3782 results about "Constant current" patented technology

The invention relates to the technical field of battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention discloses a battery health state prediction method based on multi-modal feature fusion and deep learning, and the method comprises the steps: obtaining the charging data, including current, voltage and temperature data, of each charging period during the multi-stage constant current charging of a battery; determining SOH (state of health) data of the battery; performing smooth filtering on the current and voltage data through a Savitzky-Golay filter, reconstructing a current curve, and reconstructing a voltage curve by adopting a linear interpolation method and a KF filter; constructing a comprehensive characteristic pool according to the current curve, the voltage curve and the temperature data, and directly adding the constant current value at the maximum charging amount into the comprehensive characteristic pool; screening features from the comprehensive feature pool and putting the features into a prediction feature pool; completely integrated empirical mode decomposition is carried out on the SOH data, and IMF components obtained through decomposition are put into a prediction feature pool; the method comprises the following steps: establishing a CNN (Content Number)-BILSTM-ATTENTION And performing single-step prediction and multi-step prediction on the SOH of the battery by using the trained model. According to the invention, high-precision prediction of the SOH of the electric vehicle battery can be realized.

The present disclosure relates to a battery lithium plating detection method and apparatus, and a device, a vehicle, a medium and a program product. The method comprises: determining the actual direct-current internal resistance of a battery in each charge sub-stage during step charge, wherein the step charge comprises at least two charge sub-stages, and the battery is charged at a target charge rate and a constant current in each charge sub-stage; determining the target direct-current internal resistance of the battery in a corresponding charge sub-stage, wherein the target direct-current internal resistance is a predicted value of direct-current internal resistance that is obtained by means of a preset mathematical model; and if the difference value between the actual direct-current internal resistance and the target direct-current internal resistance is greater than a preset difference threshold value, determining that the battery is in a lithium plating state.

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

The invention discloses a method for testing polarization and decoupling of a flow battery. The method aims to quantify and separate three phenomena of ohmic polarization, activation polarization and concentration polarization in the flow battery. Comprising the following steps: firstly, carrying out electrochemical impedance spectroscopy testing under open-circuit voltage and different working currents respectively to obtain ohmic resistance and charge transfer resistance, and carrying out integral calculation to obtain ohmic polarization and activated polarization; then, carrying out a step type constant current charging test to obtain a total polarization curve; and finally, subtracting the ohmic polarization and the activation polarization from the total polarization to obtain concentration polarization. The method is simple and convenient to operate and suitable for various types of flow batteries, a novel and effective solution is provided for polarization analysis of the flow batteries, an accurate test basis can be provided for optimization of battery design, and improvement of performance optimization and research level of the flow batteries is facilitated.

The invention discloses a model-data hybrid driving-based lithium ion battery variable working condition SOH (state of health) evaluation method, relates to a lithium ion battery state of health evaluation method, and aims to solve the problems of poor applicability and low precision of an existing battery fixed working condition SOH evaluation method. The method comprises the following steps: 1, accelerating aging of a battery under working conditions I and II and acquiring information; 2, aiming at the constant-current discharge curve of the battery under the working condition, performing SP + model parameter identification by using a particle swarm optimization algorithm to obtain a degradation track of the model parameter within the battery health degree of 100%-80%; 3, selecting model parameters with high relevancy with the SOH of the battery as health features, and inputting the health features into a BP neural network algorithm to estimate the SOH of the battery, wherein the first working condition data and the second working condition data are respectively a training set and a test set; 4, according to the BPNN model obtained through data training of the first working condition, after SP + model parameters of the second working condition obtained through simulation are input, the SOH of the battery under the second working condition is output, and evaluation is completed. The invention can be used in battery field.

