7208results about "Operating modes" patented technology

The preferred embodiments disclose a hangar comprising a gantry robot which includes a lifting tool. The gantry robot is provided for lifting and lowering batteries to replace them, for example, in a drone disposed in the hangar.

A system and method for directing energy use within a facility comprising charging stations for charging a fleet of electric vehicles, using an energy optimisation engine. The energy optimisation engine receives energy supply characteristics and facility operational energy consumption characteristic of energy assets associated with the facility over a first time period, and electrical vehicle operational requirements for the electric vehicles during a second period; computes energy distribution instructions for the first time period and satisfying a designated energy condition corresponding at least in part with the energy supply characteristics; and directs operation of the charge stations or the energy assets in accordance with the energy distribution instructions.

A system (100) for intelligent fault detection and energy management in fuel cell hybrid electric vehicles (FCHEVs), comprising: a data acquisition unit (102) comprising sensors (102a) configured to acquire real-time operational data from vehicle components; an energy management unit (104) comprising a fuel cell unit configured to generate electricity from hydrogen and a battery unit configured to store and supply electrical energy; a regenerative braking system (106) in conjunction with an energy management unit (104) configured to recover and store energy during vehicle deceleration; a centralized processing unit (108) configured to perform functions of the system (100) based on data acquired by the sensors (102a), wherein the centralized processing unit (108) comprises the following: a neural network processor (108a) configured to analyze real-time operational data for fault prediction; an energy optimization processor (108b) configured to dynamically manage power distribution; a maintenance prediction processor (108c) configured to identify potential component failures; a communication interface (110) configured to enable data exchange between vehicle and infrastructure (V2I) and between vehicles (V2V); a driver interface (112) configured to display system status and receive user input; and a control unit (114) configured to perform corrective actions based on neural network analysis and energy optimization calculations.

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 proposed invention relates to methods for controlling energy consumption by a motor vehicle, and can be used in transportation industry. The technical problem to be solved by the claimed invention is to provide a method, a device, a system, a motor vehicle, and a computer-readable medium that do not possess the drawbacks of the prior art and thus make it possible to generate a modified energy-efficient track based on a non-modified energy-efficient track, thus allowing to reduce the time needed to pass a portion of the route or to increase the time of useful operation of the vehicle.

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.

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.

The invention provides an energy consumption prediction and anomaly detection method and system based on digital twinning, and the method comprises the steps: collecting physical parameters in real time through a sensor, and the physical parameters comprise the cell temperature, the total current of a battery pack, the total voltage of the battery pack, the gradient, the vehicle speed and the mass; collecting battery state parameters by using a battery management system, wherein the battery state parameters comprise real-time energy consumption; the collected physical parameters are input into a Simulink and Cruise combined model for simulation, and model output prediction energy consumption is obtained; wherein the Simulink is used for establishing an equivalent circuit model of a power battery, and the control system, the sensor simulation module and the Cruise simulation model are used for establishing a whole-vehicle simulation model of the pure electric vehicle; comparing the model output energy consumption with the real-time energy consumption, and obtaining an energy consumption deviation value; whether the energy consumption deviation value exceeds a preset range or not is judged, and if yes, energy consumption is judged to be abnormal; and if not, evaluating the energy consumption of the whole vehicle so as to solve the technical problem that the actual energy consumption analysis, energy management and endurance mileage evaluation of the whole vehicle are difficult.

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.

The present invention is directed to electronic devices, systems, and methods for optimizing dispatch schedules for an electric vehicle battery in communication with the electronic device to maximize the revenue generation from vehicle-to-grid activities at a site while also preserving battery health. The devices, systems, and methods may determine, using an optimization algorithm, the dispatch schedule for the electric vehicle battery and control a charge or discharge of the electric vehicle battery based on the dispatch schedule. The optimization algorithm may determine the dispatch schedule based on forecasts for future anticipated energy needs of the site and forecasts for future anticipated energy production of the site. The optimization algorithm may include an objective function including one or more variables. The devices, systems, and methods may introduce a heuristic term to the objective function or penalize the first derivative of a variable of the objective function to optimize the dispatch schedule.

