22306 results about "Electric vehicle" patented technology

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

The invention provides a charging and discharging control strategy optimization method and system of V2G applied to a virtual power plant. The method comprises the steps of constructing a collaborative optimization strategy of comprehensive income data based on electricity price change prediction and peak regulation demand prediction; based on the user behavior mode and the user historical charging behavior data, constructing a collaborative optimization strategy matched with the user demand; constructing a collaborative optimization strategy of battery health degree management based on the health degree quantitative model of the battery; and based on a collaborative optimization strategy of comprehensive income data, user demand matching and battery health degree management, a multi-target dynamic optimization model is constructed, and charging and discharging control strategy optimization of the V2G application in the virtual power plant is realized. According to the method, full-process cooperation of strategy customization-dynamic invitation-user response is realized under the guidance of the virtual power plant, multi-mode charging and discharging control strategy combination is realized, load aggregation is carried out by effectively utilizing the distributed energy storage characteristic of the electric vehicle group, and the power grid regulation capability is maximized.

A charging method based on intelligent dynamic power allocation. The method comprises: acquiring the current SOC, a real-time power demand, a rated power and infrared position detection information of an electric vehicle; on the basis of the current SOC, the real-time power demand, the rated power and the infrared position detection information of the electric vehicle, and in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold value incentive strategy and an overall-power utilization maximization strategy, performing power allocation and generating a corresponding control instruction; and controlling the output of a charger topology switch on the basis of the control instruction. Further disclosed is a multi-gun charger apparatus using the charging method. The charging method includes a moderate power allocation strategy and takes into account the demands of both a charger control system and users, and can effectively improve the overall charging efficiency, reduce the charging time, and improve the utilization rate of charging piles.

To provide a system and an apparatus for implementing a mobile energy system.SOLUTION: In an aspect, a mobile energy system includes a cabinet having an interior space defined by an outer surface of the cabinet, and the interior space can include a battery storage area configured to receive a battery energy storage system having at least 2 kWh of storage capacity. The mobile energy system can also include an item storage area configured to receive and store one or more other items, a connector configured to connect the battery energy storage system located in the battery storage area to an external power source, and a charging cable configured to connect the battery energy storage system to a charging port of an electric vehicle.SELECTED DRAWING: Figure 4

The present invention discloses a method for diagnosing micro-short circuit fault of a battery, comprising: acquiring real-time voltage data of each battery in battery module of electric vehicle, obtaining voltage matrix after noise elimination; calculating dynamic reference voltage sequence of the each battery in the voltage matrix within sliding window length, extracting eigenvalues of the each battery at different times; calculating correlation coefficients between voltages of the each battery at different times and reference voltage within the sliding window; forming feature point matrix of the battery module based on the eigenvalues and the correlation coefficients; calculating dynamic reference feature point sequence based on the feature point matrix, and calculating improved Frechet distances between feature point sequences of the each battery and the dynamic reference feature point sequence, using Frechet distance values as anomaly scores of the each battery; thus, judging whether a short-circuit fault occurs in the each battery.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention discloses an insulation detection method for an electric automobile, belongs to the technical field of high-voltage safety monitoring of the electric automobile, and solves the problem that the insulation detection precision is reduced due to electromagnetic interference, impedance fluctuation and capacitive effect coupling in a dynamic load switching process. According to the scheme, a test sequence of discrete frequency points is applied to a high-voltage system; measuring a response current value through a current sensor and calculating a ground distributed capacitance value; connecting a test voltage source between positive and negative electrodes of the high-voltage system through a coupling circuit; applying a first pulse signal during no-load, dynamically setting the width according to the capacitance value, measuring a first response current value, and calculating an initial insulation resistance value; a second pulse signal with the same parameter is applied in the load state window, a second response current value is measured, and differential filtering is carried out in combination with the initial value to eliminate load noise; and comparing the filtered insulation resistance value with a threshold value, and outputting a fault signal when the insulation resistance value is lower than the threshold value. The method is used for monitoring the insulation state of the high-voltage system in real time and accurately early warning faults.