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

The invention relates to the technical field of power supply starting, in particular to a power supply starting system with ultra-large capacitive load energy and a starting control method.The power supply starting system comprises a power supply body and a load module, the output end of the power supply body is coupled with the load module, and the power supply body comprises a constant-current control module; the detection module is used for detecting output current in a power supply starting process and transmitting a detection result to the MCU control unit; the MCU is used for generating a PWM (Pulse Width Modulation) signal according to the received output current signal so as to control the starting current to be constant; the PWM driving module is used for adjusting the output current of the power supply according to the PWM signal generated by the MCU control unit, and the error amplifier is used for amplifying the output current signal detected by the constant current control module and transmitting the output current signal to the MCU control unit; the current sensor is used for detecting the output current of the power supply in real time; and the MOSFET switch is used for adjusting the output current of the power supply according to the PWM signal. According to the invention, the problem of restarting due to overcurrent protection caused by a large capacitive load in a power supply starting process is solved.

The embodiment of the invention provides a lithium-supplemented battery cell formation method and a lithium-supplemented battery cell, and relates to the technical field of batteries. The lithium supplement battery cell formation method comprises the following steps: carrying out constant-current charging on a lithium supplement battery cell under the conditions of a first current rate and a first negative voltage; under the condition of second current multiplying power and second negative voltage, performing constant-current charging on the lithium supplementing battery core to cut-off voltage; under the conditions of a third current rate and a third negative voltage, carrying out constant-current discharge on the lithium supplementing battery core; wherein the first negative pressure is larger than the second negative pressure, and the first negative pressure is smaller than the third negative pressure. And different negative pressure settings are adopted in different formation stages, so that the problems of lithium precipitation, black spots, bulging and the like on a battery cell interface can be improved, and the comprehensive performance of the battery cell is improved.

The invention discloses an electric vehicle battery health assessment method and system based on multi-source data fusion, and relates to the technical field of new energy vehicle battery application, and the method comprises the steps: collecting a Nyquist impedance curve, terminal voltage relaxation time sequence data and equalization current time sequence data based on a constant current stage cut-off event of a battery pack of an electric vehicle; extracting an impedance form embedding vector corresponding to the Nyquist impedance curve based on an auto-encoder; performing wavelet packet decomposition on the terminal voltage relaxation time sequence data to obtain a corresponding multi-scale voltage relaxation energy feature vector; calculating a dynamic equalization imbalance index based on the terminal voltage relaxation time sequence data and the equalization current time sequence data; and inputting the impedance form embedded vector, the multi-scale voltage relaxation energy feature vector and the dynamic equilibrium imbalance index into a battery SOH prediction model to output an SOH estimated value of the battery pack. Therefore, the accuracy of the SOH evaluation result is improved through the joint modeling of the coupling characteristics of the multi-physical process of the battery.

The invention relates to the technical field of semiconductor laser power supplies, in particular to a semiconductor laser power supply control circuit based on linear constant current and a method thereof. According to the technical scheme, the circuit comprises a voltage follower module, a linear constant-current driving module, a power switch module, a pulse control module, a sampling feedback module and a follow current protection unit. The in-phase input end of an operational amplifier OPA320 of the voltage follower module is connected with external reference voltage # imgabs0 #, the inverted input end of the operational amplifier OPA320 is grounded through a first resistor R1 and connected with the output end through a second resistor R2, and a filter capacitor C1 is connected to the two ends of the second resistor R2 in parallel. Through a linear constant current control and pulse cooperation mechanism, the current stability and the dynamic response speed of the semiconductor laser under the wide-range and high-frequency working condition are remarkably improved, and it is guaranteed that the laser output power is accurate and reliable.

A voltage limiting device (15) for constant current circuits (10) comprises input terminals (153) for connecting the voltage limiting device to a current supply (11) of a constant current circuit, output terminals (154) for connecting the voltage limiting device to a load, a voltage clamping circuit (151), and a switching circuit (152). The input terminals (153) are connected to the output terminals (154) under a normal operating condition. The voltage clamping circuit (151) is configured to clamp a voltage across the output terminals to a first voltage threshold and the switching circuit is configured to short-circuit the output terminals when a second voltage threshold across the output terminals is exceeded longer than a predetermined time period.

