28 results about "Energy-efficient driving" patented technology

The invention discloses a layered energy-saving driving speed optimization method, computer equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that a longitudinal dynamic model is constructed according to the relation between the vehicle state and the driving mileage; constructing an energy consumption model according to the relationship among the vehicle speed, the driving torque and the instantaneous battery output power; constructing a green wave window according to the energy consumption model so as to generate the optimal passing time and the entrance and exit speed of each road section; constructing and optimizing a fillet parallelogram envelope line based on the green wave window according to the maximum speed, the maximum acceleration and the maximum deceleration of the vehicle so as to construct a space-time boundary of the green wave window; and generating an energy-saving speed curve according to the longitudinal dynamic model, the energy consumption model, the space-time boundary of the green wave window, the optimal passing time and the entrance and exit speed. By adopting the method, the speed track with an excellent energy-saving effect can be quickly calculated and obtained.

1. The name of the design product: vehicle driving mode setting graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the electronic equipment. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface is used for users to set the driving mode of the vehicle, and display the parameter setting information of the vehicle driving mode. 7. The human-computer interaction mode of the graphical user interface: design 1 front view is the energy-saving driving mode interface of the vehicle, and the user clicks to trigger the "energy-saving" option card in the interface, so as to customize the setting of the energy-saving driving mode of the vehicle. Design 2 front view is the comfortable driving mode interface of the vehicle, and the user clicks to trigger the "comfortable" option card in the interface, so as to customize the setting of the comfortable driving mode of the vehicle. Design 3 front view is the sports driving mode interface of the vehicle, and the user clicks to trigger the "sports" option card in the interface, so as to customize the setting of the sports driving mode of the vehicle. Design 4 front view is the off-road driving mode interface of the vehicle, and the user clicks to trigger the "off-road" option card in the interface, so as to customize the setting of the off-road driving mode of the vehicle.

Device for providing eco-driving information for a vehicle, comprising: a vehicle weight calculation unit (1) which is configured to calculate a current weight of the vehicle based on data received from a plurality of devices within the vehicle; a computing unit (3) for the electrical load, which is equipped to calculate an electrical load of auxiliary machines currently used in the vehicle; a vehicle speed calculation unit (5) configured to calculate, using a route map, a fuel-efficiency-oriented vehicle speed that maximizes the vehicle's travel distance according to the calculated vehicle weight and electrical load; and a display device (7) which is set up to display the calculated fuel efficiency-oriented vehicle speed to a user. where the weight of the vehicle is calculated from the electricity consumption of a vehicle drive motor, an air density, a drag coefficient, a projection area of ​​the vehicle, the vehicle speed, a coefficient of friction of a current road on which the vehicle is traveling, the acceleration due to gravity and an inclination of the current road on which the vehicle is traveling, where the weight of the vehicle is calculated using the following equation m = ( TF + 1 2 ρ air C d AV 2 ) / ( μ g cos θ+ g sin θ ) where m represents vehicle weight, TF represents a driving force, ρ air which represents air density, C da drag coefficient, A a projection area of ​​the vehicle, V the vehicle speed, µ a coefficient of friction of the current road, g the acceleration due to gravity of the vehicle, and θ the current inclination of the road, and The driving force is obtained from a relationship TF = P / V, where P represents the power input of the drive motor and V represents the vehicle speed. wherein the vehicle weight calculation unit (1), the electrical load calculation unit (3) and the vehicle speed calculation unit (5) are incorporated into a single chip and the route map comprising the vehicle weight, electrical load and vehicle speed is embedded in the chip.

The invention particularly relates to an electric vehicle energy consumption prediction method for optimizing XGBoost parameters based on an improved whale algorithm. The method comprises the following steps: acquiring and preprocessing original driving data of the electric vehicle, dividing working condition fragments and extracting key driving characteristics and unit mileage energy consumption labels; an improved whale optimization algorithm is constructed, and global exploration and local development capabilities are enhanced by introducing a nonlinear time-varying convergence factor, Cat chaotic mapping initialization and a crisscross hybrid strategy; performing automatic optimization on core hyper-parameters of the XGBoost model by using the algorithm, and training by using an optimal parameter combination to obtain a final XGBoost energy consumption prediction model; the performance of the XGBoost energy consumption prediction model is verified through a test set, an SHAP method is applied to carry out interpretability analysis, and the influence of driving behaviors on energy consumption is quantified. According to the method, the precision and robustness of the prediction model are effectively improved, clear mechanism explanation is provided, and effective support can be provided for energy-saving driving and energy management.

