4605 results about "State of charge" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to the technical field of electric energy storage systems, in particular to an energy storage power station operation monitoring management system and method. The method comprises the following steps: obtaining voltage data of each energy storage node in a standing state, and carrying out periodic voltage sampling within a preset standing time, thereby obtaining voltage springback time sequence data; performing temperature correction processing on the voltage springback time sequence data so as to obtain standardized voltage springback characteristic data; acquiring charge state data of each energy storage node before and after operation; calculating a charge-discharge depth change value of each node according to the charge state data; and constructing a node health state evaluation model according to the standardized voltage rebound characteristic data and the charge and discharge depth change value, and obtaining node aging differentiation parameters. According to the method, the node charging and discharging power allocation model is constructed based on the node aging differentiation parameters, differentiation management of energy storage nodes in different states is achieved, and the problem that the aging degree between the nodes is further expanded in a traditional method is solved.

An optical storage system designed to compensate for the water hammer effect in hydropower turbines and its cooperative frequency modulation method are provided. The method involves detecting the water hammer effect and utilizing a pre-established system model integrating a hydropower unit, photovoltaic system, and hybrid energy storage system. Through model predictive control combined with a whale optimization algorithm, the governor parameters of the hydropower unit are optimized. The system then obtains the photovoltaic active power output and the battery's state of charge (SOC) within the hybrid energy storage system. Based on these parameters, a collaborative control strategy for the photovoltaic and hybrid energy storage systems is determined and executed. This strategy enables effective control of the photovoltaic system and / or hybrid energy storage to compensate for reverse power adjustments in the hydropower unit, addressing the water hammer effect and ensuring stable power grid operation.

The invention relates to the technical field of battery management systems, in particular to a distributed battery state estimation and compensation method based on voltage and current sampling, which comprises the following steps: constructing an original characteristic matrix by synchronously acquiring voltage and current waveforms and extracting ripple characteristics and slope abrupt change points, and generating a characteristic tensor with environment correction through a coupling compensator; then constructing a state parameter space based on adaptive particle injection and an electrochemical-thermodynamic coupling model; adopting a multi-source data fusion engine to realize distributed collaborative estimation of a health state index and a charge state confidence interval, and completing SOC, SOH and temperature trend prediction and residual compensation correction through a multi-state dynamic prediction model; and based on the prediction result and the uncertainty boundary, constructing an energy compensation optimization model, and searching a Pareto optimal compensation strategy. The method can be widely applied to intelligent management and control in the fields of battery energy storage systems and electric power equipment.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is improved.

The invention relates to the technical field of control and regulation systems, in particular to a photovoltaic module and system, and the photovoltaic module comprises a sensor group, a dynamic feature extraction module, a prediction model module, a collaborative optimization control module and an instruction execution module. The sensor group collects the front irradiance, the back reflectivity and the temperature gradient data of the double-sided photovoltaic array, synchronously obtains the charge state and the charge and discharge rate of the energy storage system, and instructs the execution module to drive equipment to act and calibrate a noise filtering threshold through a feedback channel. The system layer integrates an edge computing node, a cloud scheduling center and a redundant communication network, realizes local real-time control and global resource optimization, and solves the problem of multi-time scale cooperative control of double-sided photovoltaic output dynamic fluctuation and energy storage response delay in combination with virtual sensing of a fault processing module, an off-network strategy and a case library self-learning mechanism. The energy utilization rate and the power grid frequency modulation precision are improved.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention discloses a micro-grid anti-countercurrent control method and system based on multi-objective optimization, and the method comprises the steps: generating a dynamic load prediction result according to the power grid side voltage, user side load power and energy storage system charge state data collected by an energy management system in real time; based on the dynamic load prediction result, obtaining a countercurrent risk quantitative index; generating a corresponding dynamic anti-reflux threshold curve according to the reflux risk quantitative index; the dynamic anti-reflux threshold curve is corrected through a real-time collaborative verification module, and an anti-disturbance optimization threshold is generated; and converting the anti-disturbance optimization threshold value into a PCS power regulation and control instruction, synchronously displaying a threshold value execution state and reverse feed early warning information on an energy management interface, and triggering adaptive power clamping protection when detecting that the voltage is out of limit or the power is reverse. According to the embodiment of the invention, the intelligent level of countercurrent prevention and control of the micro-grid can be improved, and safe and stable operation of the grid is guaranteed.