The invention provides an active power distribution network electric vehicle V2G optimization method and system considering user will, and belongs to the technical field of power distribution network operation scheduling, and the method comprises the steps: obtaining the real-time basic load, photovoltaic output and electric vehicle charging and discharging power data of power distribution network operation; calculating the real-time net load of the power distribution network based on the acquired data; based on the real-time net load, dividing the time-of-use electricity price by using a membership function; determining a demand response price upper limit; within the interval of the upper limit and the lower limit of the demand response price, the discharge electricity price of the electric vehicle user participating in the demand response is determined according to the time-of-use electricity price and the peak clipping compensation coefficient; whether the V2G condition is met or not is inspected according to whether the electric vehicle networking time preset by the user according to the travel demand is greater than the charging and discharging time or not; if yes, dispatchable electric vehicle discharge power is obtained; and constructing an active power distribution network electric vehicle V2G optimization scheduling model, and solving the optimization scheduling model to obtain the optimal values of the discharge electricity price, the actual scheduling discharge power and the photovoltaic consumption rate.

A system and method for enabling real-time dispatch of flexibility resources to unlock grid capacity through AI-based orchestration. The invention addresses the challenge of connecting high energy demand users, such as data centers, to constrained electricity grids without requiring infrastructure upgrades. The system establishes a marketplace where flexible asset holders set temporal compensation prices and boundary conditions, enabling true market-based participation. An AI orchestration engine analyzes real-time grid conditions and modifies flexible asset behavior to create inverse consumption profiles that counterbalance new demand loads. The platform integrates hardware and software solutions for remote control and APIs for autonomous systems like electric vehicles. Aggregators and off-takers can establish long-term contracts for flexible capacity at agreed prices. The AI system ensures flexible assets meet user-defined boundary conditions while simultaneously masking high energy demand, making new loads invisible to the grid and enabling immediate connection of data centers essential for industrial deployment.

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

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

The invention discloses an intelligent AI detection method based on an infrared thermogram and automobile diagnosis, according to the technical scheme, the fireproof safety detection efficiency of an electric automobile is remarkably improved through multi-modal data fusion and intelligent analysis, and a multi-dimensional data basis is constructed through synchronous acquisition of dual-band infrared and vehicle operation parameters; secondly, a dynamic correlation model realizes accurate triggering of abnormal current on heat map enhancement, partition gradient processing is combined with dynamic threshold adjustment to optimize detection sensitivity for thermal characteristics of different parts, three-dimensional thermal field reconstruction breaks through surface detection limitation, internal potential overheat points can be identified, and the detection sensitivity is improved; and finally, closed-loop diagnosis is formed through case library intelligent matching, so that the fault positioning precision reaches a component level, and the whole scheme is coordinated through a multi-level technology, so that the problems of low infrared thermal image contrast and relatively poor detail distinguishing capability due to the fact that electric vehicle fireproof safety detection is carried out only by depending on thermal infrared images in the prior art are solved.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention relates to the field of energy allocation, and discloses a regional building group multi-energy complementary cooperative regulation and control method, which comprises the following steps of: acquisition processing: acquiring multi-energy data in a regional building group by utilizing multiple sensors, and preprocessing to obtain processed data; load prediction: inputting the processed data into an LSTM-Attention mixed deep learning model, performing fusion calculation in combination with a grey prediction model, and outputting a cold and heat power ultra-short-term load prediction result; and cooperative regulation and control: based on the load prediction result and the real-time operation parameters of the building air conditioning system, the electric vehicle and the multi-element energy storage system of the building group. Multi-energy data in a regional building group are collected and preprocessed through multiple sensors, an LSTM-Attention mixed deep learning model and a grey prediction model are fused to calculate a load prediction result, an interactive regulation and control model is constructed based on load prediction and real-time equipment parameters, and a collaborative regulation and control strategy is formulated and executed.

