize the output power of the battery and generate a power balance instruction. When the power balance instruction is generated and enters the inner loop calculation stage of the three-loop nested control structure, the current tracking controller performs the current tracking calculation. The current tracking controller accurately tracks the target current according to the real-time current demand of the battery pack and adjusts the output state of the battery pack through PWM modulation to ensure the accuracy of current distribution. During the current tracking control calculation process, a dynamic current model of the battery pack is constructed, and this model is established by the state space method based on electrochemical impedance to accurately predict the transient response of the current.Based on this model, the current tracking controller adopts proportional-integral-derivative control or sliding mode control method to track the target current and calculate the PWM control instruction. This instruction is input into the PWM modulation unit to generate the corresponding PWM duty cycle signal and adjust the power output of the battery in real time to match the optimal power distribution strategy under short-circuit fault conditions. When the PWM control instruction is input into the PWM modulation unit system, a PWM waveform signal is generated. Digital filtering is performed on the PWM waveform signal to eliminate unnecessary high-frequency components and obtain a smoother control signal. A finite impulse response (FIR) or infinite impulse response (IIR) filter is used to perform low-pass filtering on the PWM signal to remove high-frequency noise while retaining the low-frequency effective components. After the filtering process, a filtered compensation signal is obtained. The filtered compensation signal is subjected to multi-loop correction calculation with the state protection instruction and the power balance instruction to obtain an optimized control compensation signal. During the multi-loop correction calculation process, weighting is performed on the state protection instruction and the power balance instruction to ensure that the control compensation signal can maintain a reasonable weight distribution under different fault levels. Through fuzzy control or Kalman filtering method, the filtered compensation signal is optimized and adjusted to enable it to adapt to the dynamically changing system requirements and ensure the stability of the output signal.

[0039] Step S6: Input the control compensation signal into the current sharing controller to adjust the PWM duty cycle of the parallel branches and obtain the current sharing control signal.

[0040] Specifically, branch current is collected from the control compensation signal to determine the current distribution of each parallel branch. In a parallel battery system, the current distribution among branches is not completely uniform and is limited by factors such as the internal resistance of the battery, the impedance of the connecting cables, and temperature changes. Therefore, the PWM duty cycle is adjusted by real-time monitoring of the current in each branch to maintain current balance in the entire system. During this process, the current sensor samples the current of each branch with high precision, and the digital signal processing unit calculates the average current of each branch. At the same time, the branch with the largest current is identified and defined as the main branch, obtaining the master-slave branch configuration data. PI parameter calculation is performed on the control compensation signal according to the master-slave branch configuration data to obtain the optimal PI control parameters. Since the PI controller has good stability and response characteristics in current sharing control, the PI controller is used to adjust the PWM duty cycle to ensure that the current deviation of each branch is within the allowable range. The calculation of PI parameters is based on the current difference between the main branch and the slave branches and is adaptively adjusted in combination with the dynamic response characteristics of the system. In the specific implementation process, the deviation between the main branch current and the currents of each slave branch is calculated, and the proportional gain (Kp) and integral gain (Ki) of the PI control are calculated based on this deviation. Then, the PI parameters are optimized by online adjustment to ensure the fast convergence and stable operation of the current sharing control. During the PI parameter calculation process, the load characteristics and temperature influence of the battery are considered to avoid system oscillation or control lag caused by improper PI parameters.

[0041] Calculate the deviation between the real-time current and the average current of each parallel branch, and input the calculated PI control parameters into the current sharing controller to obtain the PWM duty cycle compensation value. The calculation of the PWM duty cycle compensation value is based on the current sharing control objective, that is, the currents of all parallel branches should tend to be the same. Therefore, the system calculates the deviation between the real-time current of each branch and the average current of the system, and uses a PI controller to calculate the correction amount of the PWM duty cycle, so that the branch with a larger current reduces the output, while the branch with a smaller current increases the output, thus gradually achieving current sharing. To further improve the control accuracy, an adaptive weight adjustment mechanism is adopted, that is, when calculating the PWM compensation value, the control weight is dynamically adjusted according to the load conditions and operating states of each branch to improve the adaptability of the current sharing control. Judge the overcurrent protection of the parallel branches according to the PWM duty cycle compensation value to prevent a branch from being damaged due to overcurrent caused by a short circuit or other faults. The core principle of the overcurrent protection judgment is to compare the real-time current of each branch with the set safety threshold. If the current of a certain branch exceeds the safety threshold, the system immediately triggers an overcurrent protection instruction to limit the output of the branch or directly cut off the circuit to prevent damage to the battery or related electronic devices. Input the overcurrent protection instruction into the power regulation unit to regulate the output power of the yacht energy storage battery and generate a power regulation signal. If the system detects that a certain branch has an overcurrent, it is necessary to dynamically adjust the power output of the entire system to avoid the impact of power imbalance on the yacht's energy supply. Perform current sharing compensation calculation on the power regulation signal to obtain the current sharing control signal. The current sharing compensation calculation adopts a dynamic adaptive algorithm, that is, under different load conditions, the gain coefficient of the current sharing control is automatically adjusted to ensure the stability and fast response ability of the control.

[0042] In the embodiment of the present invention, by adopting a three-stage RC parallel network model and combining with the correction of marine environment parameters, the dynamic characteristics of the yacht energy storage battery under different working conditions are accurately described; through the combined application of sliding window Fourier transform and wavelet packet decomposition, multi-scale feature extraction is realized, enhancing the integrity of feature information; a three-stage judgment mechanism based on voltage mutation, current mutation and impedance spectrum is designed, and hysteresis comparison processing is introduced to improve the reliability of fault detection; a three-loop nested control structure is proposed to realize the coordinated control of state protection, power balance and current tracking, ensuring the stability of the system output; based on the master-slave current sharing control strategy, the current distribution problem of parallel branches is effectively solved, improving the dynamic response ability of the system. Through the coordinated cooperation of multi-level protection and control strategies, the safety and reliability of the system are enhanced.

