Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Zheng, Ling; Zhu, Jianguo; Wang, Guoxiu; Lu, Dylan Dah-Chuan; He, Tingting

doi:10.1016/j.energy.2018.06.113

Cited by 85 publications

(29 citation statements)

References 40 publications

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“…Model-based filtering methods, such as the extended Kalman filter (EKF), [13][14][15] unscented Kalman filter [16][17][18] and H infinity filter, 1,[19][20][21] can achieve high accuracy online with low-cost hardware; many studies on these methods have been conducted, exhibiting remarkable potential. EKF is one of the most popular approaches used for battery SOC estimation.…”

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confidence: 99%

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

2020

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Summary This study simultaneously considers the state‐of‐charge (SOC) estimation and model parameter identification of lithium‐ion batteries with outliers in measurements. Conventional Kalman‐type filters may degrade performance in this case since they assume Gaussian‐distributed measurement noise. To improve the SOC estimation accuracy under this condition, a robust normal‐gamma (NG)‐based adaptive dual unscented Kalman filter (NG‐ADUKF) is proposed. First, by modeling the joint distribution of the state and auxiliary variables of the measurement noise as the NG distribution, the unscented Kalman filter (UKF) is integrated with the NG filter to deal with the heavy‐tailed measurement noise. Second, the online parameter identification and SOC estimation are realized simultaneously by alternatively using two NG‐based adaptive UKFs. The performance of the proposed algorithm is validated by the New European Driving Cycle and Urban Dynamometer Driving Schedule tests. Experimental results show that the proposed NG‐ADUKF algorithm has more accurate SOC estimations compared with the dual UKF (DUKF) and the variational Bayes‐based adaptive DUKF (VB‐ADUKF) in the case of mistuning and outliers. Moreover, the proposed method is more computationally efficient than VB‐ADUKF.

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Section: Introductionmentioning

confidence: 99%

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

2020

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“…PF exhibits attractive advantages in solving nonlinear, non-Gaussian distribution problems, and highlights more application potential than EKF. Thus, it has been widely developed and applied in multifarious fields, such as batteries, robotics and navigation systems [17]; however, it is limited by strong dependence upon noise and time-varying parameters of the system.In addition, battery aging is an irreversible process with operation, where the most intuitive appearance is a decline of capacity and the increase of internal impedance [18]. In general, the attenuation process is nonlinear, complex, and even difficult to predict.…”

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confidence: 99%

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Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

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“…4 The power LIB pack is constructed by the multicell cascading to realize the high-power application and provide the required energy. 8 Due to the influence of the internal structure and environmental conditions, there are many noises in the external measurement parameters. Therefore, the associated battery management system (BMS) is required, in which the realtime condition monitoring and security control should be performed to ensure its safety, power, and durability.…”

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confidence: 99%

“…7 Therefore, the accurate SoC estimation plays an important role in improving the overall performance, which is of great significance for the remaining power forecasting and safety guarantee. 8 Due to the influence of the internal structure and environmental conditions, there are many noises in the external measurement parameters. 9 It is very difficult to extract and express the feature information mathematically.…”

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A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Cited by 85 publications

References 40 publications

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

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