3876 results about "Battery charge" patented technology

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

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 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 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 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 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 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.

The invention discloses a lithium battery health state estimation method based on transfer learning, and the method comprises the steps: firstly carrying out the normalization and time sequence reconstruction of a battery charging voltage-capacity curve, and constructing a unified input sequence; extracting long-time-sequence degradation characteristics based on a Mama network, and completing SOH regression prediction through a two-stage full connection layer; in the cross-domain adaptation stage, in combination with an alignment strategy of dynamic time warping and weighted maximum mean difference, time sequence matching and feature distribution alignment in the degradation stage are realized; meanwhile, on the basis of a sample weighting mechanism of a Wasserstein distance, the effectiveness of migrating source domain knowledge to a target domain is improved; through a two-stage strategy of source domain pre-training and source-target joint training, a relatively low prediction error and a relatively high fitting degree can be kept under the condition that a target domain is not labeled; the method shows good generalization ability and robustness under different battery types and different working conditions, and can provide reference for health management of the electric vehicle.

The invention discloses a vehicle network charging and discharging optimization method and system based on a dynamic electricity price, and relates to the technical field of smart power grids and electric automobiles, and the method comprises the steps: S1, constructing a prediction model of the polarization degree of a battery through an electrochemical model and historical operation data, obtaining a quantification result of the polarization degree of the battery, and obtaining a prediction result of the polarization degree of the battery; s2, according to the quantification result of the battery polarization degree, combining the dynamic electricity price data and the user demand data, adopting a reinforcement learning algorithm, constructing a charging and discharging strategy optimization framework, outputting a preliminary charging and discharging strategy, determining the adaptability of the strategy in different scenes, and obtaining a smooth charging and discharging curve; according to the vehicle network charging and discharging optimization method and system based on the dynamic electricity price, intelligent management of the charging and discharging process of the battery is achieved, the service life of the battery is effectively prolonged, the energy utilization efficiency is improved, economic benefits are maximized, and technical support is provided for efficient operation of a battery energy storage system.

The invention provides a super-computing center battery charging and discharging strategy optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a real-time operation data set of a super-computing center battery system in real time, and carrying out the load feature extraction of the real-time operation data set; the method comprises the steps of generating a load feature set of a battery system, inputting the load feature set into a pre-trained reinforcement learning strategy network, performing charge and discharge strategy priority matching processing on the load feature set, generating a priority ranking result of a target charge and discharge strategy, adjusting a current charge and discharge strategy set according to the priority ranking result, generating an optimized charge and discharge strategy set, and outputting the optimized charge and discharge strategy set. And performing charging and discharging operation based on the optimized charging and discharging strategy set, and collecting battery performance feedback data after the charging and discharging operation so as to iteratively update strategy matching parameters of the reinforcement learning strategy network. According to the method, the problems of over-charging and over-discharging and power oscillation caused by lagging response of environmental parameters in a traditional fixed strategy are solved, and autonomous decision-making of multi-target optimization can be realized without depending on artificial experience rules.

The invention provides an electric vehicle battery charging and discharging management method, which comprises the following steps of: performing parameter fusion on a sensor data set to construct battery real-time state data; using the battery real-time state data and the environment data and the vehicle operation condition data corresponding to the battery real-time state data to construct vehicle charging and discharging state data, using the data to identify a battery state change trend and an abnormal state, and generating state change trend data and abnormal state diagnosis data; dynamically adjusting a battery charging and discharging strategy according to the vehicle charging and discharging state data and the state change trend data, and generating strategy adjustment data; and performing graded early warning according to the vehicle charging and discharging state data, the state change trend data, the abnormal state diagnosis data and the strategy adjustment data, and generating battery charging and discharging early warning data. Charging and discharging state data are constructed by fusing the real-time state, environment and working condition data of the battery, a charging and discharging strategy is dynamically adjusted, state trend recognition and abnormal early warning are carried out, and the service life of the battery is prolonged.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention relates to the field of navigation signal processing, and discloses a passenger car battery temperature control intelligent regulation and control method and system based on multi-source sensor data, environment data and battery operation state data are acquired by constructing a multi-source sensing array, and fusion processing and feature extraction are performed on the acquired data by using an edge calculation method; analyzing and predicting the charging and discharging behaviors of the battery based on the extracted characteristic parameters, judging the charging and discharging states of the battery, dynamically optimizing the switching threshold value of the refrigerating or heating mode of the battery, switching the mode according to the optimized switching threshold value, and performing matching analysis on the photovoltaic power generation energy and the temperature control energy consumption, so as to determine the temperature control energy consumption of the battery. According to a matching analysis result, an exclusive temperature control matrix is generated through a geographic position and meteorological data, and time-sharing gradient control is performed by using an adaptive PID control algorithm in combination with instantaneous wave cloud characteristics of photovoltaic energy storage, so that customized temperature control strategies can be provided for different environmental conditions, temperature fluctuation is avoided, and the temperature control stability is improved.

