2065 results about "Battery voltage" patented technology

An ID token wirelessly communicates a battery voltage to a vehicle gateway via short-range communication. A battery health may be determined based on the current battery voltage and one or more previous battery voltages associated with ID token. Replacement of the ID token may be initiated based on the battery health to avoid communication failures by the ID token due to insufficient battery charge.

The invention discloses a control system for lithium battery power management, and relates to the field of lithium battery control, and the system comprises a voltage input module which is used for externally connecting voltage and supplying power to a battery power supply module and a time delay module; the battery power supply module is used for charging the battery; receiving the battery voltage information, and adjusting the voltage output to the battery according to the battery voltage; the battery module is used for a battery to store electric energy; compared with the prior art, the system has the beneficial effects that the output voltage of the battery power supply module is adjusted through the feedback module, the output voltage of the battery power supply module is relatively large at the beginning, and the charging voltage is reduced along with the increase of the battery voltage, so that a quick charging effect is constructed; whether battery charging is abnormal or not is detected through the time delay module and the charging fault detection module, the battery power supply module is adjusted to perform constant-voltage charging when charging is abnormal, and the battery charging effect is guaranteed.

The invention relates to a battery SOC intelligent monitoring and management method based on a precise electric quantity algorithm, and belongs to the technical field of battery power generation management, and the method comprises the steps: obtaining the voltage, current, temperature and historical data of a battery in real time, and calculating an initial SOC value through an ampere-hour integral method; when the SOC is greater than or equal to 90% in a charging state, generating a first correction factor based on a voltage hysteresis characteristic and a temperature coefficient, and dynamically reducing an ampere-hour integral weight; in the discharge state and when SOC is smaller than or equal to 20%, a second correction factor is generated in combination with the polarization voltage and the temperature attenuation coefficient, and the Kalman filtering weight is increased; and fusing the correction factors through a gradient descent optimization algorithm, performing closed-loop feedback, and outputting a calibrated SOC value. According to the method, through dynamic weight distribution, a nonlinear compensation model and a closed-loop self-optimization mechanism, the problems that an extreme SOC interval is large in estimation error and a traditional method is poor in adaptability are solved, and the precision and reliability of battery management are remarkably improved.

The invention relates to the technical field of battery management, and discloses a high-voltage lithium battery cluster voltage balance control method and system for rail transit. The method comprises the following steps: acquiring battery voltage and tunnel environment parameters, and obtaining environment characteristic data through Kalman filtering; predicting a voltage deviation trend through LSTM in combination with a UPS load rule; calculating a dynamic equilibrium threshold according to the predicted value; when the voltage difference value exceeds a threshold value, the bidirectional energy transmission equalization circuit is started; and monitoring the battery state change in the equalization process, and outputting a maintenance instruction through a fault recognition algorithm. The problem that battery balance control in a rail transit UPS system cannot adapt to complex environments and dynamic load changes is solved, and the precision and the intelligent level of battery cluster voltage balance control are improved.

The invention discloses a lithium battery thermal runaway monitoring method and a related device, and relates to the field of lithium battery thermal runaway monitoring, and the method comprises the steps: obtaining a multi-sensing parameter time sequence of a target lithium battery; the multi-sensing parameter time sequence comprises a battery temperature time sequence, a battery voltage time sequence, an in-battery gas concentration time sequence and a battery deformation time sequence; inputting the multi-sensing parameter time sequence into a monitoring model to obtain the spatial-temporal characteristics of each sensing parameter; the monitoring model comprises a convolutional neural network, an LSTM network and an attention mechanism module which are connected in series; and monitoring the thermal runaway state of the lithium battery according to the spatial-temporal characteristics of the sensing parameters and the corresponding dynamic thresholds. According to the invention, the thermal runaway state of the lithium battery can be accurately monitored in real time, the occurrence of thermal runaway can be predicted in advance, and an early warning signal can be sent out in time.