The invention relates to the technical field of current compensation, and discloses a current compensation strategy and a compensation system for improving the direct current fast charging rate of an electric automobile. According to the scheme, the total current of a high-voltage loop and the input current of a battery are detected in real time, the dynamic load current difference value is calculated, and the battery health state and historical charging data are combined; and the charging pile request current value is adaptively adjusted to counteract the shunting influence of the load power. Meanwhile, a closed-loop feedback regulation mechanism is adopted, a compensation coefficient is corrected in real time through a PID algorithm, and the stability of the charging current is ensured. When the power of the charging pile is limited, the system dynamically reduces unnecessary loads (such as an air conditioner and a heater) according to a preset priority request, and the charging power of the battery is guaranteed preferentially. The charging rate is improved (the charging time is shortened by 10%-15%), the adaptability of the system to dynamic load fluctuation is enhanced, and the service life of the battery is prolonged.

The invention provides a lithium battery residual life prediction method based on a multi-feature fusion large model, and relates to the field of lithium battery health management and life prediction, and the method comprises the steps: carrying out the segmentation processing of original data through a sliding window technology, constructing a key health index soft measurement module through a KAN, and carrying out the prediction of the residual life of a lithium battery; converting the original data into key indexes representing the health state of the battery; the method comprises the following steps: splicing data and key indexes of a lithium battery to form fusion features, inputting the fusion features into a large language model LLM to construct a fusion feature prediction module, obtaining future fusion features through pre-training word embedding, a multi-head attention mechanism and natural language prefix prompt, inputting the future fused features into a sparse KAN, and obtaining a fusion feature prediction model; and constructing a residual life prediction model. And a regression relation with the residual life of the battery is established, real-time prediction of the residual life of the lithium battery is realized, and the method is suitable for state monitoring and maintenance decision of the lithium battery in scenes of electric vehicles, energy storage systems and the like.

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.

A thermal management system includes a compressor, a water-cooled condenser, a battery chiller, a valve body assembly, a first water pump, a second water pump, and a third water pump that are disposed in a centralized manner. The thermal management system can separately form a passenger compartment cooling loop, a passenger compartment heating loop, a battery cooling loop, a battery heating loop, and an electrical driver cooling loop, and any one or more of the passenger compartment, the battery, and the electrical driver can be cooled or heated.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.

The invention provides a two-wheeled electric vehicle control system and method, and the system comprises a user side control platform which is used for detecting the operation of a user on a reverse turning handle, and generating a corresponding control instruction according to the pre-configured function setting; the communication module is used for bidirectionally transmitting instruction signals and vehicle state information in real time between the user end control platform and the vehicle end control system; and the vehicle end control system is used for receiving and analyzing the control instruction, judging the running or static state of the vehicle, executing kinetic energy recovery braking or driving mode switching in the running state, executing a reversing function or a self-defining function in the static state, and feeding back a vehicle execution result or vehicle state information through the communication module. The technical problems that in the prior art, the control function of a control terminal is single, the operation mode is not convenient enough, and the integration degree of the control terminal and vehicle functions is low are at least solved.

A “one-pedal rocking mode” for extricating an electric vehicle from a stuck condition is implemented by: receiving, via a human-machine interface (HMI) of the electric vehicle, an indication of a user selection to activate the one-pedal rocking mode; operating the electric vehicle in the one-pedal rocking mode responsive to detecting movement of an accelerator pedal of the electric vehicle; controlling at least one electric motor of the electric vehicle to: (i) apply a forward torque to corresponding drive wheels when the accelerator pedal is in a first pedal position that exceeds a forward threshold, (ii) apply a reverse torque to the corresponding drive wheels when the accelerator pedal is in a second pedal position that is less than or equal to the forward threshold; and exiting the one-pedal rocking mode by switching operations of the electric vehicle to a second operating mode responsive to detecting an exit condition.