383 results about "Battery state of charge" patented technology

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 belongs to the technical field of battery management systems, and particularly relates to a battery state-of-charge estimation method for resisting a voltage sensor fault, which comprises the following steps: S1, data acquisition and system initialization; s2, state prediction; s3, measurement updating and online compensation are carried out; and S4, recursive circulation is carried out. According to the method, the direct-current bias quantity generated by a voltage sensor due to zero drift is widened into a state variable of a battery model, and the state variable, ohm internal resistance, polarization internal resistance, polarization voltage and charge state of a battery jointly form an expansion state vector; and performing real-time recursive estimation on the extended state vector by adopting an unscented Kalman filtering algorithm, and simultaneously obtaining an estimated value of the offset of the sensor and an estimated value of the SOC of the battery subjected to deviation compensation. Through combination of state extension and unscented Kalman filtering, fault bias of the voltage sensor can be identified and compensated online, non-Gaussian noise interference is effectively suppressed, and accuracy and robustness of battery SOC estimation in a complex industrial environment are remarkably improved.

The invention provides a method, a device and equipment for estimating the state of charge of a battery based on a hysteresis battery model. The method comprises the following steps: acquiring an initial charge state of an energy storage battery; determining a numerical interval where the initial charge state is located; a target parameter set matched with the numerical value interval is determined in multiple parameter sets, the target parameter set is used for recording values of all parameters in a hysteresis battery model, and the hysteresis battery model is used for simulating the hysteresis effect of the energy storage battery and compensating for errors generated by the hysteresis effect; and determining a target charge state of the energy storage battery according to the hysteresis battery model and the target parameter set. According to the method, the hysteresis effect of the energy storage battery is simulated and the error generated by the hysteresis effect is compensated by designing the hysteresis battery model, and the target parameter set most suitable for the hysteresis battery model is acquired according to the numerical interval of the initial charge state, so that the charge state of the energy storage battery can be accurately calculated.

The invention discloses a battery SOC estimation method, device and equipment, a storage medium and a product. Acquiring real-time operation parameters of the to-be-tested battery; on the basis of the real-time operation parameters, corresponding initial SOC values are obtained through calculation by using at least two different battery state of charge SOC estimation algorithms; determining the adaptive weight of each SOC estimation algorithm based on the current working condition of the to-be-tested battery and the real-time estimation error of each SOC estimation algorithm; and determining a target SOC value based on the initial SOC values and the adaptive weights of the SOC estimation algorithms corresponding to the initial SOC values. The initial SOC value is calculated by combining at least two different SOC estimation algorithms, then the adaptive weight is determined according to the current working condition of the battery and the real-time estimation error of each algorithm, and finally the weighted initial SOC values are fused to obtain the target SOC value. And through a self-adaptive fusion mechanism, the SOC estimation precision is effectively improved, and the adaptability to different working conditions and the anti-interference capability are enhanced.

The invention provides a self-adaptive pulse width modulation method, medium and system for a charging pile, and belongs to the technical field of charging piles. The self-adaptive pulse width modulation method comprises the following steps: acquiring voltage and current values of a battery, calculating a charge state parameter and a tilt change rate of the battery, and calculating a load coefficient and a third-order precision index by combining a load sensing algorithm; and analyzing the current waveform by using Fourier transform to obtain a harmonic distortion index, and calculating a delay compensation function value. The core technology is to call a deep residual neural network model of a double-path parallel architecture, input multi-dimensional characteristic parameters, output optimal pulse width modulation parameters and generate pulse width modulation signals to adjust charging characteristics. Meanwhile, the temperature and the internal resistance value of the battery are monitored in real time, parameter updating is triggered when the preset threshold value is exceeded, a complete closed-loop control cycle is formed, and the technical problem that the pulse width modulation parameter accurately adapts to the change of the charging state of the battery under the complex dynamic load condition is effectively solved.