The invention relates to a motor power self-adaptive control method and system based on multi-dimensional sensor data, and the method comprises the steps: collecting multi-dimensional signals, such as ground resistance, motor temperature and battery voltage, in real time, carrying out the self-adaptive filtering and noise reduction, analyzing the correlation of all parameters through a multivariable decoupling algorithm, and determining a power demand weight; when it is detected that the ground resistance is a dominant factor, the system generates a load feature vector through weighted fusion, a database is matched to determine an optimal power target value, and PWM output is dynamically adjusted through a fuzzy control algorithm; and a closed-loop feedback mechanism continuously optimizes control parameters to ensure that the system quickly responds to sudden change of ground resistance. According to the method, intelligent adaptation of the motor power is achieved, the problems that adjustment lags behind and multi-parameter coupling processing is insufficient in the traditional technology are effectively solved, the stability of the sweeping robot under the complex ground working condition is remarkably improved, and the service life of the motor and the service life of a battery are remarkably prolonged.

The invention relates to the technical field of vehicles, in particular to a battery charging method and device of a vehicle and the vehicle, and the method adopts a battery charging system which comprises first to eighth switches, and comprises the following steps: judging whether the vehicle is connected with a charging pile or not; if the vehicle is connected with the charging pile, acquiring a current charging mode of the battery charging system, and determining target states of first to eighth switches according to the current charging mode; and the first to eighth switches are controlled according to the target states of the first to eighth switches, and / or the multiple bridge arms are controlled based on a preset control strategy, so that the vehicle is subjected to direct fast charging or boost charging or current-rising charging. Therefore, the problem that the vehicle power battery cannot be charged or the charging efficiency is low when the charging pile is not matched with the battery voltage in the prior art is solved, and the charging experience of the vehicle power battery is improved.

The invention relates to the technical field of batteries, and discloses a battery, a battery pack and electric equipment, and the battery comprises a pole assembly arranged on a shell; the battery cell comprises a battery cell body and a tab part extending from the battery cell body, and the tab part is welded with the pole assembly to form a first welding printing area; wherein the tab part comprises an aluminum element, a manganese element and / or a silicon element, and the mass ratio of the manganese element and / or the silicon element is a%; a resistance test point is formed on the non-welding area of the tab part, the first welding printing area is located between the pole assembly and the resistance test point, and the resistance between the pole assembly and the resistance test point is b; the tab part comprises a plurality of tab layers which are stacked, and the thickness of each tab layer is c; a, b, and c satisfy 0.004 < = b * a / c < = 15. The lithium separation risk of the battery is reduced while the over-current capability between the pole component and the tab part is ensured and the voltage jump of the battery is avoided, so that the reliability of the battery is ensured.

The invention discloses a battery state-of-charge detection method based on deep learning and ultrasonic feature fusion. The method comprises the following steps: collecting and preprocessing battery ultrasonic signals and battery electrical parameters; extracting an ultrasonic amplitude; extracting and fusing the characteristics of the battery voltage, the surface temperature, the ultrasonic amplitude and the phase difference; the method comprises the following steps of: optimizing parameter values of a BiTCN-BiLSTM-Multi-Head Attention network on the basis of a WAA weighted average optimization algorithm, extracting basic characteristics related to the SOC of the battery in a characteristic matrix on the basis of a bidirectional time convolution network BiTCN, extracting long-term information related to the SOC of the battery in the basic characteristics on the basis of a BiLSTM network, extracting important characteristics related to the SOC of the battery in the long-term information on the basis of a Multi-Head Attention network, and calculating the SOC of the battery according to the extracted important characteristics. And outputting a predicted value of the SOC of the battery through the full connection layer. According to the invention, the estimation precision of the battery SOC can be improved.

The present application relates to a warming-up control method for a fuel cell system in a starting process and a fuel cell system, applied to the technical field of fuel cells. The method includes: starting the fuel cell system, turning on a small-cycle cooling circuit, and pull-loading an output power to a first power; in response to an inlet temperature of a stack reaching a first temperature, rotating a three-way valve at a first rotating rate; calculating a variance of a cell voltage value; in response to the variance being smaller than a third threshold and reducing the variance to be within the first threshold, returning to the rotating the three-way valve at the first rotating rate until all turn-on of a large-cycle cooling circuit and all turn-off of the small-cycle cooling circuit; pull-loading the output power to a rated power.

The invention discloses a thermal runaway detection method and system for a sodium ion battery. The detection method comprises the following steps: acquiring battery voltage and surface pressure of a battery shell; a voltage change curve and a pressure change curve are obtained, the voltage change curve is a curve that the battery voltage changes along with time, and the pressure change curve is a curve that the surface pressure changes along with time; and in combination with the battery voltage, the pressure change curve, the surface pressure and the change of the pressure change curve, multi-stage early warning signals are sent out. According to the method, multi-stage early warning detection with pressure as a core parameter is adopted, the severity of thermal runaway can be effectively identified, corresponding measures are taken according to different stage characteristics, the detection accuracy is improved, the thermal runaway detection reliability of the sodium ion battery is improved, and the use safety of the sodium ion battery is improved.

