2018
DOI: 10.1016/j.energy.2018.07.022
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Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning

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Cited by 112 publications
(39 citation statements)
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“…The increasing interest in sustainable mobility has led to the study and development of new hybrid architectures, which can be mainly divided in three main categories [1]: parallel [2][3][4][5][6], series/parallel (also known as power-split) [6][7][8][9][10][11][12][13][14], and series hybrid [15][16][17][18][19][20][21]. Passalacqua et al [22] proposed a new series architecture configuration for medium size cars, with a storage system based only on supercapacitors and they highlighted the benefits in terms of internal combustion engine (ICE) optimal working points.…”
Section: Introductionmentioning
confidence: 99%
“…The increasing interest in sustainable mobility has led to the study and development of new hybrid architectures, which can be mainly divided in three main categories [1]: parallel [2][3][4][5][6], series/parallel (also known as power-split) [6][7][8][9][10][11][12][13][14], and series hybrid [15][16][17][18][19][20][21]. Passalacqua et al [22] proposed a new series architecture configuration for medium size cars, with a storage system based only on supercapacitors and they highlighted the benefits in terms of internal combustion engine (ICE) optimal working points.…”
Section: Introductionmentioning
confidence: 99%
“…In Refs. [23,24], a reinforcement learning algorithm is employed to optimally control HEVs using MC-based predictors. Compared with Ref.…”
Section: Introductionmentioning
confidence: 99%
“…[23], the main improvement of Ref. [24] is to utilize an online updating policy for updating the transition probability of the MC models. Ref.…”
Section: Introductionmentioning
confidence: 99%
“…Ref. [17] proposed a different Tabular QL-based EMS, where the SOC and the speed of the engine were taken as the states, and the throttle of the engine was taken as the action. For the latter, Ref.…”
Section: Introductionmentioning
confidence: 99%