Scientometric research and critical analysis of battery state-of-charge estimation

Yang, Fan; Shi, Dongliang; Mao, Qian; Lam, Kwok-ho

doi:10.1016/j.est.2022.106283

Cited by 19 publications

(4 citation statements)

References 158 publications

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“…The convergence is underlined by the Unscented Kalman Filter (UKF) [21][22][23][24][25][26][27][28][29][30][31][32][33], a sophisticated algorithm that has done a lot in advancing the abilities of AI-based robotics [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. The ability of UKF to provide accurate state estimation in non-linear systems is not only precise but also invaluable to the development of robotic systems with higher levels of autonomy and intelligence [49][50][51][52][53][54][55]. This paper aims at giving a bibliometric analysis of the application of UKF in AI-infused robotics, with emphasis on how this technology forms the basis for the blending of control and cognition.…”

Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.

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Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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“…However, the continuous working of lithium-ion batteries in complex and changeable environments causes a variety of performance degradations, such as capacity loss, reduced endurance mileage, and power fade [4][5][6][7]. In general, battery SOH is a critical indicator for evaluating the degree of aging that can be defined as the ratio of the current available capacity to the initial capacity; as a battery's capacity degrades over time to 70-80% of the rated capacity, it will eventually need to be retired, as its performance will no longer satisfy energy and power requirements [8][9][10][11]. Therefore, it is vital to develop a method that can accurately estimate battery SOH in order to guarantee safe and efficient battery operation.…”

Section: Introductionmentioning

confidence: 99%

State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory

Luo

Zhang

et al. 2023

WEVJ

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The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden.

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“…The BMS is a critical component of EVs, ensuring the safety, longevity, and performance of the battery pack while enhancing the overall driving experience for users [8]. The development of an effective and intelligent BMS is essential to estimate remaining useful life (RUL), state of energy (SOE), state of charge (SOC) and state of health (SOH), as well as to perform charge balancing, temperature management, and fault diagnostics, [9]. The BMS employs various circuit devices and power electronics components as well as algorithms and methods to implement various functionalities such as SOC management, overvoltage and undervoltage protection, temperature control, battery cell balancing, energy efficiency and battery life expansion [10,11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

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Scientometric research and critical analysis of battery state-of-charge estimation

Cited by 19 publications

References 158 publications

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

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