The invention discloses a wireless earphone battery health monitoring method and a related device. The method comprises the following steps: constructing a first dynamic model based on an earphone battery electric signal, and constructing a second dynamic model based on the electric signal and a thermal signal; outputting a battery charging state estimation value by using the first dynamical model, and outputting a battery instantaneous internal resistance and capacity estimation value by using the second dynamical model; calculating equivalent internal resistance and equivalent capacity according to the internal resistance and capacity estimation value of the current charging and discharging period, calculating a battery health state value according to the equivalent internal resistance and the equivalent capacity, and taking the two state values as a health monitoring result together; according to the method, the health state of the earphone battery can be dynamically and accurately monitored through the double-dynamical model, compared with the prior art that only battery charging data is monitored, the degradation condition of the battery can be comprehensively reflected, a basis is provided for battery life prediction, maintenance decision making and safe use, efficient and low-cost battery health management is achieved, and the method is suitable for popularization and application. And the use safety and the user experience are improved.

The invention relates to the technical field of new energy intelligent vehicle energy management, and discloses an intelligent vehicle power supply energy management method based on deep learning, comprising the following steps: step A, collecting vehicle dynamic operation parameters, power battery electrochemical state parameters and external driving environment parameters, and constructing a multi-source heterogeneous data stream; and step B, inputting the multi-source heterogeneous data stream into a hierarchical deep learning architecture, wherein the hierarchical deep learning architecture comprises a convolution-long and short-term memory hybrid network for spatial-temporal feature extraction and an attention mechanism multi-task network for jointly estimating the state of charge and the state of health of the battery. Virtual rehearsal and safety threshold verification are carried out on a power distribution strategy through a digital twinborn model, potential risks are identified before an instruction is issued, increment retraining is triggered, a closed-loop feedback mechanism is formed, the defects that a traditional deep learning scheme is insufficient in safety and rigid in a vehicle-mounted scene are overcome, and the safety of the vehicle-mounted scene is improved. The system robustness is obviously improved; and the service life of the battery is prolonged.

A system for managing emissions of a vehicle includes a controller to perform: activating a heater responsive to a battery state of charge being at or above a predefined threshold, activating an air mover, causing the moving air to be heated to heat an aftertreatment system, receiving an aftertreatment system temperature responsive to activating the air mover, causing the engine to operate in a modified power mode where a power output of the engine is below a power output threshold, responsive to the temperature being at or above a first threshold and below a second threshold, causing the engine to operate the engine in a normal operating mode where the power output of the engine is at or above the power output threshold, responsive to the temperature being at or above the second threshold, and deactivating the heater when the temperature is at or above the second threshold.

A method and system for controlling a temperature control system based on both system reliability and battery state is disclosed. Batteries may supply power to places where the power grid is unavailable. The batteries may be housed in containers, with the temperature and humidity of the containers being controlled using a temperature control system. The system and method disclosed may select the temperature setpoint and humidity setpoint in order to operate the batteries within prescribed temperature and humidity values, while considering both the state of health of the batteries (e.g., the battery state of charge) and the state of health of the temperature control system (e.g., the failure of the compressors). In this way, the batteries may be operated within prescribed limits while accounting for both the health of the batteries and the health of the temperature control system.

The invention provides a method for estimating the state of charge of a marine lithium iron phosphate battery based on model compensation and improved Kalman filtering. The method comprises the following steps: acquiring voltage and current data of the marine lithium iron phosphate battery; constructing a novel compensation model taking an improved Play hysteresis operator as a core, and quantifying the hysteresis voltage of the battery; on the basis of a second-order RC equivalent circuit model, hysteresis voltage is used as voltage input of an ideal voltage source in the battery and is injected into model internal resistance and capacitance parameter values obtained through identification at different temperatures, and an equivalent circuit model considering hysteresis at different temperatures is formed; performing parameter identification on the equivalent circuit model with hysteresis compensation by adopting an adaptive forgetting factor recursive least square method, and adaptively optimizing a forgetting factor and an initial parameter value, so that an error between a terminal voltage value simulated by the equivalent circuit model and an actual voltage value is minimum; and carrying out joint estimation on the battery end voltage and the SOC by adopting an adaptive square root unscented Kalman filtering algorithm, so that the error of the end voltage is minimum, and an estimated value of the battery SOC is obtained.