[0043] In a specific embodiment, the process of executing step S1 may specifically include the following steps:

[0044] Differentially amplify the sampling channels of the voltage acquisition unit to obtain the voltage acquisition gain coefficient, and input the voltage acquisition gain coefficient into the voltage sampling circuit to sample multiple series-connected battery cells of the yacht energy storage battery pack with 16-bit precision to obtain voltage sampling data;

[0045] Operate and amplify the output signal of the Hall sensor of the current acquisition unit to obtain the current acquisition gain coefficient, and input the current acquisition gain coefficient into the current sampling circuit to sample two parallel branches with 16-bit precision to obtain current sampling data;

[0046] Perform 12-bit ADC conversion on the voltage sampling data and the current sampling data to obtain digitalized sampling data, and transmit the digitalized sampling data to the digital signal processor through the CAN bus to perform verification processing on the received data to obtain the original sampling data.

[0047] Specifically, differentially amplify the sampling channels of the voltage acquisition unit to reduce the influence of common-mode noise and improve the signal quality of the measurement. During the voltage sampling process, since the yacht energy storage battery pack is composed of multiple series-connected single batteries, a differential amplification circuit is constructed by a high-precision operational amplifier, so that the sampling signal can accurately reflect the terminal voltage of each battery cell. Assume that the terminal voltage of a certain battery cell is , then the signal amplified by the differential amplification circuit is expressed as:

[0048]

[0049] Among them, is the voltage acquisition gain coefficient, and are the voltages of the positive and negative terminals of the battery cell respectively. By accurately measuring this gain coefficient, it is ensured that the subsequent sampling circuit can correctly convert the voltage signal and input it into the voltage sampling circuit for high-precision sampling. In the voltage sampling circuit, in order to ensure the measurement accuracy, a 16-bit ADC (analog-to-digital converter) is used to sample multiple series-connected battery cells of the yacht energy storage battery pack. During the sampling process, the measured values of each battery cell are sequentially stored in the data register, and unified sampling is performed through a time synchronization mechanism to avoid misjudgment of the battery state due to sampling time offset. At the same time, in the current acquisition unit, since the output signal of the Hall sensor is usually weak and easily affected by external magnetic field interference, the output signal of the Hall sensor is operated and amplified to enhance the signal amplitude and improve the signal-to-noise ratio. The current signal output by the Hall sensor changes in milliamps, while the sampling circuit requires a higher signal level to ensure the measurement accuracy. An operational amplifier is used to construct a gain circuit so that the amplified signal reaches the optimal input range of the ADC. Assume that the current signal output by the Hall sensor is passes through the current-voltage conversion resistor Converted to a voltage signal , its expression is written as:

[0050]

[0051] After being amplified by an operational amplifier, the obtained voltage signal is:

[0052]

[0053] Among them, is the current acquisition gain coefficient, which determines the amplitude of the amplified signal. By accurately measuring this gain coefficient and inputting it into the current sampling circuit, high-precision measurement of the current in two parallel branches is ensured. During the current sampling process, a 16-bit ADC is used for sampling to ensure the accuracy of the sampled data. The sampled data is converted by a 12-bit ADC to reduce the burden of data storage and transmission. The process of 12-bit ADC conversion is implemented through a data reduction algorithm, such as using mean filtering, that is, weighted averaging of the 16-bit sampled data to reduce noise and improve the smoothness of the data. For example, if the 16-bit data needs to be converted to 12-bit data , the conversion formula is written as:

[0054]

[0055] This conversion process effectively reduces data redundancy while retaining the main signal characteristics, thus being suitable for subsequent data transmission and processing. After the data conversion, to ensure the reliable transmission of the sampled data to the digital signal processor (DSP), communication is carried out through the CAN (Controller Area Network) bus. The CAN bus is an efficient data transmission protocol with strong anti-interference ability, suitable for the complex electromagnetic environment of the yacht energy storage system. During the data transmission process, the digitized sampled data is packed according to a predetermined data format. For example, each data frame contains voltage data, current data, and timestamp information, and then it is encoded through the CAN protocol and sent to the DSP. The DSP receives the data and performs data verification to ensure the integrity and correctness of the transmission. The data verification methods include CRC (Cyclic Redundancy Check) and parity check. The calculation formula for CRC check is:

[0056]

[0057] Among them, represents each byte of the data frame, It is a preset polynomial check code. If the calculated CRC value matches the CRC value calculated at the receiving end, it indicates that the data transmission is normal; otherwise, a retransmission request is needed to ensure data integrity. After completing the data verification, the original sampled data is stored in the buffer of the DSP and used for subsequent short-circuit detection and energy management calculations.

[0058] In a specific embodiment, the process of executing step S2 may specifically include the following steps:

[0059] Perform ocean environment parameter correction on the original sampled data according to the temperature coefficient and humidity coefficient to obtain environment-corrected data;

[0060] Input the environment-corrected data into a three-stage RC parallel network model, set the time constant of the fast charge and discharge response branch to a first target value, set the time constant of the start-stop transition branch to a second target value, and set the time constant of the navigation steady-state branch to a third target value to obtain the RC branch parameters of the yacht working condition;

[0061] Discretize the RC branch parameters of the yacht working condition, and use the capacitor voltage and temperature state of the three-stage RC parallel network model as state variables to obtain an extended discrete state equation;

[0062] Perform least squares calculation on the response data of the battery pack under different temperature, humidity, and load conditions according to the extended discrete state equation to obtain an environment-adaptive parameter matrix;

[0063] Perform multivariable correlation calculation on the environment-adaptive parameter matrix, establish a mapping relationship between the RC parameters and temperature, humidity, and state of charge to obtain the RC parameter correction value;

[0064] Update the RC parameter correction value to the extended discrete state equation for numerical calculation to obtain the RC network dynamic response data.