The invention relates to the technical field of energy dispatching, in particular to a power dispatching optimization method based on optical energy storage, which comprises the following steps: extracting photovoltaic output and charge state fluctuation based on partitions, synchronously identifying abnormal intervals, analyzing direction matching and energy connectivity, screening abnormal units, and extracting linkage units in combination with load and power utilization characteristics. Evaluating power flow deviation, determining an unbalance level, matching a response list, and outputting an automatic linkage scheduling adjustment instruction set. According to the method, photovoltaic output and energy storage state fluctuation are identified in partitions, the accuracy of capturing an abnormal space-time coupling phenomenon is enhanced, the identification depth of scheduling imbalance hidden danger is improved, regional load distribution and power utilization behavior consistency judgment are combined, dynamic load feature evaluation of abnormal units is completed, the abnormal units are scheduled in a layered mode according to risk levels, and the scheduling efficiency is improved. The scheduling range is accurately adjusted, and the response precision and the handling capacity of the scheduling mechanism to the dynamic difference of the load side under new energy fluctuation are improved.

The invention discloses a lithium battery charge state estimation method based on a Bayes-TLCO optimization deep neural network, and belongs to the technical field of battery state monitoring. The method comprises the following steps: firstly, preprocessing a lithium battery charging and discharging data set; then, constructing a deep neural network model comprising a convolutional neural network, a long-short-term memory network and a multi-head attention mechanism, dynamically optimizing hyper-parameters of the model by using a Bayesian optimization-assisted termite life cycle optimization algorithm, introducing Bayesian optimization during iteration stagnation in a TLCO algorithm iteration process, and finally obtaining a termite life cycle optimization model; fitting historical data through a Gaussian process to construct a search empirical model, generating high-value sampling points, and accelerating model hyper-parameter convergence to a globally optimal solution; and finally, estimating the state of charge of the lithium battery. The method breaks through the limitation of a single algorithm, achieves the high-precision estimation of the state of charge of the lithium battery under a complex working condition, effectively improves the model training efficiency, is suitable for electric vehicles, energy storage systems and other scenes, and provides a key technical support for the intelligent upgrading of battery management.

The invention provides an intelligent scheduling control method, system and device of an optical storage hybrid inverter, and a medium, and belongs to the technical field of new energy power systems.The method comprises the steps that a scheduling controller collects the output power of a photovoltaic battery, the electric quantity state of an energy storage battery, the power grid electricity price and the load power; generating a photovoltaic power prediction value and a load prediction value by using the prediction model; solving an optimal power distribution scheme by taking economy optimization as a target to obtain photovoltaic side target power, energy storage side target power and power grid interaction target power; the duty ratio of the photovoltaic side bidirectional DC / DC converter is adjusted according to the photovoltaic side target power to achieve maximum power point tracking of the photovoltaic cell, the duty ratio of the energy storage side bidirectional DC / DC converter is adjusted according to the energy storage side target power to achieve charging and discharging power control of the energy storage cell, and the output current of the grid-connected inverter is adjusted according to the power grid interaction target power. According to the invention, efficient and intelligent control of the optical storage hybrid inverter can be realized.

A machine learning method of estimating State of Charge (SOC) and State of Health (SOH) of a battery pack, the method including collecting time-series data of a battery pack; processing the collected data utilizing a reconstruction algorithm to reconstruct a first phase state space reconstruction; training a first neural network model using the first phase state space reconstruction for predicting the SOH; feeding the time-series data to the first neural network to predict an estimated SOH value; processing the time-series data utilizing the reconstruction algorithm to reconstruct a second phase state space reconstruction; training a second neural network model using the second phase state space reconstruction, taking into account the estimated SOH value, for predicting the SOC, and feeding the time-series data and the estimated SOH value to the second neural network to obtain an estimated SOC value. A system configured to perform the above method is also disclosed.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention relates to a battery monomer, a battery device and a power utilization device, the battery monomer comprises an electrolyte, a positive pole piece and a negative pole piece, the positive pole piece comprises a positive pole current collecting part and a positive pole film layer located on at least one surface of the positive pole current collecting part and containing a positive pole active material, and the positive pole active material comprises lithium-containing phosphate of an olivine structure; the coating weight of one side of the positive electrode film layer is 200mg / 1540.25 mm < 2 > to 370mg / 1540.25 mm < 2 >, the negative electrode plate comprises a negative electrode current collecting part and a negative electrode film layer which is positioned on at least one surface of the negative electrode current collecting part and contains a negative electrode active material, the negative electrode active material comprises a carbon-based material, the conductivity of the electrolyte is 13-20mS / cm, the battery monomer is subjected to constant-current discharge from a 100% state of charge (0.33 C) to 2.0 V at 25 DEG C, and a discharge platform voltage V1 is 3.19-3.235 V. According to the invention, the fast charging performance, the energy density and the cycle performance of the battery monomer can be improved.