The invention discloses a power supply rapid charging pulse current regulation and control system and method based on current monitoring, and particularly relates to the technical field of power supply management. The method comprises the following steps: acquiring a pulse current signal in a battery charging process, performing nonlinear decoupling analysis, and decomposing the pulse current signal into a first characteristic sub-vector representing a real charging state of the battery and a second characteristic sub-vector representing a high-frequency harmonic interference state; predicting the change trend of the electrochemical impedance of the battery based on the first feature sub-vector to form an instant charging acceptance evaluation index, and evaluating the harmonic interference degree based on the second feature sub-vector to form a charging state misjudgment risk level index; based on the battery instant charging acceptance assessment index and the battery charging state misjudgment risk grade index, the optimal pulse current regulation and control strategy is determined, the power output parameters are adjusted in real time, the state misjudgment problem caused by high-frequency harmonic interference and electrochemical impedance coupling is effectively solved, and the battery charging performance and the long-term reliability are improved.

The invention relates to the technical field of balcony light storage, and discloses a novel balcony light storage system. The system comprises a multi-source illumination monitoring module, an energy storage state analysis module, a virtual operation feature generation module, a dynamic energy flow coordination module, a power execution control module, an abnormal response management module and a multistage response strategy engine. The multi-source illumination monitoring module generates a multi-dimensional illumination feature vector; the energy storage state analysis module generates an energy storage characteristic matrix; the virtual operation feature generation module constructs a reference operation feature set; the dynamic energy flow coordinating module generates a power adjusting instruction sequence according to the power adjusting instruction sequence; the power execution control module drives the bidirectional converter to adjust the working point of the photovoltaic module and controls the charging and discharging power threshold value of the storage battery; and the abnormal response management module monitors the execution deviation value, activates the multi-stage response strategy engine when the execution deviation value exceeds the limit, and outputs a coordination compensation instruction to the dynamic energy flow coordination module. According to the system, dynamic collaborative management and abnormal flexible response of the balcony optical storage system are realized.

The present disclosure provides a multifunctional battery charger including: an installation component. An interior of the installation component is provided with an external charging component and a charging component, the charging component is provided below the external charging component, a front of a housing is provided with a charging indicator light and a magnetic charging head, the magnetic charging head is connected to a magnetic adapter, one end of the magnetic adapter is inserted into a charging port of a battery. Some battery charging ports are provided at one side, some battery charging ports are provided on a head portion. Whether on the side or the head portion, they can be charged flexibly, solve a problem that most charging plugs of existing chargers are fixed.

The present invention relates to the technical field of batteries, and provides a battery cover plate, a battery and a battery pack, the battery cover plate comprises: a cover plate body provided with a through hole; the sealing plate is arranged at the through hole, and the sealing plate is in sealing connection with the through hole; the boss is arranged on the outer side of the sealing plate in the first direction, the boss extends out of the through hole in the first direction, a plurality of convex ribs with closed outlines are integrally formed on the end face of the side, away from the cover plate body, of the boss, and the first direction is the thickness direction of the cover plate body; the surface area of the outer surface of the boss is increased through the existence of the convex ribs, meanwhile, the heat dissipation area of the battery cover plate is greatly increased, heat generated by a system can be rapidly conducted and dissipated in the charging and discharging process of the battery, the working temperature of the battery is effectively reduced, the battery can stably adapt to the high-rate rapid charging technical scheme, and in addition, the service life of the battery cover plate is prolonged. Through the design, the surface of the boss can be coated with the structural adhesive, and the bonding area of the structural adhesive and the boss is remarkably increased.

