1697results about "Operating modes" patented technology

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.

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.

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 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.

An electric vehicle charging control device includes an electronic dynamic charging schedule generator, a non-transitory computer readable medium and an electronic communicator. The non-transitory computer readable medium stores vehicle profile data and building profile data. The electronic communicator is configured to receive profile updates to the vehicle profile data from an electronic user interface. The electronic controller is programmed to generate a driver charging profile based on the vehicle profile data and the building profile data. The electronic controller is programmed to update the driver charging profile based on the electric vehicle user's behavior. The electronic controller is programmed to generate a charging schedule for the electric vehicle based on the updated driver charging profile. The electronic controller is programmed to controlling the charging port in accordance with the charging schedule upon the electric vehicle user confirming acceptance of the charging schedule on the electronic user interface.

A vehicle includes a chassis, a body coupled to the chassis, a first accessory coupled to the body, a second accessory coupled to the body, and a hydraulic system. The body includes a first body rail and a second body rail. The hydraulic system includes an electric power takeoff coupled to the body, a first manifold coupled to the body, first conduits fluidly coupled between the electric power takeoff and the first manifold, a second manifold coupled to the body, the second manifold positioned rearward of the first manifold, and second conduits fluidly coupled between the first conduits and the second manifold. The first manifold is configured to distribute the hydraulic fluid to the first accessory. The second manifold is configured to distribute the hydraulic fluid to the second accessory. The first conduits extend through at least one of the first body rail or the second body rail.

The invention discloses a vehicle cloud collaborative vehicle thermal management method, system and device and a storage medium, relates to the technical field of new energy vehicles, and can solve the problem of energy waste caused by too low battery temperature at the end of driving in the prior art. According to the specific technical scheme, a cloud end obtains real-time operation data and travel information of a vehicle uploaded by a vehicle end; the cloud judges whether the vehicle is in a fixed road condition according to the travel information; if the vehicle is in the fixed road condition, the cloud predicts the battery prediction temperature of the vehicle at the terminal point based on the real-time operation data and the historical operation data of the fixed road condition; the cloud end compares the battery predicted temperature with a target temperature and calculates an optimal time point for stopping battery thermal management according to a comparison result, and the target temperature is an upper limit temperature of a battery working temperature range; and when the optimal opportunity point is reached, the cloud end issues a cooling stopping instruction to the vehicle end, so that the vehicle end stops the cooling operation on the battery. The method is used for vehicle thermal management.

Total charging time to charge an EV from a starting SOC to a target SOC is determined based on vehicle related data. Home energy usage of a home in an EV availability time window is also determined based on home related data. EV idle power consumptions for an EV charging mode, an EV discharging mode, and an EV idle mode with neither charging nor discharging are further determined. A genetic algorithm is set up with cost and penalty functions. These functions are built, for each candidate schedule in a solution space, based on the starting state of charge, the home energy usage and the EV idle power consumptions. The genetic algorithm is run to generate an optimized schedule that includes schedule values for the time slots in the EV availability time window to control whether the EV is to charge, discharge or idle in each of these time slots.

To suppress concentration of charging for a plurality of electric vehicles (EV) and achieve leveling. An electric vehicle charging management device 10 that manages charging for a plurality of EVs 40, the electric vehicle charging management device including: a charging time zone information creation unit 121 that creates charging time zone information 1007 indicating a charging schedule for each of the plurality of EVs 40 by using delivery course information 1001 and vehicle information 1003; a power consumption amount calculation unit 122 that calculates time zone-based power consumption amount information 1008 indicating a power consumption amount including an execution charging amount in each of the plurality of time zones; and a charging time zone information change unit 123 that changes the charging time zone information 1007 by changing charging for a changing target electric EV in a maximum time zone that is a time zone having the largest power consumption amount among the plurality of time zones.

One or more examples provide an electric vehicle or a device for use with an electric vehicle, including an electric vehicle charging system and method. In one example, an electric vehicle charging system with a charging robot rail switch is disclosed.

A device for managing charging and discharging of an electric vehicle is provided. The device includes one or more processors, and a memory configured to store one or more programs executed by the one or more processors, wherein the processor includes a first processing unit configured to cluster a plurality of electric vehicles and generate at least one cluster, a second processing unit configured to limit charging and discharging conditions for each time period according to charging and discharging conditions of individual electric vehicles belonging to the cluster, and a third processing unit configured to provide the limited charging and discharging conditions for each time period to integrate battery resources of electric vehicles in the cluster and generate one virtual battery for each cluster.

