6347 results about "Battery charge" patented technology

A system receives historical vehicle battery data from a gateway device connected to a vehicle. Some vehicles with plug-in rechargeable batteries recommend / require that the vehicle computer be turned off when recharging. Thus, obtaining a current state of charge while a vehicle is charging can be difficult because the vehicle computer can be off. While the vehicle / gateway device is unable to transmit current battery data, the systems estimate a battery charge from the historical data.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention belongs to the technical field of battery fault prediction, and relates to a storage battery health state assessment method, system and device and a medium. The method comprises the following steps: acquiring key operation parameters of the storage battery; calculating storage battery health state evaluation index parameters by using the key operation parameters; the storage battery health state evaluation index parameters comprise the storage battery internal resistance, the internal resistance change rate, the storage battery charging state, the storage battery operation age limit and the storage battery operation temperature; preprocessing the storage battery health state evaluation index parameters to obtain preprocessed health state evaluation data; inputting the preprocessed health state evaluation data into a constructed health degree evaluation model to obtain a storage battery health degree representing the health state of the storage battery; and performing trend dynamic prediction on the health degree of the storage battery through a time sequence analysis algorithm to obtain a trend prediction result. The problem that an existing method is not accurate enough in battery fault prediction is solved.

The invention provides a lithium battery hybrid power motorcycle performance improvement control strategy generation method, which comprises the steps that vehicle driving states comprise a sliding state and a braking state, if the current vehicle is in the sliding state, a preset sliding energy recovery mode is activated, and if the current vehicle is in the braking state, a preset braking energy recovery mode is activated; different energy recovery strategies are combined and optimized according to the subdivision result of the energy recovery strategies in the sliding state and the braking state, and the vehicle speed, the battery charging state and the energy recovery potential value parameters are input to obtain a target energy recovery strategy combination scheme; the intention of acceleration, deceleration or constant speed of a driver is judged according to the operation characteristics of an accelerator and a brake, the operation characteristics refer to the force of stepping on an accelerator pedal and a brake pedal, and the driver intention obtained through judgment serves as the basis for dynamic adjustment of the energy recovery strategy.

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

An apparatus may include a substrate. The apparatus may also include a first charger terminal disposed on the substrate and configured to apply a first electric potential to a first battery terminal of a battery. The apparatus may additionally include a second charger terminal disposed on the substrate and configured to apply a second electric potential to a second battery terminal of the battery. The apparatus may further include a barrier located on the substrate between the first charger terminal and the second charger terminal and configured to electrically isolate the first charger terminal and the second charger terminal by obstructing formation of a conductive path by way of a liquid disposed on the substrate.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

A vehicle provides an indication of status of an energy source that provides power for the vehicle. The vehicle includes an indicator, positioned on the vehicle at a location determined to be among a plurality of locations to most likely be intact during and after an accident. The indicator provides a predetermined indication that identifies status of an energy source that provides power for the vehicle. The indicator may provide a visual or acoustical indication or may provide a wireless message. The indication may provide an indication of one or more of make / model of the vehicle, battery charge level, type, danger condition, danger condition reoccurrence, danger condition suppression technique and a warning to not approach the vehicle.

The invention relates to the technical field of electrical engineering, and provides an electric energy storage system optimization scheduling method and system based on reinforcement learning, so as to solve the problems that it is difficult to effectively evaluate and control the battery aging risk in the frequent charging and discharging process and it is difficult to meet the real-time scheduling requirement in the edge side low computing power environment. The method comprises the steps of obtaining battery charging state and health state data in an electric energy storage system and power grid load and power grid electricity price data of an industrial and commercial park, and generating a state input data set; using the reinforcement learning agent to obtain a charging and discharging scheduling instruction; performing power distribution on the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source cooperative power flow data; in combination with historical operation data, a reinforcement learning agent is utilized to optimize the charging and discharging scheduling instruction; and performing optimal scheduling of the electric power energy storage system according to the optimized instruction. According to the invention, efficient, intelligent and adaptive optimization scheduling of the power energy storage system in the industrial and commercial park is realized.