The invention relates to a constant-current light source driver power control method and device, and the method comprises the steps: obtaining the light source type, driving parameters and working environment information of a constant-current light source driver, and carrying out the integration, and obtaining the corresponding basic control parameters of the driver; acquiring power data and temperature data of the constant-current light source driver, and performing association processing on the power data and the temperature data and light source characteristics to obtain corresponding real-time light source driving parameters; dynamically analyzing the basic control parameters of the driver according to the real-time light source driving parameters to obtain a corresponding power regulation trend; identifying the working state of the light source according to the basic control parameters of the driver to obtain corresponding working mode information, and predicting the working mode information based on the power regulation trend to obtain a corresponding protection strategy; and comprehensively analyzing the initial power control scheme and the protection strategy to obtain a corresponding global power regulation and control scheme. According to the invention, global power optimization regulation and control of the light source driver can be realized.

The invention discloses a battery SOH evaluation method combining an electrochemical model and machine learning, and the method comprises the steps: constructing a target battery electrochemical simulation model, generating voltage curves and SOH data under the conditions of different temperatures, charging rates and different battery aging states, and constructing an SOH evaluation model and a voltage curve prediction model according to the voltage curves and the SOH data; a constant-current charging segment of the target battery in the current charging cycle is obtained in real time, the actually measured temperature and charging rate of the target battery corresponding to the constant-current charging segment are obtained, multiple SEI thicknesses and lithium precipitation thicknesses are preset, and multiple voltage curves are obtained through prediction of a voltage curve prediction model; acquiring a voltage curve segment closest to the initial SOC of the constant-current charging segment in the plurality of voltage curves, and calculating corresponding SEI thickness and lithium precipitation thickness; and obtaining an SOH estimated value according to the SOH evaluation model. According to the invention, accurate evaluation can be realized for a small data set and a novel battery.

In a memory device including a ternary memory cell, the ternary memory cell may include: a first inverter and a second inverter cross-coupled in a first node and a second node and including a pull-up device and a pull-down device configured to have a constant current pass therethrough upon turn-off; a first read transistor and a first write transistor which are connected to each other in parallel between the first node and a first bit line; and a second read transistor and a second write transistor which are connected to each other in parallel between the second node and a second bit line, wherein the first read transistor and the second read transistor may have a read access current, which is less than or equal to the constant current, pass therethrough in response to an activated read word line.

The invention provides a lithium battery internal short circuit diagnosis method based on interpretable machine learning, and the method comprises the steps: collecting voltage, current and time data of a battery in a constant-current charging process under different internal short circuit degrees, and building an experiment data set; preprocessing and filtering the voltage data, calculating the ratio of the voltage increment to the capacity increment, and obtaining the IC curve characteristics of the capacity increment; extracting a battery state-of-charge-open-circuit voltage relationship, and establishing a battery equivalent circuit model for a constant current stage; estimating the state of charge of the battery through an extended Kalman filtering algorithm and battery charging data; extracting characteristic parameters from the IC curve, wherein the characteristic parameters comprise a peak value, a peak position and an area between peaks; time sequence features are extracted based on a state of charge estimation result, and a multi-dimensional feature space is constructed; carrying out standardization processing on the extracted features, and constructing a classification model; and a Shapril additive feature interpretation method is introduced to realize interpretability analysis of a classification model decision process.

The invention discloses an electric energy bidirectional multipath AC / DC conversion circuit, and relates to the technical field of electric energy conversion, and the circuit comprises a power supply multipath power distribution module which is used for inversion filtering, multipath power distribution and electric energy complementary superposition processing; the intelligent control module is used for signal receiving and module control; the first adjusting module is used for constant-voltage adjustment, constant-current adjustment and residual electric energy superposition control and supplying power to the first output module; the second adjusting module is used for constant-voltage adjustment, constant-current adjustment and residual electric energy superposition control and supplying power to the second output module; the energy storage control module is used for storing residual electric energy and supplying power; and the compensation control module is used for boosting and providing electric energy for the first adjusting module or the second adjusting module. According to the electric energy bidirectional multipath AC / DC conversion circuit, multipath power distribution processing can be carried out, constant current or constant voltage adjustment processing can be carried out according to needs, the superposed residual electric energy is stored and is supplied to the first adjustment module or the second adjustment module, the utilization rate of the electric energy is improved, and a wider voltage range can be provided.

