Battery energy management system of electric stretching equipment

Abstract

The invention relates to the technical field of battery energy management, and discloses a battery energy management system of electric stretching equipment. The system comprises a battery energy recovery module, a battery energy supply module, a battery energy dynamic distribution module and a battery energy regulation module, firstly, tension machine energy is captured and stored in a super capacitor, then super capacitor energy is stored in a battery pack, and a battery energy storage unit is formed; secondly, considering the energy contribution priority of the tension machine and the energy supply of a battery energy storage unit, optimizing the current tolerance limit and the voltage tolerance limit in the energy supply of the traction machine by using a multi-strategy fused sparrow search algorithm, and generating a battery energy dynamic distribution strategy; and finally, the working state of the battery energy storage unit is adjusted based on temperature-voltage closed-loop feedback, and battery energy management is completed. Energy management is carried out on the electric stretching equipment, the purpose of battery energy management is achieved, and the method is accurate and objective.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy management, and specifically to a battery energy management system for an electric tensioning device. Background Art

[0002] Electric tensioning devices (such as traction machines and tensioning machines) are core equipment for erecting high-voltage transmission lines, submarine cables or optical cables in fields such as electric power, communication and rail transit. With the development of new energy and smart grids, the equipment has gradually transformed from traditional fuel-driven to electric-driven, and generally uses lithium battery packs as the power source.

[0003] In traditional equipment, the traction machine and the tensioning machine are usually independently powered. When the tensioning machine is in the wire laying operation, a large amount of reverse electric energy is generated due to braking. The traditional resistance braking method converts the electric energy into heat energy and dissipates it. The battery energy management method of traditional electric tensioning devices feeds the electric energy back to the battery pack, uses a bidirectional converter to adjust the voltage and store the energy, and then calls the battery energy of the tensioning machine when the traction machine is under high load, and manages the battery energy according to a single energy distribution strategy and a centralized control logic to control the energy flow direction.

[0004] The electric tensioning device has the problem of insufficient endurance under complex working conditions, relying on a large-capacity battery pack for power supply. Frequent charging and discharging reduces the battery life and increases the operation and maintenance cost. At the same time, recycling the energy of the tensioning machine for the traction machine, the centralized control lacks real-time performance and self-adaptability, and the charging and discharging logic call is chaotic, which may lead to waste of redundant energy or the risk of overcharging / overdischarging of the battery, and is not conducive to the effective recycling of energy. Summary of the Invention

[0005] In view of the problems in the related art, the present invention provides a battery energy management system for an electric tensioning device to overcome the technical problems existing in the existing related technologies.

[0006] To solve the above technical problems, the present invention is realized through the following technical solutions: The present invention is a battery energy management system for an electric tensioning device, specifically including: a battery energy recovery module, a battery energy supply module, a battery energy dynamic distribution module and a battery energy regulation module; The battery energy recovery module is used to judge whether the tensioning machine enters the energy recovery mode, capture the energy of the tensioning machine and store it in the super capacitor first, and then store the energy of the super capacitor in the battery pack to form a battery energy storage unit; The battery energy supply module is used to predict the output power of the traction machine, obtain the predicted value of the traction machine output power, compare the output power of the battery energy storage unit with the predicted value of the traction machine output power, and realize the supply of energy from the battery energy storage unit to the traction machine; The battery energy dynamic distribution module is used to optimize the current tolerance limit and voltage tolerance limit during energy supply by using a multi-strategy fusion sparrow search algorithm when the battery energy storage unit supplies energy to the tractor, and generate a battery energy dynamic distribution strategy; The battery energy regulation module is used to combine the battery energy dynamic distribution strategy and regulate the working state of the battery energy storage unit based on temperature-voltage closed-loop feedback.

[0007] Preferably, determining whether the tension machine enters the energy recovery mode includes: Obtain the tension machine load parameters and motor operation parameters. The tension machine load parameters include cable tension and drum speed, and the motor operation parameters include motor torque and motor speed, to obtain the tension machine operation data, record the tension machine operation time series, and select the first time point and the second time point; When both the cable tension and the drum speed in the first tension machine operation data are greater than 0 and both the cable tension and the drum speed in the second tension machine operation data are greater than 0, the tension machine enters the energy recovery mode at this time.

[0008] Preferably, storing the captured tension machine energy in the super capacitor first includes: Obtain the motor torque and motor speed in the first tension machine operation data and the second tension machine operation data. When the motor torque is a reverse torque output and the direction of the motor torque is opposite to the direction of the motor speed, the tension machine enters the active braking condition at this time; then calculate the differential of the motor speed within the range from the first time point to the second time point, set a speed threshold. When the motor torque is 0 and the differential of the motor speed within the range from the first time point to the second time point is less than the speed threshold, the tension machine enters the passive braking condition at this time; Obtain the battery pack capacity, set a first capacity threshold. When the battery pack capacity is greater than the first capacity threshold, store the captured tension machine energy in the super capacitor; In the active braking condition, switch the motor to the power generation mode to charge the super capacitor; in the passive braking condition, trigger protective braking, parallel the crowbar circuit for discharging, and then charge the super capacitor.

