Edge computing for vehicle battery management: Cloud-based online state estimation

Li, Shuangqi; He, Haibo; Wei, Zhongbao; Zhao, Pengfei

doi:10.1016/j.est.2022.105502

Cited by 22 publications

(12 citation statements)

References 44 publications

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“…With the rapid development of AI technologies in recent years, 199 machine learning (ML) is being applied to battery modeling and real-time predictions, including online state estimation and diagnostics. 200 , 201 , 202 The incorporation of ML in LIB TR modeling is still emerging. We explore three potential modeling strategies that integrate TR forecasts with ML, as illustrated in Figure 5 , aiming to inspire and guide the next generation of LIB TR modeling.…”

Section: Seeking a Balance Between Accuracy And Efficiencymentioning

confidence: 99%

Advances and challenges in thermal runaway modeling of lithium-ion batteries

Wang,

Ping,

Kong

et al. 2024

The Innovation

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Section: Seeking a Balance Between Accuracy And Efficiencymentioning

confidence: 99%

Advances and challenges in thermal runaway modeling of lithium-ion batteries

Wang,

Ping,

Kong

et al. 2024

The Innovation

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“…c) Hybrid Models The synergistic integration of machine learning and physicsbased models, as championed by [163], exhibits great promise in enhancing the precision of SoC estimation. Through their innovative approach, reference [164] harnessed the strengths of both modeling paradigms, achieving a substantial improvement in the accuracy of SoC predictions.…”

Section: ) Machine Learning 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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“…Experimental results demonstrated that the proposed method exhibited a low error rate of less than 4% in estimating battery life. In another study, the authors proposed a cloud-assisted online battery management method based on AI and edge computing technologies for EVs [26]. A cloud-edge battery management system (CEBMS) was established to integrate cloud computation and big data resources into real-time vehicle battery management.…”

Section: Cloud-based Progress On Battery State Predictionmentioning

confidence: 99%

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

et al. 2023

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Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

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Edge computing for vehicle battery management: Cloud-based online state estimation

Cited by 22 publications

References 44 publications

Advances and challenges in thermal runaway modeling of lithium-ion batteries

Advances and challenges in thermal runaway modeling of lithium-ion batteries

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

Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

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