[0065] Specifically, correct the original sampled data based on the temperature coefficient and humidity coefficient to obtain environment-corrected data that better conforms to the actual operating state. The original sampled data includes voltage and current as well as the environmental variables temperature and humidity , where temperature and humidity will affect the equivalent internal resistance, polarization voltage, and charge and discharge efficiency of the battery. Use an empirical model to compensate for these effects, that is:

[0066]

[0067]

[0068] Among them, and The voltage and current after temperature and humidity correction respectively, and are the reference temperature and humidity, and is the temperature correction coefficient, and is the humidity correction coefficient. Through correction, the influence of environmental changes on battery performance is effectively compensated. The environmental correction data is input into a three-stage RC parallel network model to construct an equivalent circuit model that can accurately describe the yacht working conditions. The three-stage RC network model consists of three parallel RC branches, where each branch represents the battery dynamic characteristics at different time scales. The fast charge and discharge response branch mainly describes the double-layer capacitance effect of the battery, and its time constant is set to the first target value to capture the voltage change of the battery in a short time, expressed as:

[0069]

[0070] where, is the equivalent resistance of the fast response branch, is the equivalent capacitance of the fast charge and discharge branch. The start-stop transition branch is used to describe the electrochemical polarization that occurs during the start and stop of the battery, and its time constant is set to the second target value, which is determined by the characteristics of the SEI film:

[0071]

[0072] where, and are the equivalent resistance and capacitance of the start-stop branch respectively. The sailing steady-state branch is used to describe the slow energy release of the battery during long-term sailing, and its time constant is set to the third target value to ensure the accuracy of long-term steady-state calculation:

[0073]

[0074] where, and represent the equivalent resistance and capacitance of the steady-state branch. Through the above settings, it is ensured that the three-stage RC network model can accurately simulate the charge and discharge behavior of the battery at different time scales. When establishing the RC model, the parameters of the RC branches of the yacht working conditions are discretized for computer implementation. Since the differential equation of the RC circuit is continuous, a numerical discretization method is used to convert it into a state equation suitable for numerical calculation. During the discretization process, state variables are defined, including the capacitor voltage of each branch and the temperature state , then the continuous state equation is expressed as:

[0075]

[0076]

[0077]

[0078]

[0079] Among them, is the temperature increment coefficient determined by the Joule heating effect, is the thermal conductivity coefficient, is the ambient temperature. Through discretization, the above equation is converted into a discrete state equation:

[0080]

[0081]

[0082]

[0083]

[0084] An extended discrete state equation is obtained, which is used for battery modeling calculations under different temperature, humidity, and load conditions. To further optimize the RC parameters, the least squares method is used to fit the battery response data under different environmental conditions to obtain an environment-adaptive parameter matrix. Set the error function:

[0085]

[0086] Among them, is the measured voltage, is the voltage calculated by the model. By solving the least squares solution:

[0087]

[0088] Among them, is the feature matrix, is the measurement data, is the parameter to be estimated, and an environment-adaptive parameter matrix is obtained. Multivariate correlation calculations are performed on this matrix to establish the mapping relationship between the RC parameters and the temperature, humidity, and state of charge :

[0089]

[0090] ;

[0091] The corrected Substitute into the extended discrete state equation and perform numerical calculations to obtain the final dynamic response data of the RC network, thereby realizing battery modeling based on ocean environment correction.

[0092] In a specific embodiment, the process of executing step S3 may specifically include the following steps:

[0093] Perform sliding window processing on the dynamic response data of the RC network and the original sampling data to obtain de-spectral leakage data;

[0094] Input the de-spectral leakage data into a second-order Butterworth low-pass filter to obtain preprocessed signal data;

[0095] Perform fast Fourier transform operation on the preprocessed signal data to obtain frequency-domain characteristic data;

[0096] Calculate the phase difference between adjacent characteristic frequency points based on the frequency-domain characteristic data to obtain phase difference characteristic data;

[0097] Perform wavelet packet decomposition on the preprocessed signal data to obtain wavelet energy characteristic data;

[0098] Perform vector mapping and vector fusion on the frequency-domain characteristic data, phase difference characteristic data, and wavelet energy characteristic data to obtain the target characteristic vector.

[0099] Specifically, the dynamic response data of the RC network and the original sampling data As input signals, they are non-stationary, that is, their spectral characteristics change with time. Sliding window processing is adopted, that is, signal data is intercepted within a fixed time window, and frequency-domain analysis is performed on each window separately. Assume that the sliding window length is , then for any moment , the signal within the sliding window is expressed as:

[0100]

[0101] Among them, represents the instantaneous value of the dynamic response data of the RC network or the original sampling data. The sliding window continuously slides on the time-series data and is updated with a fixed step size so that subsequent calculations can cover the entire signal. After completing the sliding window processing, perform de-spectral leakage processing on the data. Spectral leakage is a phenomenon of spectral energy expansion caused by signal truncation and window mismatch during FFT calculation. To suppress this problem, multiply the data within the sliding window by a window function , and the window function includes Hanning window, Hamming window, etc. The signal after windowing is expressed as:

[0102]

[0103] Among them, Select the Hann window:

[0104]

[0105] Effectively reduce the spectral leakage during FFT calculation and obtain the data with spectral leakage removed. Input the data with spectral leakage removed into a second-order Butterworth low-pass filter to remove high-frequency noise and smooth the signal. The characteristic of the Butterworth filter is that its amplitude-frequency response curve is flat, enabling the signal to remain intact in the low-frequency part while the high-frequency part is effectively attenuated. Its transfer function is:

[0106]

