38863 results about "Electric vehicle" patented technology

The present disclosure relates to a blockchain-based authentication system for electric vehicles and drones, and a method for authenticating electric vehicles or drones in a smart city. The proposed blockchain-based authentication system enhances security, privacy, and scalability for electric vehicles (EVs) and drones in smart city environments. It employs a consortium blockchain managed by city authorities and stakeholders, utilizing smart contracts for identity registration, credential issuance, and access control. Device nodes store cryptographic keys securely, while an optional authentication server facilitates off-chain integration. The authentication operation of the system includes, ensuring tamper-proof identity verification through on-chain validation. Authentication attempts are immutably recorded for auditing and anomaly detection. By eliminating single points of failure and strengthening data privacy, this decentralized authentication framework offers a scalable and secure solution for integrating EVs and drones into smart city infrastructures.

The invention provides an electric vehicle energy storage battery fault diagnosis and predictive maintenance control method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting the real-time working condition data and environment parameters of an electric vehicle energy storage battery, and carrying out the data preprocessing through a cloud server; carrying out life prediction by utilizing a multi-task deep learning framework, and generating a comprehensive state evaluation report; constructing a multi-target maintenance decision optimization model based on the evaluation report, and predicting an optimal maintenance time window; historical maintenance data and fault diagnosis experience are fused, and a maintenance operation list is generated; and performing resource scheduling optimization through a mixed integer programming algorithm to form an optimal battery maintenance scheme.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

To ensure the rigidity of a floor panel against a load in a vertical direction in an electric vehicle having a step in the floor panel.SOLUTION: A rear floor panel 10 of an electric vehicle has an inclined part 16 that extends while inclining upward as it heads for backward from a rear end of a plane part 12. A floor cross member 20 is disposed in a front part of the inclined part 16 on the side of an upper surface of the rear floor panel 10. An under-floor reinforcement 30 is disposed in the front part of the inclined part 16 on the side of a lower surface of the rear floor panel 10. A front end of the under-floor reinforcement 30 is disposed ahead of the front end of the inclined part 16, and a rear end of the under-floor reinforcement 30 is disposed on the inclined part 16. The under-floor reinforcement 30 and the floor cross member 20 are joined with the rear floor panel 10 therebetween.SELECTED DRAWING: Figure 6

A system and method for electric vehicle operational optimization is disclosed. The system comprises a memory storing processor-executable instructions and a processor, communicably coupled with the memory. The system obtains input data and predict health and performance parameters. The system generates computer simulated instances which emulate a behavior and a performance of the electric vehicle. The system, further, validates the health and the performance parameters by simulating the computer simulated instances in a virtual environment. The system determines a behavior status, a performance status and a health status of the electric vehicle. Thereafter, the system determines abnormality associated with the electric vehicle, followed by determining action for rectifying the abnormality. Consequently, the system controls an operation by performing the determined action at the electric vehicle.

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

A system and method that manages charging of Electric Vehicles (EVs) at a depot equipped with EV chargers. For each EV arriving at the depot, data is collected from the EV including a current state of charge (SoC), an SoC goal, a route estimate, and a charging schedule. A depot controller determines from the collected data a charging priority for the EV relative to other EVs at the depot. When charging is required to meet the SoC goal of the EV, a charger is selected for charging the EV and a charging start time and charging duration is determined by an optimization algorithm that concurrently optimizes a minimal total cost of electricity for total power transferred to the EV during charging while maximizing a probability of the EV leaving the depot with its SoC goal satisfied.

The invention discloses a vehicle-mounted boost inductance monitoring simulation test system and a test method thereof. The vehicle-mounted boost inductance monitoring simulation test system comprises a vehicle-mounted power supply simulation module, an accelerated life test module, an environment test box, a fault early warning module and a data analysis and diagnosis module. The actual stress state of the inductor is comprehensively simulated by constructing a power supply simulation model matched with the actual working condition of the electric vehicle and combining a multi-environmental-factor coupled accelerated life test; real-time monitoring and fault early warning of inductive performance degradation are realized by adopting an acoustic emission technology; an inductance fault diagnosis knowledge base and an intelligent reasoning system are constructed by applying big data analysis and machine learning technologies. According to the system and the method provided by the invention, the inductance reliability test efficiency can be remarkably improved, the early warning of inductance degradation is realized, the accuracy and the intelligent level of fault diagnosis are improved, a solid basic guarantee is provided for safe operation of a new energy electric vehicle, and the system and the method have wide application prospects and popularization values.

