Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Lin, Qizhe; Li, Xiaoqi; Tu, Bicheng; Cao, Junwei; Zhang, Ming; Xiang, Jiawei

doi:10.3390/s23010467

Cited by 16 publications

(13 citation statements)

References 38 publications

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“…The method of estimating SOC can be simply divided into four categories: experiment-based SOC assessment method, model-based SOC assessment method, data-driven SOC assessment method, and SOC assessment method based on fusion method [2].…”

Section: Estimation Methods Of Charge State For Lithium-ion Batteriesmentioning

confidence: 99%

Estimation of the Charge State in Lithium-ion Batteries Based on an Adaptive Particle Filter

Zhao

Pan²

2023

J. Phys.: Conf. Ser.

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Lithium-ion batteries have the advantages of high energy density, long life, and environmental friendliness, and are widely used as sources of energy in new energy vehicles. The charge state (SOC) of lithium-ion battery greatly represents the remaining service time of the battery, and in electric vehicles, it greatly determines the range of the electric vehicle. Therefore, how to estimate SOC from physical quantities such as end voltage and end current is crucial. This paper introduces the common lithium-ion battery charge state estimation method and its state model, and estimates the charge state based on the adaptive particle filter algorithm.

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Section: Estimation Methods Of Charge State For Lithium-ion Batteriesmentioning

confidence: 99%

Estimation of the Charge State in Lithium-ion Batteries Based on an Adaptive Particle Filter

Zhao

Pan²

2023

J. Phys.: Conf. Ser.

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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. The hybrid model, combining the adaptability of machine learning with the foundational understanding of physics-based models, demonstrated a marked advancement in capturing the intricate dynamics inherent in EV systems [165].…”

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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“…The BMS requires an accurate estimation of the SOC and SOH to ensure the safety, life, and performance of the batteries [ 32 , 33 , 34 ]. In conventional methods, voltage, current, and surface temperature are employed as input parameters to estimate SOC and SOH [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Lithium-Ion Battery Safety Monitoring Based on Fiber Bragg Grating Sensors

Chen

Zhao

Zheng

et al. 2023

Sensors

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Lithium-ion batteries are widely used in a variety of fields due to their high energy density, high power density, long service life, and environmental friendliness. However, safety accidents with lithium-ion batteries occur frequently. The real-time safety monitoring of lithium-ion batteries is particularly important during their use. The fiber Bragg grating (FBG) sensors have some additional advantages over conventional electrochemical sensors, such as low invasiveness, electromagnetic anti-interference, and insulating properties. This paper reviews lithium-ion battery safety monitoring based on FBG sensors. The principles and sensing performance of FBG sensors are described. The single-parameter monitoring and dual-parameter monitoring of lithium-ion batteries based on FBG sensors are reviewed. The current application state of the monitored data in lithium-ion batteries is summarized. We also present a brief overview of the recent developments in FBG sensors used in lithium-ion batteries. Finally, we discuss future trends in lithium-ion battery safety monitoring based on FBG sensors.

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Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Cited by 16 publications

References 38 publications

Estimation of the Charge State in Lithium-ion Batteries Based on an Adaptive Particle Filter

Estimation of the Charge State in Lithium-ion Batteries Based on an Adaptive Particle Filter

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

Recent Progress in Lithium-Ion Battery Safety Monitoring Based on Fiber Bragg Grating Sensors

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