A frequency regulation control method and apparatus, a device, and a storage medium. The method comprises: according to the frequency at a current moment and the calibration frequency of a wind / photovoltaic grid-connected system, determining the frequency deviation value at the current moment and, according to the frequency at the current moment and the frequency at the previous moment, obtaining a frequency deviation change rate (S110); if the absolute value of the frequency deviation value is greater than the absolute value of a preset frequency regulation threshold value, then, according to the frequency deviation value and the frequency deviation change rate, determining inertia response power (S120); according to the frequency deviation value, the preset frequency regulation threshold value, and a preset system maximum frequency deviation, determining droop response power (S130); according to the state of charge of battery energy storage, the inertia response power, and the droop response power, determining battery energy storage target charging and discharging power (S140); and, according to the battery energy storage target charging and discharging power, regulating the frequency of the system (S150).

The invention provides a dynamic reconfigurable battery energy storage system balance control method and device based on PWM duty ratio modulation. Belongs to the battery system control field. Voltage information of all single batteries in the energy storage system is collected in real time, total loop current, system operation modes and temperature distribution are monitored synchronously, a controller dynamically calculates charge state values of all the single batteries based on the information, a target object to be balanced is recognized, and meanwhile a global battery consistency evaluation index is established. The method dynamically adjusts the maximum allowable equalization current and generates an equalization topology. The modulation frequency, the duty ratio spectrum amplitude and the phase distribution are adjusted through PWM, and the maximum duty ratio is limited. All the operation instruction sets take effect synchronously at the starting moment of the next equalization period. Besides, the efficiency and working condition mapping table in the historical learning library is synchronously updated to form a closed-loop self-evolution optimization mechanism, so that the balance efficiency optimization of the system in a dynamic scene is realized, and the voltage overshoot is ensured to be within a set threshold value.

The invention belongs to the technical field of voltage prediction, and relates to a lithium battery SOC / SOH joint estimation method based on a parameter updating mechanism model and an LSTM algorithm, and the method comprises the steps: estimating the SOC of a lithium battery through employing a Kalman filtering AEKF algorithm; estimating the SOH (state of health) of the lithium battery by using the LSTM neural network; updating the input of the LSTM neural network through the estimated state of charge SOC, correcting the parameters of Kalman filtering AEKF through the estimated state of health SOH, and constructing a joint estimation model; and calculating a loss function of the joint estimation model, and training the joint estimation model to realize joint estimation of the state of charge (SOC) and the state of health (SOH). According to the method, the charge state and the health state of the battery are comprehensively considered and are coupled, so that the evaluation precision of the health state of the battery is effectively improved, the full-life-cycle management of the battery is optimized, and the energy utilization efficiency and the safety of the battery are improved.

The invention relates to the technical field of new energy vehicles, in particular to a multi-working-condition energy management method and system for a hybrid chariot, and the method comprises the steps: determining a corresponding working condition weight coefficient through obtaining the altitude, environment temperature and road condition data in real time, and the real-time working parameters of three power sources, namely an engine, a fuel cell and a lithium battery, so as to achieve the multi-working-condition energy management of the hybrid chariot; the system constructs a multi-objective optimization model based on the data to realize comprehensive fuel consumption minimization, lithium battery SOC balance and lithium battery life protection, and the model comprises establishment of a power source efficiency parameter mapping relation, determination of a corresponding relation between lithium battery power and cycle life, and construction of a membership function of an optimization objective. By adopting a method of combining Pareto local optimization and fuzzy evaluation, the optimal power distribution proportion among the engine, the fuel cell and the lithium battery is calculated, the overall efficiency of the hybrid chariot is effectively improved, and the service life of the hybrid chariot is effectively prolonged.