[0107] Among them, is the cut-off angular frequency, is the Laplace transform variable. By discretizing the input signal, the difference equation of the second-order Butterworth filter is written as:

[0108]

[0109] Among them, is the filtered signal, is the input signal, and the coefficient is determined by the filter design. The signal after filtering is the preprocessed signal data. To analyze the frequency-domain characteristics of the signal, perform a fast Fourier transform (FFT) on the preprocessed signal data. Its mathematical expression is:

[0110]

[0111] Among them, is the frequency-domain signal, is the time-domain signal, is the number of FFT points, is the frequency. The calculation result of the FFT can intuitively reflect the spectral characteristics of the signal and is used for subsequent feature analysis. Based on the frequency-domain feature data calculated by the FFT, calculate the phase difference between adjacent characteristic frequency points to extract the phase feature. The formula for calculating the phase difference is:

[0112]

[0113] Among them, represents the phase of the spectrum at a certain frequency point, is the th frequency point. The phase difference feature data can reflect the phase shift caused by internal short circuit or non-linear impedance change in the battery. Perform wavelet packet decomposition on the preprocessed signal data. Wavelet packet decomposition is a time-frequency analysis method that decomposes the signal into components of different frequency bands to obtain more detailed spectral information. Set the mother wavelet function as , the wavelet packet transform of the signal is expressed as:

[0114]

[0115] Wherein, represents the decomposition level, represents the frequency band index. The calculation formula for the energy feature obtained by wavelet packet decomposition is:

[0116]

[0117] This energy feature can effectively characterize the energy distribution of the signal in different frequency bands, and thus is used for fault feature extraction. Vector mapping is performed on the frequency domain feature data, phase difference feature data, and wavelet energy feature data to normalize and standardize different types of features. The normalization process uses min-max normalization:

[0118]

[0119] Wherein, and are respectively the minimum and maximum values of the feature data. The standardization process uses zero-mean normalization:

[0120]

[0121] Wherein, is the feature mean, is the feature standard deviation. The feature data after normalization and standardization processing are fused to form the final target feature vector. The fusion method uses principal component analysis:

[0122]

[0123] Wherein, is the dimensionality reduction matrix, is the feature vector. The finally generated target feature vector is used for short circuit detection and subsequent battery management optimization.

[0124] In a specific embodiment, the process of executing step S4 may specifically include the following steps:

[0125] Perform vector demapping processing on the target feature vector to obtain feature component data;

[0126] Calculate the voltage drop of the frequency domain feature in the feature component data to obtain a voltage feature judgment result;

[0127] Calculate the current slope of the frequency domain feature in the feature component data according to the voltage feature judgment result to obtain a current feature judgment result;

[0128] Reconstruct the complex impedance with the phase difference feature and wavelet energy feature in the feature component data to obtain impedance spectrum data;

[0129] Calculate the Euclidean distance of the impedance spectrum data to obtain the impedance feature judgment result;

[0130] Based on the voltage feature judgment result, current feature judgment result and impedance feature judgment result, perform a hysteresis judgment to obtain a multi-level fault judgment result.

[0131] Specifically, perform a vector demapping process on the target feature vector to extract independent feature component data. The target feature vector is composed of the fusion of different types of signal features, including frequency domain features, voltage mutation features, current mutation features, phase difference features, and wavelet energy features. During the demapping process, according to the feature category, the fused feature vector is mapped back to its original feature space. Assume the target feature vector is:

[0132]

[0133] where, represents the frequency domain feature, represents the phase difference feature, represents the wavelet energy feature. The demapping process is carried out by the method of inverse principal component analysis or independent component analysis, reducing the feature vector to independent feature component data for subsequent calculations. Calculate the voltage drop of the frequency domain feature in the feature component data to judge whether the voltage feature is abnormal. A short circuit fault is accompanied by a sudden drop in voltage, so calculate the transient voltage drop to judge whether there is a short circuit phenomenon. The voltage drop calculation is expressed as:

[0134]

[0135] where, is the voltage drop, is the reference voltage under normal working conditions, is the currently measured voltage. If exceeds the set threshold , it is determined that the battery may have a short circuit trend:

[0136]

[0137] The calculation result of this step is the voltage feature judgment result. Based on the voltage feature judgment result, calculate the current slope of the frequency domain feature to determine the mutation of the current. During a short circuit, the current will rise sharply, so calculate the slope of the current to judge whether there is an abnormality. Let the current sampling data be , then the current slope calculation formula is:

[0138]

[0139] Among them, represents the current change rate, is the current at the current moment, is the current at the previous moment, is the sampling time interval. If the current slope exceeds the set mutation threshold , it is determined that the current is abnormal:

[0140]

[0141] This result is the current characteristic judgment result. To analyze the change of the internal impedance of the battery, the phase difference characteristic and the wavelet energy characteristic in the characteristic component data are used for complex impedance reconstruction. The representation form of the complex impedance is:

[0142]

[0143] Among them, is the equivalent resistance component of the battery, is the equivalent reactance component of the battery. The key to impedance reconstruction lies in using the phase difference characteristic to calculate the impedance angle:

[0144]

[0145] At the same time, the wavelet energy characteristic reflects the energy change in different frequency bands and is used to calculate the impedance amplitude:

[0146]

[0147] Thereby, the real part and the imaginary part of the impedance are solved:

[0148]

[0149] Finally, the impedance spectrum data is obtained. To quantify the deviation between the impedance spectrum data and the normal working state, the Euclidean distance of the impedance spectrum data is calculated to obtain the impedance characteristic judgment result. Assuming that the impedance data under normal working conditions is , the Euclidean distance between the current impedance and the normal impedance is:

[0150]

[0151] If exceeds the set threshold , it is determined that the impedance is abnormal:

[0152]

[0153] This result is the impedance characteristic judgment result. Hysteresis judgment is carried out based on the voltage characteristic judgment result, the current characteristic judgment result and the impedance characteristic judgment result to obtain the final multi-level fault judgment result. The purpose of hysteresis judgment is to avoid misjudgment caused by short-term noise or transient changes. During the final judgment process, a hysteresis interval is set. Assume the fault judgment variable is defined by the combination of the three as:

[0154]

[0155] where, is the weight factor, respectively represent the fault judgment results of voltage, current and impedance. Then set the hysteresis threshold:

[0156]

[0157] When , the fault is determined as a short circuit; when , the system is determined to be normal; when , the previous state is maintained to prevent misjudgment:

[0158] ;

[0159] This judgment strategy can effectively reduce misjudgment and improve the reliability of fault detection.

