Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang, Chao; Zha, Mingjun; Wang, Weida; Liu, Kaijia; Chen, Xiang

doi:10.1049/iet-its.2019.0606

Cited by 167 publications

(61 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the development of V2I, V2V communication, and CV techniques, the traffic information can be collected. Moreover, studies revealed that the performance of the EMS was strongly dependent on the driver's power demand, and the EMS combined with traffic information in a connected environment can enhance energy management [18–20].…”

Section: Introductionmentioning

confidence: 99%

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Zhang

Shen

2020

IET intell. transp. syst

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Zhang

Shen

2020

IET intell. transp. syst

View full text Add to dashboard Cite

“…The rapid development of intelligent transport systems has led to the rise of online car‐hailing. [ 17 ] Different from buses and private cars for daily commuting, online car‐hailing has a common feature, that is, the driving cycle is uncertain. Therefore, it is essential to study the EMS that can be applied to variable driving cycles.…”

Section: Introductionmentioning

confidence: 99%

A Double‐Deep Q‐Network‐Based Energy Management Strategy for Hybrid Electric Vehicles under Variable Driving Cycles

2021

Self Cite

View full text Add to dashboard Cite

As a core part of hybrid electric vehicles (HEVs), energy management strategy (EMS) directly affects the vehicle fuel‐saving performance by regulating energy flow between engine and battery. Currently, most studies on EMS are focused on buses or commuter private cars, whose driving cycles are relatively fixed. However, there is also a great demand for the EMS that adapts to variable driving cycles. The rise of machine learning, especially deep learning and reinforcement learning, provides a new opportunity for the design of EMS for HEVs. Motivated by this issue, herein, a double‐deep Q‐network (DDQN)‐based EMS for HEVs under variable driving cycles is proposed. The distance traveled of the driving cycle is creatively introduced as states into the DDQN‐based EMS of HEV. The relevant problem of “curse of dimensionality” caused by choosing too many states in the process of training is solved via the good generalization of deep neural network. For the problem of overestimation in model training, two different neural networks are designed for action selection and target value calculation, respectively. The effectiveness and adaptability to variable driving cycles of the proposed DDQN‐based EMS are verified by simulation comparison with Q‐learning‐based EMS and rule‐based EMS for improving fuel economy.

show abstract

“…Progress in the field of sustainable energy scenario over the previous span of time has been exceptional. The two main sustainable energy resources are the sun and the wind [4]. The growth in electricity generation from renewable was substantially increased.…”

Section: Introductionmentioning

confidence: 99%

Energy Storage and Electric Vehicles: Technology, Operation, Challenges, and Cost-Benefit Analysis

Salkuti¹

2021

IJACSA

View full text Add to dashboard Cite

With ever-increasing oil prices and concerns for the natural environment, there is a fast-growing interest in electric vehicles (EVs) and renewable energy resources (RERs), and they play an important role in a gradual transition. However, energy storage is the weak point of EVs that delays their progress. The world's EV industry is accelerating to faster adoption with appropriate incentives to the EV owners, policy support, and encouraging local manufacturing. The increasing demand for EV's has presented itself as an authentic alternative to internal combustion engines (ICE). The main feature of the RERs is their variability and intermittency. These drawbacks are overcome by integrating more than one renewable energy source including backup sources and storage systems. This paper presents various technologies, operations, challenges, and costbenefit analysis of energy storage systems and EVs.

show abstract

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Cited by 167 publications

References 127 publications

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

A Double‐Deep Q‐Network‐Based Energy Management Strategy for Hybrid Electric Vehicles under Variable Driving Cycles

Energy Storage and Electric Vehicles: Technology, Operation, Challenges, and Cost-Benefit Analysis

Contact Info

Product

Resources

About