Disclosed is a battery health state assessment method, including performing discharge control on a cell until a cutoff voltage; performing charge control on the cell until a preset cell load rated capacity, and performing discharge control on the cell until the cutoff voltage, and generating cell discharge efficiency information; performing charge and discharge cycle control on the cell and acquiring corresponding charge and discharge cycle control efficiency information; using a cell load algorithm to calculate and generate a cell load actual capacity information; and generating a cell health state assessment information according to the ratio between the cell load actual capacity and the cell load rated capacity. The accuracy of the SOH assessment process can be improved by implementing the methods of the embodiments of the present application to optimize the battery performance assessment process and improve the battery charge and discharge efficiency assessment capability.

The invention relates to a lithium battery health degree detection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the lithium battery health degree detection method comprises the steps: evaluating vehicle basic features according to vehicle source data, and obtaining a battery feature data table; taking the charging state of the battery as a research object, analyzing a charging segment, determining a sampling time threshold value, and judging data missing and interpolation filling according to the residual charge of the battery; calculating the battery capacity based on the charge charging time, and analyzing the battery capacity transversely and longitudinally to obtain an initial capacity value; extracting effective capacity data in all the initial capacity values according to a preset travel constraint condition; training a single-pack health degree evaluation matrix or a double-pack health degree evaluation matrix, predicting a charging fragment sequence, and constructing a battery health degree evaluation model; the single-pack health degree evaluation matrix or the double-pack health degree evaluation matrix is corrected, an optimal health degree evaluation weight matrix is obtained, and lithium battery health degree detection is completed in combination with a physical compensation mechanism; according to the method, upward fluctuation of the SOH is avoided, and the SOH estimation precision and the model adaptability are improved.

An apparatus may include a substrate. The apparatus may also include a first charger terminal disposed on the substrate, including silver, and configured to apply a first electric potential to a first battery terminal of a battery. The apparatus may additionally include a second charger terminal disposed on the substrate, comprising silver, and configured to apply a second electric potential to a second battery terminal of the battery.

The invention relates to the technical field of battery charging and discharging optimization, and discloses a charging and discharging optimization method, device and equipment for a robot energy storage battery, and the method comprises the steps: collecting a working condition feature data set of the robot energy storage battery; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, and then accurate prediction of the battery performance is achieved. And stable operation of the charging and discharging control system is ensured.

A battery management system, a battery pack, an electric vehicle and a battery charging time prediction method are provided. The battery management system includes a memory for storing a plurality of reference charging maps, a sensing unit for detecting voltage, current, and temperature of a battery, and a control unit for determining an estimated SOC value of the battery. Each reference charging map includes first to Mth reference current values and first to Mth reference power curves associated with first to Mth SOC sections. The control unit obtains a mth reference power curve and a mth reference current value associated with a mth SOC section to which the estimated SOC value belongs, from the reference charging map associated with the temperature section to which the temperature detection value of the battery belongs. The control unit determines the time required for the SOC of the battery to reach an end point of the mth SOC section, according to the result of comparing a maximum output power of a charger and the mth reference power curve, based on the difference between the end point of the mth SOC section and the estimated SOC value, and at least one of the mth reference current value, the maximum output power and the mth reference power curve.

The invention discloses a battery pack equalization charging and discharging intelligent control method and system based on adaptive learning. The method comprises the steps that the SOC change trend of battery monomers is predicted, an SOC-time curve is obtained after correction, and the monomers are clustered and recombined to obtain a target battery pack; monitoring a real-time operation state of the target battery pack, when a voltage difference value, a temperature difference value, an SOC difference value or an internal resistance difference value between at least two batteries exceeds a first preset value, judging that the target battery pack is in an unbalanced state, and determining a regulation and control priority according to an unbalance degree; and when the priority is greater than a second preset value, adaptively adjusting the energy of different battery monomers through energy transfer, and otherwise, adaptively adjusting the charging and discharging rates of different battery monomers. According to the method, the SOC change trend of the single body can be predicted in advance, the consistency of the battery pack is ensured through clustering and regrouping, the battery charging and discharging are balanced by adopting a grading strategy when the battery pack is unbalanced, and the operation energy efficiency and the safety of the battery pack are greatly improved.