The invention discloses a three-phase vehicle-mounted charger hierarchical control method and system based on a battery state, and the method comprises the steps: obtaining the battery state data of a vehicle-mounted power battery, the battery state data at least comprising a battery voltage and a battery charge state; determining a charging mode according to the battery voltage and / or the battery charge state; when the charging mode is a first charging mode, controlling the PFC converter to operate in a constant voltage output mode, and executing voltage following control on the bidirectional DC-DC converter based on the battery voltage; and when the charging mode is a second charging mode, executing voltage following control on the PFC converter based on the battery voltage, and controlling the bidirectional DC-DC converter to operate in a constant-frequency or constant-phase output mode at the same time. By setting hierarchical control strategies under different working conditions, the output voltage of the PFC converter is adaptively adjusted, the voltage regulation transformation ratio under different working conditions is optimized, the overall circuit structure is improved, and the charging efficiency of the vehicle-mounted charger within the full working condition range is improved.

The invention relates to the technical field of energy storage lithium batteries, in particular to a method for detecting the health state of a household energy storage lithium battery. The method for detecting the state of health of the household energy storage lithium battery is characterized by comprising the following specific steps: S1, preprocessing charge-discharge cycle data; s2, performing multi-domain feature extraction; s3, an SA-XGBoost model oriented to static data and a TSO-LSTM-AM model oriented to dynamic data are constructed respectively, and SOH estimation is carried out on the SA-XGBoost model and the TSO-LSTM-AM model; and S4, finally, performing compression optimization on the model through knowledge distillation and quantification technologies to meet a lightweight deployment requirement. Compared with the prior art, in combination with historical charging and discharging parameters such as battery voltage, current and temperature, feature vectors are constructed through adaptive data cleaning and multi-domain feature extraction, the SOH estimation capability of static and dynamic data is optimized by adopting machine learning and a deep learning model respectively, and the deployment requirement of practical application is met by utilizing a model compression technology.

The invention discloses an internet of things data optimization processing method and device and a medium, and relates to the technical field of internet of things edge computing, and the method comprises the steps: collecting a battery voltage, a CPU utilization rate and a network bandwidth of an internet of things device in real time, calculating an available energy consumption budget, and configuring a basic sampling frequency; acquiring a time sequence data stream by using a basic sampling frequency, measuring a nonlinear information entropy increment through a sliding window, and dynamically evaluating the information value density of the time sequence data stream; according to a comparison result of the information value density and the available energy consumption budget, obtaining an optimal sampling frequency, and performing time sequence data flow anomaly judgment to generate a time sequence data subset; and inputting the time sequence data subset into the pulse neural network, calculating a current continuous data point variation, and activating a processing channel if the current continuous data point variation exceeds a device dynamic threshold. According to the method, the basic sampling frequency is configured through the non-cooperative game model, and combined modeling and game optimization of the equipment operation state and resource constraint are realized.

The invention discloses a battery pack intelligent fire extinguishing system based on multistage thermal runaway early warning, which comprises a sensing monitoring module, a data processing module, an execution mechanism module and a communication interaction module, the sensing monitoring module comprises a distributed temperature sensor array for monitoring the surface temperature of the battery in real time and detecting abnormal temperature rise, a voltage / current monitoring circuit for detecting the voltage fluctuation of the battery and identifying the risk of internal short circuit, and a multi-gas detector for detecting the CO / CO / H concentration and judging the initial decomposition reaction of the electrolyte. According to the invention, three-level early warning can be carried out at the potential risk stage of thermal runaway, the initial stage of thermal runaway and thermal runaway outbreak, so that early warning or fire-fighting treatment can be carried out in advance, and disaster expansion is avoided; the situation of false alarm caused by temperature rise in the battery charging process can be reduced, and the early warning accuracy of the system is improved.