The invention discloses a lithium battery SOH estimation method based on knowledge distillation, and the method comprises the steps: collecting voltage data in a constant-current charging stage and current data in a constant-voltage charging stage, and carrying out the preprocessing of the data, and then forming a data set; building a teacher-student knowledge distillation model, wherein the model comprises a local feature extraction module, a time sequence degradation memory module and a global interaction module; a distillation method of each module is designed, a local feature extraction module adopts layer attention distillation, a time sequence degeneration memory module adopts time sequence attention distillation, and a global interaction module adopts feature alignment distillation; through knowledge distillation, lithium battery degradation characteristics extracted by the teacher model are migrated to the student model, and a lightweight SOH estimation model is obtained; and estimating SOH on line by using the lightweight SOH estimation model. According to the method, a knowledge distillation framework is built, knowledge is transferred from a complex teacher model to a light student model, and the number of model parameters is remarkably reduced while high-precision estimation of the SOH of the lithium battery is achieved.

The invention provides a battery health state evaluation method and system, a storage medium and a computer. The evaluation method comprises the following steps: acquiring basic data of a battery during a mixed pulse test; constructing a battery model, performing parameter fitting on the battery model according to the basic data, and obtaining parameter values of each element in the battery model after parameter fitting; performing constant current-constant voltage charging and discharging simulation on the fitted battery model, acquiring SOC data of the battery model in the charging and discharging simulation process, and acquiring SOH data according to the element parameter values; taking the voltage, current, temperature and SOC data of the battery model in the charge and discharge simulation process as the input of the convolutional neural network, and taking the SOH data as the output to train the convolutional neural network; and taking the trained convolutional neural network as an evaluation model of the battery, and evaluating the health state of the battery according to the evaluation model. The battery health state evaluation method provided by the invention is high in adaptability and high in accuracy.

A soft start circuit of a synchronous boost DC-DC converter is used for restraining current overshoot and voltage overshoot of the synchronous boost DC-DC converter in the starting process and comprises a first soft start module and a second soft start module. The first soft start module belongs to system open-loop work, and suppresses a channel length modulation effect through the same drain end voltage of a clamping matching tube and a power upper tube, so as to obtain a precisely controlled charging current. A second soft start module belongs to system closed-loop work, and the method comprises the following steps: in the first stage, VOUT partial voltage is synchronized; in the second stage, constant-current charging is carried out; and in the third stage, the error amplifier vref rises gently through preset voltage RC charging. According to the invention, current overshoot and voltage overshoot are effectively suppressed through two soft start stages, and the starting circuit has a wide application prospect in the field of starting circuits.

The invention relates to the technical field of storage battery charging, in particular to a multi-stage power storage battery charging control method and system based on intelligent dynamic adjustment. According to the invention, the pre-charging current is determined through the battery health state value and the initial SOC of each single battery, so that the single batteries are pre-charged through the pre-charging current which is individually designed, and after the pre-charging stage is finished, the constant-current charging current is determined according to the first SOC so as to carry out constant-current charging. The constant-current charging current is made to be matched with the current state of each single battery, then a second SOC of each single battery after constant-current charging is finished is obtained, after it is indicated that the single battery is charged to be close to the full-charge state through the second SOC, a constant-voltage charging voltage is obtained for the second SOC of the single battery to conduct constant-voltage charging, and meanwhile electric quantity transferring is conducted on the single battery; finally, whether the electric quantity of the battery pack is balanced or not is judged, and if the electric quantity is not balanced, the SOC difference between the single batteries is gradually eliminated by continuously optimizing the charging process.

The invention relates to the technical field of wire harness detection, and particularly discloses a new energy automobile wire harness conduction test system, which is used for solving the problems of low detection precision and poor abnormity identification and data tracing capabilities in a complex structure wire harness and an extreme abnormal state in the prior art. The system comprises a data acquisition module for multi-group constant current excitation and high-resolution acquisition based on a four-line method, a dynamic anomaly analysis module with sliding window processing and adaptive threshold discrimination capabilities, and a level-to-level management module for automatic multi-layer grouping according to parameterized feature tags, through high-frequency multi-round synchronous detection, trend change modeling and grouped closed-loop parameter adjustment, high-precision detection, whole-process intelligent recheck and whole-process dynamic traceability of the multi-loop conduction state and multi-type abnormity of the new energy automobile wire harness are realized; and the safety and the detection management level of the wire harness system are effectively improved.

