Article
Chemistry, Physical
Jonas A. Braun, Rene Behmann, David Schmider, Wolfgang G. Bessler
Summary: Accurately diagnosing the SOC and SOH of batteries is crucial for battery users and manufacturers. This study presents a new algorithm that uses battery voltage as input for a voltage-controlled model to accurately estimate SOC and SOH. The algorithm is self-calibrating, robust against cell aging, allows SOH estimation from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.
JOURNAL OF POWER SOURCES
(2022)
Article
Energy & Fuels
Shulin Liu, Xia Dong, Xiaodong Yu, Xiaoqing Ren, Jinfeng Zhang, Rui Zhu
Summary: This paper presents an adaptive unscented Kalman filter algorithm (AUKF) for the joint estimation of SOC and SOH of lithium-ion batteries. The proposed method is verified to be accurate and reliable through experiments.
Article
Energy & Fuels
Bashar Mohammad Othman, Zainal Salam, Abdul Rahid Hussain
Summary: This paper proposes a simple and fast online adaptive observer for SOC estimation of Lithium-ion battery. The observer has attractive features such as proven stability, low computational requirements, and simultaneous estimation of SOC and most of the battery parameters.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Electrical & Electronic
Hussein Obeid, Raffaele Petrone, Hicham Chaoui, Hamid Gualous
Summary: This paper proposes a new higher-order sliding mode-based approach for online estimation of state of charge (SOC) and state of health (SOH) for Lithium-ion batteries. The approach combines a Higher-Order Sliding Mode (HOSM) observer with two Generalized Super-Twisting (GST) observer-based identification algorithms. The HOSM observer provides an exact estimation of SOC, while the identification algorithms ensure finite time estimation of battery capacity and inner resistance, leading to accurate estimation of SOH. The proposed approach has the advantages of using only one observer for SOC estimation and not requiring any assumptions on system states. Experimental results using the Worldwide Harmonized Light Vehicles Test procedures (WLTP) demonstrate the high efficiency of the approach.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Tiancheng Ouyang, Peihang Xu, Jie Lu, Xiaoyi Hu, Benlong Liu, Nan Chen
Summary: This article proposes a multithread dynamic optimization method for accurate estimation of state-of-charge (SOC) and state-of-health (SOH) of power batteries. The method utilizes a fractional-order model and an unscented Kalman filter for SOC estimation, and proposes Gaussian linear models based on parameters of six commonly used open-circuit-voltage models for SOH estimation. Experimental results confirm the effectiveness of the proposed method in improving estimation accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Peihang Xu, Chengchao Wang, Jinlu Ye, Tiancheng Ouyang
Summary: In this study, an integrated attention mechanism and a multi-dimensional temperature compensation method are proposed to enhance the feature extraction and battery state prediction capabilities of LSTM networks in long time series. Experimental results show that the proposed method significantly improves prediction accuracy under current conditions and outperforms traditional methods in battery health prognosis.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Chemistry, Physical
Yohei Kawahara, Kei Sakabe, Ryohei Nakao, Kenichiro Tsuru, Keiichiro Okawa, Yoshinori Aoshima, Akihiko Kudo, Akihiko Emori
Summary: A new method is developed to detect the status of HEV batteries, accurately obtaining the state of charge and health of the batteries. The method also has an auto-tuning function for battery parameters, as confirmed by simulation evaluations showing less than 5% SOC error. The state of health gradually converges to the true value by repeated simulation evaluations.
JOURNAL OF POWER SOURCES
(2021)
Review
Green & Sustainable Science & Technology
Jiahui Zhao, Yong Zhu, Bin Zhang, Mingyi Liu, Jianxing Wang, Chenghao Liu, Xiaowei Hao
Summary: The accurate estimation of the state of charge (SOC), the state of health (SOH) and the prediction of remaining useful life (RUL) of lithium-ion batteries is essential for battery management. Researchers have conducted extensive research on battery state evaluation and RUL prediction methods, proposing various approaches. This paper introduces the definitions and existing estimation methods for SOC and SOH, presents the definition of RUL and compares different approaches. Lastly, it summarizes the challenges in lithium-ion battery state estimation and RUL prediction and proposes future directions for development.
Article
Thermodynamics
Jingwen Wei, Chunlin Chen
Summary: This paper presents a multi-timescale framework for SOC estimation and lifetime prediction of lithium-ion batteries, combining recursive least squares-based parameter identification, robust observer, dual extended Kalman filter, and particle filtering based on capacity for lifetime prediction. The proposed method demonstrates effectiveness and superiority in accuracy and robustness through simulation and experimental results, achieving low root-mean-square errors for SOC and capacity estimation, as well as accurate lifetime prediction.
Article
Energy & Fuels
Mehmet Korkmaz
Summary: Accurate state-of-charge (SoC) estimation is crucial for the efficient management and protection of Li-Ion batteries, especially in electrified vehicles. However, the complexity of electrochemical reactions and environmental variables make accurate SoC estimation challenging. Traditional methods suffer from limitations, while data-driven approaches have gained popularity for building models based on battery parameters. This study aims to comprehensively compare ML methods and evaluate the effectiveness of different filters for outlier removal in improving SoC estimation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Shuzhi Zhang, Nian Peng, Haibin Lu, Rui Li, Xiongwen Zhang
Summary: This paper presents a systematic and low-complexity multi-state estimation framework for a series-connected lithium-ion battery pack under passive balance control. The framework includes the estimation of pack state-of-charge (SOC), state-of-health (SOH), and cell SOC inconsistencies. The results show that the proposed framework can accurately estimate the battery pack's states and track the cell SOC inconsistencies.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Omid Rezaei, Ali Rahdan, Sohrab Sardari, Masoud Dahmardeh, Zhanle Wang
Summary: This paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method for the practical state of charge (SoC) estimation of lithium-ion batteries. The proposed estimator is able to estimate the model uncertainties without requiring the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, eliminating the destructive effect of model inaccuracy on the estimation accuracy.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Jiabo Li, Min Ye, Kangping Gao, Xinxin Xu, Meng Wei, Shengjie Jiao
Summary: This paper proposes a novel dual Kalman filter method to achieve simultaneous SOC and SOH estimation and improves the estimation accuracy of SOC and SOH from aspects such as model establishment, parameter identification, error model proposal, and algorithm improvement. The experimental results show that the proposed model can control the estimation error of SOC and SOH within 1%.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Multidisciplinary Sciences
Rajakumar Sakile, Umesh Kumar Sinha
Summary: This paper proposes a new adaptive nonlinear observer (ANO) method for estimating the accurate SOC and SOH of lithium-ion batteries. Compared to conventional methods, the new approach provides better dynamic results and high convergence capability.
ADVANCED THEORY AND SIMULATIONS
(2021)
Article
Automation & Control Systems
Yizhao Gao, Kailong Liu, Chong Zhu, Xi Zhang, Dong Zhang
Summary: This article presents a scheme using a simplified reduced-order electrochemical model and dual nonlinear filters for the reliable co-estimations of cell state-of-charge (SOC) and state-of-health (SOH). By accessing unmeasurable physical variables such as surface and bulk solid-phase concentration, the feasibility and performance of SOC estimator are revealed. Aging factors including loss of lithium ions, loss of active materials, and resistance increment are identified to improve the precision of SOC estimation for aged cells.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
