Titlebook: Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles; Teng Liu Book 2019 Springer Nature Switzerland

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Powertrain Modeling and Reinforcement Learning,ptimization objective). To this end, the detailed modeling of the powertrain components need to be created. Two perspectives are often leveraged to establish modeling for energy management problem: simulator-oriented and control-oriented. The former is suitable for testing the performance of existin

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Prediction and Updating of Driving Information, future vehicle velocity and power demand are able to be predicted accurately, the corresponding EMS could be regulated appropriately a priori to accommodate driving conditions. Especially when driving situations are switched (e.g., from the highway to urban), the predicted driving information is im

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Evaluation of Intelligent Energy Management System,in the energy management field, and the performance of these approaches has also been estimated in different powertrain architectures and driving situations. This book chooses dynamic programming (DP) and stochastic dynamic programming (SDP) as benchmark technologies to evaluate the RL-based control

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978-3-031-00375-2Springer Nature Switzerland AG 2019

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Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles978-3-031-01503-8Series ISSN 2576-8107 Series E-ISSN 2576-8131

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Meinert A. Meyer,Josef Keuffere access control for handling entities who can use the data. . can partially provide such flexibility by specifying privileged receivers so that only they can decrypt a ciphertext. However, once privileged receivers are specified, they can be no longer dynamically added and/or removed. In this paper

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volunteer internship is institutionalized for students seeking global working experience and native English. Specifically, using the concept of migration infrastructure (Xiang & Lindquist, .: S122–S148, 2014), this research investigates the roles that intermediaries, such as study abroad agencies an