The invention discloses an energy-saving driving method, device and equipment based on real-time load identification and a storage medium, and relates to the technical field of vehicle control, and the energy-saving driving method based on real-time load identification comprises the following steps: obtaining operation data of a vehicle in real time; inputting the operation data into a preset load identification model to obtain load information of the vehicle, the preset load identification model being obtained by training historical operation data and historical load information of the vehicle; and an energy-saving driving strategy containing power system control parameters is generated according to the load information, and the vehicle is controlled to conduct energy-saving driving according to the energy-saving driving strategy. The power energy consumption of vehicle driving can be reduced.

The application provides a hydrogen fuel cell bus energy-saving driving control method and system, and the method comprises the following steps: a hierarchical double-layer deep reinforcement learning architecture is designed; the upper-layer deep reinforcement learning controls the longitudinal speed of the hydrogen fuel cell bus by acquiring the speed, acceleration and ramp road condition and other traffic environment information of the hydrogen fuel cell bus and the front vehicle, so as to ensure the safe and stable driving of the hydrogen fuel cell bus; the lower-layer deep reinforcement learning optimizes the energy management of the hydrogen fuel cell by the speed output by the upper layer; meanwhile, a rapid road comprising three ramps with different traffic flows is designed to perform offline training on the deep reinforcement learning algorithm, so as to obtain the optimized energy-saving driving control. The application realizes the longitudinal speed control and energy management optimization of the hydrogen fuel cell bus by using the double-layer deep reinforcement learning method, so that the hydrogen fuel cell bus can realize energy-saving driving.

The invention relates to the technical field of intelligent networked electric vehicles, in particular to an intelligent networked electric vehicle speed optimization method considering a side vehicle cut-in scene, which comprises the following steps: 1) obtaining historical tracks of each side vehicle and surrounding vehicles corresponding to the side vehicle; 2) designing a hierarchical control strategy of side vehicle lane changing track prediction and economic vehicle speed optimization; 3) obtaining space-time interaction characteristics; 4) decoding the space-time interaction features, and predicting the future lane changing trajectory of the side vehicle; 5) constructing a multi-constraint optimization problem of vehicle speed optimization in a side vehicle cut-in scene; 6) predicting a lane changing intention and a lane changing track of the side vehicle and a motion conflict scene under the cut-in scene; and 7) generating an economic vehicle speed curve of the vehicle by adopting a QP method. According to the method, the lane changing track of the adjacent side vehicle can be predicted more accurately, the optimal solution can be found quickly, the future lane changing track of the adjacent side vehicle can be processed in real time, and the vehicle speed can be optimized in real time; the energy consumption of the BEV in the driving process is effectively reduced, the purpose of energy-saving driving is achieved, and the overall energy efficiency of the vehicle is improved.

The present invention discloses an energy-saving driving method for a networked fuel cell bus with comprehensive comfort enhancement. The method takes a networked fuel cell bus as the research object, utilizes the correlation between IRI and WRMSA, introduces vertical comfort and road surface information into the energy-saving driving strategy; integrates the energy management system and adaptive cruise control, quantifies the collaborative optimization target into a reward coefficient, sets the state space, action space and reward function, and obtains a comprehensive comfort-enhanced energy-saving driving strategy based on a deep reinforcement learning algorithm; obtains an inheritable parameterized neural network strategy by offline training of the energy-saving driving strategy, and downloads it to the vehicle controller to realize the real-time online application of the energy-saving driving strategy. The present invention can achieve energy saving and comfort levels comparable to dynamic programming, and greatly improves computational efficiency for real-time application. While maintaining the safety, durability and other indicators of the vehicle under optimal energy-saving regulation, the present invention increases the comprehensive driving comfort.

