Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

Xia, Bizhong; Zhang, Zheng; Lao, Zizhou; Wang, Wei; Sun, Wei; Lai, Yongzhi; Wang, Mingwang

doi:10.3390/en11061481

Cited by 32 publications

(33 citation statements)

References 42 publications

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“…Different filters exhibit distinct features and computing complexities. An HIF is applied in this paper as it is capable of tolerating uncertainty in battery modeling and works well without the knowledge of noise statistics [36]. Fundamentally, a set of discrete-time and nonlinear state-space models should be defined firstly, i.e.,…”

Section: H-infinity Filtermentioning

confidence: 99%

Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

2019

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The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells.Energies 2019, 12, 3005 2 of 16 the SOC [9]. This method, albeit accurate, is ex-situ and not suitable for real-time application. In a recent study, the electrolyte viscosity-determined by measuring the pressure drop across the VRB cell-was used to estimate the SOC [10]. This method demonstrated good potential for online SOC monitoring, although the quantitative relationships among SOC, viscosity, and pressure drop have to be calibrated accurately to ensure a reliable estimate. Uncertainties associated with other environmental or operating conditions also have to be ruled out.A new category of methods for SOC estimation of VRBs is the model-based observers. Firstly, a battery model is established to describe the inner physical processes and electrochemical reactions [11,12]. A typical model-based observer then links the measurable input and output to the internal state via a nonlinear state-space model [13]. This can provide a closed-loop and robust solution, which is favourable for online application, although a highly accurate battery model is a prerequisite. Different types of models have been proposed for VRBs. Numerical models [14][15][16] can describe the complicated electrochemical processes and multi-physics feature of a VRB well, but the high computing cost restricts their use in real-time application. Tang et al. developed a simplified mathematical model to describe the key processes of a VRB [17]. However, the model parameters involved had to be determined empirically based on extensive testing and knowledge of the VRB's dynamics. In contrast to mechanism-based models, equivalent circuit models (ECMs) show an expected trade-off between the computing complexity and the model accuracy. A simple ECM has been proposed for system-level integration, although this overlooks the dynamic variability of VRBs [18][19][20]. More recently, first-order [21-23] and second-order [24,25] RC models were developed to take into account the ...

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Section: H-infinity Filtermentioning

confidence: 99%

Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

2019

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“…Compared with the well-known Kalman filtering (KF)-based methods, the HIF can better withstand modeling uncertainty and the estimation accuracy is not dependent on knowledge of the noise statistics. It is thereby expected that the estimation will have a better robustness to model uncertainty and noise statistics [46]. A general nonlinear discrete-time state-space equation is expressed as:…”

Section: H-infinity Filter (Hif)mentioning

confidence: 99%

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Wei

Feng

et al. 2018

Energies

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The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation's alertness and numerical stability so as to achieve an accurate online adaption of model parameters.Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data-model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy.

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“…SOC estimation of lithium-ion batteries is commonly estimated using three methods, namely, conventional 8,9 , model-based [10][11][12] , and machine learning (ML) approaches [13][14][15] . Conventional approaches are simple but are unsuitable for online operations 16 .…”

mentioning

confidence: 99%

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan

Lipu

Hussain

et al. 2020

Sci Rep

182

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State of charge (Soc) is a crucial index used in the assessment of electric vehicle (eV) battery storage systems. thus, Soc estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise Soc assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced Soc estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced Soc estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

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Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation

Cited by 32 publications

References 42 publications

Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

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