An Improved SOC Estimator Using Time-Varying Discrete Sliding Mode Observer

Dai, Kangwei; Wang, Ju; He, Haibo

doi:10.1109/access.2019.2932507

Cited by 18 publications

(3 citation statements)

References 38 publications

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“…Moreover, there is a need for low pass filter to extract the estimated signal from these methods, which incurs phase lag and further affects the estimation accuracy. The adaptive gain sliding mode observer (AGSMO), second-order sliding mode observer (SOSMO) and time-varying discrete sliding mode observer (TVDSMO) are employed for SOC estimation to reduce the chattering [26][27][28][29]. They still use the discontinuous control injection and low pass filter for implementation.…”

Section: Introductionmentioning

confidence: 99%

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Sethia

Majhi

Nayak

et al. 2021

IET Renewable Power Gen

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The effective implementation of battery management system (BMS) in various applications such as electric vehicles (EVs), renewable energy sources (RES) integrated smart‐grids and micro‐grids, necessitates accurate estimation of the battery parameters and states. This paper primarily focuses on offering an improved solution to the state of charge (SOC) estimation problem of lithium‐ion (Li‐ion) batteries. After extensive analysis of the current state‐of‐the‐art methods, a new strict Lyapunov super twisting algorithm (SLSTA) based approach is proposed for precise estimation of SOC under a comprehensive range of uncertainties. The error convergence and robustness of the proposed state observer are demonstrated using Lyapunov stability theory. Since the modelling parameters of the battery equivalent circuit utilised in this paper vary with various operational and external factors, a standard online method is employed for their real‐time identification. The presented method is executed on a lithium‐polymer (LiPo) battery with the help of a dynamic stress test (DST). The experimental results demonstrate that the proposed approach outperforms the well‐known approaches in terms of accuracy, computational complexity, and convergence time.

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Section: Introductionmentioning

confidence: 99%

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Sethia

Majhi

Nayak

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…In order to maintain an accurate state monitoring, the BMS is required to precisely estimate the remaining state of charge (SoC) and state of health (SoH) of a battery [1], [2]. Most electrified vehicle manufactures require the SoC RMSE and the maximum SoC error to be less than 1% and 3%, respectively in a wide temperature range [24], [27], [28]. An elaborate battery-cell model is of utmost importance in the BMS in order to achieve such SoC estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Temperature-Dependent State of Charge Estimation Method Including Hysteresis for Lithium-Ion Batteries in Hybrid Electric Vehicles

Choi

Chang

2020

IEEE Access

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Currently, lithium-ion batteries are mainly used as central power components in electric vehicles. Usually, accurate battery cell modeling and state of charge estimation are required in order to effectively use the lithium-ion batteries in electrified vehicles. For an elaborate battery cell modeling on a wide temperature range, we select a temperature-dependent battery cell modeling approach, which relies on one resistance plus second-order resistance-capacitance equivalent circuit model. In order to corporate some temperature effects into the modeling, we derive the sixth-order polynomial functions using the curve fitting algorithm. The functions contribute to the accurate battery cell modeling on the wide temperature range. For a practical usage of the functions in real-time embedded systems, we propose an offset-based lookup table technique. In addition, we propose a novel hysteresis voltage unit model for more accurate parameter estimation of hysteresis voltages. This also leads to more accurate battery cell modeling. Based on the battery cell modeling, we propose a temperature-dependent estimation method for state of charge. This approach exploits the extended Kalman filter, which is suitable for nonlinear characteristics such as the hysteresis effect. Experimental evaluation exhibits that the proposed estimation method outperforms the conventional approaches in the wide temperature range.

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“…The most frequently used observers in model-based SOC estimation methods include the H-infinity filter [32], derivate Kalman filters [9,10], particle filter [8], and sliding mode observer [33,34]. While the use of these observers can improve the accuracy of the model and the SOC estimation, they bring extra complexity and computational efforts to the estimation algorithms.…”

mentioning

confidence: 99%

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

2020

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Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively. INDEX TERMS Lithium-ion battery, state of charge (SOC), electrochemical model, Nernst equation I. INTRODUCTION With numerous advantages, such as high energy density, low self-discharging rate, no memory effects, and long lifespan, and so on, lithium-ion batteries are generally regarded as a leading energy storage candidate for electric vehicles, renewable energy systems, portable electronic devices, and many other applications [1-3]. In most lithium-ion battery energy storage systems, a battery management system (BMS) is employed to manage the performance and maintain the longevity of battery cells [1, 4, 5]. The functions of a BMS include mainly cell voltage detection and balancing [6, 7], estimation of state of charge (SOC) [8-11] and state of health (SOH) [12-14], and thermal management [15-18]. Defined as the ratio of the residual charge stored in a battery to its maximum available capacity, the SOC is one of the primary and critical parameters for the development of battery management strategies [19]. Accurate SOC estimation plays a vital role in controlling the safe charging and discharging of the battery while guaranteeing its efficiency. However, the SOC cannot be measured directly and accurately by sensors like ordinary physical quantities in practices. Despite the huge research efforts, the battery SOC estimation still poses a significant challenge due to the complexity and nonlinearity of e...

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An Improved SOC Estimator Using Time-Varying Discrete Sliding Mode Observer

Cited by 18 publications

References 38 publications

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries

A Temperature-Dependent State of Charge Estimation Method Including Hysteresis for Lithium-Ion Batteries in Hybrid Electric Vehicles

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

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