The application discloses a layered energy-saving driving speed optimization method, a computer device and a storage medium, relates to the technical field of data processing, and comprises the following steps: constructing a longitudinal dynamics model according to the relationship between the vehicle state and the driving mileage; constructing an energy consumption model according to the relationship among the vehicle speed, the driving torque and the instantaneous battery output power; constructing a green wave window according to the energy consumption model to generate the optimal passing time of each road section and the entrance and exit speed; constructing and optimizing a round-cornered parallelogram envelope based on the green wave window according to the maximum speed, the maximum acceleration and the maximum deceleration of the vehicle to construct the space-time boundary of the green wave window; and generating an energy-saving speed curve according to the longitudinal dynamics model, the energy consumption model, the space-time boundary of the green wave window, the optimal passing time and the entrance and exit speed. According to the application, the energy-saving effect of the speed trajectory can be quickly calculated and obtained.

The application relates to a PHEV energy-saving driving control method based on integrated learning and considering battery life, which comprises the following steps: S1, constructing a comprehensive driving condition data set; S2, obtaining a global optimal comprehensive energy efficiency cost and an optimal control decision data set; S3, discretely extracting an optimal energy consumption cost, a battery life cost and a current data set; S4, calculating a battery SOC according to a vehicle driving state; S5, establishing a comprehensive energy efficiency cost model considering the energy consumption cost and the battery life cost; S6, designing a hierarchical energy-saving driving control architecture based on integrated learning; S7, performing economic vehicle speed optimization based on integrated learning and considering battery life in the upper layer; and S8, performing power system energy management control according to a planned vehicle speed trajectory in the lower layer. A fusion energy efficiency cost model and a system state transition model considering energy consumption and battery life are established by using a neural network, a complex coupling relationship existing between energy consumption characteristics of a hybrid power system and economic vehicle speed is efficiently decoupled, and the service life of the battery is prolonged.

The operation control system includes an information processing device having a first control unit, and plural control devices mounted on plural vehicles and each having a second control unit. The second control unit acquires, for each vehicle occupant, the set temperature of the air conditioning device as first information, and acquires the detected accelerator operation amount as second information. The first control unit calculates, for each vehicle occupant, a first suppression control value corresponding to the suppression rate of the output of the air conditioning device and a second suppression control value corresponding to the suppression rate of the acceleration output based on the first and second information. When one vehicle with the second control unit is in the eco-driving mode, the second control unit performs suppression control of the air conditioning device of the one vehicle and the acceleration output based on the first and second suppression control values.

The invention discloses a new energy commercial vehicle driving suggestion generation method, device and system, and belongs to the technical field of new energy commercial vehicle driving guidance, and the method comprises the following steps: collecting vehicle state information and vehicle driving data information of a new energy vehicle; generating a driving track according to the vehicle driving data information, comparing the driving track with a preset map, and dividing the driving track into a plurality of sections of different vehicle driving scenes; according to the vehicle driving scene, in combination with the vehicle state information and the vehicle driving data information, scoring the vehicle driving behavior to obtain a scoring result; and according to the scoring result and the vehicle driving scene, extracting a driving suggestion from a driving behavior suggestion library. The system adapts to the characteristics of the new energy commercial vehicle, can improve the driving economy, reduces the driving energy consumption of the vehicle, and trains the driver to adapt to the energy-saving driving mode of the new energy commercial vehicle on the basis.

The invention relates to the technical field of vehicle energy consumption analysis, and discloses a vehicle energy consumption improvement method based on user behavior feedback, and the method comprises the following steps: searching a benchmarking vehicle; user behavior comparison and diagnosis: based on Internet of Vehicles big data, user driving behavior classification is established, driving behaviors and corresponding energy consumption of a target vehicle and a benchmarking vehicle are compared, and an association rule of a high-energy-consumption association behavior and a low-energy-consumption association behavior is identified; and improvement and verification of a diagnosis result: pushing personalized driving improvement suggestions, recording oil consumption data before and after the user adopts the suggestions, calculating an energy consumption improvement degree, and forming closed-loop feedback. The invention further discloses a vehicle energy consumption improvement system based on user behavior feedback. According to the vehicle energy consumption improvement method and system based on the user behavior feedback, by constructing a multi-dimensional benchmarking vehicle search mechanism, a refined driving behavior diagnosis model and a closed-loop feedback verification system, energy-saving driving guidance of thousands of people and thousands of faces is achieved, and the vehicle economy is remarkably improved.

