Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles

Lee, Seongjun; Kim, Jong-Hoon

doi:10.1016/j.energy.2015.02.046

Cited by 58 publications

(18 citation statements)

References 44 publications

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“…To overcome the disadvantages of the above methods summarized in Table 1, researchers have proposed nonlinear methods, e.g., extended Kalman filter (EKF) [13,14], unscented Kalman filter (UKF) [15,16], particle filter [17,18], Bayesian framework [19,20], sliding mode [21,22], nonlinear observer [23,24], wavelet analysis [25,26], and H-infinity [27][28][29]. These methods are applicable to any battery and can simultaneously identify the parameters of prebuilt models and thereby estimate battery SOC through their nonlinear mapping capabilities without the need of initial SOC values.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

2019

Processes

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In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery's intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the'Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.Keywords: battery energy storage system (BESS); state of charge (SOC); adaptive network-based fuzzy inference system (ANFIS) Processes 2020, 8, 2 2 of 22 As a result, real-time SOC can be determined based on a given initial SOC value. This process can be expressed as (1):

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Section: Advantages Disadvantagesmentioning

confidence: 99%

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

2019

Processes

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“…Nowadays, lithium-ion (Li-ion) battery is attracting increasing attention as the optimal choice of energy storage in current electrical equipment, given that it has properties of high density of specific energy, long lifetime cycles, and low self-discharge [1,2]. To address online battery operational management and lifetime extension, a BMS (battery manage system) is employed to provide estimations of SOC (state of charge), SOH (state of health), and SOL (state of life) [3e5].…”

Section: Introductionmentioning

confidence: 99%

Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

Cheng

Tieying

et al. 2015

Energy

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“…Most researches mainly focus on the denoising ability of discrete wavelet transform (DWT) [40][41][42]. The hybrid WNN [43] -based SOC estimation methods; adaptive WNN-based [32] in 2005; and momentum-optimized, adaptive WNN [31]-based in 2013 SOC estimation methods are discussed, which have good performance facing the high nonlinear battery system, but the training algorithm is based on the steepest descent method, and the study of the hybrid WNN using the wavelet multi-resolution decomposition method to optimized the adaptive WNN is quite limited.…”

Section: Estimation Model Based On Wnnmentioning

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

et al. 2018

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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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Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles

Cited by 58 publications

References 44 publications

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

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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