The invention relates to the technical field of extended-range electric vehicle control, and discloses an engine starting control method and system for an extended-range electric vehicle, and the method comprises the following steps: obtaining the operation information of the extended-range electric vehicle, the operation information comprises the actual temperature of an engine, the actual rotating speed of a range extender, the state of charge (SOC) of a power battery and the maximum dischargeable power; when the starting request mark position of the engine is set, the generator is controlled to enter a torque control mode, and the engine enters a starting mode; and based on the actual temperature of the engine and the actual rotating speed of the range extender, the pre-control torque of the generator is activated through the enabling flag bit. By accurately controlling the rotating speed and the torque of the generator, smooth starting of the engine is achieved, the requirement for starting the engine in various vehicle using scenes is met, meanwhile, the actual temperature of the engine, the SOC of a power battery and the like are considered, the target rotating speed of the started engine and the starting torque of the generator are flexibly controlled, and the starting efficiency is improved. The starting performance of the engine is improved.

The invention discloses a lithium battery charge state and service life combined prediction method based on deep learning, and belongs to the technical field of lithium batteries, the lithium battery is subjected to full life cycle charge and discharge cycle life experiment under specific working conditions, a deep learning model Autoformer is trained through experimental data, and the lithium battery charge state and service life combined prediction method based on deep learning is obtained. The method comprises the following steps: acquiring a combined prediction model of the state of charge and the residual service life of a lithium battery, inputting historical data into the combined prediction model, firstly acquiring a predicted SOC and a predicted voltage, and then performing autocorrelation calculation on a predicted voltage curve and a voltage curve of the first cycle of the lithium battery to obtain an autocorrelation coefficient; and inputting the self-correlation coefficient into a residual service life sub-prediction model to obtain a lithium battery capacity prediction value. According to the method, the excellent learning capability of Autoformer on super-long sequence data is utilized, the charge and discharge and capacity attenuation characteristics of the lithium battery are extracted from the full life cycle data, and an accurate prediction means is provided for the joint state estimation of the lithium battery.

The invention particularly relates to a lead-acid battery SOC accurate calculation and lead-acid battery polymorphic anomaly detection and protection method, which comprises the following steps: firstly, collecting and preprocessing multi-dimensional data information of a lead-acid battery, and then respectively calculating the preprocessed data based on a preset ampere-hour integral method and an improved extended Kalman filtering method; meanwhile, the battery operation condition is recognized through a preset condition recognition algorithm, and then dynamic weight fusion is conducted on calculation results of the two methods according to the condition to obtain a first fusion result; and then temperature compensation correction is performed on the result by using a preset temperature compensation model, and finally correction is performed in sequence by using a preset internal resistance-SOC relationship and an aging correction model to obtain a first SOC value. Through multi-dimensional data fusion, dynamic working condition weight distribution, temperature compensation and multi-mode cooperative protection, the SOC estimation error of the lead-acid battery is reduced, the safety and environmental adaptability under complex working conditions are improved, and meanwhile, intelligent interaction and low-power-consumption management are supported.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

The invention relates to the technical field of distributed energy aggregation cooperative regulation and control, and discloses a virtual power plant hierarchical cooperative intelligent scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining the operation parameters and prediction data of each subsystem of a virtual power plant, generating an energy storage system hour-level charge state target trajectory, and carrying out the prediction of the target trajectory; establishing an energy storage system charge state constraint budget pool; generating a budget allocation table; monitoring a short-term budget allowance state, when the short-term budget allowance state is lower than a safety threshold value, borrowing a part of budget from a long-term budget for redistribution, and dynamically adjusting a power amplitude limit value of deviation correction according to charge state constraint tensity; and when the accumulated deviation exceeds the autonomous correction capability or the constraint tensity reaches a critical value, generating an interlayer deviation report and triggering global re-optimization, and adjusting the budget distribution proportion of the next period according to the actual budget consumption condition at the end of the hour-level period. According to the invention, the continuous optimization and self-learning capability of the hierarchical scheduling strategy are realized.

The invention relates to the field of photovoltaic energy storage, and discloses an off-grid photovoltaic energy storage system control method comprising the following steps: collecting photovoltaic power generation data, load demand data, meteorological prediction data and battery state data and carrying out data preprocessing; constructing a deep reinforcement learning model based on the collected data, wherein the deep reinforcement learning model is used for optimizing a charging and discharging strategy of the energy storage system; constructing a multi-objective optimization algorithm for optimizing battery management, energy efficiency and load stability and determining an optimal charging and discharging strategy; dynamically adjusting a charging and discharging strategy based on the photovoltaic generating capacity, the load demand and the battery state monitored in real time, and performing cross-seasonal energy scheduling according to meteorological prediction data; and based on real-time feedback and system evaluation, the deep reinforcement learning model is updated, and a multi-objective optimization algorithm and a discharge strategy are adjusted. By constructing a closed-loop feedback mechanism based on the state of charge and the power supply error, the dynamic correction of the energy storage scheduling strategy is realized.

