Machine learning approach for electric vehicle availability forecast to provide vehicle-to-home services

Aguilar-Dominguez, Donovan; Ejeh, Jude O.; Dunbar, Alan D. F.; Brown, Solomon

doi:10.1016/j.egyr.2021.02.053

Cited by 26 publications

(14 citation statements)

References 10 publications

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“…The ECM represents the battery as a combination of resistors, capacitors, and voltage sources. According to figure (9), the ECM equations relate the SoC (SoCt) to current (It) and voltage (Vt) measurements [157]:…”

Section: ) Mathematical Formulation For Soc Estimationmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing state of charge management efficiency and effectiveness for electric vehicles, offering critical insights.. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

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Section: ) Mathematical Formulation For Soc Estimationmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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“…However, the application of these methods in real-time systems is limited due to extensive computations, which makes them slow. Thus, data-driven approaches, including machine learning (ML), are being introduced to offer more advanced services [18][19][20]. Furthermore, reinforcement learning (RL) algorithms provide superior alternative energy decisions, control, and management solutions due to their outstanding ability to make decisions and resolve issues without prior knowledge of the environment.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers proposed a supervised ML model to predict EV availability in V2H services to understand the vehicle availability's impact on capacity [19]. First, the model predicts the EV's start and end locations (classification problem).…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

2023

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These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

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“…Das et al [7] designed an ML-based algorithm to learn the EV's status and inform when the EVs should perform G2V/V2G using support vector machines (SVM). Donovan et al [8] presented a prediction mechanism for the EV's availability to perform energy trading (ET) and used the Gurobi optimization to minimize the consumer's energy bill.…”

Section: Introductionmentioning

confidence: 99%

SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

Kumari

Trivedi

Tanwar

et al. 2022

International Transactions on Electrical Energy Systems

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In recent years, advancements in electric vehicle (EV) technology and rising petrol prices have increased the demand for EVs and also made them important for the Smart Grid (SG) economy. During the high energy demand, Vehicle to Grid (V2G) comprises a notable feature that returns the stored energy back to the grid. However, due to dynamic nature of energy prices and EVs availability, determining the best charging and discharging strategy is quite difficult. The existing approaches need a model to predict the uncertainty and optimize the scheduling problem. Further, other issues like security, scalability, and real-time data accessibility of EVs energy trading (ET) data at low cost also exist. Though many solutions exist, they are not adequate to handle the aforementioned issues. This paper proposes a Secure V2G-Energy Trading (SV2G-ET) scheme using deep Reinforcement Learning (RL) and Ethereum Blockchain Technology (EBT). The proposed SV2G-ET scheme employs a deep Q-network for EVs scheduling for charging/discharging. SV2G-ET scheme uses InterPlanetary File System (IPFS) and smart contract (SC) for secure access of EV’s ET data in real time. The experimental results prove the efficacy of the proposed SV2G-ET scheme that leads to improved scalability, saving the EVs charging cost, low ET data storage cost, and increased EV owner’s profit.

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Machine learning approach for electric vehicle availability forecast to provide vehicle-to-home services

Cited by 26 publications

References 10 publications

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

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