The invention relates to the technical field of battery charging and discharging optimization, and discloses an energy management system data analysis method, device and equipment and a storage medium, and the method comprises the steps: collecting a working condition feature data set of a robot energy storage battery through an energy management system; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, accurate prediction of battery performance is further achieved, and stable operation of an energy management system is ensured.

The invention discloses an energy storage battery pack PACK thermal runaway detection method, and relates to the technical field of battery thermal runaway detection. Comprising the following steps: collecting mechanical stress change data in battery charge-discharge circulation, and establishing a stress change data set; monitoring the pressure fluctuation trend in the single body in real time, recording pressure change data, and performing correlation analysis with the stress change data set through data synchronization to form a stress-pressure combined data set; inputting a material aging model and an interface fatigue analysis model, extracting a sensitive area and a failure point of interface aging, and quantifying a gas release risk; and on the basis of the quantized gas release risk, an abnormal trend curve is constructed in combination with historical data, feedback is performed, and early warning information is generated. According to the invention, accurate identification of the aging sensitive area and the high-risk monomer is realized.

The invention discloses an intelligent battery charging management system for a battery charging and replacing cabinet. The intelligent battery charging management system comprises a multi-source data acquisition module 101, a spatial-temporal characteristic analysis engine 102, a demand prediction model 103, a dynamic scheduling decision maker 104, a charging parameter optimizer 105, a battery health monitoring unit 106 and a system communication module. The intelligent battery charging method for the battery charging and replacing cabinet comprises the following steps: acquiring battery state data and operation data in real time; generating a demand thermodynamic diagram and network topology features through a spatial-temporal feature analysis engine; predicting demand distribution in future 6 hours based on LSTM and random forest dual models; generating a scheduling scheme meeting the electricity price, load and SOH constraints by using mixed integer linear programming (MILP); charging parameters are dynamically adjusted according to a gradient charging strategy, and valley electricity resources are preferentially used; and monitoring the health degree of the battery and feeding back and correcting the charging plan in real time. The method effectively reduces the charging cost, prolongs the service life of equipment, improves the health degree of a battery, and guarantees the operation reliability.

The invention discloses an active power distribution network two-stage island division method and system considering flexible resource support. The method comprises the following steps: establishing a first resource allocation model considering energy storage and controllable load, the first resource allocation model comprises energy storage battery charging and discharging upper and lower limit constraints, an energy storage battery degradation cost model, a controllable load model and a controllable load related user protocol; obtaining parameter data of a target power distribution system, wherein the parameter data comprises a line parameter, a load level, a load level, a network topology connection relationship, a system operation voltage level and branch current limitation, a system fault position, a reference voltage and a reference power initial value; and according to the parameter data, establishing a mathematical model for two-stage island power supply recovery based on a first resource allocation model. The method for solving the island is simple and convenient, the island recovery amount can achieve the optimal recovery effect, meanwhile, the search time is greatly shortened, the solving efficiency is higher, and the rapid power supply recovery capacity of the power distribution network is improved.

