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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles (Paperback)
Loot Price: R1,601
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Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles (Paperback)
Series: Synthesis Lectures on Advances in Automotive Technology
Expected to ship within 18 - 22 working days
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The urgent need for vehicle electrification and improvement in fuel
efficiency has gained increasing attention worldwide. Regarding
this concern, the solution of hybrid vehicle systems has proven its
value from academic research and industry applications, where
energy management plays a key role in taking full advantage of
hybrid electric vehicles (HEVs). There are many well-established
energy management approaches, ranging from rules-based strategies
to optimization-based methods, that can provide diverse options to
achieve higher fuel economy performance. However, the research
scope for energy management is still expanding with the development
of intelligent transportation systems and the improvement in
onboard sensing and computing resources. Owing to the boom in
machine learning, especially deep learning and deep reinforcement
learning (DRL), research on learning-based energy management
strategies (EMSs) is gradually gaining more momentum. They have
shown great promise in not only being capable of dealing with big
data, but also in generalizing previously learned rules to new
scenarios without complex manually tunning. Focusing on
learning-based energy management with DRL as the core, this book
begins with an introduction to the background of DRL in HEV energy
management. The strengths and limitations of typical DRL-based EMSs
are identified according to the types of state space and action
space in energy management. Accordingly, value-based, policy
gradient-based, and hybrid action space-oriented energy management
methods via DRL are discussed, respectively. Finally, a general
online integration scheme for DRL-based EMS is described to bridge
the gap between strategy learning in the simulator and strategy
deployment on the vehicle controller.
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