The invention provides a battery system health state equalization control method based on an LSTM-D3QN algorithm, and relates to the technical field of battery management, and the method comprises the steps: obtaining the historical health state value of each single battery based on the constant current complete discharge process of a plurality of single batteries; performing feature extraction based on the historical health state value of each single battery in the battery system to obtain aging features of the single batteries; inputting the aging features to an LSTM network to obtain a health state prediction value evolved along with time; and inputting the state space including the health state prediction value into the constructed Markov decision process, and generating a switch control strategy of the battery system in the current state by using a D3QN algorithm. According to the invention, efficient equalization control can be realized for the reconfigurable battery system with SOH inconsistency.

The invention discloses an active island detection method and system based on harmonic impedance characteristic identification, and the method comprises the steps: analyzing the harmonic impedance difference of grid-connected points in a grid-connected state and an island state, and building a state criterion based on resonant frequency deviation and impedance amplitude through the comparison of the frequency response characteristics of island impedance and grid-connected impedance; fitting the grid impedance into an RLC parallel model in a selected frequency band, and mapping the topological difference of the power grid into a parameter difference; embedding a low-voltage ride-through strategy in the control of the voltage source converter, and adjusting reactive current in a grading manner according to the voltage drop degree of a grid-connected point; fundamental waves and harmonic waves are separated through a cascade notch filter, two synchronous harmonic waves are injected into the system through constant current control, and it is ensured that injection signals of a multi-voltage source converter are in the same phase in a phase locking link; and solving an overdetermined equation set based on the double-frequency response data to identify RLC parameters, setting an island criterion, and synchronously completing island judgment in a low-voltage ride-through time window. Detection precision is improved, dynamic response is optimized, and power generation efficiency is improved.

The present invention discloses a formation method for a sodium-ion single battery, which includes constant current charging of the sodium-ion single battery at a charging rate of 0.1C - 0.5C under the condition of 20 - 25°C, and the charging cut-off voltage is V C1 and standing still for 30 - 60 minutes, placing the single battery under the condition of 2 - 10°C for 1 - 2 h, and using voltage V C2 to perform constant voltage charging for 2 - 5 seconds and then standing still for 3 - 8 seconds, standing still for 30 - 60 minutes under the condition of 20 - 25°C, and performing constant current discharging of the sodium-ion single battery at a discharging rate of 0.1C - 0.5C, and the discharging cut-off voltage is V D1、 placing the single battery under the condition of 35 - 55°C for 1 - 2 h, and using voltage V D2 to perform constant voltage discharging for 3 - 6 seconds and standing still for 5 - 8 seconds. The formation method of the sodium-ion single battery significantly changes the performance of the sodium-ion battery, effectively improving the first capacity, charge and discharge efficiency, and cycle life of the sodium-ion battery.

The invention relates to the technical field of lithium battery charging under photovoltaic panel power supply, in particular to a Buck-Boost lithium battery intelligent charging method based on ML-MPPT and dynamic reference voltage regulation, and the method comprises the steps: dividing a charging stage into a trickle charging stage, a constant-current charging stage and a constant-voltage charging stage according to the voltage of a lithium battery; the method comprises the following steps: acquiring environment temperature, solar illuminance and maximum power point voltage data in a constant current stage, preprocessing the data, training a maximum power point voltage prediction machine learning model, distilling the model to obtain a compression model, and deploying the compression model on a microcontroller; real-time environment data is input into the model to predict and obtain the maximum power point voltage as the reference voltage, the output voltage of the photovoltaic panel is sampled, and the input voltage of the Buck-Boost circuit is controlled by using the innovative PWM control system, that is, the duty ratio of PWM pulses is controlled through the microcontroller, the input impedance of the Buck-Boost circuit is adjusted, and the output voltage of the Buck-Boost circuit is adjusted. The output voltage of the photovoltaic panel, namely the input voltage of the switching power supply, is adjusted to the maximum power point voltage according to environmental conditions; and dynamically switching the lithium battery in different charging stages according to the battery state and the input power. The method integrates machine learning into prediction of the maximum power point voltage, and is suitable for an intelligent charging scene of the lithium battery under photovoltaic power supply.