Systems and methods for providing control in relation to multiple EVs, for example an EV fleet, are provided. A system generates charging control information for EVs based on a receding horizon optimization. The optimization may be based on EV charging goal information related to the EVs including information relating to target charging completion time, and target EV battery state of charge (SoC) information at target charging completion times. The optimization may also be based on prediction information relating to EVs predicted to become available for charging during the optimization horizon. The charging control information may comprise indications of individual EVs to charge during a given time interval during the horizon. The system utilizes various available information as well as predicted data to provide more optimal control to EVs, thereby taking advantage of previously missed opportunities for enhanced optimization.

The invention discloses an exhaust heat management method, device and equipment of a hybrid vehicle. The hybrid vehicle comprises electric heating equipment used for heating exhaust, and the electric heating equipment is powered by a power battery of the hybrid vehicle; the method comprises the steps that the battery state of charge (SOC) and the braking energy recovery state of a power battery and the temperature of a selective catalytic reduction (SCR) aftertreatment system are obtained; if the current battery SOC is larger than the first electric quantity threshold value and the braking energy recovery state is activated, electric heating equipment is controlled to heat and exhaust, and the temperature of the SCR aftertreatment system reaches the target working temperature; wherein the current battery SOC exceeds a first electric quantity threshold value, and the braking energy recovery state is that the braking electric energy representing that the power battery cannot completely store and recover is activated. According to the exhaust heat management system, on the premise that the heat management requirement of the SCR system is met, surplus electric energy generated in the brake recycling process is utilized for heating exhaust, and therefore the environment friendliness and economical efficiency of the exhaust heat management system are improved.

The invention discloses an energy storage converter operation control method and system. The severity of a power grid frequency event is accurately captured and quantified through high-precision state perception. On the basis, weight factors of inertia control and droop control are dynamically generated, and smooth fusion and seamless transition of the two control modes are achieved. In the initial stage of frequency disturbance, the inertia support weight dominated by the frequency change rate is maximum, and a rapid and powerful response is provided to suppress frequency abrupt change; as the frequency tends to be stable, the weight is smoothly transferred to frequency deviation dominated droop control. Finally, by introducing a battery state of charge (SOC) modulation link, it is ensured that the output of the supporting power can meet the emergency demand of the power grid, the safety and sustainable operation capacity of the energy storage system are also considered, and intelligent, efficient and reliable frequency supporting of the power grid is achieved.

The embodiment of the invention provides a battery state-of-charge estimation method, device and equipment based on end-cloud collaboration, and relates to the technical field related to batteries. The method comprises the following steps: acquiring a first state-of-charge estimation value of a single battery in an electrochemical energy storage battery cabin from a cloud end; the first state-of-charge estimation value is obtained by the cloud through prediction based on real-time operation data of a single battery based on a CNN-LSTM hybrid model fused with negative resistance physical characteristics; obtaining a second state of charge estimation value of the single battery from the terminal; the second state-of-charge estimation value is obtained by the terminal through state estimation based on real-time operation data through a target equivalent circuit model corresponding to the current working condition of the single battery and a preset state estimation algorithm; and through an adaptive Kalman filtering algorithm, carrying out fusion processing on the first state-of-charge estimation value and the second state-of-charge estimation value to obtain a target state-of-charge estimation value of the single battery. The method is used for improving the estimation precision and efficiency of the state of charge.

