4740results about "Operating modes" 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.

A robotic emulation device includes a processor, memory and multimedia interfaces to capture a video signal from a target device, analyze it, and transmit manipulation control signals to the target device input by emulating a peripheral input device, wherein the manipulation control signals determine manipulation or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. Furthermore, the robotic emulation device may be connected via an embedded transceiver to at least one computer or computer tenant for relaying the captured video data for analysis, and based on the analysis, applying inferred manipulation semantics.

A vehicle includes a chassis including a frame rail extending longitudinally, a tractive element coupled to the chassis, a cab coupled to the chassis and configured to support an operator, an electric motor configured to drive the tractive element to propel the vehicle, a battery configured to supply electrical energy to the electric motor, a heat exchanger, a cable electrically coupling the electric motor and the battery, and a conduit fluidly coupling the battery and the heat exchanger. The cable and the conduit extend laterally from the battery to the frame rail, and the cable and the conduit extend longitudinally along the frame rail.

The present disclosure relates generally to flight control of electric aircraft and other powered aerial vehicles. In one embodiment, a method is disclosed, comprising: receiving a descent rate command from a pilot input device, determining a proximity of each propeller of at least two propellers to a vortex ring state; and controlling the aircraft's descent rate to be less than the commanded descent rate when at least one of the at least two propellers is within a first threshold proximity to the vortex ring state.

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

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.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

A robotic emulation device includes a processor, memory and multimedia interfaces to capture a video signal from a target device, analyze it, and transmit manipulation control signals to the target device input by emulating a peripheral input device, wherein the manipulation control signals determine manipulation or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. Furthermore, the robotic emulation device may be connected via an embedded transceiver to at least one computer or computer tenant for relaying the captured video data for analysis, and based on the analysis, applying inferred manipulation semantics.

A charging system comprises a local energy storage disposed at a charging site; an electric coupling configured to provide an electrical interconnect between the charging site and an electric vehicle (EV); and one or more controllers configured to: (i) detect a condition related to availability of electrical energy at the charging station, (ii) in response to the detecting of the condition: (a) estimate a number of EVs to which a charge remaining at the local energy storage is to be distributed within a certain time interval, (b) initiate a charging session for transferring electric energy from the local energy storage to the EV, via the electric coupling, and (c) limit an amount of electrical energy in the charging session in view of at least the estimated number of EVs.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes a plurality of energy storage units that include a supercapacitor and an electrochemical battery. The system includes an energy controller that tracks historical power draw from the plurality of energy storage units over time in power tracking data, and that identifies a power draw based on the power tracking data. The energy controller switches between a first configuration and a second configuration based on the identified power draw. The first configuration is configured for drawing power from the electrochemical battery and disconnecting from the supercapacitor, while wherein the second configuration is configured for drawing power from the supercapacitor and disconnecting from the electrochemical battery.

A battery temperature acquirer acquires a measured battery temperature of a battery module in a battery pack installed in an electric vehicle. A target temperature determination unit determines a target temperature of the battery module at the time of arrival at an installation place of a charger to which the electric vehicle is to head. A set value determination unit determines a combination of a set value for regenerative charging, a set value for output suppression, and a set value for a temperature adjustment unit such that the battery temperature becomes closer to the target temperature.

A refuse vehicle includes a cab, a refuse compartment, a chassis, a high voltage power distribution unit (HVPDU) including a battery, a plurality of cables and a manual service disconnect. The manual service disconnect is accessed via a through hole of a wheel well of the refuse vehicle. The refuse vehicle further includes a platform removably coupled to the chassis and the HVPDU and configured to enclose the high voltage power distribution unit.

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.

An apparatus configured to collect and predict charging data for an electric vehicle (EV) from a mobile device associated with a user or the EV or an infotainment unit of the EV.

A method includes receiving a turning angle indication indicating a turn of front wheels of the vehicle in one of a left direction or a right direction by a turning angle relative to a first direction of travel of the vehicle, and determining that a magnitude of the turning angle satisfies a threshold turn angle. Based on determining that the magnitude of the turning angle satisfies the threshold turn angle, the method also includes operating the vehicle in a drift mode by determining that an inner rear wheel includes one of a left rear wheel or a right rear wheel of the vehicle, determining that an outer rear wheel includes the other one of the left rear wheel or the right rear wheel, and commanding at least one torque machine to provide forward torque to the inner rear wheel without providing any forward torque to the outer rear wheel of the vehicle.

A modular, multi-directional, smart electric vehicle (EV) charging station. In an embodiment, the EV charging station has a field-serviceable, replaceable modular construction, a fully multi-directional power routing architecture, buffered EV-to-grid and EV-to-EV charging, multi-EV charging capability, and the ability to integrate multiple modular charging stations into a networked local and / or wide-area charging system.

A motor controller for pulse heating of a power battery. The motor controller includes an inverter circuit, the inverter circuit includes three switching transistor bridge arms, two ends of each switching transistor bridge arm are respectively configured to connect to positive and negative electrodes of the power battery through a direct current bus, and bridge arm midpoints of the three switching transistor bridge arms are separately configured to connect to a three-phase winding of a motor. The bridge arm midpoints of the three switching transistor bridge arms are configured to output a three-phase current, the three-phase current is used to generate a pulse alternating current on the direct current bus, and the pulse alternating current is used to heat the power battery. A current of each phase of the three-phase current is a unipolar triangular wave, and a sum of the three-phase current is zero.