A method is disclosed for managing and safeguarding a battery environment. The method includes continuous monitoring of individual battery voltages in a battery room and automatic isolation of defective batteries, employing a controller-solenoid switch mechanism responsive to voltage abnormalities. The methodology accounts for the state of battery charging, invoking fan activation when charging occurs or hydrogen levels rise. Also disclosed is a system for managing a battery system in a battery room and isolating defective batteries. Enhanced system features include hydrogen sensors strategically placed throughout, including the ceiling, with outcome-driven fan operation and comprehensive notification mechanisms to inform on-site personnel of gas release locations, maintaining a secure and efficient battery room environment.

The invention provides a battery micro short circuit diagnosis method and system based on an auto-encoder and abnormal clustering, and relates to the technical field of battery pack fault diagnosis. Comprising the steps that a target battery pack is determined, battery voltage signals in the target battery pack are sampled, the improved Pearson's correlation coefficient and the improved cosine similarity of the sampled voltage signals between the batteries are calculated, and similarity fusion features are calculated; inputting the similarity fusion features into an auto-encoder fault detection model to obtain reconstruction features; calculating a loss function between the similarity fusion feature and the reconstruction feature, taking the loss function value as a fault detection index, and judging whether the target battery pack has a micro short circuit fault or not; when a micro-short-circuit fault occurs in the target battery pack, constructing a residual distance matrix; and based on the residual distance matrix, determining a fault variable by adopting a clustering method, and finally determining a short-circuit fault battery. According to the method, the two-dimensional features are extracted, and the faulty single battery is accurately positioned.

The present invention relates to the technical field of batteries, and discloses a battery and a battery pack, the battery comprises: a battery cell comprising a battery cell body and a tab part extending from the battery cell body; the pole assembly is welded with the tab part to form a first welding mark area, and a non-welding area is formed on the part, without the welding mark, of the tab part; the tab part comprises a plurality of tab layers which are stacked in the z direction, the surface roughness of the tab layers is R, the orthographic projection area of the first welding printing area on the tab part in the z direction is S, the thickness of the non-welding area in the z direction is T1, the thickness of the first welding printing area is T2, and the formula that (T2 * R) / (T1 * S) is larger than or equal to 1.4 * 10 <-6 > and smaller than or equal to 5.4 * 10 <-5 > is met. According to the invention, the over-current capability between the pole assembly and the tab part is ensured, the charging and discharging rate of the battery is improved, the voltage jump of the battery is avoided, the reliability of the battery is ensured, the welding strength of the tab part and the pole assembly is improved, the difficulty degree of the welding process is reduced, and the welding efficiency is ensured.

The invention belongs to the technical field of water traffic transportation safety and navigation mark navigation aiding, and mainly relates to a navigation mark anti-collision early warning method based on multi-sensor fusion, and the method comprises the steps: constructing a multi-sensor fusion architecture integrating an AIS terminal, a laser radar and an AI camera, and achieving the omnibearing monitoring and precise recognition of a ship in a navigation channel; a grading response mechanism is adopted, corresponding functions are dynamically started according to the real-time distance between the ship and the navigation mark, and targeted response from early warning to emergency warning is achieved; a legal evidence chain packaging mechanism is introduced, a unique ID is generated for each early warning event, formatted evidence is stored in an associated mode, and integrity and traceability of data are ensured; through a dynamic energy supply strategy, an equipment operation mode is intelligently adjusted according to a storage battery voltage threshold value, and continuous and stable work of the system under the conditions of cloudy and rainy days, night and the like is guaranteed; according to the invention, the accuracy, timeliness and reliability of navigation mark collision early warning information are effectively improved.

The invention discloses an underwater vehicle battery state monitoring system and a power supply system based on wireless communication, and the system comprises a wireless power supply unit which employs a circuit structure of LCC-S compensation topology and is used for achieving constant voltage output; the battery monitoring unit is used for acquiring voltage, current and temperature data of the battery in real time; the communication unit comprises an RS-485 communication module and a matched LORA wireless communication module, and after sensor data is converted into an RS-485 signal, the RS-485 signal is transmitted through the LORA module after being converted by a serial port; and the control unit is located at an undersea wireless charging station end and is used for receiving the data transmitted by the LORA wireless communication module and calculating a phase shift angle based on a phase shift control strategy so as to generate a PWM signal to adjust the output voltage / current of the high-frequency inverter. By means of the system, real-time monitoring of the battery of the underwater vehicle is achieved, phase shift control is conducted on output of the primary side of the undersea wireless power supply system according to the battery charging information, and the safety and reliability of battery charging of the underwater vehicle in the marine environment are guaranteed.