An electric vehicle charging recommendation method is disclosed. The method may include obtaining a vehicle information associated with a vehicle, a driver information, and a vehicle trip information. The vehicle information may include a current state of charge (SOC) of a vehicle battery. The method may further include determining a distance to be travelled by the vehicle from a vehicle current location based on the vehicle trip information. The method may further include determining an expected SOC required to travel the distance from the vehicle current location based on the vehicle information, the driver information and the vehicle trip information. The method may additionally include transmitting a recommendation notification to a user device to charge the vehicle battery when the expected SOC may be greater than the current SOC.

An electrified vehicle may include: a main battery and an auxiliary battery; a motor system including a dual inverter connected to the main battery and driving a motor depending on a motor driving mode, and a transfer switch for switching between first and second driving modes included in the motor driving mode; and a charging switch connected between the motor system and the auxiliary battery, and configured to form a boost charging path connected from the auxiliary battery to the main battery through the motor system when the first driving mode switches to a charging mode while driving.

Systems and methods for selecting a charging entity based on occupancy status are provided. In one embodiment, a method includes determining a current geo-location and state of charge of a requesting vehicle and a plurality of charging entities that are within a remaining distance of the requesting vehicle. Occupancy statuses for one or more charging entities of the plurality of charging entities are determined based on a number charging vehicles and a charging parameter. The method includes estimating charging speeds for the one or more charging entities based on the occupancy statuses. A charging station map user interface that pin points the current geo-location of the requesting vehicle and the one or more charging entities is presented. The one or more charging entities are presented with labels. The method further includes reserving a charging station of a selected charging entity of the plurality of charging entities by selecting a label.

There is provided a control system for an electric vehicle comprising a power train, the control system comprising: a first controller configured to automatically select a drive mode for the vehicle based on a whole vehicle operation model; and a second controller coupled to the first controller, the second controller configured to adjust a set of tuning parameters for the vehicle, whereby the second controller is configured to receive a signal from the first controller to adjust the set of tuning parameters such that the vehicle operates in the selected drive mode. Thus enhancing the operation and durability characteristics of the vehicle.

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.

An electric vehicle may comprise a chassis extending between a front and a rear and having a steering axle coupled to the chassis and arranged near the front. The electric vehicle may further comprise a first drive axle and a second drive axle coupled to the vehicle chassis and arranged near the rear. The first drive axle may comprise a first axle housing, a first drive wheel rotatably supported by the first axle housing, and a first electric machine operably coupled to the first drive wheel through a first power path, the first power path including a first gear reduction. The second drive axle may comprise a second axle housing, a second drive wheel rotatably supported by the second axle housing, and a second electric machine operably coupled to the second drive wheel through a second power path, the second power path having no speed change.

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 dense charging station for charging the battery may have lanes arranged in parallel, and each of the lanes may have sequential charging locations. A vehicle utilizing the charging station may position itself at the first available charging location, being receiving energy, and determine if a subsequent charging station becomes available in the lane, and then position itself at the subsequent charging location, once available. Multiple charging stations may be required to maintain a threshold power state for individual vehicles in a fleet of vehicles providing a service for a geographic region. A charging coordinator may determine when a battery of a vehicle does not satisfy a threshold power state and requires a recharge. Additionally, the charging coordinator may determine a candidate charging station from among multiple charging stations associated with the geographic region.

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.

The invention discloses a public vehicle operation management method based on big data analysis and related equipment, and the method comprises the steps: receiving a battery alarm signal of an electric public vehicle, and obtaining a battery parameter and vehicle operation data; inputting the current environment data and the real-time road condition data into a big data model to obtain a current operation scene of the vehicle; inquiring to obtain a battery parameter interval corresponding to the current operation scene of the vehicle; under the condition that the battery parameters are located in the battery parameter interval, vehicle energy consumption data in the preset kilometer interval are obtained, and according to the battery parameters, the vehicle energy consumption data and the current operation scene of the vehicle, the energy consumption trend of the electric public vehicle in the future preset time period is predicted; generating a power output mode of the electric public vehicle according to the energy consumption trend, the vehicle operation data and the driving behavior data; generating a brake adjusting instruction according to the power output mode; and the brake adjusting instruction is sent to the vehicle terminal, so that the vehicle terminal executes a brake strategy.