State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

Luping, Chen; Xu, Li; Wang, Ruoyu

doi:10.1155/2017/5489356

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…The KF has also been merged with other SOC estimation techniques, such as the CC, OCV [72], GA [74], and back-propagation neural network [75]. Some variants of KF, such as the square root cubature KF (SRCKF) [76] and dual square root cubature KF (DSRCKF) [77] have been also reported. Table 6 lists the previous studies.…”

Section: H Infinity Filter (H∞f)mentioning

confidence: 99%

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Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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Section: H Infinity Filter (H∞f)mentioning

confidence: 99%

“…Urbain et al 2007 [73] ME ≤ ± 5% Yatsui et al 2011 [72] ≤± 1.76% Zhao et al 2018 [75] ≤± 0.04% Cui et al 2018 [76] ≤± 0.71% Chen et al 2017 [77] ≤± 1.2%…”

Section: Reference Mae (%)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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“…V(t) is negative, for the second item of . V(t), it can be easily proved that, when h > h 0 , Equations (18) and (19) is true.…”

Section: Design Of Fuzzy Logic Sliding Mode Observer For Soc Estimationmentioning

confidence: 99%

“…Under certain condition, the UKF method has been proven to be more accurate than EKF. Other improved Kalman filters include the cubature Kalman filter [19], the adaptive Kalman filter [20], and so on. Kalman-based filters have significant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Zheng

Xia

Wang³

et al. 2019

Energies

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State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

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“…But asymmetric or non-positive definite covariance often arises during the iteration of CKF, which results in accuracy degradation or iteration interruption. To cope with the numerical unstability of the CKF, square root cubature Kalman filter (SRCKF) [22,23] was developed, which can ensure the positive definiteness of the covariance. However, these KF-based methods also have some accuracy limitations.…”

Section: Introductionmentioning

confidence: 99%

A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Hou

Yan

et al. 2019

Energies

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An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

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State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

Cited by 14 publications

References 19 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

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