Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320482342> ?p ?o ?g. }
- W4320482342 endingPage "26" @default.
- W4320482342 startingPage "1" @default.
- W4320482342 abstract "Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario." @default.
- W4320482342 created "2023-02-14" @default.
- W4320482342 creator A5003850660 @default.
- W4320482342 creator A5011894823 @default.
- W4320482342 creator A5026934870 @default.
- W4320482342 creator A5057119084 @default.
- W4320482342 creator A5060663455 @default.
- W4320482342 creator A5074882171 @default.
- W4320482342 creator A5075660433 @default.
- W4320482342 creator A5085021426 @default.
- W4320482342 date "2023-02-13" @default.
- W4320482342 modified "2023-10-13" @default.
- W4320482342 title "Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios" @default.
- W4320482342 cites W1987813263 @default.
- W4320482342 cites W2029515813 @default.
- W4320482342 cites W2065788296 @default.
- W4320482342 cites W2066449147 @default.
- W4320482342 cites W2067279931 @default.
- W4320482342 cites W2118412444 @default.
- W4320482342 cites W2145339207 @default.
- W4320482342 cites W2148881573 @default.
- W4320482342 cites W2186737955 @default.
- W4320482342 cites W2236204240 @default.
- W4320482342 cites W2466636338 @default.
- W4320482342 cites W2596696538 @default.
- W4320482342 cites W2740748703 @default.
- W4320482342 cites W2750856959 @default.
- W4320482342 cites W2752306854 @default.
- W4320482342 cites W2797688571 @default.
- W4320482342 cites W2801441281 @default.
- W4320482342 cites W2936616423 @default.
- W4320482342 cites W2963334388 @default.
- W4320482342 cites W2970566041 @default.
- W4320482342 cites W2973588514 @default.
- W4320482342 cites W2974193225 @default.
- W4320482342 cites W3007711103 @default.
- W4320482342 cites W3009193590 @default.
- W4320482342 cites W3011524553 @default.
- W4320482342 cites W3016533673 @default.
- W4320482342 cites W3033556475 @default.
- W4320482342 cites W3086953995 @default.
- W4320482342 cites W3088335723 @default.
- W4320482342 cites W3123642769 @default.
- W4320482342 cites W3135672429 @default.
- W4320482342 cites W3193771755 @default.
- W4320482342 cites W3196143163 @default.
- W4320482342 cites W3197437737 @default.
- W4320482342 cites W3206820790 @default.
- W4320482342 cites W3215576994 @default.
- W4320482342 cites W4200612083 @default.
- W4320482342 cites W4206209083 @default.
- W4320482342 cites W4212840895 @default.
- W4320482342 cites W4213339220 @default.
- W4320482342 cites W4221138616 @default.
- W4320482342 cites W4301373990 @default.
- W4320482342 cites W4310054160 @default.
- W4320482342 doi "https://doi.org/10.1155/2023/1875380" @default.
- W4320482342 hasPublicationYear "2023" @default.
- W4320482342 type Work @default.
- W4320482342 citedByCount "0" @default.
- W4320482342 crossrefType "journal-article" @default.
- W4320482342 hasAuthorship W4320482342A5003850660 @default.
- W4320482342 hasAuthorship W4320482342A5011894823 @default.
- W4320482342 hasAuthorship W4320482342A5026934870 @default.
- W4320482342 hasAuthorship W4320482342A5057119084 @default.
- W4320482342 hasAuthorship W4320482342A5060663455 @default.
- W4320482342 hasAuthorship W4320482342A5074882171 @default.
- W4320482342 hasAuthorship W4320482342A5075660433 @default.
- W4320482342 hasAuthorship W4320482342A5085021426 @default.
- W4320482342 hasBestOaLocation W43204823421 @default.
- W4320482342 hasConcept C105795698 @default.
- W4320482342 hasConcept C108583219 @default.
- W4320482342 hasConcept C119599485 @default.
- W4320482342 hasConcept C127413603 @default.
- W4320482342 hasConcept C154945302 @default.
- W4320482342 hasConcept C171146098 @default.
- W4320482342 hasConcept C186370098 @default.
- W4320482342 hasConcept C188116033 @default.
- W4320482342 hasConcept C2780165032 @default.
- W4320482342 hasConcept C33923547 @default.
- W4320482342 hasConcept C40567965 @default.
- W4320482342 hasConcept C41008148 @default.
- W4320482342 hasConcept C50644808 @default.
- W4320482342 hasConcept C52121051 @default.
- W4320482342 hasConcept C7817414 @default.
- W4320482342 hasConcept C97541855 @default.
- W4320482342 hasConceptScore W4320482342C105795698 @default.
- W4320482342 hasConceptScore W4320482342C108583219 @default.
- W4320482342 hasConceptScore W4320482342C119599485 @default.
- W4320482342 hasConceptScore W4320482342C127413603 @default.
- W4320482342 hasConceptScore W4320482342C154945302 @default.
- W4320482342 hasConceptScore W4320482342C171146098 @default.
- W4320482342 hasConceptScore W4320482342C186370098 @default.
- W4320482342 hasConceptScore W4320482342C188116033 @default.
- W4320482342 hasConceptScore W4320482342C2780165032 @default.
- W4320482342 hasConceptScore W4320482342C33923547 @default.
- W4320482342 hasConceptScore W4320482342C40567965 @default.
- W4320482342 hasConceptScore W4320482342C41008148 @default.