An apparatus, including a vehicle computer located at a vehicle which is an electric vehicle or hybrid vehicle; battery recharging equipment which includes a plug-in recharging system, an electric road inductive charging system, or an electric road electrified contact charging system, and a radio frequency identification (RFID) reader. The vehicle computer processes information and determines a battery charge level or a battery charge state for a battery of the vehicle. The RFID reader obtains information from a battery recharging system located at a battery recharging facility. The vehicle computer receives an activity report regarding the recharging of the battery by the battery recharging facility and displays information contained in the activity report. The activity report or information contained in the activity report is stored in a distributed ledger and blockchain technology system.

Provided herein is an electric drive machine including a battery having a state of charge ranging from a depleted state to a full state; a resistor grid electrically coupled to the battery; and a control circuit comprising one or more processors and a memory structured to store instructions that, when executed by the one or more processors, cause the control circuit to: determine the state of charge of the battery; determine a target state of charge of the battery according to an anticipated charge event; and control a flow of electrical current from the battery to the resistor grid, to modify the state of charge of the battery to the target state of charge, prior to the anticipated charge event.

The invention relates to the field of battery state detection, in particular to a method for determining the state of charge of a battery. The hybrid power pulse data of different temperature intervals are collected and the corresponding state equations are constructed, so that neglect of a single normal temperature / high temperature model to a low-temperature working condition is made up, and the problem of insufficient model precision under low temperature and high internal resistance is solved; secondly, weighting and integrating multiple temperature state equations through temperature related weights, realizing effective fusion of RC models at different temperatures, and fully utilizing battery characteristic parameters at each temperature; secondly, noise matrix optimization is replaced with weight optimization, and the algorithm stability defect caused by noise uncertainty is avoided; and finally, based on the target equivalent state equation, Kalman filtering is performed to estimate the SOC, so that the estimation precision under the full-temperature working condition is ensured, the stability of the algorithm is improved, and the technical problem that the existing scheme cannot give consideration to the precision and stability of the full-temperature range is effectively solved.

This application provides a battery state detection method, apparatus, device, storage medium, and program product. The method includes: obtaining the battery electrode thickness of a target battery and the battery state of health (SOH); and obtaining the battery state of charge (SOC) of the target battery based on the battery electrode thickness and the SOH. The method of this application can improve the accuracy of SOC detection.

The invention relates to a battery charge state evaluation method and device, computer equipment and a storage medium. Real-time parameters and actual charging and discharging parameters of a battery are obtained, the real-time parameters comprise the current charge state and the real-time temperature, and the actual charging and discharging parameters comprise state parameters and power parameters; according to the real-time parameters and the actual charging and discharging parameters, the average temperature of the battery in the process of reaching the target state of charge is determined; obtaining a first mapping relation, wherein the first mapping relation is a three-dimensional mapping curved surface for calibrating the temperature, calibrating the charging and discharging parameters and calibrating the state of charge; determining a target charge state evaluation value according to the average temperature, the actual charge and discharge parameters and a first mapping relation; the target charge state evaluation value comprises a discharge cut-off SOC evaluation value and a full charge cut-off SOC evaluation value, and the problem that the prediction accuracy of the charge and discharge cut-off SOC of the battery is low is effectively solved.

Methods and systems for estimating driving range for an electric vehicle. With a given vehicle at a given location and given battery state, which may include battery state of charge, a control system identifies a plurality of potential destinations or routes in different directions from the vehicle. For each destination or route, the control system then uses terrain, traffic and / or other data, along with the battery state, an expected battery state of charge trajectory is calculated. These calculations are used to map the potential range for driving the vehicle. Analogous methods and systems are disclosed for fueled vehicles.

The application relates to a battery state of charge prediction method, device, equipment, medium and program product. The method comprises the following steps: acquiring discharge sequence data of a target battery, wherein the discharge sequence data comprises real-time discharge data and historical discharge data before the real-time discharge data; inputting the historical discharge data into an encoder of a target state of charge prediction model to extract discharge time sequence features of the historical discharge data; inputting the discharge time sequence features and the real-time discharge data into a decoder of the target state of charge prediction model together, and outputting a state of charge prediction result of the target battery under the guidance of the real-time discharge data. The method can improve the accuracy of the state of charge prediction of the battery.