The invention relates to the technical field of electric vehicles, and discloses a full-automatic AGV charging collaborative optimization system which comprises an energy consumption prediction module, a consensus mechanism design module and an AGV scheduling optimization module. The energy consumption prediction module is used for acquiring a pose topological feature vector and a potential margin attenuation gradient, constructing a multi-dimensional input vector, and generating a dynamic energy consumption prediction value based on an energy consumption track residual model of multi-physics field coupling; the consensus mechanism design module is used for constructing and verifying a dynamic consensus factor and providing a priority decision basis; the AGV scheduling optimization module is used for discretizing an operation space of a battery swap station, generating a space-time folding unit, constructing a space-time conflict resolution atlas, and generating an optimal battery swap sequence and a collision avoidance path; through multi-module cooperation, efficient cooperation of multi-AGV charging and battery swap is realized, and congestion of the battery swap station and resource waste are avoided.

A battery case structure for an electric vehicle, includes a side member in which a flange of the side member laterally provided in a battery case is deformed or broken from a partition to absorb an impact when a side collision occurs, minimizing the impact which is caused by the collision and is provided to penetrate into a battery. Furthermore, the partition of the side member is designed based on topology optimization to secure rigidity, protecting the battery against the impact.

A power distribution utility panel is provided with a first panel area configured to be accessed only by personnel of a utility, the first panel area including a current transformer, a second panel area including at least one remote controllable device for connecting and disconnecting power to / from one or more loads, and a third panel area including at least one power converter assembly, a controller and a power meter configured to monitor power from a power source, the at least one power converter assembly being configured to route power from the power source to an electric vehicle charger and / or an energy storage system.

A method of repairing a used battery pack from an electric vehicle include removing the battery pack from the vehicle. Battery tests are performed on at least some of the plurality of batteries and a battery test result for each of the batteries tested are obtained and stored in a database. A plurality of replacement batteries are tested and test results for each of the replacement batteries are stored in the database. The battery test results from the database are retrieved and used to create a refurbished battery pack. An apparatus includes a database for storing test results.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

1. The name of the product of this design: New energy electric vehicle maintenance auxiliary intelligent robot. 2. Purpose of this design product: This design is an intelligent robot used to assist and guide maintenance engineers in repairing electric vehicles. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: Stereoscopic diagram 1.

The present application relates to the technical field of electric vehicles, and discloses a fault-tolerant actuator control method and apparatus for an electric vehicle, a storage medium, and a vehicle. The method comprises: acquiring vehicle-mounted sensor information and a reference trajectory of a distributed drive electric vehicle; using an MPC algorithm to perform trajectory tracking on the basis of the vehicle-mounted sensor information and the reference trajectory to obtain an expected front wheel steering angle; when at least one steering actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a differential power moment on the basis of the expected front wheel steering angle and an actual front wheel steering angle corresponding to a previous moment by using a sliding mode control (SMC) algorithm; when at least one driving actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a torque corresponding to the current moment on the basis of the differential power moment by using a distribution optimization algorithm based on minimum tire adhesion utilization; and controlling steering of the distributed drive electric vehicle on the basis of the torque corresponding to the current moment. The present application implements reliable control of the vehicle in a case where a steering actuator and a driving actuator fail at the same time.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention relates to the technical field of electric vehicle heat management, in particular to a direct heat pump type heat management system. Comprising a cooling liquid loop and a refrigerant loop, and the cooling liquid loop comprises a first cooling loop used for heat management of a battery pack, a second cooling loop provided with a plate heat exchanger, a third cooling loop used for heat management of an electric drive assembly and a fourth cooling loop provided with a radiator; the first cooling loop, the second cooling loop, the third cooling loop and the fourth cooling loop are connected through a seven-way valve; the refrigerant loop comprises a first refrigerating loop used for conducting heat management on the passenger compartment, a second refrigerating loop provided with an evaporator and a third refrigerating loop connected with the plate heat exchanger. The direct heat pump type heat management system is simple in structure, heat pump heating can be achieved in the heat pump mode that a refrigerant releases heat in the indoor condenser and absorbs heat in the outdoor heat exchanger under the condition that the external air temperature is low, and the energy-saving effect of an air source heat pump is reflected to the maximum extent.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention provides a charging control method and device for charging piles, equipment and a storage medium, and relates to the technical field of electric vehicle charging, and the method comprises the steps: determining the total charging load of a parking lot according to the output power of each charging pile in the parking lot when a valley-peak balance instruction sent by a power grid dispatching center is received; when the total charging load exceeds a set threshold value, calculating a difference value between the total charging load and the set threshold value; determining the change trend of the power grid load according to the real-time load data and the historical load data; determining a target power value needing to be adjusted of the parking lot by combining the difference value and the change trend of the power grid load; according to the target power value and the charging mode of the vehicle corresponding to each charging pile, adjusting the output power to obtain first output power; and adjusting the first output power according to the battery state information of the vehicle corresponding to each charging pile to obtain second output power. The technical effect of the invention is that the balance between the charging demand and the stable operation of the power grid is realized.