[0160] In this embodiment, before starting the three-loop nested control structure according to the multi-level short-circuit fault judgment result and inputting the calculation results of current tracking control, power balance control, and state protection control into the PWM modulation unit to obtain the control compensation signal, the following steps are further included: performing offline training on historical operating condition data, inputting the voltage, current, and power data of the yacht energy storage battery pack under different navigation states into a recursive equalization network to obtain network training parameters; constructing a neural network prediction model based on the network training parameters, using the multi-level short-circuit fault judgment result as the input variable to obtain fault condition prediction data; performing a fast response strategy calculation on the fault condition prediction data, setting the control period to 10 μs and the calculation accuracy to 16 bits to obtain control strategy parameters; inputting the control strategy parameters into a digital signal processor to perform online optimization of the control gain of the three-loop nested control structure to obtain optimal control parameters; dynamically adjusting the bandwidth of the current tracking control loop based on the optimal control parameters, setting the bandwidth range between 500 Hz and 2 kHz to obtain a bandwidth adjustment coefficient; correcting the response speed of the power balance control loop according to the bandwidth adjustment coefficient, controlling the response time within 2 ms to obtain a response speed compensation value; performing a stability analysis on the response speed compensation value and the state feedback signal of the three-loop control structure, constructing a Lyapunov function for closed-loop stability determination to obtain a stability criterion; performing asymptotic convergence calculation on the control parameters of the three-loop nested control structure according to the stability criterion to obtain convergence control parameters, and updating the convergence control parameters to the three-loop nested control structure.

[0161] In a specific embodiment, the process of executing step S5 may specifically include the following steps:

[0162] Start the three-loop nested control structure according to the multi-level fault judgment result, perform an outer loop calculation through the state protection controller in the three-loop nested control structure, and construct a safe operating boundary based on the temperature limit, voltage limit, and current limit of the yacht energy storage battery pack to obtain a state protection instruction;

[0163] Perform a middle loop calculation on the power balance controller in the three-loop nested control structure according to the state protection instruction, and perform dynamic allocation based on the power requirements under the yacht start-stop transition condition and the navigation steady-state condition to obtain a power balance instruction;

[0164] According to the power balance instruction, perform an inner loop current tracking calculation on the current tracking controller in the three-loop nested control structure to obtain a PWM control instruction, and input the PWM control instruction into the PWM modulation unit to adjust the duty cycle in real time to obtain a PWM waveform signal;

[0165] Perform digital filtering processing on the PWM waveform signal to obtain a filtered compensation signal, and perform multi-loop correction calculation on the filtered compensation signal, the state protection instruction, and the power balance instruction to obtain a control compensation signal.

[0166] Specifically, based on the multi-level fault judgment result, the three-loop nested control structure is started, and the outer loop calculation is performed by the state protection controller to ensure the operation safety of the yacht energy storage battery pack. The core of the state protection controller lies in the temperature limit value of the battery pack , voltage limit value , current limit value to construct a safe operation boundary to ensure that the system operates within a reasonable working range. To construct this boundary, a safety discrimination function is defined:

[0167]

[0168] where represents whether the system is in a safe area. When , the system can operate normally; when , it means that a certain parameter exceeds the allowable range and the system operation state needs to be adjusted. At this time, the state protection controller will generate a state protection instruction :

[0169]

[0170] where are the weight factors of temperature, voltage and current respectively, used to adjust the sensitivity of the protection strategy. If , it means that the system needs to take protection measures, such as reducing the charging power or discharging current. The state protection instruction is input into the power balance controller of the three-loop nested control structure, and the middle loop calculation is performed. The power balance controller dynamically distributes the power output of the energy storage battery pack based on different operating conditions of the yacht, such as start-stop transition conditions and navigation steady-state conditions. Under start-stop transition conditions, the electrical energy demand of the yacht fluctuates greatly, so a quick response is made through the power balance controller to maintain the stability of the system. Under navigation steady-state conditions, the system optimizes the power output to extend the battery life and improve the energy utilization efficiency. The power demand is expressed as:

[0171]

[0172] where is the current total power demand of the yacht, is the power demand of the propulsion system, is the power demand of the auxiliary system (such as lighting, communication, etc.). The power balance controller adjusts the power distribution strategy according to the state protection instruction :

[0173]

[0174] where is the power value finally allocated to the battery system. To ensure the smoothness of power distribution, a PI controller is used to adjust the power error:

[0175]

[0176] where and are the proportional and integral gain parameters, is the power balance control instruction. This instruction is used to optimize the battery output to adapt to the current load demand. After obtaining the power balance instruction enters the inner loop calculation stage of the three-loop nested control structure, that is, current tracking calculation is performed through a current tracking controller, and a PWM control instruction is generated. The goal of current tracking control is to ensure that the current output of the energy storage battery pack matches the system demand to maintain a stable voltage output. Let the target current be , then the current tracking error is:

[0177]

[0178] where is the actually measured current value. Sliding mode control or PI control is used to calculate the PWM duty cycle:

[0179]

