3137results 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.

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.

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.

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.

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.

A method of operating a mowing system, the method including mowing within a work area, wherein mowing is performed by a robotic lawn mower, and wherein the work area comprises a plurality of base stations each configured to charge the robotic lawn mower; determining when to charge the robotic lawn mower in view of a current charge level and a current location of the robotic lawn mower.

The present disclosure relates to techniques for scheduling electric vehicle (EV) charging sessions using an EV charger scheduling service. A method includes presenting a user interface on a client computing device, which prompts an end-user to select a time slot for using an EV charger. Upon receiving the selected time slot, the scheduling service reserves the EV charger for the end-user. The service then receives a signal indicating that an EV is connected to the charger during the reserved time. In response, the user interface is updated with a control element that, when activated by the end-user, instructs the scheduling service to command the EV charger to initiate power delivery. The method enhances the efficiency of EV charging station utilization and provides end-users with a convenient and controlled charging experience.

To solve the problem that large commercial electric vehicles with range extenders require a large-capacity battery and a motor generator with high output power, which limits the number of passengers and loaded cargo, and generates large amounts of exhaust gas from the internal combustion engine of the power plant, a planned power generation and energy storage control technique is applied, in which, as a method for estimating the amount of power generation required during travel and the pre-travel power generation distance in a route plan of an electric vehicle with range extenders, assuming that travel from the starting point to the destination is carried out using exactly the energy stored in the battery, an absolute value in which a minimum storage requirement (SCL) of the battery has been deducted from a negative energy storage quantity (SOC) of the battery at the destination,as the amount of energy generation required during the journey, whereby a reduction in the installed secondary battery and the installed generator is preferably achieved by correcting the route plan during the journey.

The structure and operation of a logistical system for charging electrical vehicles (EVs) are described. The logistical system may include synergistically interacting hardware and software components on a computer of a charging service, a plurality of EVs, and a plurality of charging stations. Charge information describing conditions of a battery of an EV, and EV location information, may be sent to the charging service computer. An application operating on the EV and / or driver's mobile device may receive data from the charging service, which may include a location of a charging station capable of charging the EV and an estimated charging time and / or cost for the battery charging. Charging stations, in communication with the charging service, provide information that assists the charging service to select an appropriate charging station. Mobile charging-vehicles may be directed to particularly needed locations by the charging service, and may charge electrically assisted bicycles and other vehicles.

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 plurality of energy storage units including a supercapacitor and an electrochemical battery, the supercapacitor comprising a plurality of selectable power sources, and an adder module including a processor. The processor is configured to execute instructions to selectively connect the supercapacitor or the electrochemical battery to an electric drivetrain to propel the vehicle. The processor may be configured to measure the selectable power sources and determine a set of the selectable power sources to connect to the system.

A facility for predicting on site electric power requirements for maintaining a fully electric fleet of vehicles in a mining operation to ensure sustainable electric operations, comprising a server comprising communications module configured to receive data and to transmit load management instructions; plurality of electrical charging stations; plurality of electric vehicles configured to be recharged by a selected electrical charging station; wherein the server comprises processor coupled with non-volatile memory, the non-volatile memory storing processing instructions that, when executed by the processor, causes the processor to simulate a total electrical power demand of the mine site due to the plurality of cabled mine equipment and the plurality of electric vehicles for each electric vehicle and each charging station within the facility over a prescribed period thereby to simulate and determine an energy storage capacity requirement of the charging stations of the facility through the prescribed period; thereby to forecast the electrical load management needs of the facility through the prescribed period.

A system for controlling a vehicle combination comprising a tractor unit and at least one trailing unit is disclosed. The system has a target generator to determine a virtual control input for the vehicle combination based on a reference input for the vehicle combination. A power manager determines a power allocation input for the vehicle combination based on the reference input and a power capability of one or more units of the vehicle combination. A combination control allocator determines a control input for the vehicle combination based on the power allocation input and the virtual control input.

The invention relates to a slope parking control method and system for an electric truck and the electric truck. According to the slope parking control method for the electric truck, a motor controller MCU executes the steps, and state information of the truck is obtained; when the slope sliding condition is met, a slope holding mode is executed; in the hill-holding mode, the MCU determines control parameters according to the current gear and the load state so as to realize zero setting of the rotating speed of the motor; the control parameters comprise a P I parameter, a rotating speed slope parameter and a torque slope parameter. When the truck is parked on the slope, different control parameters can be output to adapt to different gears and different loading states of the truck, and smoothness of the truck in the slope parking process is effectively guaranteed.