A system includes a traction battery and a controller. The traction battery includes first and second arrays of battery cells. The controller is programmed to implement first and second filters, such as first and second bar-delta filters, configured to respectively generate output indicative of a state-of-charge (SOC) of the first and second arrays from models of the first and second arrays. The controller is further programmed to charge and / or discharge the traction battery according to power limits defined by the SOC of the first and second arrays.

A battery electric vehicle includes a motor configured to output torque for driving to a drive shaft, a mode selector configured to select a driving mode from a plurality of driving modes based on driver's operation including a motor driving mode that the battery electric vehicle is driven by outputting a required torque required for driving from the motor and a gear shift driving mode that the battery electric vehicle is driven with behavior of torque output from the motor as behavior of the torque in an engine vehicle including an engine and a transmission based on the driver's shift operation, and a controller programmed to control the motor such that the battery electric vehicle drives in the driving mode selected by the mode selector. The controller is programmed to permit switching between the motor driving mode and the gear shift driving mode when a steering wheel is gripped.

An example operation may include one or more of determining, by a vehicle, points of interest (POIs) and attributes of the POIs based on preferences of an occupant of the vehicle, capturing measurements, by the vehicle, of a state of charge (SOC) of a battery installed within the vehicle as the vehicle moves along a route, determining that the SOC of the battery has fallen below a threshold based on the measurements, identifying, by the vehicle, a plurality of charging stations based on a current location of the vehicle, determining, by the vehicle, an optimal charging station among the plurality of charging stations based on distances from the current location of the vehicle to the plurality of charging stations, historical travel routes of the vehicle, and the attributes of the POIs, and routing, by the vehicle, the vehicle to the optimal charging station.

An arrangement is for increasing the range of an electrically driven vehicle combination. The arrangement includes a power adaptation and charging control unit for bidirectionally transmitting voltage between a first high-voltage battery of a towing vehicle of the vehicle combination and a second high-voltage battery of a trailer of the vehicle combination.

Relocatable axles and energy storage is provided. First axle mounts are configured to receive first rear axles. The first axle mounts are longitudinally offset rearward from second axle mounts configured to receive corresponding second rear axles. First batteries are configured to couple with the vehicle to occupy a longitudinally offset location. At a first of the longitudinally offset locations, a rear portion of the first batteries is disposed forward of a wheel and tire assembly for each of the first rear axles. At a second of the longitudinally offset locations, a rear portion of the first batteries is disposed forward of a wheel and tire assembly for each of the second rear axles. The first of the longitudinally offset locations overlaps with one or more wheel and tire assemblies for the second rear axles.

A system is described. The system comprises: a global positioning unit; a battery management system; and a processor storing instructions in non-transitory memory. The processor is operable to: detect, via the global positioning unit, that a vehicle moved from a first geographical range to a second geographical range during a first scheduled session corresponding to a first grid; identify a second grid upon the vehicle entering the second geographical range; establish a connection between the vehicle and the second grid located within the second geographical range; and communicate a command to the battery management system to perform one of charging and discharging at the second grid, using an artificial intelligence engine trained based on first charging information and first discharging information obtained from all first grids and second grids over time.

A mobile apparatus, a charging control method, and a chip relate to the field of electric vehicle charging, where the mobile apparatus is configured to connect to a charging pile to form a charging system. The charging pile is configured to charge a cell of the mobile apparatus. The method for fast charging of the cell includes: detecting a temperature of the cell; and if the temperature of the cell is equal to a target temperature, disabling a heating function for the cell until charging ends, where a temperature change of the cell is positively correlated with internal resistance of the cell and a charging current provided by the charging pile.

A fleet and trolley system, a first method of charging a work machine, and a second method of charging a fleet of work machines are disclosed. The fleet and trolley system includes a trolley network, at least one zero-emission work machine, and a controller. The controller manages a scheduled usage of the trolley network, a power draw, and a distribution of the power draw by the work machine. The first method includes monitoring states of the work machine, scheduling a usage of the trolley network, and supplying electric power to the work machine. The second method includes monitoring states of a fleet, monitoring states of the trolley network, scheduling usages of the trolley network, and supplying electric power to one or more machines of the fleet. Advantageously, the disclosed system and methods may improve an efficiency, productivity, and longevity of a fleet of zero-emission work machines.

A torque control system for a drive system of an electric vehicle includes a controller that generates a front-wheel torque command and a rear-wheel torque command having torque values distributed from required torque for vehicle driving, a front-wheel motor, wherein operation of the front-wheel motor is controlled according to the front-wheel torque command, and a rear-wheel motor, wherein operation of the rear-wheel motor is controlled according to the rear-wheel torque command. The controller determines whether there is a change request of a direction of the required torque, and determines, in a case where there is the change request of the direction of the required torque, the front-wheel torque command and the rear-wheel torque command determined from the required torque as values for sequential zero-crossing while the required torque determined in real time changes while performing zero-crossing of passing through 0 torque for direction change.