[0009] Preferably, storing the super capacitor energy in the battery pack again to form a battery energy storage unit includes: Set a second capacity threshold. When the battery pack capacity is less than or equal to the first capacity threshold or the super capacitor capacity is greater than the second capacity threshold, at this time, use a two-stage energy storage method to store the super capacitor energy in the battery pack; Collect the battery cell voltages in the battery pack to obtain a battery cell voltage set; Set the first protection voltage and the second protection voltage. When the voltage of a battery cell in the battery cell voltage set is less than the first protection voltage, the supercapacitor charges the battery pack normally at this time; When the voltage of a battery cell in the battery cell voltage set is greater than or equal to the first protection voltage and less than or equal to the second protection voltage, reduce the charging current; When the voltage of a battery cell in the battery cell voltage set is greater than the second protection voltage, disconnect the supercapacitor from charging the battery pack to complete the charging of the battery pack; Form a battery energy storage unit with the supercapacitor and the battery pack.

[0010] Preferably, predicting the output power of the traction machine, and the output of the predicted value of the traction machine output power includes: Obtain the motor current, motor voltage, motor speed, motor temperature, load weight and traction speed of the traction machine to generate a set of traction machine operation data samples; Set the BiLSTM neural network to include an input layer, a bidirectional LSTM layer, an attention mechanism layer, a dense layer and an output layer, and the initial learning rate is 0.001; obtain the historical operation data of the traction machine, extract the feature sequence of the historical operation data of the traction machine and perform normalization processing to obtain a sample data set, divide the sample data set into a sample training set and a sample test set, and then input the sample training set into the BiLSTM neural network for iteration until the BiLSTM neural network converges to obtain a trained BiLSTM neural network; Then input the sample test set into the trained BiLSTM neural network, output the prediction result, set the accuracy threshold. When the accuracy of the prediction result is greater than the accuracy threshold, obtain the traction machine output power prediction model, otherwise adjust the weights until the accuracy of the prediction result is greater than the accuracy threshold; input the set of traction machine operation data samples into the traction machine output power prediction model to output the predicted value of the traction machine output power.

[0011] Preferably, comparing the output power of the battery energy storage unit and the predicted value of the traction machine output power to enable the battery energy storage unit to supply energy to the traction machine includes: Calculate the path loss and remaining energy factor of the m th tension machine, and use the weighted priority algorithm to calculate the priority score of the m th tension machine; calculate the priority scores of all tension machines in turn, select the tension machine corresponding to the highest priority score and record it as the priority tension machine; Compare the predicted output power of the tractor with the output power of the battery pack in the battery energy storage unit. When the predicted output power of the tractor is less than the output power of the battery pack in the battery energy storage unit, the battery pack provides energy; otherwise, the priority tensioner is used to charge the super capacitor, and the super capacitor cooperates with the battery pack to provide energy to complete the energy supply of the tractor.

[0012] Preferably, the use of the multi-strategy fusion sparrow search algorithm to optimize the current tolerance limit and voltage tolerance limit during energy supply includes: After the tractor is energized, obtain the transmission current, transmission resistance, internal resistance and voltage of the battery pack in the battery energy storage unit, calculate the transmission resistance loss and the output power of the battery energy storage unit, and take the maximum difference between the output power of the battery energy storage unit and the transmission resistance loss as the objective function; Set the current tolerance limit and voltage tolerance limit to constrain the range of the transmission current and the voltage of the battery pack in the battery energy storage unit; take the objective function as the fitness function, regard the process of solving the objective function as the search space, there is a sparrow population in the search space, and the current tolerance limit and voltage tolerance limit are combined into a feasible solution. The position of the sparrow individual in the sparrow population represents the feasible solution, and the process of iteratively changing the position of the sparrow individual is regarded as the process of optimizing the current tolerance limit and voltage tolerance limit; divide the sparrow population into discoverers and joiners, use chaotic mapping for population initialization, and then introduce the elite opposition-based learning strategy to calculate the opposition-based solution of the position of the i nth sparrow individual in the sparrow population; Introduce the sine-cosine algorithm to update the position of the discoverer, and then update the position of the joiner; calculate the current best fitness function value and the current worst fitness function value, control the sparrow population to move in the direction of the sparrow individual corresponding to the current best fitness function value, complete the position update of the current all stages, and generate the next generation of sparrow population; until the current iteration number reaches the maximum iteration number, stop the iteration, obtain the final sparrow population, find the position of the sparrow individual corresponding to the best fitness function value, and obtain the optimized current tolerance limit and optimized voltage tolerance limit.

[0013] Preferably, the generation of the battery energy dynamic allocation strategy includes: During the energy supply of the tractor, keep the transmission current within the optimized current tolerance limit range and the voltage of the battery pack in the battery energy storage unit within the optimized voltage tolerance limit range, and the battery energy storage unit provides energy for the tractor to generate the battery energy dynamic allocation strategy.

[0014] Preferably, the work state of the battery energy storage unit is regulated based on temperature-voltage closed-loop feedback to complete the battery energy management, including: When the battery energy dynamic allocation strategy is run, the battery energy storage unit supplies power to the tractor, the temperature and voltage of the battery pack in the battery energy storage unit are collected, and the dynamic voltage threshold is calculated. When the battery energy storage unit is supplying power, a temperature threshold is set. When the voltage of the battery pack in the battery energy storage unit is less than the dynamic voltage threshold, the power supply is aborted. When the temperature of the battery pack in the battery energy storage unit is greater than the temperature threshold, the supply current is reduced to form a closed-loop feedback regulation to complete the battery energy management.

[0015] The present invention has the following beneficial effects: 1. By determining whether the tension machine enters the energy recovery mode, the invention captures the energy of the tension machine and stores it in the super capacitor first, and then stores the energy of the super capacitor in the battery pack to form a battery energy storage unit. By coordinating the operation of the super capacitor and the battery pack, it takes into account both instantaneous high-output power recovery and long-term energy storage, solves the problems of traditional power supply relying on large-capacity battery packs and insufficient endurance, and solves the problem that traditional single battery packs are vulnerable to high-voltage impacts, effectively protecting the battery pack.

