2499results about "Operating modes" patented technology

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 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.

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.

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.

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 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.

Methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. The reinforcement learning model can determine state-of-health characteristics of the battery over the charge cycles, and in a second phase, the reinforcement learning model is augmented accordingly. During this augmentation, the reinforcement learning model is trained with rewards given on a charge cycle-by-cycle basis, wherein rewards are given after looking at the charging optimization after the conclusion of each charge cycle. Commands are given to charge an on-field battery based on the augmented reinforcement learning model, associated state-of-health characteristics of the on-field battery are determined, and the reinforcement learning model is further augmented accordingly.

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.

The present invention provides an en-route battery charging / swapping scheduling strategy for an electric container truck serving inter-terminal container transportation, aiming to quantify the impact of battery charging and swapping requirements for a container truck in inter-terminal container transportation on system efficiency by means of an information-based method. Provided is an en-route battery charging / swapping scheduling strategy for an electric container truck serving inter-terminal container transportation having the minimum impact on the system efficiency. The strategy comprises four modules: a system state acquisition module, a battery charging and swapping demand evaluation module, a reachable battery charging and swapping device search module, and a battery charging and swapping device specifying and service insertion module. A decision is made in real time by means of the modules, and a container truck battery charging and swapping scheduling solution is determined, thereby reducing the adverse impact of container truck battery charging and swapping activities on the inter-terminal transportation efficiency.

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.

A speed limiting control method and apparatus for a vehicle, and a vehicle and a storage medium. The method comprises: when it is detected that a vehicle meets a speed limiting condition, acquiring in real time a travelling scenario of the vehicle; and during the travel of the vehicle, performing speed limiting control on the vehicle on the basis of a speed limiting policy corresponding to the travelling scenario. By means of the speed limiting control method for a vehicle, speed limiting is performed on a vehicle on the basis of travelling scenarios of the vehicle, thereby avoiding the problem of poor user experience caused by the use of a single speed limit.

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.

Provided are methods for managing vehicle sensors, which can include: determining a stopping distance for a vehicle travelling on a route, identifying one or more sensors of the vehicle that have respective detection ranges less than the stopping distance, and upon identifying the one or more sensors, deactivating at least one operation of at least one sensor of the one or more sensors. Systems and computer program products are also provided.

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.

An exemplary system and method thereof are disclosed that employs a networked relay device that operates with an optimizer algorithm to optimally enable or disable power flow to any installed electric-vehicle charging-system, without the need for a control interface to the charging system and do so while maximizing or maintaining grid stability, site stability, or managing the site based on user-provided preferences. The operation can be performed without any interface or communication with the electric-vehicle charger and is deployable in a large scale for any manufacturer equipment or utility deployment.

A battery management device according to one embodiment of the present invention includes: a profile acquisition unit configured to acquire a measured full-cell profile indicating a correspondence relationship between the voltage and capacity of a battery; a profile determination unit configured to divide at least one of a preset reference positive electrode profile and a reference negative electrode profile into a plurality of electrode sections, adjust the reference positive electrode profile and the reference negative electrode profile to correspond to the measured full-cell profile while adjusting each of the plurality of electrode sections, and generate an adjusted positive electrode profile and an adjusted negative electrode profile according to the adjustment results; and a control unit configured to determine a diagnostic factor for the battery from at least one of the adjusted positive electrode profile and the adjusted negative electrode profile.

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.