A battery management system includes a voltage measurement unit that measures a voltage of each of a plurality of batteries; and a controller. The controller controls the voltage measurement unit to measure the voltage of each of the plurality of batteries; calculates a first deviation, which is a deviation between long-term and short-term moving average values of the voltage of each of the plurality of batteries; calculates a second deviation, which is a deviation between long-term and short-term moving average values of an average voltage of the plurality of batteries; calculates a first diagnosis deviation, which is a difference between the first and second deviations; calculates a second diagnosis deviation based on a reference value obtained by multiplying the second deviation by a threshold constant; and based on the second diagnosis deviation, diagnoses an abnormality of at least one battery among the plurality of batteries.

The application provides a vehicle control method and device, a storage medium and a vehicle, and belongs to the technical field of vehicles. In the case where the brake energy recovery function of the vehicle is in an activated state, a first available brake energy recovery capability value is input into a first prediction model, so that a second available brake energy recovery capability value at a second time can be predicted. Then, based on the first available brake energy recovery capability value, the second available brake energy recovery capability value and a brake pedal opening degree, a first brake energy recovery torque request value at a first time is determined, and in combination with a first coasting energy recovery torque, a first motor actual execution torque is obtained. Finally, based on the first motor actual execution torque and a second brake energy recovery torque request value at the second time, it is determined whether to disable the brake energy recovery function at the second time, so that the influence of CAN signal delay can be eliminated to a certain extent, and the brake energy recovery function can be prevented from being abnormally disabled.

A method of operating an EV in one of various states provides an inverter control system having a high voltage circuit powered by a high DC voltage power source; a low voltage circuit powered by a low DC voltage power source that is substantially less than the voltage of the high voltage power source, the low voltage circuit being configured to power a microcontroller; and an inverter circuit having a plurality of high side switches and a plurality of low side switches, the inverter circuit being configured to deliver AC power to a traction motor. Upon powerup of the high voltage circuit and with the low voltage circuit being unpowered, the method establishes an emergency tow state with each of the high side switches and each of the low side switches being open permitting the EV to be towed without damaging the motor.

A vehicle including a Direct Current-to-Direct Current (DC to DC) converter, a low-voltage battery, a memory and a processor is disclosed. The DC to DC converter may be configured to convert a high voltage electric power to a low voltage electric power, and the low-voltage battery may be configured to be charged by the DC to DC converter via the low voltage electric power. The memory may be configured to store inputs associated with a vehicle user. The processor may be configured to determine that a trigger event has occurred, and estimate a future vehicle usage based on the inputs responsive to determining that the trigger event has occurred. The processor may further control a charging operation of the low-voltage battery by the DC to DC converter based on the future vehicle usage.

A fire fighting vehicle includes an energy storage system coupled to the chassis and a charging assembly configured to interface with a charging plug. The energy storage system includes battery cells. The charging assembly includes a housing, a charging port disposed within the housing and electrically coupled to the battery cells, a retainer positioned proximate the charging port, a first actuator, and a second actuator. The charging port is configured to engage with a charging interface of the charging plug. The retainer is configured to engage with a retaining interface of the charging plug to secure the charging interface within the charging port. The first actuator is positioned to release the retaining interface from engagement with the retainer by repositioning the retaining interface into a release position. The second actuator is positioned to eject the charging plug from the charging assembly when the retaining interface is in the release position.

The present disclosure relates to a method and apparatus for determining a state of charge (SOC) of a battery and a battery management system (BMS), so as to resolve a problem such as inaccurate estimation of the SOC. The method includes: acquiring state data of the battery, where the state data comprises current data and voltage data; determining each element parameter value in an equivalent circuit model of the battery based on the equivalent circuit model, error information, battery characteristic data, and the state data by using a recursive least square (RLS) prediction model; and determining an estimated value of the SOC of the battery based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data according to a technique of an observer.

The present disclosure relates to a vehicle control system and method for SoX management. The vehicle control system comprises a control circuitry and a battery management system. The control circuitry determines an operational schedule of the vehicle and monitors at least one parameter of battery state. The battery management unit is communicatively coupled to the control circuitry. The battery management unit defines and implements an adaptive battery threshold window based on an operational schedule of the vehicle and the at least one parameter of battery state. The adaptative battery threshold window comprises multiple soft threshold values.