Hybrid Electric Vehicle Powertrain Control Based on Reinforcement
                    Learning

Yao, Zhengyu; Yoon, Hwan‐Sik

doi:10.4271/14-11-02-0013

Cited by 10 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Further investigations could delve into the precise mechanisms and long-term behavior of these modified asphalt mixtures to optimize their composition and performance [82][83][84]. Also, various statistical analyses, machine learning, and optimization methods can be applied for further investigation [85][86][87][88][89][90][91][92][93][94][95][96][97][98].…”

Section: Discussionmentioning

confidence: 99%

A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles

Bozhi,

Mohamed,

Gilani

et al. 2023

Sustainability

View full text Add to dashboard Cite

The proliferation of electric vehicles (EVs) hinges upon the availability of robust and efficient charging infrastructure, notably encompassing swift and convenient solutions. Among these, dynamic wireless charging systems have garnered substantial attention for their potential to revolutionize EV charging experiences. Inductive power transfer (IPT) systems, in particular, exhibit a promising avenue, enabling seamless wireless charging through integrated pavements for EVs. This review engages in an in-depth exploration of pertinent parameters that influence the inductivity and conductivity performance of pavements, alongside the assessment of potential damage inflicted by IPT pads. Moreover, the study delves into the realm of additive materials as a strategic approach to augment conductivity and pavement performance. In essence, the review consolidates a diverse array of studies that scrutinize IPT pad materials, coil dimensions, pavement characteristics (both static and dynamic), and adhesive properties. These studies collectively illuminate the intricate dynamics of power transfer to EVs while considering potential repercussions on pavement integrity. Furthermore, the review sheds light on the efficacy of various additive materials, including metal and nanocomposite additives with an SBS base, in amplifying both conductivity and pavement performance. The culmination of these findings underscores the pivotal role of geometry optimization for IPT pads and the strategic adaptation of aggregate and bitumen characteristics to unlock enhanced performance within wireless pavements.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles

Bozhi,

Mohamed,

Gilani

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Recently, Yao Z et al [31] applied a relatively new approach called Twin-Delayed Deep Deterministic Policy Gradient (TD3) [32] to maximize the fuel economy of a mild HEV. As an extension of the DDPG algorithm, TD3 can prevent the overestimation of the value function, and further improve performance.…”

Section: Policy Gradient Approachmentioning

confidence: 99%

Review on Reinforcement Learning-Based Energy Management Strategies for Hybrid Electric Vehicles

Yoon

2022

EME

Self Cite

View full text Add to dashboard Cite

Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. These power sources have conventionally been controlled by a rule-based algorithm or optimization-based control. Besides these conventional approaches, reinforcement learning-based control algorithms have actively been studied recently. Reinforcement learning, which is one of three machine learning paradigms, has the capability of determining optimal control actions to maximize a vehicle’s fuel economy without the vehicle model nor a priori driving route information. To provide a useful reference to researchers interested in this technology, this article reviews reinforcement learning-based energy management strategies for HEVs with their advantages and disadvantages.

show abstract

“…f uel,electrical − w 1 |δSOC| parallel HEV[74,75] − ṁ f uel − ṁe. f uel,electrical + w 1 δSoC 2 − parallel HEV [76,77] 1 − (w 1 ṁ f uel + w 2 ṁe.…”

unclassified

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

2023

View full text Add to dashboard Cite

One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed.

show abstract

Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning

Cited by 10 publications

References 0 publications

A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles

A Review of Wireless Pavement System Based on the Inductive Power Transfer in Electric Vehicles

Review on Reinforcement Learning-Based Energy Management Strategies for Hybrid Electric Vehicles

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

Contact Info

Product

Resources

About