The invention discloses a UPS lithium battery system for an IDC data machine room, and relates to the technical field of battery monitoring, when the system operates, the problems of slow response to environmental influence and inaccurate evaluation in a traditional system are solved by dynamically acquiring a comprehensive influence index Timp of a microenvironment on battery temperature, and a strategy optimization module combines the operation requirement of a UPS lithium battery to improve the reliability of the system. According to the method, the optimization logic is triggered, and the optimized charging power Pr is dynamically calculated, so that the problems of unreasonable power distribution and insufficient battery protection in a battery charging strategy are effectively solved, the difference value of the actual charging power Po is monitored in real time, and the accuracy of an execution result and the safety of system operation are ensured. In addition, the evaluation feedback module monitors the running state of the UPS lithium battery after the dynamic adjustment charging strategy result is executed, and closed-loop optimization control is achieved. The method can adapt to the complex thermal environment of the IDC data room, reduce the overheating risk caused by the temperature rise of the battery, and significantly prolong the service life of the battery.

The invention provides a positive electrode, a low-temperature electrolyte, a low-temperature sodium-ion battery and a preparation method and application of the low-temperature sodium-ion battery, and belongs to the technical field of battery materials. The low-temperature sodium ion battery comprises a positive electrode, a negative electrode and a low-temperature electrolyte, wherein the positive electrode comprises a positive electrode active material, a binder, sodium salt containing fluorine and sulfur and conductive carbon; the positive electrode is improved by adding sodium salt containing fluorine and sulfur, sulfur / fluorine rich in anion derivatives can be formed on a contact interface of the positive electrode and an electrolyte to form a firm SEI film, the internal resistance of the battery is further reduced, and the coulombic efficiency of the battery during charging and discharging at normal and low temperatures is improved. The low-temperature electrolyte comprises an ester solvent and an ether additive, by adding the ether solvent, the physicochemical property of the electrolyte and the composition of an SEI film on a contact interface of an electrode and the electrolyte are optimized, the viscosity of the electrolyte at low temperature is reduced, the ionic conductivity is improved, and formation of the SEI film rich in sodium fluoride on the surface of a negative electrode can be promoted; and thus, the problem of low coulombic efficiency of battery charge-discharge cycle at low temperature is solved.

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

The invention provides a lithium battery SOH estimation method based on a time sequence diagram neural network under physical constraints. The method comprises the following steps: collecting battery charge-discharge cycle data; time-related statistical characteristics and IC curve characteristics are extracted from the battery charge-discharge cycle data, and analysis characteristics are obtained after PCC analysis; calculating a time sequence distance between analysis features based on a sliding window and multi-channel collaborative dynamic time warping, and constructing a graph structure; constructing a time sequence diagram neural network, firstly extracting spatial features through diagram convolution, and then modeling a degradation law through time convolution to obtain a preliminary SOH predicted value; defining a physical residual error loss and a monotonicity regular term, and performing weighted combination on the physical residual error loss and the monotonicity regular term and data fitting loss to construct a joint optimization objective function; based on the optimization target, a final constrained SOH predicted value is obtained through a back propagation training model; therefore, in combination with a physical information constraint mechanism, high-precision and physically consistent SOH prediction is realized.

The invention discloses a lithium battery diaphragm with a gradient pore structure and a preparation method thereof, the lithium battery diaphragm with the gradient pore structure is of a layered structure, the layered structure comprises a base membrane, a first coating and a second coating, the first coating and the second coating are located on the same side of the base membrane, the first coating is connected with one side of the base membrane, and the second coating is connected with the other side of the base membrane. The second coating is connected with the first coating, the porosity of the first coating is 40-50%, the porosity of the second coating is 30-40%, the first coating comprises large-aperture porous alumina, and the second coating comprises small-aperture porous alumina. A double-layer structural design is adopted, and the two coatings have different pore diameters, so that a gradient pore diameter structure is formed. The structure can realize gradual adaptation of ions in the charging and discharging process of the battery and reduce migration energy obstacles.