[0180] where is the initial duty cycle, is the adjusted PWM duty cycle. The calculated PWM control instruction is input into the PWM modulation unit to adjust the duty cycle in real time and generate a PWM waveform signal to control the energy output of the battery. To improve the stability of the PWM signal, digital filtering processing is performed on the PWM waveform signal to reduce the influence of high-frequency harmonics. The filtering process uses a finite impulse response (FIR) filter or a Butterworth filter, and its transfer function is:

[0181]

[0182] where is the cut-off angular frequency, is the Laplace transform variable. The filtered signal is the filtered compensation signal , and its calculation formula is:

[0183]

[0184] where is the weight coefficient of the filter, is the filter order. After filtering, the PWM signal becomes smoother, which helps reduce current ripple and improve the stability of the system. The filtered compensation signal is subjected to multi-loop correction calculation with the state protection instruction and the power balance instruction to obtain the final control compensation signal . Using a weighted fusion method, the three control signals are combined:

[0185]

[0186] where is the weighting coefficient, used to adjust the contribution degree of different signals. The final signal is sent to the battery management system (BMS) to adjust the battery output in real time to adapt to the current working condition requirements and ensure the safety and stability of the system operation.

[0187] Optionally, according to the state protection instruction, the middle loop calculation is performed on the power balance controller in the three-loop nested control structure, and dynamic allocation is performed based on the power requirements under the yacht start-stop transition condition and the sailing steady-state condition. The obtained power balance instruction includes: constructing a unified energy boundary model for multiple parallel branches of the yacht energy storage battery pack, performing coupled calculation on the total power and energy capacity between the branches to obtain energy storage boundary parameters; constructing a multiple regression prediction model based on the energy storage boundary parameters and historical sailing data, performing probability distribution calculation on the power requirements for future voyages to obtain power prediction data; performing risk value assessment on the power prediction data, dividing the sailing conditions into high-speed sailing section, low-speed cruising section and port-side berthing section to obtain sectional power thresholds; performing power optimization allocation on the energy storage system based on the sectional power thresholds, constructing a two-stage stochastic optimization model to obtain a power allocation strategy; inputting the power allocation strategy into an adaptive prediction controller, performing online correction on the allocation strategy according to the real-time sailing state to obtain a corrected control instruction; performing dynamic allocation of energy storage capacity according to the corrected control instruction, performing real-time adjustment on the standby power during sailing to obtain a dynamic power compensation value; performing power balance calculation on the dynamic power compensation value, restricting the output power of each parallel branch within a set interval to obtain a power limit instruction; performing cooperative processing on the power limit instruction and the state protection instruction to uniformly regulate the output power of the energy storage system to obtain a power balance instruction.

[0188] In a specific embodiment, the process of executing step S6 may specifically include the following steps:

[0189] Collect the branch currents of the control compensation signal, set the branch with the largest current as the main branch to obtain the master-slave branch configuration data;

[0190] Calculate the PI parameters of the control compensation signal according to the master-slave branch configuration data to obtain the PI control parameters;

[0191] Calculate the deviation between the real-time current and the average current of each parallel branch, input the PI control parameters into the current sharing controller, and obtain the PWM duty cycle compensation value;

[0192] Judge the overcurrent protection of the parallel branch according to the PWM duty cycle compensation value, and obtain the overcurrent protection instruction;

[0193] Input the overcurrent protection instruction into the power regulation unit, regulate the output power of the yacht energy storage battery, obtain the power regulation signal, and perform current sharing compensation calculation on the power regulation signal to obtain the current sharing control signal.

[0194] Specifically, sample the instantaneous current of each parallel branch with high precision to ensure that the system can correctly identify the branch with the largest current and set it as the main branch. Assume that the yacht energy storage battery pack has parallel branches, and the current acquisition values of each branch are respectively , then the current of the main branch is defined as:

[0195]

[0196] At this time, the branch corresponding to the largest current is the main branch, and its index is determined by the following formula:

[0197]

[0198] The other branches are set as slave branches, record the configuration data of the master-slave branches, and use this configuration data for subsequent current sharing control calculations. Calculate the Pl parameters of the control compensation signal based on the master-slave branch configuration data to obtain optimized Pl control parameters. The core task of the PI controller is to adjust the current of each branch to make it tend to be balanced. Therefore, the objective function of PI control is defined as:

[0199]

[0200] Among them, is the average current of all branches, and the calculation formula is as follows:

[0201]

[0202] The error represents the current deviation of a certain branch. When this value is not zero, it means that the current distribution is unbalanced and is corrected by adjusting the PWM duty cycle. The control law of the PI controller is:

[0203]

[0204] Among them, is the compensation value of the PWM duty cycle of this branch, is the proportional gain, is the integral gain. This formula ensures that the branch with a large current deviation can adjust its output through PWM to approach the current sharing state. After calculating the PI control parameters, the deviation between the real-time current and the average current of each parallel branch is calculated, and the PWM duty cycle compensation value is calculated using the PI control parameters for input to the current sharing controller for final current sharing adjustment. The input signals of the current sharing controller include the current error at the current moment and the compensation signal calculated by the PI controller , and its output is the corrected PWM duty cycle:

[0205]

[0206] where, is the initial PWM duty cycle, and the adjusted is input to the PWM modulation unit to correct the power output of each branch. After obtaining the PWM duty cycle compensation value, it is judged whether there is an overcurrent risk for each branch to ensure the safe operation of the system. The core idea of overcurrent protection judgment is to compare the current of each branch with the set overcurrent threshold . If the current of a certain branch exceeds this threshold, the system should trigger an overcurrent protection instruction :

[0207]

[0208] If , it means that this branch has entered the overcurrent state. At this time, its PWM duty cycle is reduced to reduce the current output and avoid damaging the battery or other components. The specific adjustment strategy is as follows:

[0209]

