State of charge estimation of lithium-ion batteries using fractional order sliding mode observer

Zhong, Qishui; Zhong, Fuli; Cheng, Jun; Li, Hui; Zhong, Shouming

doi:10.1016/j.isatra.2016.09.017

Cited by 55 publications

(18 citation statements)

References 33 publications

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“…By employing the resistor, capacitor, and voltage source, ECMs can describe the dynamic, external characteristics of LIBs. ECMs come in various types, including the first-order RC ECM [8,13,34], second-order RC ECM [7,35], fractional-order RC ECM [20], and so on.…”

Section: Equivalent Circuit Model Of Lithium-ion Batteriesmentioning

confidence: 99%

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

et al. 2018

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Abstract:The accuracy of state-of-charge (SOC) estimation, one of the most important functions of a battery management system (BMS), is the basis for the proper operation of an electric vehicle. This study proposes a method for accurate SOC estimation. To achieve a balance between accuracy and simplicity, a second-order resistor-capacitor equivalent circuit model is applied before the algorithm is deduced, and the parameters of the established model are determined using a fitting technique. Battery state space equations are then described. A strong tracking H-infinity filter (STHF) is proposed based on an H-infinity filter (HF) and a strong tracking filter. By introducing a suboptimal fading factor, the STHF approach can use the relevant information in the estimation residual sequence to update the estimation results. To verify the robustness of this approach, battery test experiments are performed at different temperatures on lithium-ion batteries. Finally, the SOC estimation results obtained using the STHF suggest that the STHF method exhibits high robustness against the measured noises and initial error. For comparison, the estimation results of the commonly used extended Kalman filter (EKF) and HF methods are also displayed. It is suggested that the proposed STHF approach obtains a more accurate SOC estimation.

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Section: Equivalent Circuit Model Of Lithium-ion Batteriesmentioning

confidence: 99%

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

et al. 2018

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“…In [148], the dual SMO was proposed considering the capacity fading effect for SOC estimation. To improve the efficiency, accuracy, and robustness of the SMO, some variants, such as the adaptive gain SMO (AGSMO) [149], adaptive switching gain SMO (ASGSMO) [150], super-twisting SMO (STSMO) [151], fractional order SMO (FOSMO) [152], and Fuzzy SMO (FSMO) [153], have been reported in the literature. Table 13 shows the comparison of the SMO-based SOC estimation methods.…”

Section: Sliding Mode Observer (Smo)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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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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“…(3) Sliding Mode Observer (SMO) SMO can guarantee the stability and robustness of the system against model uncertainties and environmental disturbances [58][59][60][61][62][63]. The model is established by using the state equation as the output state and a feedback switching gain is designed to control the sliding regime to ensure the …”

Section: Non-linear Observermentioning

confidence: 99%

“…Non-linear observers (NLO), sliding mode observer (SMO), and proportional-integral observer (PIO) theories are proposed to estimate the SOC of lithium-ion batteries [58][59][60][61][62][63]124,125].…”

Section: Non-linear Observermentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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State of charge estimation of lithium-ion batteries using fractional order sliding mode observer

Cited by 55 publications

References 33 publications

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

Strong Tracking of a H-Infinity Filter in 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 the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

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