IoT-Based Proactive Energy Supply Control for Connected Electric Vehicles

Hu, Yuying; Chen, Cailian; He, Jianping; Yang, Bo; Guan, Xinping

doi:10.1109/jiot.2019.2899928

Cited by 28 publications

(6 citation statements)

References 37 publications

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“…A vehicular network scenario with two electric vehicles traveling in a row based on work in [24] is considered in this paper. Both vehicles support HESS and sensors to measure their states, i.e., speed and acceleration.…”

Section: Problem Statement and Solving Methodsmentioning

confidence: 99%

“…Traffic data are essential in obtaining power demand prediction from the proposed power management strategy. However, V2V technologies are not widely used yet, which means that obtaining data from other EVs is difficult in experiments [24]. Therefore, the data for this simulation are pre-processed data from the driving cycle profile.…”

Section: Simulation Parameters and Setupmentioning

confidence: 99%

See 1 more Smart Citation

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

2021

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In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion batteries and a supercapacitor (SC) as the EV’s energy sources is known as a hybrid energy storage system (HESS) and is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from vehicular network could not directly be used to improve HESS, a two levels control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in the level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in a level two control structure. The ANN prediction is direct, which could approximate the future power demand prediction with the assumption that the vehicular network scenario that is used to obtain surrounding information is deployed. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3, respectively, when compared to the battery-only case.

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Section: Problem Statement and Solving Methodsmentioning

confidence: 99%

Section: Simulation Parameters and Setupmentioning

confidence: 99%

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

2021

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“…Hu et al have proposed the IoT‐based proactive energy supply control in connected EVs 9 . The IoT devices are employed for predicting the future power requirements by using a conditional linear Gaussian model.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

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In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

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“…The online EV charging problem is more practical, as it does not assume any future information but only relies on the current and past EV profiles, including the arrival time, the departure time, and the charging demand of EVs. The advances in the Internet of Thing (IoT) technology and the intelligent transportation system have paved the way for EVs [3]. The information shared between EVs can improve realtime transportation and make smart decisions for individual EVs [4].…”

Section: Introductionmentioning

confidence: 99%

Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning

Cao,

Wang,

et al. 2021

Preprint

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With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This paper investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor-critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor-critic learning charging algorithm (CALC) by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%, compared with the Eagerly Charging Algorithm, Online Charging Algorithm, RL-based Adaptive Energy Management Algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

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IoT-Based Proactive Energy Supply Control for Connected Electric Vehicles

Cited by 28 publications

References 37 publications

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning

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