Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048479713> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W3048479713 endingPage "3087" @default.
- W3048479713 startingPage "3075" @default.
- W3048479713 abstract "Electric vehicle (EV) has become one of the most critical components in the smart grid with the applications of the Internet-of-Things (IoT) technologies. Real-time charging control is pivotal to ensure the efficient operation of EVs. However, the charging control performance is limited by the uncertainty of the environment. On the other hand, it is challenging to determine a charging control strategy that is able to optimize multiple objectives simultaneously. In this article, we formulate the EV charging control model as a Markov decision process (MDP) by constructing state, action, transition function, and reward. Then, we propose a deep-reinforcement-learning-based approach: charging control deep deterministic policy gradient (CDDPG) to learn the optimal charging control strategy for satisfying the user’s requirement of battery energy while minimizing the user’s charging expense. We utilize the long short-term memory (LSTM) network that extracts the information of previous energy price to determine the current charging control strategy. Moreover, Gaussian noise is added to the output of the actor network to prevent the agent from sticking into the nonoptimal strategy. In addition, we address the limitation of sparse rewards by using two replay buffers, of which one is used to store the rewards during the charging phase and another is used to store the rewards after charging is completed. The simulation results prove that the CDDPG-based approach outperforms the deep- $Q$ -learning-based approach (DQL) and the deep-deterministic-policy-gradient-based approach (DDPG) in satisfying the user’s requirement for the battery energy and reducing the charging cost." @default.
- W3048479713 created "2020-08-18" @default.
- W3048479713 creator A5035313256 @default.
- W3048479713 creator A5066227184 @default.
- W3048479713 creator A5087373711 @default.
- W3048479713 date "2021-03-01" @default.
- W3048479713 modified "2023-10-17" @default.
- W3048479713 title "CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control" @default.
- W3048479713 cites W1967231569 @default.
- W3048479713 cites W1978995606 @default.
- W3048479713 cites W1987809617 @default.
- W3048479713 cites W2012160694 @default.
- W3048479713 cites W2024931064 @default.
- W3048479713 cites W2024998154 @default.
- W3048479713 cites W2029243470 @default.
- W3048479713 cites W2047902637 @default.
- W3048479713 cites W2050304929 @default.
- W3048479713 cites W2051323064 @default.
- W3048479713 cites W2076587863 @default.
- W3048479713 cites W2079082996 @default.
- W3048479713 cites W2092860676 @default.
- W3048479713 cites W2103496339 @default.
- W3048479713 cites W2105402504 @default.
- W3048479713 cites W2111619626 @default.
- W3048479713 cites W2111708305 @default.
- W3048479713 cites W2115594466 @default.
- W3048479713 cites W2136848157 @default.
- W3048479713 cites W2138289719 @default.
- W3048479713 cites W2144572409 @default.
- W3048479713 cites W2145339207 @default.
- W3048479713 cites W2342981086 @default.
- W3048479713 cites W2412864723 @default.
- W3048479713 cites W2440681194 @default.
- W3048479713 cites W2471802579 @default.
- W3048479713 cites W2515947593 @default.
- W3048479713 cites W2780336025 @default.
- W3048479713 cites W2803890045 @default.
- W3048479713 cites W2899639849 @default.
- W3048479713 cites W2901645090 @default.
- W3048479713 cites W2904635416 @default.
- W3048479713 cites W2920406591 @default.
- W3048479713 cites W4237591687 @default.
- W3048479713 cites W4251616545 @default.
- W3048479713 cites W2068014733 @default.
- W3048479713 doi "https://doi.org/10.1109/jiot.2020.3015204" @default.
- W3048479713 hasPublicationYear "2021" @default.
- W3048479713 type Work @default.
- W3048479713 sameAs 3048479713 @default.
- W3048479713 citedByCount "55" @default.
- W3048479713 countsByYear W30484797132021 @default.
- W3048479713 countsByYear W30484797132022 @default.
- W3048479713 countsByYear W30484797132023 @default.
- W3048479713 crossrefType "journal-article" @default.
- W3048479713 hasAuthorship W3048479713A5035313256 @default.
- W3048479713 hasAuthorship W3048479713A5066227184 @default.
- W3048479713 hasAuthorship W3048479713A5087373711 @default.
- W3048479713 hasConcept C121332964 @default.
- W3048479713 hasConcept C127413603 @default.
- W3048479713 hasConcept C154945302 @default.
- W3048479713 hasConcept C163258240 @default.
- W3048479713 hasConcept C171146098 @default.
- W3048479713 hasConcept C2775924081 @default.
- W3048479713 hasConcept C2776422217 @default.
- W3048479713 hasConcept C41008148 @default.
- W3048479713 hasConcept C62520636 @default.
- W3048479713 hasConcept C97541855 @default.
- W3048479713 hasConceptScore W3048479713C121332964 @default.
- W3048479713 hasConceptScore W3048479713C127413603 @default.
- W3048479713 hasConceptScore W3048479713C154945302 @default.
- W3048479713 hasConceptScore W3048479713C163258240 @default.
- W3048479713 hasConceptScore W3048479713C171146098 @default.
- W3048479713 hasConceptScore W3048479713C2775924081 @default.
- W3048479713 hasConceptScore W3048479713C2776422217 @default.
- W3048479713 hasConceptScore W3048479713C41008148 @default.
- W3048479713 hasConceptScore W3048479713C62520636 @default.
- W3048479713 hasConceptScore W3048479713C97541855 @default.
- W3048479713 hasFunder F4320321001 @default.
- W3048479713 hasFunder F4320335768 @default.
- W3048479713 hasIssue "5" @default.
- W3048479713 hasLocation W30484797131 @default.
- W3048479713 hasOpenAccess W3048479713 @default.
- W3048479713 hasPrimaryLocation W30484797131 @default.
- W3048479713 hasRelatedWork W1562959674 @default.
- W3048479713 hasRelatedWork W2923653485 @default.
- W3048479713 hasRelatedWork W2952472710 @default.
- W3048479713 hasRelatedWork W2957776456 @default.
- W3048479713 hasRelatedWork W3005560120 @default.
- W3048479713 hasRelatedWork W3037422413 @default.
- W3048479713 hasRelatedWork W4206669594 @default.
- W3048479713 hasRelatedWork W4210912933 @default.
- W3048479713 hasRelatedWork W4224287422 @default.
- W3048479713 hasRelatedWork W4255994452 @default.
- W3048479713 hasVolume "8" @default.
- W3048479713 isParatext "false" @default.
- W3048479713 isRetracted "false" @default.
- W3048479713 magId "3048479713" @default.
- W3048479713 workType "article" @default.