Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine

Jain, Arpit; Verma, Chaman; Kumar, Neerendra; Răboaca, Maria Simona; Baliya, Jyoti Narayan; Suciu, George

doi:10.3390/info14010029

Cited by 8 publications

(10 citation statements)

References 28 publications

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“…a) Sequential Dependency RNNs excel in capturing sequential dependencies in timeseries data [91]. In the context of EV SoC estimation, they can effectively model how past SoC values and input data influence the current SoC.…”

Section: ) Applications and Further Advantages Of Rnns In Soc Estimationmentioning

confidence: 99%

“…In the context of EV SoC estimation, they can effectively model how past SoC values and input data influence the current SoC. Studies comparing RNNs to traditional models revealed a 33.78% improvement in capturing intricate sequential dependencies, showcasing the efficacy of RNNs in modeling complex temporal relationships [91].…”

Section: ) Applications and Further Advantages Of Rnns In Soc Estimationmentioning

confidence: 99%

“…d) Battery Aging Consideration LSTMs can be extended to incorporate aging models into SoC estimation, accounting for battery degradation effects [91].…”

Section: ) Advantages Of Lstms In 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) Generalization Capability SVMs have strong generalization capabilities, allowing them to provide accurate SoC estimates across various driving conditions and battery types [91].…”

Section: ) Advantages Of Svms In Soc Estimationmentioning

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: ) Applications and Further Advantages Of Rnns In Soc Estimationmentioning

confidence: 99%

Section: ) Applications and Further Advantages Of Rnns In Soc Estimationmentioning

confidence: 99%

“…d) Battery Aging Consideration LSTMs can be extended to incorporate aging models into SoC estimation, accounting for battery degradation effects [91].…”

Section: ) Advantages Of Lstms In Soc Estimationmentioning

confidence: 99%

Section: Support Vector Machines (Svms) For Soc Estimationmentioning

confidence: 99%

“…c) Generalization Capability SVMs have strong generalization capabilities, allowing them to provide accurate SoC estimates across various driving conditions and battery types [91].…”

Section: ) Advantages Of Svms In Soc Estimationmentioning

confidence: 99%

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