[0016] 2. By constructing a model to predict the output power of the tractor, considering the priority of the energy contribution of the tension machine, and using a weighted priority algorithm to supply energy to the battery energy storage unit, this method supports multiple tension machines to supply power to the tractor simultaneously, avoids the delay and conflict of centralized control, reduces the risk of battery overcharging / overdischarging, and at the same time, the neural network model can effectively handle the non-linear time-varying characteristics of the tractor's output power, ensuring the reliability of the system.

[0017] 3. When supplying energy to the tractor, the invention establishes an objective function and uses a multi-strategy fusion sparrow search algorithm for optimization to generate a battery energy dynamic allocation strategy. This algorithm overcomes the disadvantages of traditional algorithms that the population diversity gradually decreases and is prone to falling into local optimal solutions, improves the convergence speed and algorithm performance, dynamically optimizes the charge and discharge logic according to the output power demand, provides an efficient and reliable energy allocation strategy for the tensioning equipment, realizes the maximization of energy recovery efficiency and the extension of equipment life, and is conducive to the effective recycling of energy.

[0018] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the invention. For those of ordinary skill in the art, without creative efforts, additional drawings can be obtained based on these drawings.

[0020] Figure 1The figure is a schematic flow chart of a battery energy management method for an electric tensioning device provided by the present invention. Detailed implementation manners

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] The electric tensioning device has a problem of insufficient endurance under complex working conditions. It relies on a large-capacity battery pack for power supply. Frequent charging and discharging reduce the battery life and increase the operation and maintenance costs. At the same time, the energy of the tensioning machine is recovered for the traction machine. The centralized control lacks real-time performance and self-adaptability, and the charge and discharge logic calls are chaotic, which may lead to waste of redundant energy or risks of overcharging / overdischarging of the battery, and is not conducive to the effective recycling of energy. The tensioning machine is used to keep construction riggings such as conductors and traction ropes being deployed or towed under tension conditions. The electric tensioning machine can generate electricity by reversing the motor under passive working conditions (the conductor drives the tension wheel to rotate), convert mechanical energy into electrical energy, and store it through a battery pack. The above is based on Faraday's law of electromagnetic induction and the principle of conservation of energy, and realizes the effective recovery and storage of energy through the motor and the battery pack. The electric energy generated by the electric tensioning machine under passive working conditions (i.e., tension working conditions) is recovered and stored, and supplied to the traction machine and other electrified devices for use, so as to realize the effective recycling of energy.

[0023] To solve the above technical problems, the embodiments of the present invention provide a battery energy management system for an electric tensioning device, aiming to provide power for the traction machine after storing the electric energy generated when multiple electric tensioning machines deploy conductors, and a control system for the electrified tensioning devices in the entire tension field, specifically including: A battery energy recovery module, a battery energy supply module, a battery energy dynamic distribution module, and a battery energy regulation module; the battery energy recovery module is used to determine whether the tension machine enters the energy recovery mode, capture the energy of the tension machine and store it in the supercapacitor first, and then store the energy of the supercapacitor in the battery pack to form a battery energy storage unit; the battery energy supply module is used to predict the output power of the tractor, obtain the predicted value of the tractor output power, compare the output power of the battery energy storage unit and the predicted value of the tractor output power, and realize the power supply of the battery energy storage unit to the tractor; the battery energy dynamic distribution module is used to optimize the current tolerance limit and voltage tolerance limit during power supply by using a multi-strategy fusion sparrow search algorithm when the battery energy storage unit supplies power to the tractor, and generate a battery energy dynamic distribution strategy; the battery energy regulation module is used to combine the battery energy dynamic distribution strategy and adjust the working state of the battery energy storage unit based on temperature-voltage closed-loop feedback.

