Optimized State of Charge Estimation of Lithium-Ion Battery in SMES/Battery Hybrid Energy Storage System for Electric Vehicles

Sun, Qiang; Lv, Haiying; Wang, Shasha; Gao, Shuang; Wei, Kexin

doi:10.1109/tasc.2021.3091119

Cited by 26 publications

(11 citation statements)

References 14 publications

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“…The discharge and charge capacity ratio for the identical cycle process is referred to as coulombic effectiveness, which is utilized to describe the discharge properties [36]. The charging capacity and percentage of the discharge capacity (D) can then be represented using (2).…”

Section: State Of Charge Estimationmentioning

confidence: 99%

“…Nowadays, the electric vehicle (EVs) industry is one of the most promising industries worldwide because of the high gasoline consumption rate and environmental hazards caused by vehicles using internal combustion engines (ICE). In order to reduce global warming, EVs have been recognized as a practical and environmentally friendly substitute [1,2]. Lithium-ion batteries are considered a top choice for the EVs because of their numerous benefits, such as the huge capacity of energy storage, long life span, low maintenance cost, and easiness in transfer [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Monirul

Pan

Ruby

et al. 2023

IET Control Theory & Appl

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The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.

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Section: State Of Charge Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Monirul

Pan

Ruby

et al. 2023

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…In the above equation, k 1 , k 2 , k 3 , k 4, and k 5 represent the conversion coefficients, which have a direct mathematical relationship with the parameters to be identified and T, and these coefficients are taken advantage of to represent R 0 , R 1 , R 2 , C 1 , and C 2 . As shown in Equation (18).…”

Section: Soc and Soh Joint Estimation Based On Forgetting Factor Dual...mentioning

confidence: 99%

“…In this method, parts of daily charging profiles are sampled and directly fed into a convolutional neural network to estimate EAPs without feature extraction. However, the process of the method is cumbersome and time‐consuming, and the linear relationship between SOC and OCV is not perfect 18 . Therefore, the linear fitting of SOC and OCV will inevitably lead to large nonlinear errors 19 …”

Section: Introductionmentioning

confidence: 99%

“…However, the process of the method is cumbersome and time-consuming, and the linear relationship between SOC and OCV is not perfect. 18 Therefore, the linear fitting of SOC and OCV will inevitably lead to large nonlinear errors. 19 The essence of the filtering method is the process of estimating the true value according to the measured and observed value, 20 the method is divided into two parts: prediction and positive, and covariance estimation is constantly updated.…”

Section: Introductionmentioning

confidence: 99%

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