The application provides a lithium ion battery state of charge prediction method based on EKF combined with CNN-BiLSTM-Attention, and belongs to the field of electrochemical energy storage. The prediction comprises constructing an equivalent circuit model and identifying key parameters of the model online; pseudo battery state of charge estimation values are obtained through extended Kalman filtering. Convolutional neural network layers are used to extract complex feature vectors, attention mechanism layers are used to obtain weighted feature vectors, BiLSTM feature vectors are obtained through bidirectional long short-term memory network layers, and finally, the final battery state of charge prediction value SOC is output pred The application cooperates battery characteristics through physical models and data-driven models, effectively improves the SOC prediction accuracy, greatly reduces the influence of data noise and sensor errors, adapts to various lithium ion battery-dependent scenes such as industrial and commercial energy storage power stations and electric vehicles, and guarantees the accuracy and robustness of SOC prediction.

The invention relates to the technical field of ultra-low field magnetic resonance systems, in particular to a battery charge state detection method and system based on closed-loop adaptive ultra-low field MRI, and the method comprises the steps: collecting the phase change of a water molecule H signal caused by the magnetic field disturbance of a to-be-detected battery, and reconstructing a first signal phase image; extracting to obtain a high-information-content area mask, and then performing resampling to obtain a second signal phase image; and inputting the second signal phase image into a state-of-charge detection model to predict a state-of-charge value of the to-be-detected battery. In order to solve the problem that a battery magnetic resonance detection scheme in the prior art needs to adopt a high-field magnetic resonance system and is not beneficial to production line use, detection equipment is replaced with an ultralow-field magnetic resonance system, so that structures such as a shielding room in a traditional magnetic resonance system are omitted, and a high-information-content area is positioned firstly, so that the detection efficiency is improved; and resampling is carried out on the region, so that the imaging precision is improved, and the accuracy of state of charge estimation of the detection model is improved.

A battery management method and apparatus, used for improving the charging speed and utilizing the charging capability of a battery as fully as possible. The method comprises: a vehicle acquiring a first charging power, wherein the first charging power comprises a charging power of a first charging apparatus used for supplying power to a battery of the vehicle in the future, or comprises an average charging power of a second charging apparatus used during m historical charging sessions of the battery of the vehicle, m being an integer greater than or equal to 2; estimating a first battery state of charge (SOC) when the vehicle reaches a target charging apparatus; on the basis of the first charging power and the first SOC, determining a target temperature; and pre-adjusting a battery temperature of the vehicle to the target temperature, the target temperature being an initial battery temperature when the vehicle is charged at the target charging apparatus.

The invention provides a full-time video recording low-power-consumption battery camera and a working method thereof. The camera comprises a main control chip; the main control chip comprises an RTC module used for timed sleep wake-up control. The main control chip internally comprises a DDR module for temporarily storing full-time recorded videos; a carried camera; a microphone and a loudspeaker which are mounted; the carried 4G / 5G mobile communication module is used for data transmission between the camera and a cloud server and communication support for interaction between the camera and a video monitoring APP; the carried single-chip microcomputer MCU is used for controlling power supply of the main control chip, performing information interaction with the main control chip and detecting the state of the camera peripheral module, and the state comprises voltameter low-power interruption detection, battery charging state, USB power supply insertion detection, ADC detection, control of operation of the 4G / 5G mobile communication module and information interaction; and the carried SD card is used for local storage of recorded videos and audios. The method is applicable to the camera.