[0024] Specifically, three prototype electric tension machines without batteries, one prototype electric small tractor without batteries, and one prototype power battery container (battery energy storage unit) can be used to realize that 3 tension machines supply energy to the tractor; among them, one prototype electric tension machine can generate about 40 kWh of surplus power per hour, and three prototype electric tension machines can generate about 120 kWh of electric energy per hour when working simultaneously. The prototype electric small tractor requires between 50-100 kWh of electric energy per hour during the traction process; In the specific implementation process of the above embodiments, first, the tension machine controller collects the load parameters and motor operation parameters of the tension machine, including cable tension, drum speed, motor torque, and motor speed, determines whether the tension machine enters the energy recovery mode, captures the energy of the tension machine and stores it in the supercapacitor first, and then stores the energy of the supercapacitor in the battery pack to form a battery energy storage unit; this method coordinates the work of the supercapacitor and the battery pack, uses the supercapacitor to take into account the instantaneous high-output power recovery, solves the problem that the traditional single battery pack is vulnerable to high-voltage impact, effectively protects the battery pack, and at the same time recovers the energy of multiple tension machines to solve the problem of insufficient traditional endurance, realizing the recovery and storage of electric energy; secondly, a BiLSTM neural network model is constructed to predict the output power of the tractor, considering the priority of the energy contribution of the tension machine, using the weighted priority algorithm combined with the predicted value of the tractor output power, and the battery energy storage unit coordinates to provide energy to realize the energy supply of the tractor; this method supports multi-machine energy sharing, that is, it supports multiple tension machines to supply power to the tractor at the same time, avoids the delay and conflict of centralized control, reduces the risk of overcharging / overdischarging of the battery, and at the same time the neural network model can effectively process the non-linear time-varying characteristics of the tractor output power to ensure the reliability of the system; when the tractor is supplied with energy, an objective function is established based on the maximum energy utilization rate, and the chaos mapping, sine-cosine algorithm, and elite opposition-based learning strategy are integrated, and the multi-strategy fusion sparrow search algorithm is used to optimize the current tolerance limit and voltage tolerance limit in the energy supply of the tractor, ensuring the energy distribution within the allowable range of current and voltage, and generating a dynamic battery energy distribution strategy; this algorithm overcomes the disadvantages of the traditional algorithm that the population diversity gradually decreases and is easy to fall into the local optimal solution, improves the convergence speed and algorithm performance, dynamically optimizes the charge and discharge logic, provides an efficient and reliable energy distribution strategy for the tensioning equipment, and realizes the maximization of energy recovery efficiency and the extension of equipment life; the whole process can efficiently search for the optimal combination of current and voltage tolerance limits, ensure that the current and voltage are within the safe range, significantly improve the energy transmission efficiency, and is conducive to the effective recycling of energy; finally, combined with the dynamic battery energy distribution strategy, the working state of the battery energy storage unit is adjusted based on the temperature-voltage closed-loop feedback to complete the battery energy management; the temperature-voltage closed-loop feedback regulation mechanism avoids the risk of thermal runaway through real-time feedback and dynamic adjustment, ensuring the safety and reliability of the energy call process.