The present invention provides a multi-section energy-saving operation curve planning method for trains based on grouped backpacks, belonging to the technical field of train operation energy-saving optimization. The method comprises the following steps: first, obtaining the optimal operating Pareto frontier between stations, obtaining the "time-energy consumption" relationship from an offline database, further optimizing and adjusting the operation time between stations, and then generating energy-saving operation curves for each section based on a single-section energy-saving driving algorithm. This method minimizes the total energy consumption of the entire train line and achieves the goal of energy-saving driving for the entire line. The present invention solves the problems of high online computing time complexity and inability to guarantee calculation accuracy.

The application discloses an energy-saving driving and energy-saving refrigeration integrated control method for a pure electric vehicle, and comprises the following steps: S1, constructing a transfer equation of a vehicle motion state in a space domain; S2, fitting a torque boundary of a motor based on motor calibration data; S3, calibrating and fitting temperature characteristics and energy consumption characteristics of a vehicle-mounted air conditioner; S4, constructing and solving an energy-saving vehicle speed planning problem in a rolling time domain; and S5, constructing and solving an air conditioner refrigeration optimization problem in the rolling time domain. The energy-saving driving and energy-saving refrigeration are integrated controlled, the obtained planning vehicle speed sequence can guide the refrigeration optimization of the air conditioner, and based on a variable cost function of an optimization target, the energy-saving refrigeration effect of the air conditioner under a second-level planning time domain length is improved, and the adaptability of the integrated control method to an expressway and an urban road is promoted.

To provide a driving support system that can assist in driving with high energy efficiency. [Solution] A driving support system comprising: a satellite signal receiver, an inertial sensor, and an imaging device mounted on a moving body; a storage device for storing map information; a self-position estimation unit that estimates the position, attitude, and direction of the moving body based on positioning data from the satellite signal receiver, detection data from the inertial sensor, and the map information; a road surface flatness detection unit that detects the flatness of the road surface based on the position, attitude, and direction and an image from the imaging device; and a trajectory planning unit that plans the trajectory of the moving body based on the position, attitude, direction, and flatness, wherein the inertial sensor includes a sensor that detects angular velocity around the Z axis and has a bias instability of 1.0° / h or less; and the trajectory planning unit plans the trajectory to change from a first driving line to a second driving line with a smaller inclination angle.

The application relates to a new energy automobile energy consumption optimization reminding system and method, and belongs to the technical field of new energy automobile intelligent control, which comprises a multi-source data acquisition module, a central processing and analysis module, an intelligent prompting strategy module and a man-machine interaction module, the modules are sequentially connected, and the real-time monitoring, analysis and optimization reminding of new energy automobile energy consumption are realized in cooperation, vehicle dynamic parameters, state parameters, basic parameters and environmental parameters are systematically fused and analyzed for the first time, energy consumption pain points are accurately positioned, passive display is changed into active intervention, timely reminding is realized when non-optimization energy consumption occurs, energy-saving driving habits of users are helped to be formed, the reminding strategy is not fixed and unchangeable, but is combined with real-time driving scenes to provide the most timely and most relevant suggestions, and user experience is better.

The present disclosure relates to a driving control system and an information processing apparatus. The driving control system includes: an information processing apparatus having a first control section; and a plurality of control apparatuses each having a second control section mounted on a plurality of vehicles. The second control section acquires, as first information, information of a set temperature of an air conditioning apparatus and acquires, as second information, information of an accelerator opening degree detected by an accelerator sensor, for each occupant of the vehicle. The first control section calculates, for each occupant of each vehicle, a first suppression control value corresponding to a suppression rate of an output of the air conditioning apparatus and a second suppression control value corresponding to a suppression rate of an acceleration output, based on the first information and the second information acquired from the control apparatuses. When one of the vehicles mounted with the second control section is in an energy-saving driving mode, the second control section performs suppression control of the air conditioning apparatus mounted on the one vehicle and the acceleration output, based on the first suppression control value and the second suppression control value acquired from the information processing apparatus.