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 discloses a new energy electric vehicle ordered charging method considering multiple factors, which realizes ordered charging through a closed-loop framework of data driving, multi-objective optimization, intelligent scheduling and dynamic feedback based on multi-dimensional data such as user demand, power grid stability, economical efficiency and renewable energy utilization, and can improve the charging efficiency on the basis of meeting the charging demand of a user. Peak clipping and valley filling of power grid loads, efficient utilization of renewable energy sources and user charging cost optimization are achieved, power grid load fluctuation is reduced, the electric energy quality is improved, the renewable energy sources are utilized to the maximum extent, it is ensured that the requirements of new energy electric vehicle users are met, and meanwhile the economical efficiency and stability of a power grid are achieved. Meanwhile, through an intelligent scheduling system and load prediction, charging tasks are uniformly distributed to night valley periods, so that a power grid load curve is more stable, and the utilization efficiency of power grid resources is optimized; and meanwhile, through an intelligent algorithm and a big data technology, the charging demand prediction accuracy is improved, and charging scheduling is more intelligent and accurate.

Disclosed is an adaptive control method and system for train operating condition identification. The method includes acquiring train operating state data under various traction conditions and calculating traction and braking performance data. An adhesion condition characteristic data set is formed, and train adhesion conditions are divided into Nz grades based on this data set, where Nz is a natural number. The current adhesion condition grade Nd is identified using the number of adhesion condition grades and current operating state data, where Nd≤Nz, establishing a mapping relationship between an adhesion coefficient and maximum deceleration for braking under the identified grade. An optimal train protection curve is generated. By clustering and grading adhesion conditions through a clustering algorithm, the method achieves more accurate adhesion condition analysis. Adaptively generating optimal train protection curves based on adhesion condition grades enhances the reliability and safety of train operations.

The invention discloses a fault diagnosis and early warning method for an intelligent thermal management system of a power battery, and relates to the technical field of new energy automobile power battery safety. Comprising the following steps executed in sequence: S1) based on real-time monitoring data of a distributed temperature sensor in a battery module, constructing a dynamic space-time neural field model through an adaptive graph neural network, and generating a thermal field fingerprint representing a thermal propagation topology mode; according to the fault diagnosis and early warning method for the intelligent thermal management system of the power battery, a dynamic space-time neural field model is constructed, a sub-DEG C weak thermal abnormal signal is accurately captured, and a micro short circuit fault is recognized in advance in combination with electric heating delay analysis, so that the problem that a fault precursor signal is submerged by a complex working condition is solved; a double-channel verification mechanism depends on double mutual verification of physical law rigid constraint and intelligent residual feature recognition, the misjudgment risk caused by cooling system fluctuation and sensor noise is eliminated, and the early warning accuracy is improved.

In one aspect, a computer-implemented method for executing an artificial intelligence (AI) engine, including executing a categorization model configured to categorize, into categories, vehicles based on factors comprising age, temperature conditions, usage patterns, battery health metrics, or some combination thereof, executing a behavior analysis model configured to analyze behavior of the vehicles in each of the categories to identify battery performance metrics including charging habits, discharge rates, charge rates, state of charge, state of health, state of power, or some combination thereof, executing a recommendation generation model configured to generate, based on the battery performance metrics, recommendations for enhancing battery management strategies, wherein the recommendation generation model accounts for a current state of a vehicle to suggest actions to improve battery health; and executing a battery model configured to determine power and energy consumption based on the recommendations generated by the recommendation generation model.

A vehicle including an energy storage device and processing circuitry. The processing circuitry detects a charging session being initiated to charge the energy storage device. The processing circuitry further obtains a current state of charge (SoC) associated with the energy storage device and first vehicle usage information that indicates a usage of the vehicle over a historical time-interval. The processing circuitry further predicts vehicle usage over a first time-interval based on the obtained first vehicle usage information and rate the charging session at current time-instance based on the predicted vehicle usage and the current SoC. The processing circuitry further generates an alternate charging plan based on the charging session at the current time-instance being rated suboptimal and outputs the generated alternate charging plan at the current time-instance.

A battery performance management system and method using an electric vehicle charging station. The battery performance management server collects battery performance evaluation information including identification information and operation characteristic accumulative information of a battery, identification information and driving characteristic accumulative information of the electric vehicle, and latest charging characteristic information of the battery from a plurality of charging stations through a network. The server determines a current state of health (SOH) corresponding to the collected battery performance evaluation information by using an artificial intelligence model that is trained in advance to receive the battery performance evaluation information and output a SOH of the battery. The server determines a latest control factor corresponding to the current SOH, and transmits the latest control factor to the charging station so that the charging station may transmit the latest control factor to a control system of the electric vehicle to update the control factor.

A battery control component of an electric vehicle may limit charging of a battery on the electric vehicle to a target state of charge (SOC), such as to reserve battery capacity for regenerative braking during downgrades. A computing device associated with the vehicle may calculate an estimated gradient profile and / or an estimated total energy consumption of a predicted upcoming route of the vehicle based on one or more data items, such as GPS data, altitude data, and / or map data. The computing device may determine a target SOC based on the estimated gradient profile. The computing device may determine a confidence factor associated with the estimated gradient profile, where the confidence factor depends on the quantity of data items used to calculate the estimated gradient profile. The computing device may adjust the target SOC based on the confidence factor.

Systems, methods, and computer program products for providing recommendations and features related to charging of batteries for electrically powered motor vehicles are provided herein. Various systems and methods utilize current motor vehicle data and / or user preference data to determine recommended charge devices for a user as they travel, removing the need to research / plan out charge device options during travel and the worry of running out of charge. Various systems and methods provide tailored recommendations and features that help a user select which charge device to use and provide options to occupy the user while the charging occurs. Additionally, relevant offers or promotions for nearby accommodations are provided herein. Various systems and methods also recommend turning off certain devices to elongate the estimated travel distance. Desirable maps for aiding in travel planning and visualizing where the current amount of charge will reach are also provided herein.