On the public citation graph

Deep reinforcement learning-based vehicle energy efficiency autonomous learning system

0 signed · 77 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.jpowsour.2013.09.085openalex-snapshot
Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming
inferredcites →https://doi.org/10.1049/iet-its.2014.0075openalex-snapshot
Fuzzy‐based blended control for the energy management of a parallel plug‐in hybrid electric vehicle
inferredcites →https://doi.org/10.1109/itec.2016.7520190openalex-snapshot
Range anxiety reduction in battery-powered vehicles
inferredcites →https://doi.org/10.1109/tits.2013.2294342openalex-snapshot
Development and Evaluation of an Intelligent Energy-Management Strategy for Plug-in Hybrid Electric Vehicles
inferredcites →https://doi.org/10.1109/tits.2013.2294723openalex-snapshot
Commuter Route Optimized Energy Management of Hybrid Electric Vehicles
inferredcites →https://doi.org/10.1109/tits.2016.2633542openalex-snapshot
Development and Evaluation of an Evolutionary Algorithm-Based OnLine Energy Management System for Plug-In Hybrid Electric Vehicles
inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.3141/2572-01openalex-snapshot
Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles
inferredcites →https://doi.org/10.3182/20080706-5-kr-1001.00785openalex-snapshot
Trip Based Near Globally Optimal Power Management of Plug-in Hybrid Electric Vehicles Using Gas-Kinetic Traffic Flow Model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4586364openalex-extracted
Energy Management for Hev Combining Naturalistic Driving Data and Deep Reinforcement Learning with High Generalization
inferred← citeshttps://doi.org/10.2139/ssrn.4773027openalex-extracted
inferred← citeshttps://doi.org/10.2139/ssrn.4879863openalex-extracted
inferred← citeshttps://doi.org/10.1002/2050-7038.12531openalex-snapshot
Deep learning and reinforcement learning approach on microgrid
inferred← citeshttps://doi.org/10.1002/9781119893998.ch11openalex-snapshot
Deep Reinforcement Learning and Energy Price Prediction
inferred← citeshttps://doi.org/10.1002/ett.4427openalex-snapshot
Machine learning for next‐generation intelligent transportation systems: A survey
inferred← citeshttps://doi.org/10.1007/978-3-032-06732-6_2openalex-snapshot
Deep Reinforcement Learning in Energy Management System for Fuel Cell Hybrid Vehicles: A Review on Reward Design and Testing Framework
inferred← citeshttps://doi.org/10.1007/978-981-15-2862-0_38openalex-snapshot
A Survey: Learning-Based Energy Management Strategy for Heterogeneous Multi-energy Drive System
inferred← citeshttps://doi.org/10.1007/978-981-16-2090-4_53openalex-snapshot
Reinforcement Learning Energy Management for Hybrid Electric Tracked Vehicle with Deep Deterministic Policy Gradient
inferred← citeshttps://doi.org/10.1007/s10586-021-03350-zopenalex-snapshot
Artificial intelligent system for multimedia services in smart home environments
inferred← citeshttps://doi.org/10.1007/s40313-025-01148-7openalex-snapshot
A Coordinated Optimization Strategy for Energy Management of Hybrid Electric Vehicle Fleets
inferred← citeshttps://doi.org/10.1007/s40684-022-00476-2openalex-snapshot
Recent Progress in Learning Algorithms Applied in Energy Management of Hybrid Vehicles: A Comprehensive Review
inferred← citeshttps://doi.org/10.1016/j.apenergy.2019.113762openalex-snapshot
Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information
inferred← citeshttps://doi.org/10.1016/j.apenergy.2024.124350openalex-snapshot
Real-time energy management for HEV combining naturalistic driving data and deep reinforcement learning with high generalization
inferred← citeshttps://doi.org/10.1016/j.est.2021.102355openalex-snapshot
Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles
inferred← citeshttps://doi.org/10.1016/j.jestch.2023.101434openalex-snapshot
Extended-deep Q-network: A functional reinforcement learning-based energy management strategy for plug-in hybrid electric vehicles
inferred← citeshttps://doi.org/10.1016/j.rser.2021.111521openalex-snapshot
