Enhanced Support Vector Machine with Particle Swarm Optimization and Genetic Algorithm for Estimating Discharge Coefficients of Circular-Crested Oblique Weirs

Nourani, Bahram; Arvanaghi, Hadi; Pourhosseini, Fatemeh Akhoni; Javidnia, Mohammad; Abraham, John

doi:10.1007/s40996-023-01110-0

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…LSTM networks have gained prominence in SoC estimation for EVs due to their ability to capture long-term dependencies in time-series data effectively [94][95][96][97]. LSTMs are a type of RNN designed to mitigate the vanishing gradient problem, allowing them to capture information over longer time horizons.…”

Section: B Long Short-term Memory (Lstm) Network For Soc Estimationmentioning

confidence: 99%

“…SVM is an emerging technique for SoC estimation in EVs. SVMs aim to find a hyperplane that maximizes the margin between different classes of data points while minimizing prediction errors [91,94]. In the context of SoC estimation, SVMs are used for regression tasks to predict SoC values based on input features.…”

Section: Support Vector Machines (Svms) For Soc Estimationmentioning

confidence: 99%

“…c) AI-Based Fault Detection Reference [94] presented AI-driven fault detection methods that contribute to enhanced SoC management by identifying abnormal battery behavior.…”

Section: ) Broad Applications and Adaptations A) Battery Health Monit...mentioning

confidence: 99%

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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: B Long Short-term Memory (Lstm) Network For Soc Estimationmentioning

confidence: 99%

Section: Support Vector Machines (Svms) For Soc Estimationmentioning

confidence: 99%