The invention relates to the technical field of new energy automobile intelligent network connection, in particular to a vehicle energy-saving driving assistance system and method based on a cloud road spectrum, and the system comprises a vehicle CAN bus, a positioning module, an accelerator execution mechanism, a wireless communication network, a vehicle-mounted intelligent controller and a cloud server. The road slope road spectrum model is generated based on the high-precision map and the historical driving data, the driving route and the future preset driving road section are determined by combining the positioning information, the energy-saving driving scheme has perspectiveness, and meanwhile the driving data of the same vehicle, the same load and the same road section are continuously collected and the energy consumption model is trained and optimized. The scheme can continuously fit real road and vehicle working conditions along with data accumulation, and the energy-saving effect and adaptability are improved.

A vehicle management system is applied to an electric vehicle that uses an electric motor as a power device for running. The vehicle management system generates a sound, and outputs the sound through a speaker mounted on the electric vehicle. The vehicle management system acquires driving state information indicating a driving state of the electric vehicle. The vehicle management system acquires an aggressive driving degree indicating a degree of safe driving and / or energy-saving driving of the electric vehicle, based on the driving state information. The vehicle management system changes the sound and / or changes an option of the sound in accordance with the aggressive driving degree.

Provided is an information processing apparatus capable of proposing, to a user, battery degradation reduction control adapted to the orientation of the user toward use of a vehicle. An information processing apparatus 1 includes: an acquisition unit 12 configured to acquire driving information of a vehicle 3; a determination unit 14 configured to determine whether or not eco-driving is being performed on the vehicle 3 on the basis of the driving information acquired by the acquisition unit 12; and a proposal unit 15 configured to output information for proposing, to a user of the vehicle 3, battery degradation reduction control that reduces degradation of a battery 34 mounted on the vehicle 3 upon the determination unit 14 determining that eco-driving is being performed on the vehicle 3.

This invention relates to a hierarchical control method for hybrid electric vehicle (HEV) platooning energy-saving driving based on deep reinforcement learning, belonging to the field of new energy vehicle technology. The method comprises the following steps: S1: constructing a vehicle platoon motion model and establishing platoon motion constraints based on the vehicle longitudinal dynamics model; S2: acquiring vehicle operating data using vehicle-to-vehicle (V2V) communication technology; S3: constructing an upper-layer collaborative adaptive cruise control model based on a dual-delay deep deterministic policy gradient algorithm to provide vehicle speed data to a lower-layer energy management model; S4: constructing a lower-layer energy management model based on a deep Q-network that considers cabin temperature control; and S5: training the lower-layer energy management model. This method can effectively improve the safety and energy efficiency of HEV platooning, while also enhancing cabin thermal comfort.

The invention provides a fuel cell heavy truck energy management method and system considering energy-saving driving. The method comprises the steps that an energy-saving driving index is determined according to driving state parameters obtained in real time; based on the energy-saving driving index, the front road information, the driving motor power and the driving state parameters, utilizing a space time sequence fused graph neural network prediction model to predict an instantaneous vehicle power demand; constructing a multi-objective optimization model and solving the model to obtain the target output power of the fuel cell stack and the target charging and discharging power of the energy storage device; issuing a power instruction to the fuel cell stack and the energy storage device according to the target output power and the target charging and discharging power, and triggering an energy storage compensation mode when the deviation between the actual driving power and the predicted power demand exceeds a preset condition; and the weight of the model is updated based on the deviation and the change of the energy-saving driving index, and energy-saving driving information is fed back to the driver. In this way, the driver is guided to optimize operation so as to further excavate the energy-saving potential, and the energy consumption economy and the driving range of the whole vehicle are improved.