[0025] Further, in order to better introduce the technical solution of the embodiments of the present invention, based on the above battery energy management system of an electric tensioning device, as Figure 1 shown, the embodiments of the present invention provide a battery energy management method for an electric tensioning device, which specifically includes the following contents: S1. Obtain the load parameters and motor operation parameters of the tensioner, determine whether the tensioner enters the energy recovery mode, capture the energy of the tensioner and store it in the supercapacitor first, and then use the secondary energy storage method and hierarchical protection mechanism to store the energy of the supercapacitor in the battery pack to form a battery energy storage unit; The S1 includes the following steps: S11. Obtain the load parameters and motor operation parameters of the tensioner. The load parameters of the tensioner include the cable tension and the drum speed, and the motor operation parameters include the motor torque and the motor speed to obtain the operation data of the tensioner; then obtain the time points for collecting the load parameters and motor operation parameters of the tensioner, generate the operation time series of the tensioner, find the operation data of the tensioner corresponding to the time points in sequence in the operation time series of the tensioner, and form a set of operation data of the tensioner; set a time window, select the time points separated by the time window in the operation time series of the tensioner, and record them as the first time point and the second time point respectively, obtain the operation data of the tensioner at the first time point, record it as the first operation data of the tensioner, and then obtain the operation data of the tensioner at the second time point, record it as the second operation data of the tensioner; S12. When both the cable tension and the drum speed in the first operation data of the tensioner are greater than 0 and both the cable tension and the drum speed in the second operation data of the tensioner are greater than 0, at this time the tensioner enters the energy recovery mode, and capture the energy of the tensioner and store it in the supercapacitor. The specific steps are as follows: S121. Obtain the motor torque and motor speed in the first operation data of the tensioner and the second operation data of the tensioner. When the motor torque is a reverse torque output and the direction of the motor torque is opposite to the direction of the motor speed, at this time the tensioner enters the active braking condition; then calculate the differential of the motor speed within the range from the first time point to the second time point, set a speed threshold, and when the motor torque is 0 and the differential of the motor speed within the range from the first time point to the second time point is less than the speed threshold, at this time the tensioner enters the passive braking condition, and judge the braking conditions of the set of operation data of the tensioner in turn; S122. Obtain the battery pack capacity, set a first capacity threshold, and store the captured energy of the tensioner in the supercapacitor when the battery pack capacity is greater than the first capacity threshold; in the active braking condition, switch the motor to the power generation mode to charge the supercapacitor; in the passive braking condition, trigger the protective braking, parallel the crowbar circuit for discharging, and then charge the supercapacitor; S13. Set a second capacity threshold. When the battery pack capacity is less than or equal to the first capacity threshold or the supercapacitor capacity is greater than the second capacity threshold, at this time use the secondary energy storage method to store the energy of the supercapacitor in the battery pack and use the hierarchical protection mechanism to form a battery energy storage unit. The specific steps are as follows: S131. Collect the voltages of the battery cells in the battery pack to obtain a set of battery cell voltages; S132. Set the first protection voltage and the second protection voltage. When the voltage of a battery cell in the battery cell voltage set is less than the first protection voltage, the supercapacitor charges the battery pack normally at this time; when the voltage of a battery cell in the battery cell voltage set is greater than or equal to the first protection voltage and less than or equal to the second protection voltage, the charging current is reduced; when the voltage of a battery cell in the battery cell voltage set is greater than the second protection voltage, the charging of the battery pack by the supercapacitor is disconnected to complete the charging of the battery pack; S133. Combine the supercapacitor and the battery pack to form a battery energy storage unit; In this embodiment, the tension machine controller collects the load parameters and motor operation parameters of the tension machine, determines whether the tension machine enters the energy recovery mode, captures the energy of the tension machine and stores it in the supercapacitor first, and then stores the energy of the supercapacitor in the battery pack to form a battery energy storage unit; this method coordinates the work of the supercapacitor and the battery pack, uses the supercapacitor to take into account the recovery of instantaneous high output power, solves the problem that a traditional single battery pack is vulnerable to high voltage impact, effectively protects the battery pack, and at the same time recovers the energy of multiple tension machines to solve the problem of insufficient traditional endurance and realizes the recovery and storage of electric energy; specifically, for example, the collected cable tension is 1200N, the drum speed is 60rpm, the motor torque is -50N·m, the motor speed is -55rpm, the time window is set to 5 seconds, and two adjacent time points t1 = 10:00:00 and t2 = 10:00:05 are selected; at t1, the motor torque is -50N·m (<0), the speed is -55rpm (opposite to the torque direction), active braking is triggered, and the energy is stored in the supercapacitor. The motor torque suddenly becomes 0, and the speed differential dω / dt = -60rpm / s (exceeds the threshold -50rpm / s), triggering passive braking to release instantaneous energy; the current capacity of the battery pack is 85% (> the first capacity threshold 80%), and the supercapacitor is preferentially used for energy storage. When the capacity of the supercapacitor reaches 95% (> the second capacity threshold 90%), secondary storage is started; the battery cell voltage is detected: the voltage of cell 1 is 3.7V (< the first protection voltage 3.6V), normal charging (current 20A), the voltage of cell 2 is 4.0V (between 3.6V and 4.2V), the current is reduced to 10A, and the voltage of cell 3 is 4.3V (> the second protection voltage 4.2V), disconnecting the charging circuit; storing electric energy through the energy recovery system to improve energy utilization efficiency; S2. Predict the output power of the tractor based on a neural network, output the predicted value of the tractor output power, and then consider the priority of the energy contribution of the tension machine, and use the weighted priority algorithm to combine the predicted value of the tractor output power, and the battery energy storage unit provides energy in coordination to achieve the energy supply of the tractor; The S2 includes the following steps: S21. Obtain the motor current, motor voltage, motor speed, motor temperature, load weight, and traction speed of the tractor to obtain the tractor operation data. Then, obtain the time point for collecting the tractor operation data to obtain the tractor operation time series, and form a tractor operation data set; set the sliding window size to , place the sliding window in the tractor operation data set, and select, according to the tractor operation time series, the tractor operation data corresponding to time points to form a feature sequence, and then perform normalization processing to generate a tractor operation data sample set; S22. Train a BiLSTM neural network to obtain a tractor output power prediction model, and output the tractor output power prediction value according to the sample data set. The specific steps are as follows: S221. Set the BiLSTM neural network to include an input layer, a bidirectional LSTM layer, an attention mechanism layer, a dense layer, and an output layer, and the initial learning rate is 0.001; obtain the historical tractor operation data, extract the feature sequence of the historical tractor operation data and perform normalization processing to obtain a sample data set, divide the sample data set into a sample training set and a sample test set, and then input the sample training set into the BiLSTM neural network for iteration until the BiLSTM neural network converges to obtain a trained BiLSTM neural network; S222. Then input the sample test set into the trained BiLSTM neural network, output the prediction result, set the accuracy threshold, and when the prediction result accuracy is greater than the accuracy threshold, obtain the tractor output power prediction model, otherwise adjust the weights until the prediction result accuracy is greater than the accuracy threshold; input the tractor operation data sample set into the tractor output power prediction model and output the tractor output power prediction value; S23. Denote the distance between the m th tensioner and the tractor as , and the transmission loss as b , then the path loss of the m th tensioner . Set the rated energy generated by the m th tensioner, obtain the remaining energy generated by the m th tensioner, and denote the quotient of the remaining energy generated by the m th tensioner and the rated energy generated by the m th tensioner as the remaining energy factor m of the th tensioner. Use the weighted priority algorithm to calculate the priority score of the m th tensioner. The calculation formula is as follows: ; where, represents them Priority score of a tension machine; Calculate the priority scores of all tension machines in sequence, sort them in descending order, and select the tension machine corresponding to the highest priority score, which is denoted as the priority tension machine; compare the predicted output power of the tractor and the output power of the battery pack in the battery energy storage unit. When the predicted output power of the tractor is less than the output power of the battery pack in the battery energy storage unit, the battery pack provides energy; otherwise, use the priority tension machine to charge the super capacitor, and the super capacitor cooperates with the battery pack to provide energy to complete the energy supply for the tractor. In this embodiment, a model is constructed to predict the output power of the tractor. Considering the priority of the energy contribution of the tension machine, the weighted priority algorithm is used in combination with the predicted value of the tractor output power, and the battery energy storage unit cooperates to provide energy to achieve the energy supply for the tractor. This method supports multiple