Driving conditions-driven energy management strategies for hybrid electric vehicles: A review
inferred← citeshttps://doi.org/10.1016/j.trc.2021.102980openalex-snapshot
Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors
inferred← citeshttps://doi.org/10.1016/j.trc.2022.103863openalex-snapshot
Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.trc.2025.105005openalex-snapshot
Robust lane change decision for autonomous vehicles in mixed traffic: A safety-aware multi-agent adversarial reinforcement learning approach
inferred← citeshttps://doi.org/10.1016/j.trip.2021.100425openalex-snapshot
Deep reinforcement learning in transportation research: A review
inferred← citeshttps://doi.org/10.1016/j.vehcom.2021.100398openalex-snapshot
Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G
inferred← citeshttps://doi.org/10.1080/15435075.2023.2266724openalex-snapshot
A novel multi-stack fuel cell hybrid system energy management strategy for improving the fuel cell durability of the hydrogen electric Multiple Units
inferred← citeshttps://doi.org/10.1080/15472450.2025.2576907openalex-snapshot
Follow the leader: a deep reinforcement learning framework for safe and efficient autonomous car-following
inferred← citeshttps://doi.org/10.1109/access.2024.3445302openalex-snapshot
MPC-Based EMS for a Series Hybrid Electric Tractor
inferred← citeshttps://doi.org/10.1109/aim.2019.8868667openalex-snapshot
Actor-Critic based Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles
inferred← citeshttps://doi.org/10.1109/case48305.2020.9216797openalex-snapshot
Risk-aware Energy Management of Extended Range Electric Delivery Vehicles with Implicit Quantile Network
inferred← citeshttps://doi.org/10.1109/case48305.2020.9216999openalex-snapshot
Constrained Double Deep Q-learning Network for EVs Charging Scheduling with Renewable Energy
inferred← citeshttps://doi.org/10.1109/cvci56766.2022.9965049openalex-snapshot
Deep Reinforcement Learning based Multi-Objective Energy Management Strategy for a Plug-in Hybrid Electric Bus Considering Driving Style Recognition
inferred← citeshttps://doi.org/10.1109/ddcls66240.2025.11065684openalex-snapshot
Adaptive Hierarchical Energy Management Strategy for Hybrid Electric Vehicles Based on Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/iccece46942.2019.8941819openalex-snapshot
Reflex-Augmented Reinforcement Learning for Operating Strategies in Automotive Electrical Energy Management
inferred← citeshttps://doi.org/10.1109/iceem52022.2021.9480636openalex-snapshot
Management of Energy Based on Intelligent Controller for Plug-in Fuel Cell Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.1109/icpects62210.2024.10780029openalex-snapshot
Cascaded Neural Network Controller Development for Electric Vehicle Energy Management
inferred← citeshttps://doi.org/10.1109/is.2018.8710466openalex-snapshot
On-line Energy Optimization of Hybrid Production Systems Using Actor-Critic Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/itsc.2019.8916968openalex-snapshot
Energy Management for Unmanned Tracked Vehicles Based on Global Path
inferred← citeshttps://doi.org/10.1109/iv66570.2026.11623933openalex-snapshot
ETSC-HD : A Safety-Aware, ODD-Robust Benchmark and Composite Score for SPaT- and Grade-Aware DRL-Based Eco-Driving
inferred← citeshttps://doi.org/10.1109/ivs.2019.8813890openalex-snapshot
A Deep Reinforcement Learning Framework for Energy Management of Extended Range Electric Delivery Vehicles
inferred← citeshttps://doi.org/10.1109/mie.2019.2913015openalex-snapshot
Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
inferred← citeshttps://doi.org/10.1109/peas53589.2021.9628686openalex-snapshot
A Double-layered Energy Management Strategy for Fuel Cell Hybrid Power Source System of More Electric Aircraft
inferred← citeshttps://doi.org/10.1109/ssci51031.2022.10022233openalex-snapshot
Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data
inferred← citeshttps://doi.org/10.1109/tii.2018.2797322openalex-snapshot
A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle
inferred← citeshttps://doi.org/10.1109/tits.2023.3283010openalex-snapshot
Intelligent Learning Algorithm and Intelligent Transportation-Based Energy Management Strategies for Hybrid Electric Vehicles: A Review
inferred← citeshttps://doi.org/10.1109/tiv.2017.2788185openalex-snapshot
Intelligent Energy Management Strategy Based on Artificial Neural Fuzzy for Hybrid Vehicle
inferred← citeshttps://doi.org/10.1109/tte.2017.2779267openalex-snapshot
Whole-Day Driving Prediction Control Strategy: Analysis on Real-World Drive Cycles
inferred← citeshttps://doi.org/10.1109/tte.2023.3263927openalex-snapshot
Hierarchical Rewarding Deep Deterministic Policy Gradient Strategy for Energy Management of Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.1109/tte.2025.3645050openalex-snapshot
Collaborative Safe Lane-Change Decision and Multiagent Reinforcement Learning-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.1109/tvt.2022.3209817openalex-snapshot
A Novel Energy Management Strategy Integrating Deep Reinforcement Learning and Rule Based on Condition Identification
inferred← citeshttps://doi.org/10.1186/s10033-024-01026-4openalex-snapshot
Reinforcement Learning-Based Energy Management for Hybrid Power Systems: State-of-the-Art Survey, Review, and Perspectives
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i9s.7396openalex-snapshot
Deep Reinforcement Learning DDPG Algorithm with AM based Transferable EMS for FCHEVs
inferred← citeshttps://doi.org/10.2200/s00934ed1v01y201907aat009openalex-snapshot
Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.23919/sice56594.2022.9905847openalex-snapshot
Energy Optimization of Hybrid electric Vehicles Using Deep Q-Network
inferred← citeshttps://doi.org/10.26599/tst.2023.9010094openalex-snapshot
Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models
inferred← citeshttps://doi.org/10.3384/diss.diva-178628openalex-snapshot
Energy Management Strategy Design for Series Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.3390/a11030033openalex-snapshot
An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
inferred← citeshttps://doi.org/10.3390/app8020187openalex-snapshot
Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.3390/electronics11121850openalex-snapshot
Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.3390/en11092212openalex-snapshot
A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor
inferred← citeshttps://doi.org/10.3390/en13133352openalex-snapshot
Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook
inferred← citeshttps://doi.org/10.3390/en16083450openalex-snapshot
A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation
inferred← citeshttps://doi.org/10.3390/wevj10030057openalex-snapshot
Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV
inferred← citeshttps://doi.org/10.3390/wevj15090424openalex-snapshot
Energy Management Strategies for Hybrid Electric Vehicles: A Technology Roadmap
inferred← citeshttps://doi.org/10.35833/mpce.2020.000552openalex-snapshot
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
inferred← citeshttps://doi.org/10.35940/ijitee.a1053.1191s19openalex-snapshot
Examination on Classification of EVs and Energy Management Strategies of HEV
inferred← citeshttps://doi.org/10.47839/ijc.18.4.1607openalex-snapshot
ACTOR-CRITIC REINFORCEMENT LEARNING FOR ENERGY OPTIMIZATION IN HYBRID PRODUCTION ENVIRONMENTS
inferred← citeshttps://doi.org/10.48550/arxiv.2007.10880openalex-snapshot
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
inferred← citeshttps://doi.org/10.48550/arxiv.2010.06187openalex-snapshot
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
inferred← citeshttps://doi.org/10.4995/thesis/10251/181892openalex-snapshot
Interconnection Architecture of Proximity Smart IoE-Networks with Centralised Management
inferred← citeshttps://doi.org/10.70675/87c84718zafe4z412cza804zc70115a173ddopenalex-snapshot
Optimisation d’un système de stockage hybride de l’énergie électrique avec batterie et supercondensateurs pour véhicule électrique
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.