[0210] where, is the adjustment coefficient, and the value of which determines the response speed of overcurrent protection. The overcurrent protection instruction is input to the power regulation unit to adjust the overall power output of the yacht energy storage battery and generate a power regulation signal. The basic principle of power regulation is to ensure that the total power meets the load demand and at the same time maintains the current sharing state:

[0211]

[0212] where, the power output of each branch is:

[0213]

[0214] If the power output of some branches decreases due to overcurrent protection, the power of other branches is dynamically adjusted so that the total power can still meet the load requirements. The adjustment strategy adopts the proportional distribution method, that is:

[0215]

[0216] Among them, is the total power reduced due to overcurrent protection, and the remaining branches distribute this power proportionally to maintain power stability. The current sharing compensation calculation is performed on the power adjustment signal to finally generate the current sharing control signal. The key to current sharing compensation is to ensure relatively balanced power output of each branch. Therefore, the corrected current sharing error is calculated:

[0217]

[0218] If this value exceeds the set threshold, the parameters of the current sharing controller are adjusted to optimize the PWM duty cycle distribution:

[0219]

[0220] Among them, is the current sharing compensation coefficient, whose function is to accelerate the convergence speed of current sharing control.

[0221] The internal short - circuit detection method of the yacht energy storage battery in the embodiment of the present invention is described above. Next, the internal short - circuit detection device of the yacht energy storage battery in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the internal short - circuit detection device of the yacht energy storage battery in the embodiment of the present invention includes:

[0222] A sampling module for sampling the voltage and current of the yacht energy storage battery pack to obtain the original sampling data;

[0223] A parameter identification module for inputting the original sampling data into a three - stage RC parallel network model for parameter identification to obtain the RC network dynamic response data;

[0224] A decomposition module for performing sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain the target feature vector;

[0225] A judgment module for performing voltage mutation feature judgment, current mutation feature judgment, and impedance spectrum feature judgment based on the target feature vector to obtain a multi - level fault judgment result;

[0226] A control compensation module for starting a three - loop nested control structure according to the multi - level fault judgment result, inputting the calculation results of current tracking control, power balance control, and state protection control into the PWM modulation unit to obtain the control compensation signal;

[0227] The current sharing control module is used to input the control compensation signal into the current sharing controller to adjust the PWM duty ratio of the parallel branches and obtain the current sharing control signal.

[0228] Through the collaborative cooperation of the above-mentioned various components, by adopting a three-stage RC parallel network model and combining with the correction of marine environmental parameters, the dynamic characteristics of the yacht energy storage battery under different working conditions are accurately described; through the combined application of the sliding window Fourier transform and wavelet packet decomposition, multi-scale feature extraction is realized, enhancing the integrity of feature information; a three-stage judgment mechanism based on voltage mutation, current mutation and impedance spectrum is designed, and hysteresis comparison processing is introduced to improve the reliability of fault detection; a three-loop nested control structure is proposed to realize the collaborative control of state protection, power balance and current tracking, ensuring the stability of the system output; based on the master-slave current sharing control strategy, the current distribution problem of parallel branches is effectively solved, improving the dynamic response ability of the system. Through the collaborative cooperation of multi-level protection and control strategies, the safety and reliability of the system are enhanced in the present invention.

[0229] Refer to Figure 3 , in the embodiment of the present invention, a computer device is further provided. The computer device may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0230] Those skilled in the art can understand that Figure 3 the structure shown in

[0231] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0232] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0233] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, systems, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0234] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc., which can store program codes.

[0235] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for detecting internal short circuit of a yacht energy storage battery, characterized in that, The method includes: Sampling the voltage and current of the yacht energy storage battery pack to obtain original sampling data; Inputting the original sampling data into a three-stage RC parallel network model for parameter identification to obtain RC network dynamic response data; specifically including: correcting the original sampling data according to the temperature coefficient and humidity coefficient to obtain environment-corrected data; inputting the environment-corrected data into the three-stage RC parallel network model, setting the time constant of the fast charge and discharge response branch to a first target value, setting the time constant of the start-stop transition branch to a second target value, setting the time constant of the navigation steady-state branch to a third target value to obtain the RC branch parameters under yacht working conditions; discretizing the RC branch parameters under yacht working conditions, using the capacitor voltage and temperature state of the three-stage RC parallel network model as state variables to obtain an extended discrete state equation; performing least squares calculation on the response data of the battery pack under different temperature, humidity, and load conditions according to the extended discrete state equation to obtain an environment-adaptive parameter matrix; performing multivariable correlation calculation on the environment-adaptive parameter matrix to establish a mapping relationship between the RC parameters and temperature, humidity, and state of charge to obtain an RC parameter correction value; updating the RC parameter correction value to the extended discrete state equation for numerical calculation to obtain RC network dynamic response data; Performing sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain a target feature vector; Based on the target feature vector, performing voltage mutation feature judgment, current mutation feature judgment, and impedance spectrum feature judgment to obtain a multi-level fault judgment result; According to the multi-level fault judgment result, starting a three-loop nested control structure, and inputting the calculation results of current tracking control, power balance control, and state protection control into a PWM modulation unit to obtain a control compensation signal; Inputting the control compensation signal into a current sharing controller to adjust the PWM duty ratio of the parallel branches to obtain a current sharing control signal.

2. The internal short - circuit detection method of the yacht energy storage battery according to claim 1, characterized in that, The sampling of the voltage and current of the yacht energy storage battery pack to obtain original sampling data includes: Performing differential amplification on the sampling channels of the voltage acquisition unit to obtain a voltage acquisition gain coefficient, and inputting the voltage acquisition gain coefficient into a voltage sampling circuit to perform 16-bit precision sampling on multiple series battery cells of the yacht energy storage battery pack to obtain voltage sampling data; Performing operational amplification on the output signal of the Hall sensor of the current acquisition unit to obtain a current acquisition gain coefficient, and inputting the current acquisition gain coefficient into a current sampling circuit to perform 16-bit precision sampling on 2 parallel branches to obtain current sampling data; Performing 12-bit ADC conversion on the voltage sampling data and the current sampling data to obtain digital sampling data, and transmitting the digital sampling data to a digital signal processor through a CAN bus to perform verification processing on the received data to obtain original sampling data.