The present invention discloses a voltage-configurable rechargeable battery device and charging terminal and charging station, wherein the voltage-configurable rechargeable battery device comprises a rechargeable battery, a battery voltage management circuit, and a battery capacity acquisition circuit; the rechargeable battery is electrically connected to a charging terminal; the battery voltage management circuit comprises a boost / buck management circuit; the battery capacity acquisition circuit is connected to the battery voltage management circuit; the rechargeable battery can realize discharge curve following according to the current capacity via the battery voltage management circuit. The present invention can ensure basically consistence of the actual power level and the displayed power level, and improve users' experience.

The invention discloses a multi-rotor trajectory tracking optimization method based on MLP acceleration estimation and reinforcement learning, and relates to the technical field of flight control, and the method comprises the steps: receiving a target position of an unmanned aerial vehicle, synthesizing a man-machine position, speed, attitude, relative position error and a current battery voltage value into an observation vector, inputting the observation vector into a reinforcement learning model, and carrying out the reasoning, outputting the planned reference acceleration to a controller; the controller obtains an expected attitude and an expected thrust of the unmanned aerial vehicle according to a kinematics formula; and adjusting an optimization algorithm of control parameters in real time through the thrust-accelerator compensation network model, calculating a corresponding accelerator value, and solving an optimal bottom layer control instruction. According to the method, the target acceleration generated by reinforcement learning is taken as a core control quantity, high-performance flight control is realized through a hierarchical control strategy, and the system can still keep excellent trajectory tracking performance and control stability even under the condition that obvious battery voltage fluctuation exists.

The invention relates to the technical field of battery management, in particular to an intelligent charging adjustment method and system based on dynamic monitoring, and the method comprises the steps: determining a tested energy storage battery, determining a target battery based on a charging adjustment instruction, carrying out the feature extraction of a plurality of historical charging data and the previous full capacitance, and obtaining a charging emergency coefficient and a battery aging coefficient; a humidity sensor and a temperature sensor are used for obtaining current humidity and current temperature respectively, particle swarm simulation is carried out on a charging emergency coefficient, a battery aging coefficient, a constant current curve set, a constant voltage curve set, a switching curve set, the current humidity and the current temperature, and target constant current, target constant voltage and a target switching threshold value are obtained; and if the target battery voltage is greater than or equal to the target switching threshold, performing charging adjustment on the target battery according to the target constant voltage and the target switching threshold to obtain a final battery, and completing intelligent charging adjustment. The safety and stability of the charging process can be improved, and the service life of the battery is prolonged.

The invention relates to the technical field of charge and discharge condition detection, in particular to a detection system and method integrating charge and discharge condition data. The method comprises the steps of working condition excitation in a charging mode, calculating target current according to rated capacity of a battery, and charging the battery with the target current until the voltage of the battery is close to cut-off voltage; the cut-off voltage is kept, and the current is gradually reduced to the termination current; in the constant voltage stage, the charging moments are marked through a voltage threshold value, and the electric quantity variable quantity between the moments is calculated; dynamically adjusting charging and discharging current according to battery parameters collected in real time; data acquisition in a discharge mode, including a V2G reverse feed mode and a PTC heating discharge mode; establishing a mapping relation between the electric quantity variation and the SOH; establishing a battery state space model, and fusing voltage, current and temperature data to estimate the SOC and SOH of the battery; a three-layer BP neural network is constructed, and the weight and bias are optimized through a sample data training model. According to the technical scheme, the charging and discharging condition data detection efficiency and accuracy can be improved.

The invention belongs to the technical field of flow batteries, particularly relates to the technical field of flow batteries, and particularly relates to an electrolyte for a chromium-cerium flow battery as well as a preparation method and application of the electrolyte. The electrolyte comprises a positive electrode electrolyte and a negative electrode electrolyte, the positive electrode electrolyte and the negative electrode electrolyte are both acidic aqueous solutions, the positive electrode electrolyte contains cerium (III) ions, and the negative electrode electrolyte contains chromium (III) ions. The cost of the electrolyte is lower than that of an existing redox flow battery, and the battery based on the electrolyte is high in voltage and good in cycling stability.