The invention discloses a photovoltaic centralized charging control method and device for a light commercial electric vehicle and a medium, and belongs to the technical field of new energy charging. The method comprises the following steps: acquiring weather data, a vehicle battery charge state and task scheduling data; photovoltaic module parameters and environment data are processed through an LSTM neural network model, and a photovoltaic power generation power prediction curve is generated; constructing a multi-dimensional evaluation index system including task emergency degree, charging efficiency and power grid interaction factors, and generating a charging priority sequence in combination with an entropy weight TOPSIS algorithm; solving a real-time power distribution scheme based on a mixed integer programming algorithm, and dynamically adjusting the output power of the charging pile; monitoring the operation state of the system, and triggering a multi-stage power compensation mechanism when the photovoltaic power is insufficient or the electric quantity is low; and the battery state and the photovoltaic power are updated in real time, and a charging strategy is corrected through a rolling optimization algorithm. By means of the method, centralized charging control over the light commercial electric vehicle is achieved, and the technical effect of meeting the requirements of a motorcade is achieved.

The invention relates to the field of battery charging control, in particular to a lithium ion battery equalizing charge control method and system based on a BMS (Battery Management System). The method comprises the following steps: continuously obtaining battery cell monitoring parameters of a battery charging state based on a BMS, and carrying out high-frequency filtering and denoising processing and battery cell charging behavior analysis to obtain a battery cell behavior characteristic time sequence; identifying a charging voltage response signal of each battery cell based on a BMS, performing multi-frequency impedance change trend evolution, performing battery cell charging response characteristic perception based on a battery cell behavior characteristic time sequence, and constructing a plurality of battery cell charging state maps; and carrying out quantitative calculation on charging energy flow affinity according to the battery cell monitoring parameters, carrying out state deviation trend distinguishing by combining the plurality of battery cell charging state maps, constructing a topological connection diagram among the battery cells, and carrying out energy deviation complementary path mining and dynamic deviation balance planning on weak battery cells and strong battery cells. According to the invention, efficient and accurate energy balanced distribution and charging control are realized.

In one aspect, a computer-implemented method may include determining whether an anode potential offset of a battery pack has reached an upper threshold of a target range, responsive to determining that the anode potential offset of the battery pack has reached the upper threshold of the target range, receiving tear-down data associated with the battery pack, training, based on the tear-down data associated with the battery pack, cloud prediction models that are trained to predict a likelihood of occurrence of lithium plating associated with battery packs, and transmitting, to edge processors of fleet vehicles, parameters associated with the trained prediction models to cause the edge processors to train edge prediction models using the edge processors, wherein the edge processors modify a battery charging policy based on a prediction from the edge prediction models.

The invention discloses an unmanned aerial vehicle energy-power conversion system, and relates to the technical field of unmanned aerial vehicle energy, and the system comprises a battery module which is formed by combining a plurality of single batteries in a series connection or parallel connection manner, and is responsible for providing electric energy required by the flight of an unmanned aerial vehicle. And the energy management module performs real-time monitoring, optimal distribution and intelligent regulation and control on energy use of the unmanned aerial vehicle through an integrated sensor, algorithm and control technology, and comprises a data acquisition unit, a charging management unit and a power conversion unit. The charging management unit is used for managing an unmanned aerial vehicle battery charging process, externally connecting a charging power supply and controlling an unmanned aerial vehicle battery charging strategy according to the battery power reserve level; and the power conversion unit adopts a dynamic power distribution strategy, implements closed-loop control based on the real-time power spectrum characteristics of the driving module, and realizes on-demand power output adjustment. And the configuration of longer endurance time of the same power supply weight or fewer batteries under the same endurance time is realized.

This application relates to the technical field of electric vehicles, and in particular, to charging systems and methods. An example charging system includes: a low-voltage battery capable of supplying power to a low-voltage electrical device of a vehicle; a power battery module; a direct current-to-direct current (DC-DC) converter capable of receiving a voltage supplied by the power battery module in response to at least that a positive relay and a negative relay of the power battery module are closed, and converting the voltage into a charging voltage of the low-voltage battery to charge the low-voltage battery; and a DC-DC controller electrically connected to the low-voltage battery and capable of monitoring a voltage of the low-voltage battery in response to at least that the vehicle is in a dormant state.

Techniques are described for using machine learning to extract physics-based cell model parameters from battery charge data provided by the battery module of the battery management system. This data may be processed via machine learning techniques in order to periodically update or adapt a physics-based model that may capture cell electrical dynamics and degradation mechanisms. The techniques may utilize a “digital twin” to adaptively charge the battery while preserving the life of the cells.