GA-LSTM speed prediction-based DDQN energy management for extended-range vehicles-Reference-Cited by-同舟云学术

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GA-LSTM speed prediction-based DDQN energy management for extended-range vehicles

Published:2024-09 Issue: Volume:17 Page:100367
ISSN:2666-5468
Container-title:Energy and AI
language:en
Short-container-title:Energy and AI

Author:

Lu Laiwei^ORCID,Zhao Hong^ORCID,Xv Fuliang,Luo Yong,Chen Junjie,Ding Xiaoyun

Publisher

Elsevier BV

Reference31 articles.

1. Unregulated emissions from natural gas taxi based on IVE model;Zhao;Atmosphere,2021

2. Real-time self-adaptive Q-learning controller for energy management of conventional autonomous vehicles;Fayyazi;Expert Syst Appl,2023

3. Optimization strategies for real-time energy management of electric vehicles based on LSTM network learning;Zhu;Energy Rep,2022

4. A reinforcement learning-based energy management strategy for fuel cell hybrid vehicle considering real-time velocity prediction;Yang;Energy Convers Manage,2022

5. Intelligent energy management through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in plugin parallel hybrid electric vehicle;Vignesh;Energy Convers Manage,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net;Agriculture;2024-08-05

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