tension machines to supply power to the tractor simultaneously, avoids the delay and conflict of centralized control, reduces the risk of overcharging / overdischarging of the battery, and at the same time, the neural network model can effectively handle the non-linear time-varying characteristics of the tractor output power to ensure the reliability of the system; specifically, for example, the tractor data is collected once every 10 seconds for 24 hours continuously to form 8640 sets of time series data. Set the window size to 15 (2.5 minutes of data), the window sliding step size to 5 seconds, generate 1728 feature sequences, after normalization processing, input them into the tractor output power prediction model, and the predicted value of the tractor output power is 58.7 kW; there are 3 tension machines T1, T2, and T3, with distances of 0.8 km, 1.2 km, and 0.5 km respectively, and the remaining energy factors are 0.9, 0.6, and 0.3 respectively, and the priority scores are 3.68, 2.92, and 6.67 respectively. Select the priority tension machine T3, provide a peak output power of 10 kW through the super capacitor, the battery is downloaded to 48.7 kW, and cooperate to provide energy of 48.7 + 10 = 58.7 kW. S3. When supplying energy to the tractor, establish an objective function based on the maximum energy utilization rate, and use the multi-strategy fusion sparrow search algorithm to optimize the current tolerance limit and voltage tolerance limit in the tractor energy supply to generate a dynamic battery energy distribution strategy. The S3 includes the following steps: S31. After the tractor is supplied with energy, obtain the transmission current, transmission resistance, internal resistance and voltage of the battery pack in the battery energy storage unit, and calculate the transmission resistance loss , where represents the transmission current, represents the transmission resistance, and then calculate the output power of the battery energy storage unit , where V represents the voltage of the battery pack in the battery energy storage unit, It represents the internal resistance of the battery pack in the battery energy storage unit, and takes the maximum difference between the output power of the battery energy storage unit and the transmission resistance loss as the objective function; S32. Set the current tolerance limit and voltage tolerance limit to restrict the range of the transmission current and the voltage of the battery pack in the battery energy storage unit, improve the sparrow search algorithm, and obtain a sparrow search algorithm with multi-strategy fusion; during the energy supply process of the tractor, use the sparrow search algorithm with multi-strategy fusion to optimize the current tolerance limit and voltage tolerance limit, and obtain the optimized current tolerance limit and optimized voltage tolerance limit. The specific steps are as follows: S321. Take the objective function as the fitness function, regard the process of solving the objective function as the search space. There is a sparrow population in the search space. The number of the sparrow population is p , and the dimension of the sparrow population is q . Combine the current tolerance limit and voltage tolerance limit to form a feasible solution. The position of the sparrow individual in the sparrow population represents the feasible solution, and regard the process of iterating the position of the sparrow individual as the process of optimizing the current tolerance limit and voltage tolerance limit; divide the sparrow population into discoverers and joiners, use chaotic mapping for population initialization, then introduce the elite opposition-based learning strategy, select the initial best position and initial worst position in the sparrow population, and set to represent a random number between the interval [0, 1], to represent the position of the i th sparrow individual in the sparrow population, calculate the reverse solution i of the position of the th sparrow individual in the sparrow population, where represents the position of the best sparrow individual in the sparrow population, represents the position of the worst sparrow individual in the sparrow population; S322. Set the current iteration number to t , the maximum iteration number to T , , and to represent random numbers between the interval (0, 1], to represent the warning value and a random number between the interval [0, 1], to represent the safety value and a random number between the interval [0.5, 1], to represent the t th dimension position of the i th sparrow individual in the sparrow population at the j th iteration. Find the best sparrow individual in the sparrow population at the t th iteration, denoted as ; introduce the sine-cosine algorithm to update the position of the discoverer. When , obtain the position of the t +1th iteration of the iThe j -dimensional position of a sparrow individual , when , the position is obtained; S323. Search for the best sparrow individual in the sparrow population at the t +1-th iteration, denoted as , and the worst sparrow individual in the sparrow population at the t +1-th iteration, denoted as . Set to represent a random number between the interval [-1, 1], and C to represent a random number following a standard normal distribution. At this time, update the position of the joiner. When , the position is obtained. When , the position is obtained. Calculate the current best fitness function value and the current worst fitness function value, control the sparrow population to move in the direction of the sparrow individual corresponding to the current best fitness function value, complete the position update of all current stages, and generate the next generation of sparrow population. Until the current iteration number reaches the maximum iteration number, stop the iteration, obtain the final sparrow population, search for the position of the sparrow individual corresponding to the best fitness function value, and obtain the optimized current tolerance limit and the optimized voltage tolerance limit; S33. During the energy supply of the tractor, keep the transmission current within the optimized current tolerance limit, and the voltage of the battery pack in the battery energy storage unit within the optimized voltage tolerance limit. The battery energy storage unit provides energy for the tractor to generate a dynamic battery energy distribution strategy; In this embodiment, when the tractor is powered, a target function is established based on the maximum energy utilization rate, and the chaotic mapping, sine-cosine algorithm, and elite opposition-based learning strategy are integrated. The multi-strategy fusion sparrow search algorithm is used to optimize the current tolerance limit and voltage tolerance limit in the tractor power supply, generating a battery energy dynamic allocation strategy, which overcomes the shortcomings of the traditional algorithm that the population diversity gradually decreases and is prone to falling into local optimal solutions, improving the convergence speed and algorithm performance; dynamically optimizing the charge and discharge logic, providing an efficient and reliable energy allocation strategy for the tensioning equipment, achieving the maximization of energy recovery efficiency and the extension of equipment life; the whole process can efficiently search for the optimal combination of current and voltage tolerance limits, ensure that the current and voltage are within a safe range, significantly improve the energy transmission efficiency, and is conducive to the effective recycling of energy; specifically, for example, the current tolerance limit constraint ∈ [1%, 10%] (initial range), the voltage tolerance limit constraint ∈ [1%, 5%] (initial range), the sparrow population size p = 50, the dimension q = 2 (current and voltage tolerance limits), the maximum number of iterations T = 100, the chaotic mapping uses the Logistic mapping, the elite opposition-based learning strategy introduces the opposition solution, the initial sparrow individual position is set as [8%, 4%], the transmission current is 98 A, the battery pack voltage in the battery energy storage unit is 45.5 V, the internal resistance of the battery pack in the battery energy storage unit is 0.05 Ω, the transmission resistance is 0.1 Ω, and the energy utilization rate at this time is ; update the sparrow individuals in the sparrow population. For the discoverer update: when 0.3 < 0.8, use the sine update. For the joiner update: when 20 ≤ p / 2 = 25, update around the best position, complete this iteration, and converge to [3.2%, 1.8%] at the maximum number of iterations. At this time, the energy utilization rate is 94%. The optimization of the current and voltage tolerance limits before and after improves the energy utilization rate and balances the efficiency and equipment life at the same time; S4. Combine the battery energy dynamic allocation strategy, and based on the temperature-voltage closed-loop feedback, adjust the working state of the battery energy storage unit to complete the battery energy management; The S4 includes the following steps: S41. Run the battery energy dynamic allocation strategy. The battery energy storage unit supplies power to the tractor, collect the battery pack temperature and battery pack voltage in the battery energy storage unit to obtain a temperature-voltage array, and then calculate the average temperature of the battery pack in the battery energy storage unit ; set the temperature coefficient as , the standard voltage at u temperature is , the battery pack temperature in the battery energy storage unit at u temperature is , and the dynamic voltage threshold at this time is ; S42. When the battery energy storage unit is powered, set a temperature threshold. When the voltage of the battery pack in the battery energy storage unit is less than the dynamic voltage threshold, suspend the power supply. When the temperature of the battery pack in the battery energy storage unit is greater than the temperature threshold, reduce the supply current to form a closed-loop feedback regulation to complete the battery energy management. In this embodiment, by combining the battery energy dynamic distribution strategy, based on the temperature-voltage closed-loop feedback, adjust the working state of the battery energy storage unit, with real-time feedback and dynamic adjustment, to avoid the risk of thermal runaway, extend the battery life, ensure the safety and reliability of the energy call process, and complete the battery energy management. Specifically, for example, set the temperature coefficient to 0.05V / ℃ (assumed empirical value), the current ambient temperature is 25℃, the standard battery voltage at this temperature is 3.7V, the temperature threshold is set to 50℃, the collected temperatures are 25℃, 28℃, 31℃, the average temperature is 28℃, and the current monitored point temperature is 25℃. Calculate the dynamic voltage threshold of 3.55V. When the voltage of a single cell drops to 3.5V (<3.55V), the system immediately suspends the power supply to prevent over-discharge. When the temperature rises to 52℃ (>50℃), the system automatically reduces the supply current from 100A to 70A and dissipates heat through the cooling system.