A battery management device according to one embodiment of the present invention includes: a profile acquisition unit configured to acquire a battery profile and a differential profile based on the voltage and capacity of the battery; and a control unit configured to generate a comparative full-cell profile and a comparative differential profile based on a preset reference positive electrode profile and a preset reference negative electrode profile, adjust the reference positive electrode profile and the reference negative electrode profile so that the comparative full-cell profile and the comparative differential profile correspond to the battery profile and the differential profile, respectively, and determine the adjusted positive electrode profile and adjusted negative electrode profile as the positive electrode profile and the negative electrode profile of the battery, respectively.

The invention specifically discloses an optical storage system dynamic damping control method based on direct current capacitor energy, and relates to the technical field of new energy. The method comprises the following steps: firstly, acquiring a direct-current bus voltage value, photovoltaic output voltage and current, energy storage battery voltage and current, LC filter inductive current and grid-connected point voltage and current in the current optical storage system; then, the photovoltaic system and the energy storage system are adjusted to operate cooperatively, the photovoltaic system achieves maximum power point tracking through a disturbance observation method, and the energy storage system stabilizes the direct-current bus voltage through a voltage-current double closed loop; secondly, based on a dynamic damping control strategy of direct-current capacitor energy, introducing a difference value between actual direct-current capacitor energy and a reference value into a network construction control active control loop through a direct-current damping coefficient; and finally, performing network construction control by combining an inverter virtual synchronous generator control technology. According to the method, existing hardware resources are effectively utilized, a complex algorithm is not needed, the dynamic performance of the system is remarkably improved, and the method has high adaptability and wide application prospects.

The invention discloses a sodium ion battery thermal runaway early warning method and system, and the method comprises the steps: obtaining the state parameters of a battery, and the state parameters comprise the voltage of the battery, the surface pressure of the battery, the temperature of the battery and the gas concentration; calculating a risk index of each state parameter based on the state parameters; calculating a thermal runaway risk comprehensive index of the sodium-ion battery based on the risk index; and judging a thermal runaway stage based on the thermal runaway risk comprehensive index, and sending out an early warning signal. According to the method, the state parameters including multiple parameters of the sodium ion battery are subjected to fusion analysis, and the multi-stage early warning strategy is constructed according to the thermal runaway risk comprehensive index, so that the system can take intervention measures in advance, the possibility of safety accidents is reduced, and the reliability of the energy storage system is improved.

The invention provides an energy storage system parallel operation power control method, which comprises the steps of firstly defining an initialized active droop coefficient m, then obtaining an adjustment coefficient K1 according to an SOC mean value of an input battery of each PCS and a maximum SOC mean value of a parallel operation system, obtaining an adjustment coefficient K2 according to a battery voltage mean value of each PCS and a maximum battery voltage mean value of the parallel operation system, an adjustment coefficient K3 is obtained by calculating the battery cluster number of each PCS, an adjustment coefficient K4 is obtained according to the PCS battery maximum allowable discharge power, the inversion side maximum allowable discharge power and the load power percentage, the final droop coefficient M = m * K1 * K2 * K3 * K4 is adjusted, the droop coefficient of each PCS is adjusted in real time, then the output power of the PCS is adjusted, multi-output of the PCS with the large load capacity in a parallel operation system is achieved, and the output power of the PCS with the large load capacity in the parallel operation system is improved. And the PCS with small load capacity is less in output, so that long-term parallel operation and load operation of the system are realized, and the robustness of the system is improved.

According to the intelligent storage battery state detection system and the control method, a dynamic early warning mechanism is constructed by monitoring voltage parameters of a storage battery device of an aerial work platform and real-time working condition parameters of a vehicle in real time, and early warning prompt is actively triggered when voltage abnormity or an electric quantity critical threshold value is detected; operation interruption caused by insufficient electric quantity in the operation process is effectively prevented, meanwhile, accurate monitoring of storage battery voltage early warning is enhanced through a closed-loop control algorithm, the operation reliability and the use safety of equipment are remarkably improved, and potential safety hazards caused by power supply faults are reduced to the maximum extent; according to the method, a self-adaptive charging strategy is synchronously integrated, bidirectional communication with a lithium battery management system is carried out based on periodic voltage monitoring data, accurate charging control is realized, continuous and stable operation of the aerial work platform is guaranteed, the use efficiency of the storage battery is synchronously improved, the maintenance cost is reduced, and a complete storage battery health management solution is formed.