The invention relates to the technical field of state-of-charge calibration, and discloses a battery state-of-charge self-adaptive calibration method and device, and the method comprises the steps: judging whether a to-be-calibrated battery meets a charging and discharging mode confirmation condition or not according to a target cumulative capacity parameter of the to-be-calibrated battery, and if yes, carrying out the self-adaptive calibration of the battery state-of-charge according to the target cumulative capacity parameter; determining a current charging and discharging mode of the battery to be calibrated; and based on the current charging and discharging mode, performing target state-of-charge calibration operation on the to-be-calibrated battery to obtain a calibrated state-of-charge value of the to-be-calibrated battery. It can be seen that when it is judged that the to-be-calibrated battery meets the charging and discharging mode confirmation condition according to the target accumulated capacity parameter, the current charging and discharging mode of the to-be-calibrated battery is determined, and the state of charge calibration is performed on the to-be-calibrated battery according to the current charging and discharging mode, so that the calibration efficiency is improved. The reliability and the accuracy of determining the charge state value of the to-be-calibrated battery are improved, so that the judgment accuracy of the running state of the to-be-calibrated battery is improved.

The invention relates to the technical field of new energy battery management, and discloses a sodium ion battery state-of-charge and state-of-health joint estimation method, which comprises the following steps: acquiring a state equation and an observation equation of a sodium ion battery; initializing a state estimation value, a state covariance, a capacity estimation value and a capacity covariance; performing unscented transformation on the state equation to obtain a plurality of key points and weights; a state equation and an observation equation are utilized to spread key points at the # imgabs0 # moment, the state at the # imgabs1 # moment, an observation nonlinear transmission result, a prediction state, an observation estimation value, a corresponding prediction state covariance and a corresponding prediction observation covariance are obtained, Kalman gain at the # imgabs2 # moment is constructed, and an extended Kalman gain matrix is obtained; presetting an innovation length, calculating a historical observation residual error, and constructing a multi-innovation matrix; and based on the extended Kalman gain matrix, the multi-information matrix and the actual observation value at the # imgabs3 # moment, correcting to obtain the sodium ion battery charge state and the battery health state at the # imgabs4 # moment.

The invention discloses a first-order ECM-based parameter and SOC joint estimation method and system, and the method comprises the steps: constructing a first-order RC equivalent circuit model, and constructing a state-space equation of a lithium ion battery based on the first-order RC equivalent circuit model; corresponding open-circuit voltage OCV values under different SOCs are collected according to an HPPC experiment, and the OCV values are fitted to obtain an OCV-SOC relation curve; constructing a state-space equation based on model parameters, performing parameter identification on the model by using a Kalman filtering algorithm optimized based on a suburb wolf algorithm, and calculating a terminal voltage value through an identification result to verify the identification precision; updating a state-space equation related to the SOC based on a parameter identification result; and on the basis of the state-space equation, rapid convergence from the SOC estimated value to the SOC actual value is realized by using a self-adaptive Kalman filtering algorithm based on an innovation matrix. The method is high in estimation precision.

The invention relates to the technical field of energy storage lithium batteries, and discloses a remote monitoring operation and maintenance management device for energy storage lithium batteries, which is characterized in that a battery temperature acquisition module, a battery voltage acquisition module, a battery current acquisition module and an environment temperature acquisition module are respectively used for acquiring the surface temperature, voltage and current of a battery cell of an energy storage lithium battery group and the environment temperature of the battery; the state of charge calculation module calculates the state of charge (SOC) of the energy storage lithium battery group; the battery fault diagnosis module performs battery abnormity detection to identify a fault battery; and the early warning module generates alarm information when the SOC is not in a preset range and detects that a fault battery exists in the energy storage lithium battery group. According to the method, more data are collected through the intelligent sensor, multiple calculation modes are established to calculate the SOC of the battery, the calculation result can accurately reflect the actual working state of the energy storage power station, real-time fault detection is carried out on the energy storage lithium battery, and the use safety of the energy storage lithium battery is guaranteed.