The invention relates to a fragmented charging data-based lithium ion battery health state estimation method, which comprises the following steps of: acquiring voltage, current and timestamp original data of a constant current charging stage of a lithium ion battery, and intercepting a 100-600-second standardized fragmented charging fragment based on a window parameter consisting of an initial voltage threshold Vstart, a sampling interval delta t and a sampling point number n; the method comprises the following steps: calculating an accumulated charging capacity through a coulomb counting method, generating a voltage-capacity curve, deducing a capacity increment-voltage curve and a voltage increment-capacity curve, and carrying out smooth filtering, multi-modal data splicing and normalization processing to obtain a 3 * n multi-channel characteristic tensor; according to the method, complete charging and discharging data are not needed, different battery types and complex working conditions are adapted, the lowest SOH estimation root-mean-square error can reach 0.00355, the highest determination coefficient (R) can reach 0.99485, and the practicability and estimation efficiency in practical application are greatly improved.

The invention relates to the technical field of resistance test equipment, in particular to a portable loop resistance tester, which comprises a test and acquisition module used for acquiring current and voltage signal data of a tested product, analyzing a loop contact state and calculating a loop resistance value and impedance characteristic parameters; the control and output module is used for performing high-precision constant-current output and self-adaptive adjustment; the interaction and printing module is used for receiving a user operation instruction and displaying current and voltage signal data and a resistance value of a tested product, and meanwhile, printing is performed through a built-in thermal printer according to the user instruction; the power supply module is used for stably supplying power to equipment; and the intelligent diagnosis module is in butt joint with the cloud case library, automatically diagnoses related defect types influencing the loop resistance test, and generates a health assessment report. Therefore, the problems of small current output, insufficient sampling precision, single operation mode and the like in the prior art are solved.

The invention discloses a physical therapy control circuit and method, a storage medium, a program product and physical therapy equipment, and relates to the field of physical therapy, and the method comprises the steps that the physical therapy control circuit is arranged in the physical therapy equipment, and the physical therapy control circuit comprises a physical therapy control module, a touch screen, a driving power supply, a constant current control module and a plurality of lamp groups, the plurality of lamp banks comprise at least two lamp banks with different wavelengths; the physiotherapy control module is connected with the touch screen, the driving power supply and the constant current control module, the constant current control module is connected with the driving power supply, the constant current control module is respectively connected with the plurality of lamp banks, and the plurality of lamp banks are respectively and independently arranged on the physiotherapy equipment; the physiotherapy control module is used for receiving a lamp group control instruction triggered by a user on the touch screen; and the constant current control module is used for adjusting the current output to the target lamp group based on the target lamp group in the lamp group control indication and the target brightness of the target lamp group. The technical problem that the adjustment flexibility of the physiotherapy equipment is low is solved.

The invention relates to the field of battery management systems, and particularly discloses a multi-scene fusion battery health degree calculation method and system, and the method comprises the steps: a standard working condition initial calibration step: triggering a standard working condition calibration process when the electric quantity of a battery pack is lower than a preset initial calibration threshold value, and charging the battery pack to a full charge state with constant current, the actual input capacity in the charging process is recorded, the current available capacity Cnow is calculated in combination with the voltage platform characteristic curve, the initial calibration health degree is calculated through the formula # imgabs0 #, and Cinitial is the factory nominal initial capacity of the battery; and a dynamic use updating step: in the continuous charging and discharging process of the battery pack, accumulating the discharging capacity Cd < ischarge > and the charging capacity Ccharge in real time through a coulomb integral method, calculating the real-time available capacity in combination with the ampere-hour efficiency correction model, and updating the current available capacity Cnow according to a preset period. By adopting the technical scheme of the invention, a multi-scene fusion SOH evaluation algorithm can be constructed for different battery material characteristics, and the reliability of a battery management system is improved.