Intelligent eco mode optimization in a battery electric vehicle (BEV) includes collecting data from one or more systems of a vehicle in which the vehicle includes a battery. A predicted route is generated based on the collected data. The collected data includes a navigation map for a portion of a vehicle transportation network. A state of the vehicle is determined based on the collected data and the predicted route. A drive mode is determined, using a decision-making model, for the vehicle based on the state of the vehicle and the predicted route. The drive mode is either a first drive mode having a first acceleration curve or a second drive mode have a second acceleration curve and the second drive mode reduces a rate of discharge of the battery as compared to the first drive mode. The vehicle is set to use the drive mode.

The invention discloses an extended-range hybrid power system and a control method therefor, and a crane, and belongs to the technical field of construction machinery. The extended-range hybrid power system comprises a power battery, an all-in-one controller, a first motor, a second motor and an engine, wherein the first motor is connected to a lower traveling mechanism; the engine is connected to the second motor, and the second motor is connected to an upper operating mechanism; the power battery, the first motor and the second motor are connected to the all-in-one motor, and the all-in-one motor is connected to an external power supply.

Computer-implemented methods and computer systems are disclosed herein as implemented by a controller operatively coupled to a network of electric vehicles. The methods and systems include the controller (i) receiving a notification that an electric vehicle is stranded without sufficient power to operate; (ii) receiving information regarding the stranded electric vehicle; (iii) detecting one or more other electric vehicles in a vicinity of the stranded electric vehicle; (iv) receiving information regarding the detected one or more other electric vehicles; and / or (v) determining, based upon the received information, which of the detected one or more other electric vehicles to send a power source request. Alternatively, the notification may indicate that an electric vehicle has a low state of charge (SOC), or is otherwise has a battery in need of being recharged to facilitate the electric vehicle traveling to a destination.

An example operation includes one or more of determining a predicted charging duration for a rechargeable battery of an electric vehicle (EV) at a plurality of charging stations, determining, by an artificial intelligence (AI) model, an activity based on the predicted charging duration and a profile of a user of the EV, determining a charging station of the plurality of charging stations proximate the activity and notifying the EV of the charging station and the activity, collecting data of the user during the activity by one or more of the EV and a device associated with the user, and training the AI model based on the data of the user.

The invention relates to the field of power control, and particularly discloses an electric four-wheel drive torque distribution method based on a climbing working condition, which comprises the following steps: defining vehicle state parameters, power system parameters, vehicle real-time driving state parameters and road condition parameters; carrying out torque calculation by utilizing Julia, considering shaft load, friction coefficient, motor efficiency and yaw velocity correction, obtaining a comprehensive weight of a front shaft and a rear shaft, and carrying out normalized torque distribution; and establishing model simulation through Modelica, and verifying the effectiveness of the distribution scheme. According to the invention, the climbing performance of the vehicle can be improved, the control flexibility and driving stability of the vehicle are improved, the energy consumption is reduced, the dynamic stability is enhanced, and the method is suitable for power control of the electric four-wheel-drive vehicle under the climbing working condition.

The invention discloses a compensation control method, device and system for traction force of an electric traction machine, a storage medium and a computer program product. The method comprises the steps that operation parameters and working condition disturbance parameters of the traction machine are collected; pre-judging a working condition disturbance change trend in a future preset duration based on the working condition disturbance parameters, and calculating a load torque variation corresponding to the disturbance change trend; calculating a pre-compensation amount based on the load torque variation, calculating a real-time compensation amount in combination with the control target and the operation parameters of the current traction working condition, and superposing the pre-compensation amount and the real-time compensation amount to obtain a preliminary compensation amount; performing calibration and amplitude limiting processing on the initial compensation amount to obtain a final compensation amount; and adjusting the output torque of the motor of the traction machine based on the final compensation amount to realize dynamic compensation of the traction force. According to the scheme, pre-judgment type dynamic compensation of the traction force is achieved, compensation deviation is reduced, the adaptability of the traction machine to working condition disturbance is improved, and operation stability is improved.

Apparatus, systems, and methods for charging electric vehicles (EVs) at an EV charging site having a plurality of EV chargers, controlling by a management server the charging performed by a plurality of EV chargers at a charging site when connectivity quality between the management server and any of the EV chargers may be unstable, and electrical load reserve for use by an EV charging system of a site.

