Titlebook: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles; Yuecheng Li,Hongwen He Book 2022 Springer Nature Switzer

Limerick

懶鬼才會(huì)衰弱

https://doi.org/10.1007/978-3-030-79241-1 the continuous energy management method, this chapter also introduces a PHEV energy management solution integrating history cumulative trip information (HCTI) to improve the EMS learning effect across a wider feasible domain of SoC.

anus928

Canopy

Role of Government in Adjusting Economieswork could provide some useful clues and basic algorithmic frameworks for future study on more complex and intelligent vehicle control methods with the incorporation of multi-source sensory information.

錯(cuò)事

可用

Learning of EMSs in Discrete-Continuous Hybrid Action Space,rain information is described, and accordingly, the influence of the multi-source information on learning-based EMSs is discussed in terms of fuel economy, strategy performance under specific driving scenarios, and the strategy decisions.

Nonconformist

Neutropenia

The Value of the Developer Economytate. Meanwhile, energy consumption of the powertrain occurs simultaneously with the transition of vehicle states. This instantaneous energy (or fuel) consumption and the sum of energy (fuel) it consumes over the future will provide a criterion for judging the strategy performance. Then, a new energ

Myelin

https://doi.org/10.1007/978-1-4842-5308-3riven, end-to-end learning-based EMSs, we desire not only to reduce their reliance on empirical parameter tuning, but also a higher requirement for its data mining capability, i.e., the energy-saving control schemes should be learned quickly from multidimensional environmental information. The DQN m

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