[0026] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0027] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the invention, so that those skilled in the relevant technical field can understand and utilize the invention well.

Claims

1. A battery energy management system for an electric tensioning device, characterized in that: include: The battery energy recovery module is used to determine whether the tension machine has entered the energy recovery mode, capture the energy of the tension machine and store it in the supercapacitor first, and then store the supercapacitor energy in the battery pack to form a battery energy storage unit; The battery energy supply module is used to predict the output power of the traction machine, obtain the predicted value of the output power of the traction machine, compare the output power of the battery energy storage unit with the predicted value of the output power of the traction machine, and realize that the battery energy storage unit supplies energy to the traction machine; A battery energy dynamic allocation module is used to optimize the current tolerance limit and the voltage tolerance limit when the battery energy storage unit supplies energy to the traction machine using a multi-strategy fusion sparrow search algorithm to generate a battery energy dynamic allocation strategy; The battery energy regulation module is used to adjust the working state of the battery energy storage unit based on the temperature-voltage closed-loop feedback in combination with the battery energy dynamic allocation strategy.

2. A battery energy management system for an electric stretching device according to claim 1, characterized in that: The determination of whether the tension machine enters the energy recovery mode comprises: Obtaining tension machine load parameters and motor operating parameters, wherein the tension machine load parameters include cable tension and drum speed, and the motor operating parameters include motor torque and motor speed, obtaining tension machine operating data, recording the tension machine operating time series, and selecting a first time point and a second time point; When the cable tension and the roller speed in the first tension machine operation data are both greater than 0 and the cable tension and the roller speed in the second tension machine operation data are both greater than 0, the tension machine enters the energy recovery mode.

3. A battery energy management system for an electric stretching device according to claim 2, characterized in that: The energy of the capture tension machine is first stored in the super capacitor, including: The motor torque and motor speed in the first tension machine operation data and the second tension machine operation data are obtained. When the motor torque is a reverse torque output and the direction of the motor torque is opposite to that of the motor speed, the tension machine enters an active braking condition. Then, the differential of the motor speed in the range from the first time point to the second time point is calculated, and a speed threshold is set. When the motor torque is 0 and the differential of the motor speed in the range from the first time point to the second time point is less than the speed threshold, the tension machine enters a passive braking condition. Acquire the capacity of the battery pack, set a first capacity threshold, and when the capacity of the battery pack is greater than the first capacity threshold, store the energy of the capture tension machine in the supercapacitor; In the active braking condition, the motor is switched to the power generation mode to charge the supercapacitor; in the passive braking condition, protective braking is triggered, the parallel crowbar circuit is discharged, and then the supercapacitor is charged.