An autonomous vehicle can include multiple low voltage power domains. Each low voltage power domain can comprise a low voltage power distribution unit (LVPDU). An LVPDU can include a failsafe switch that can selectively couple or de-couple the low voltage power domain from a shared backup low voltage power source, which can be configured to concurrently supply backup power multiple low voltage power domains or LVPDUs. A first LVPDU of a first low voltage power domain can generate a control signal to control the failsafe switch of a second LVPDU of the second low voltage power domain to disconnect the second low voltage power domain from the shared low voltage backup power source, and vice versa.

The invention relates to the technical field of vehicle control, and discloses a VCU torque control system based on multi-sensor fusion, and the VCU torque control system comprises a system monitoring and fault diagnosis module which is used for generating a sensor failure mark when a yaw velocity sensor is diagnosed to be failed; and the data acquisition and preprocessing module is used for receiving the sensor failure mark generated by the system monitoring and fault diagnosis module and acquiring real-time data of the health sensor so as to generate a real-time feature vector. A Gaussian process regression state observer and random model predictive controller fusion scheme is adopted, state perception uncertainty information is transmitted to a control decision-making layer, and the problem that in the prior art, when a deterministic state observer is cascaded with a conventional controller, the controller cannot perceive the upstream state estimation quality, and the control accuracy is poor is solved. And the system is unstable or the performance is reduced due to over-excitation control when the estimation is inaccurate.

The invention discloses an electric vehicle energy management method and system based on vehicle speed prediction, and relates to the technical field of electric vehicle energy management, and the method comprises the steps: collecting vehicle driving data to construct a state vector, constructing a quantile LSTM vehicle speed distribution predictor, and predicting the quantile vehicle speed track of each step in the future; mapping the quantile vehicle speed track into traction power distribution through longitudinal dynamics, and carrying out battery power, current and SOC quantile rolling prediction; predicting and defining predicted time domain energy consumption based on the battery power quantile so as to construct an MPC optimization objective function of the CVaR risk constraint; constructing a deterministic constraint set based on a quantile prediction trajectory, defining a terminal risk cost according to a predicted terminal state, and merging the terminal risk cost into an MPC optimization target to obtain a final target function; the robustness and adaptability of the electric vehicle energy management system are obviously improved, especially in complex and uncertain road and traffic environments.

An illustrative system includes a location sensor configured to sense location of an electric vehicle and to generate a signal indicative of an electric vehicle's location. An electric vehicle battery cooling system is configured to provide cooling to an electric vehicle's battery. A controller is electrically couplable to receive the signal indicative of an electric vehicle's location from the location sensor and is configured to determine battery energy for the electric vehicle to reach a location of a charging station. The battery energy is based on the location of the electric vehicle and an amount of electrical energy remaining in the electric battery. The controller is configured to initiate cooling of the electric battery based on the amount of electrical energy remaining in the electric battery and the battery energy for the electric vehicle to reach the location of the charging station.

A vehicle thermal management system includes: a fluid transfer device that consumes power to perform a thermal management function, and a control unit that determines an operation mode of the fluid transfer device. In particular, the fluid transfer device operates in the determined operation mode based on an optimal control value derived using a control model for a predicted state value.

A charging management apparatus is configured to manage charging of a battery of an electric vehicle serving as a vehicle. The charging management apparatus includes one or more processors and one or more memories. The one or more memories are communicably coupled to the one or more processors. The one or more processors are configured to: acquire data on a current location and a destination of the vehicle; set charging facilities of the battery to stop during a travel from the current location to the destination, based on a predetermined criterion; and calculate a necessary charging time necessary at a first charging facility of the charging facilities to cause an expected remaining capacity of the battery upon a scheduled arrival at a second charging facility of the charging facilities after the first charging facility to be equal to or greater than a predetermined reference charging capacity.

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 plurality of energy storage units including a supercapacitor and an electrochemical battery, the supercapacitor comprising a plurality of selectable power sources, and an adder module including a processor. The processor is configured to execute instructions to selectively connect the supercapacitor or the electrochemical battery to an electric drivetrain to propel the vehicle. The processor may be configured to measure the selectable power sources and determine a set of the selectable power sources to connect to the system.

A method for controlling a marine drive configured to propel a marine vessel. The method includes receiving a destination for the marine vessel, receiving a demand request for operating the marine drive, and operating the marine drive at an actual demand corresponding to the demand request. The method further includes determining a required energy for the marine drive to propel the marine vessel to the destination at the actual demand and determining a remaining energy of an energy source available for operating the marine drive. The method further includes comparing the required energy to the remaining energy and, when the remaining energy is less than the required energy, automatically iteratively reducing the actual demand for operating the marine drive until the remaining energy equals or exceeds the required energy determined for the marine vessel to reach the destination.