The invention discloses a dynamic carpooling scheduling method for unmanned electric taxis. The method comprises the following steps: firstly, acquiring data and cleaning the data; mapping the data in each hexagonal grid; establishing a travel demand feature matrix and a spatial dependency matrix; constructing a space-time diagram convolutional neural network based on an attention mechanism, and obtaining a travel demand prediction result; setting constraint conditions matched with the car orders in the dynamic car-pooling scene; based on the travel demand prediction result and the vehicle order matching constraint condition, constructing a vehicle order matching decision model based on distributed deep reinforcement learning; and designing a Double DQN solving algorithm, carrying out real-time order matching and idle vehicle scheduling based on a result, notifying the matched vehicle to complete a corresponding order, and notifying the unmatched idle vehicle to carry out regional scheduling or charging energy supplementation. According to the method, in dynamic carpooling of the multi-passenger bicycle, the endurance process constraint of the electric automobile and the charging energy complementation requirement of the bicycle are considered, the actual operation requirement is better met, and the quality is improved.

The invention relates to the technical field of energy consumption analysis, in particular to an electric vehicle energy consumption analysis system and method, and the system comprises an operation factor correlation module, a speed slope coupling extraction module, a slope interference correction module, an energy consumption stability discrimination module and a deviation trend recognition module. According to the method, the associated gradient and vehicle speed data are extracted and combined with the vehicle mass to form a three-dimensional data structure, energy consumption distribution can be predicted and evaluated more accurately, and power fluctuation caused by gradient change can be effectively identified by associating electric power output with the gradient of the gradient and the vehicle speed change rate. By comparing the deviation between the actual power state and the ideal power state and correcting gradient interference, the accuracy of energy consumption data is improved, the fine granularity of electric vehicle energy consumption management is enhanced, and by comparing the electric power and kinetic energy changes in a cross mode, power abnormal deviation can be recognized. And more reliable data support is provided for energy-saving optimization of the electric vehicle.

The invention relates to the technical field of electric vehicle control, and provides an intelligent new energy locomotive power distribution and energy recovery control system and method. The system comprises a multi-source information sensing layer, an intelligent decision control layer and an execution feedback layer. The multi-source information sensing layer collects the state of a vehicle and external environment data. The intelligent decision control layer generates a power distribution and energy recovery instruction through data fusion and preprocessing, fuzzy logic assistance, reinforcement learning decision and cooperative work of a multi-objective optimization module; and the execution feedback layer executes the instruction, monitors feedback in real time, and optimizes related actions. According to the method, advanced technologies such as reinforcement learning and fuzzy logic are fused, complex and changeable driving scenes are accurately dealt with, self-adaptive optimization of power distribution and energy recovery strategies is achieved, the energy recovery efficiency is improved, power distribution is accurate and efficient, dynamic adjustment can be achieved according to driving intentions and working conditions, the driving safety and comfort are guaranteed, and meanwhile the driving efficiency is improved. And efficient utilization of energy is realized.

The invention provides a vehicle network interaction type ordered charging scheduling method. The method comprises the following steps: acquiring photovoltaic, energy storage and charging pile configuration parameters, electric vehicle charging requirements, system topology, operation data of each device and meteorological data; regression analysis is carried out on charging behaviors and energy storage operation modes of various charging pile electric vehicles, and controllable load and energy storage response capability of each response time period are evaluated; generating power and load power are predicted, and the overall response capability is evaluated; according to the issued instruction, determining a response time period, a response task type and a planned operation load; based on the response demand, the operation mode of each device, the response subsidy profit sharing scheme and the demand constraint, an optimization scheduling model is established; solving an optimal planning problem, outputting a day-ahead response scheme, and declaring and confirming a clearing response demand; and correcting the scheduling strategy in real time. According to the invention, an effective resource control scheme is provided for the vehicle network interaction system, the electric vehicle and the power grid are decoupled, and the utilization rate and the economical efficiency of resources 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.