State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer

Tian, Yong; Li, Dong; Tian, Jing; Xia, Bizhong

doi:10.1016/j.electacta.2016.12.119

Cited by 79 publications

(33 citation statements)

References 43 publications

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“…Among various types of batteries, lithium-ion batteries (LIBs) provide several advantages (e.g., high power/energy density, long lifespan, no memory effect, high operating voltage, and low self-discharge rate) [1] and are widely used in EVs. To ensure the normal operation of the entire system, a battery management system (BMS) plays an important role in an EV [2].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Linear observers are commonly used, but will increase the SOC estimation error. Hence, the non-linear observer is used in a linear system with non-linear observation mathematical equations [124]. Chaoren et al [126] proposed the SOC estimation method based on NLO.…”

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 (SOC) has an undoubted critical position for BMS to realize management functions, and it indicates the remaining energy, whose accuracy is of great significance for service safety and life of batteries [2,3]. Nevertheless, the limitation on the progress of BMS is mostly due to SOC unmeasurable and dynamic properties that are similar to the characteristics of the batteries, which are influenced by various factors [4], such as discharge rate, ambient temperature, battery degeneration, and external disturbance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

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State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test).

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State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer

Cited by 79 publications

References 43 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

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

A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

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