Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2021 - Google Patents

A novel learning-based model predictive control strategy for plug-in hybrid electric vehicle

Zhang et al., 2021

View PDF
Document ID
15618945932883880391
Author
Zhang Y
Huang Y
Chen Z
Li G
Liu Y
Publication year
Publication venue
IEEE Transactions on Transportation Electrification

External Links

Snippet

The multisource electromechanical coupling renders the energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving …
Continue reading at qmro.qmul.ac.uk (PDF) (other versions)

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/62—Hybrid vehicles
    • Y02T10/6286—Control systems for power distribution between ICE and other motor or motors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage for electromobility
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage for electromobility
    • Y02T10/7005—Batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce green house gasses emissions common to all road transportation technologies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies related to electric vehicle charging
    • Y02T90/12—Electric charging stations
    • Y02T90/128—Energy exchange control or determination
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
    • B60L11/00—Electric propulsion with power supplied within the vehicle
    • B60L11/18—Electric propulsion with power supplied within the vehicle using power supply from primary cells, secondary cells, or fuel cells
    • B60L11/1851—Battery monitoring or controlling; Arrangements of batteries, structures or switching circuits therefore
    • B60L11/1861—Monitoring or controlling state of charge [SOC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—ELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries

Similar Documents

Publication Publication Date Title
Zhang et al. A novel learning-based model predictive control strategy for plug-in hybrid electric vehicle
Xie et al. Pontryagin’s minimum principle based model predictive control of energy management for a plug-in hybrid electric bus
Vatanparvar et al. Extended range electric vehicle with driving behavior estimation in energy management
Li et al. Research on equivalent factor boundary of equivalent consumption minimization strategy for PHEVs
Chen et al. Energy management for a power-split plug-in hybrid electric vehicle based on dynamic programming and neural networks
Ramadan et al. Energy management improvement of hybrid electric vehicles via combined GPS/rule-based methodology
Salmasi Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends
Khayyam et al. Adaptive intelligent energy management system of plug-in hybrid electric vehicle
Zhang et al. A deep reinforcement learning-based energy management framework with Lagrangian relaxation for plug-in hybrid electric vehicle
Simmons et al. Modeling and energy management control design for a fuel cell hybrid passenger bus
Climent et al. Exploiting driving history for optimising the Energy Management in plug-in Hybrid Electric Vehicles
Liu et al. Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment
Fu et al. Real-time energy management and sensitivity study for hybrid electric vehicles
Han et al. Energy management in plug-in hybrid electric vehicles: preheating the battery packs in low-temperature driving scenarios
Yang et al. Energy management of the power-split hybrid electric city bus based on the stochastic model predictive control
Peng et al. Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning
CN113554337A (en) Construction method of energy management strategy for plug-in hybrid vehicle integrating traffic information
Zhang et al. A novel model predictive control based co-optimization strategy for velocity planning and energy management of intelligent PHEVs
Xu et al. Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy
Zhang et al. An optimal control strategy for plug-in hybrid electric vehicles based on enhanced model predictive control with efficient numerical method
Guo et al. Clustered energy management strategy of plug-in hybrid electric logistics vehicle based on Gaussian mixture model and stochastic dynamic programming
Li et al. Electrothermal dynamics-conscious many-objective modular design for power-split plug-in hybrid electric vehicles
Lü et al. Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles
Yang et al. A real time multi-objective optimization Guided-MPC strategy for power-split hybrid electric bus based on velocity prediction
Shen et al. Real-time energy management for plug-in hybrid electric vehicles via incorporating double-delay Q-learning and model prediction control