The invention discloses a battery charging control method for a battery replacement cabinet based on the Internet of Things, relates to the technical field of charging control, and has higher adaptability in the aspects of battery replacement demand prediction, charging mode switching and charging safety management compared with a traditional fixed charging mode. The problems of sudden increase of battery replacement demands, changes of environmental factors and the like can be effectively solved, and the operation stability of the battery replacement cabinet is improved; the method comprises the following steps: acquiring a battery state, a battery replacement demand and meteorological data, predicting a future battery replacement demand curve in combination with a long-short term memory (LSTM) network and a multiple linear regression model, introducing a meteorological influence factor, adjusting a charging plan of a battery replacement cabinet 24 hours before severe weather early warning, and matching the battery stock with a peak demand; the charging modes comprise a fast charging mode, a general charging mode and a slow charging mode, dynamic switching is carried out according to the battery replacement demand peak value and the daily average battery replacement demand, the fast charging mode is preferentially adopted in the battery replacement peak period, the battery supply rate is increased, and meanwhile part of charging resources are reserved.

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

The invention provides a multi-parameter collaborative optimization battery charging and discharging management system integrated with a thermal management technology, and aims to improve the charging and discharging efficiency and safety of a battery and prolong the service life of the battery. The system monitors key parameters such as the temperature, the voltage, the current and the internal resistance of the battery in real time through a high-precision sensor, and the parameters are comprehensively analyzed through the data fusion technology. A core optimization algorithm adopts a genetic algorithm or deep learning, a charging and discharging strategy is dynamically adjusted, and battery performance maximization is achieved. The thermal management module effectively controls the temperature of the battery through a cooling fin, a liquid cooling system or a phase change material, overheating and supercooling are prevented, and the safety and stability of the battery are improved. The user interface is friendly, the battery state is convenient to monitor and adjust, and the system has wide application prospects in the fields of electric automobiles, renewable energy storage and the like.

The invention discloses a health assessment method and system for a decommissioned battery, and relates to the technical field of battery state assessment, and the method comprises the steps: recognizing a decommissioned battery identifier, automatically generating an assessment task number, and setting the battery state as "to-be-detected"; a battery charging mode is set, battery state data are collected in real time, battery features are extracted, and a feature vector G is constructed; and inputting the feature vector G into a parallel attention neural network to carry out static SOH estimation to obtain a preliminary SOH estimated value, carrying out SOH correction by using joint extended Kalman filtering, and obtaining the current internal resistance. According to the invention, the method can achieve the efficient and precise evaluation of the health state of the decommissioned battery, remarkably improves the comprehensiveness and deep modeling capability of feature extraction, improves the reliability and dynamic adaptability of an SOH estimation result, effectively reduces the error fluctuation, and improves the generalization capability of an evaluation system for a complex decommissioned state.

The invention relates to the technical field of electronic equipment battery management, and discloses a battery charging management method and an electric quantity accurate display method based on electronic equipment. The dynamic adjusting module generates a multi-dimensional strategy through a staged charging control unit (including a fast, balanced and trickle charging sub-unit), a parameter transition processing layer and a global charging state pooling layer; the environment adaptation module adjusts the charging power and the temperature threshold according to the environment data, and has the functions of environment feature extraction, self-adaptive compensation and anti-interference switching; and the strategy fusion module outputs a control instruction in a weighted manner, and is additionally provided with an exception handling and wireless charging optimization mechanism. When the electric quantity is displayed, the collected data is dynamically calibrated to compensate polarization errors, and the capacity is fused with a matching historical curve to generate a high-precision electric quantity percentage which is dynamically rendered and displayed. According to the scheme, the charging safety, efficiency and electric quantity display precision are improved.

A battery charging system includes a charging rack comprising multiple receptacle, multiple battery assemblies. Each of the battery assemblies includes a housing having a handle, rechargeable battery cells disposed within the housing, and a mating feature protruding away from the housing, wherein the mating feature is configured to selectively connect the battery assembly with the receptacle of the charging rack and includes multiple ports electrically connected to the battery cells and configured receive power from the charging rack to charge the battery cells, and wherein the handle comprises a release button configured to selectively disengage the battery assembly from the receptacle of the charging rack.