4. A battery energy management system for an electric stretching device according to claim 3, characterized in that: The supercapacitor energy is then stored in a battery pack, and the battery energy storage unit comprises: A second capacity threshold is set, and when the battery pack capacity is less than or equal to the first capacity threshold or the supercapacitor capacity is greater than the second capacity threshold, a secondary energy storage method is used to store supercapacitor energy in the battery pack; Collecting the voltage of battery cells in the battery pack to obtain a battery cell voltage set; A first protection voltage and a second protection voltage are set, and when the battery cell voltage in the battery cell voltage set is less than the first protection voltage, the supercapacitor charges the battery pack normally; When the battery cell voltage in the battery cell voltage set is greater than or equal to the first protection voltage and less than or equal to the second protection voltage, reducing the charging current; When the battery cell voltage in the battery cell voltage set is greater than the second protection voltage, disconnecting the supercapacitor to charge the battery pack, thereby completing the charging of the battery pack; The supercapacitor and the battery pack form a battery energy storage unit.

5. A battery energy management system for an electric stretching device according to claim 4, characterized in that: The predicted output power of the traction machine and the output predicted value of the traction machine output power include: Obtain the motor current, motor voltage, motor speed, motor temperature, load weight and traction speed of the traction machine, and generate a traction machine operation data sample set; The BiLSTM neural network is set to include an input layer, a bidirectional LSTM layer, an attention mechanism layer, a dense layer and an output layer, and the initial learning rate is 0.001; historical traction machine operation data is obtained, and the feature sequence of the historical traction machine operation data is extracted and normalized to obtain a sample data set, and the sample data set is divided into a sample training set and a sample test set, and then the sample training set is input into the BiLSTM neural network for iteration until the BiLSTM neural network converges to obtain a trained BiLSTM neural network; Then input the sample test set into the trained BiLSTM neural network, output the prediction result, set the accuracy threshold, and when the prediction result accuracy is greater than the accuracy threshold, obtain the traction machine output power prediction model, otherwise adjust the weight until the prediction result accuracy is greater than the accuracy threshold; input the traction machine operation data sample set into the traction machine output power prediction model, and output the traction machine output power prediction value.

6. A battery energy management system for an electric stretching device according to claim 5, characterized in that: The comparing the output power of the battery energy storage unit and the predicted value of the output power of the traction machine to enable the battery energy storage unit to supply energy to the traction machine comprises: Calculate the m The path loss and residual energy factor of the tension machine are calculated using the weighted priority algorithm. m The priority scores of the tension machines are calculated; the priority scores of all tension machines are calculated in sequence, and the tension machine corresponding to the highest priority score is selected and recorded as the priority tension machine; The predicted value of the traction machine output power is compared with the output power of the battery pack in the battery energy storage unit. When the predicted value of the traction machine output power is less than the output power of the battery pack in the battery energy storage unit, the battery pack provides energy; otherwise, the priority tension machine is used to charge the supercapacitor, and the supercapacitor cooperates with the battery pack to provide energy to complete the energy supply of the traction machine.

7. A battery energy management system for an electric stretching device according to claim 6, characterized in that: The method of using the sparrow search algorithm with multi-strategy fusion to optimize the current tolerance limit and the voltage tolerance limit during energy supply includes: After the traction machine supplies energy, the transmission current, transmission resistance, internal resistance and voltage of the battery pack in the battery energy storage unit are obtained, the transmission resistance loss and the output power of the battery energy storage unit are calculated, and the maximum difference between the output power of the battery energy storage unit and the transmission resistance loss is used as the objective function; The current tolerance limit and the voltage tolerance limit are set to constrain the range of the transmission current and the battery pack voltage in the battery energy storage unit; the objective function is used as the fitness function, and the process of solving the objective function is regarded as a search space, in which there is a sparrow population, the current tolerance limit and the voltage tolerance limit are composed of a feasible solution, and the individual position of the sparrow in the sparrow population represents a feasible solution, and the process of iterating the individual position of the sparrow is regarded as a process of optimizing the current tolerance limit and the voltage tolerance limit; the sparrow population is divided into discoverers and joiners, and the population is initialized using chaotic mapping, and then the elite reverse learning strategy is introduced to calculate the first in the sparrow population. i The reverse solution of the individual positions of sparrows; The sine-cosine algorithm is introduced to update the position of the discoverer, and then the position of the joiner is updated; the current best fitness function value and the current worst fitness function value are calculated, and the sparrow population is controlled to move in the direction of the sparrow individual corresponding to the current best fitness function value, so as to complete the position update of all current stages and generate the next generation of sparrow population; until the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final sparrow population, and the position of the sparrow individual corresponding to the best fitness function value is found to obtain the optimized current tolerance limit and the optimized voltage tolerance limit.

8. A battery energy management system for an electric stretching device according to claim 7, characterized in that: The generation of a dynamic battery energy allocation strategy includes: When the traction machine is powered, the transmission current is kept within the optimized current tolerance limit, and the battery pack voltage in the battery energy storage unit is within the optimized voltage tolerance limit. The battery energy storage unit provides energy for the traction machine and generates a dynamic battery energy allocation strategy.

9. A battery energy management system for an electric stretching device according to claim 8, characterized in that: The method of adjusting the working state of the battery energy storage unit based on the temperature-voltage closed-loop feedback to complete the battery energy management includes: The battery energy dynamic allocation strategy is run, the battery energy storage unit supplies power to the traction machine, the battery pack temperature and battery pack voltage in the battery energy storage unit are collected, and the dynamic voltage threshold is calculated; When the battery energy storage unit is powered, a temperature threshold is set. When the battery pack voltage in the battery energy storage unit is less than the dynamic voltage threshold, the power supply is terminated. When the battery pack temperature in the battery energy storage unit is greater than the temperature threshold, the power supply current is reduced to form a closed-loop feedback regulation to complete battery energy management.

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