To provide a travel control device and a vehicle capable of reducing fuel consumption or electric power consumption.SOLUTION: The travel control device can execute the following (1) to (3). (1) when first and second preceding vehicles traveling in parallel in front of the vehicle are detected in a case where the ACC is enabled, inquiring of the driver whether or not to give priority to energy-saving traveling, (2) in a case where a response that does not give priority to energy-saving traveling is obtained, performing automatic traveling control to a position that faces a gap between the first and second preceding vehicles and corresponds to a set inter-vehicle distance, that is, automatic travel control is performed to a position corresponding to the shortest inter-vehicle distance SELECTED DRAWING: Figure 3

The invention relates to a networked vehicle energy-saving driving optimization method adaptive to steering and gradient working conditions, and the method comprises the steps: building a transmission system model through a networked vehicle based on the road steering angle and gradient data obtained in real time according to a front-wheel steering and rear-wheel driving tandem HEV; a slope-curve driving model is used for deducing a longitudinal kinematic model under the steering condition, a power total power flow model is generated according to the longitudinal kinematic model, a battery life model is synchronously integrated, and integrated energy-saving driving of the steering condition and battery health is constructed in a combined mode, so that the attenuation life of the battery is quantified. The method comprises the following steps: establishing a stroke-level protection mechanism of a battery state of health (SOH), implementing non-convex relation convex approximation and convex relaxation operation on integrated energy-saving driving, constructing an integrated energy-saving driving problem in a second-order cone programming form considering steering resistance and a driving gradient, and solving the problem through a convex optimization method. And quickly generating a real-time optimization strategy and issuing the real-time optimization strategy to the vehicle-mounted controller for execution through the network connection system.

The application discloses a kind of track traffic driving curve planning method and system based on multi-source perception data, the application includes the travel line of train is divided into multiple intervals;During train operation, the current travel position of train is detected, if it is found that train is currently driven to the boundary of adjacent arbitrary interval q i , then the control sequence u2 (k) of interval q i And its real-time optimization energy consumption J si Are calculated based on the various perception data of train;Real-time optimization energy consumption J si And the preset experience optimization energy consumption J i Of interval q yi Are compared, if real-time optimization energy consumption J si Is better than experience optimization energy consumption J yi , then the control sequence u2 (k) is selected to control train travel in interval q i ; Otherwise, the preset control sequence u1 (k) corresponding to experience optimization energy consumption J yi Controls train travel in interval q i . The application can meet the actual energy consumption demand of train operation, reduce the performance energy consumption of train, reduce train operation cost, and has great significance for studying train energy-saving driving optimization.

This invention discloses a cloud-supported energy management control method and system for connected buses based on energy-saving driving. The method includes acquiring traffic flow, signal timing, and intersection queuing prediction results; combining these with bus stop stopping time windows; constructing and solving a multi-constraint, multi-objective speed planning optimization model to obtain the globally optimal smooth speed trajectory; calculating the vehicle's required power based on this trajectory and acceleration trajectory, converting it into battery power; obtaining the State of Charge (SOC) state transition equation through a battery model; finding the optimal SOC feasible region; and generating a reference trajectory. Finally, acquiring vehicle and traffic information to calculate tracking errors; integrating and constructing a reinforcement learning state space and action space; training a policy network and updating parameters to achieve adaptive tracking execution of the vehicle speed and SOC reference trajectory. This invention can improve the overall energy efficiency, operational stability, and control robustness of new energy buses in complex urban traffic environments.

Systems and methods for controlling the speed of a vehicle traveling over a terrain. The methods comprise: predicting, by a mobile device (MD), a queue length defined by the number of vehicles in a queue at a downstream intersection based on a traffic volume and vehicle speeds that were detected during a past period of time by a detector located at or near an upstream intersection; sensing, by MD's sensor(s), the vehicle's velocity and location; computing a distance to a downstream intersection from the vehicle's location; generating a desired speed for the vehicle based on the traffic volume and vehicle speeds, signal phase and timing data for at least the next two cycles, the predicted queue length at the downstream intersection, the vehicle's velocity and location, the distance to the downstream intersection, and a current time; and using the desired speed to facilitate control of the speed of the vehicle.