The application provides a battery state of charge calibration method, device, medium, terminal and program product, the method comprises: obtaining the first full charge calibration compensation value and the second full charge calibration compensation value of each full charge state, after the first full charge is completed, if the next charging meets the calibration precondition and the single highest voltage of the current charging state reaches the virtual voltage of the preset virtual point, the state of charge compensation value is calculated according to the compensation formula, and whether the state of charge compensation value is in the allowed calibration range is judged, if yes, the further calibration is executed according to the preset calibration formula, if not, it is ended; if the calibration precondition is met, the single highest voltage does not reach the virtual voltage and the actual state of charge value reaches the virtual state of charge value, then the calibration is carried out according to the preset retention strategy until the single highest voltage reaches the virtual voltage. The application can realize the state of charge calibration under the long time unfull charging condition, and reduces the dependence on open circuit voltage data.

The invention discloses a new energy hybrid power system multi-performance index system establishment method, and relates to the technical field of new energy automobile energy management, and the method comprises the steps: firstly establishing a state-space equation of a new energy hybrid power system, and constructing a power balance relation based on a vehicle longitudinal dynamics model; factors such as air resistance, rolling resistance, slope resistance and acceleration resistance are comprehensively considered, and a dynamic coupling relation among engine power, motor / battery power, a battery state of charge (SOC) and emission variables is established; secondly, constructing a Hamiltonian function of a system performance index by utilizing a Pontryagin minimum principle, and introducing a co-state variable to establish an adjoint equation, so as to form a multi-dimensional performance index system including economical efficiency, dynamic property, emission property and battery stability;

The invention provides a mining lithium battery pack data processing method and system, and relates to the technical field of mining battery management and intelligent charging control. The method comprises the following steps: collecting real-time voltage data of the mining lithium battery pack and preprocessing the real-time voltage data to eliminate noise interference; analyzing a current battery charging state based on the preprocessed voltage data and generating state characteristic parameters; constructing a charging power regulation and control model according to the state characteristic parameters, and calculating a target charging power value; sending the target charging power value to a charging control system to dynamically adjust the charging power; according to the mining lithium battery pack data processing method and system, the intelligent level, stability and safety of the charging process of the mining lithium battery pack are remarkably improved, and the mining lithium battery pack data processing method and system have good industrial application value and popularization prospects.

The invention discloses an automobile low-voltage storage battery intelligent electricity supplement model based on multi-physics field coupling analysis, which mainly realizes accurate state evaluation and dynamic electricity supplement strategy optimization of a low-voltage storage battery by establishing a nonlinear state space equation and a fuzzy decision rule. The system specifically comprises a multi-parameter coupling modeling module used for evaluating the terminal voltage of the storage battery, a battery state evaluation module used for combining the terminal voltage to evaluate the charge state of the storage battery, a fuzzy decision module used for determining the charging voltage and current according to the charge state, and a control module used for determining the charging current and the charging voltage in the charging process through the charging voltage and the charging current. And the self-adaptive correction module is used for identifying the internal resistance of the battery on line and carrying out dynamic voltage compensation. According to the method, all key factors can be comprehensively integrated, accurate estimation of the capacity of the low-voltage storage battery is realized, so that the electricity supplementing accuracy is improved, the service life of the low-voltage storage battery is prolonged while the power consumption is reduced, and the method is suitable for accurate evaluation of the battery state and optimization of the electricity supplementing strategy when the vehicle is in a dynamic working condition.

A method for determining a state of charge (SOC) of a battery may include determining a measured physical battery current flowing through the battery. The method further may include determining an estimated physical battery current using a physical battery cell model. The physical battery cell model is an equivalent circuit model of a first battery cell. The method further may include determining an estimated SOC of the battery based at least in part on the measured physical battery current and the estimated physical battery current using a virtual battery cell model. The virtual battery cell model is an equivalent circuit model of a second battery cell. The second battery cell is a computer-simulated electrochemical battery cell modeled in series with the physical battery cell model. The method further may include determining the SOC of the battery based at least in part on the estimated SOC of the battery.