3. The method for detecting internal short circuit of a yacht energy storage battery according to claim 1, wherein The performing sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain a target feature vector includes: Perform sliding window processing on the dynamic response data of the RC network and the original sampling data to obtain de-spectral leakage data; Input the de-spectral leakage data into a second-order Butterworth low-pass filter to obtain preprocessed signal data; Perform fast Fourier transform operation on the preprocessed signal data to obtain frequency-domain characteristic data; Calculate the phase difference between adjacent characteristic frequency points according to the frequency-domain characteristic data to obtain phase difference characteristic data; Perform wavelet packet decomposition on the preprocessed signal data to obtain wavelet energy characteristic data; Perform vector mapping and vector fusion on the frequency-domain characteristic data, the phase difference characteristic data, and the wavelet energy characteristic data to obtain a target feature vector.

4. The method for detecting internal short circuit of the yacht energy storage battery according to claim 1, characterized in that, Based on the target feature vector, perform voltage mutation feature judgment, current mutation feature judgment, and impedance spectrum feature judgment to obtain a multi-level fault judgment result, including: Perform vector demapping processing on the target feature vector to obtain feature component data; Calculate the voltage drop of the frequency-domain feature in the feature component data to obtain a voltage feature judgment result; Calculate the current slope of the frequency-domain feature in the feature component data according to the voltage feature judgment result to obtain a current feature judgment result; Reconstruct the complex impedance of the phase difference feature and the wavelet energy feature in the feature component data to obtain impedance spectrum data; Calculate the Euclidean distance of the impedance spectrum data to obtain an impedance feature judgment result; Perform hysteresis judgment based on the voltage feature judgment result, the current feature judgment result, and the impedance feature judgment result to obtain a multi-level fault judgment result.

5. The internal short - circuit detection method of the yacht energy storage battery according to claim 1, characterized in that, According to the multi-level fault judgment result, start a three-loop nested control structure, and input the calculation results of current tracking control, power balance control, and state protection control into the PWM modulation unit to obtain a control compensation signal, including: According to the multi-level fault judgment result, start a three-loop nested control structure, perform outer-loop calculation through the state protection controller in the three-loop nested control structure, and construct a safe operation boundary according to the temperature limit, voltage limit, and current limit of the yacht energy storage battery pack to obtain a state protection instruction; Perform middle-loop calculation on the power balance controller in the three-loop nested control structure according to the state protection instruction, and perform dynamic allocation based on the power requirements under the yacht start-stop transition condition and the sailing steady-state condition to obtain a power balance instruction; According to the power balance instruction, perform current tracking inner-loop calculation on the current tracking controller in the three-loop nested control structure to obtain a PWM control instruction, and input the PWM control instruction into the PWM modulation unit to adjust the duty cycle in real time to obtain a PWM waveform signal; Perform digital filtering processing on the PWM waveform signal to obtain a filtered compensation signal, and perform multi-loop correction calculation on the filtered compensation signal, the state protection instruction, and the power balance instruction to obtain a control compensation signal.

6. The internal short circuit detection method of the yacht energy storage battery according to claim 1, characterized in that, Input the control compensation signal into the current sharing controller to adjust the PWM duty cycle of the parallel branches to obtain a current sharing control signal, including: Collect the branch current of the control compensation signal, set the branch with the largest current as the main branch, and obtain the master-slave branch configuration data; Calculate the PI parameters of the control compensation signal according to the master-slave branch configuration data to obtain the PI control parameters; Calculate the deviation between the real-time current and the average current of each parallel branch, input the PI control parameters into the current sharing controller, and obtain the PWM duty cycle compensation value; Judge the overcurrent protection of the parallel branch according to the PWM duty cycle compensation value to obtain the overcurrent protection instruction; Input the overcurrent protection instruction into the power regulation unit to regulate the output power of the yacht energy storage battery, obtain the power regulation signal, and perform current sharing compensation calculation on the power regulation signal to obtain the current sharing control signal.

7. An internal short circuit detection device for a yacht energy storage battery, characterized in that, For executing the internal short-circuit detection method of the yacht energy storage battery according to any one of claims 1-6, the internal short-circuit detection device of the yacht energy storage battery includes: A sampling module for sampling the voltage and current of the yacht energy storage battery pack to obtain the original sampling data; A parameter identification module for inputting the original sampling data into a three-stage RC parallel network model for parameter identification to obtain the RC network dynamic response data; A decomposition module for performing sliding window Fourier transform and wavelet packet decomposition on the RC network dynamic response data and the original sampling data to obtain the target feature vector; A judgment module for performing voltage mutation feature judgment, current mutation feature judgment and impedance spectrum feature judgment based on the target feature vector to obtain a multi-level fault judgment result; A control compensation module for starting a three-loop nested control structure according to the multi-level fault judgment result, and inputting the calculation results of current tracking control, power balance control and state protection control into the PWM modulation unit to obtain a control compensation signal; A current sharing control module for inputting the control compensation signal into a current sharing controller to adjust the PWM duty cycle of the parallel branch to obtain a current sharing control signal.

8. A computer device, characterized in that, Comprising a memory and a processor, the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the internal short-circuit detection method of the yacht energy storage battery according to any one of claims 1 to 6 is realized.

9. A computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the processor is caused to execute the internal short-circuit detection method of the yacht energy storage battery according to any one of claims 1 to 6.

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