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

Abstract

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).

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

“…Some variants of the NN were also used to SOC estimation [203][204][205][206][207][208][209]. Recently, Cui et al [210] combined the discrete wavelet transform and wavelet NN methods to estimate the SOC. The wavelet NN was trained using the Levenberg Marquardt (L-M) technique.…”

“…Affanni et al 2003 [196] ME ≤ ± 4.6% Rui-hao et al 2011 [197] ME ≤ ± 4.91% Chen et al 2011 [212] ≤± 1.0% He et al 2014 [199] ME ≤ ± 2.5% Dong et al 2015 [200] ME ≤ ± 4.0% Wang et al 2016 [202] ME ≤ ± 2.5% Hussein 2015 [203] ≤± 0.03% Tong et al 2016 [204] ≤± 3.8% Dang et al 2016 [205] ≤± 0.75% Shi et al 2010 [207] ≤± 1.25% Kang et al 2014 [208] ≤± 3.0% Cui et al 2018 [210] ≤± 0.93% Hannan et al 2018 [211] ≤± 0.87%…”

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

“…Therefore, SOC should be examined against bias and random noises, where bias noise corresponds to the sensor precision, and random noise is related to EMI noises. The robustness of the proposed method was checked under positive bias noises by injecting 0.1 A and 0.01 V to the current and voltage measurements, respectively 51 . In addition to biased noises, a standard random noise with an amplitude of 0.1 A and 0.01 V was added to current and voltage measurements 52 .…”

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

“…1 As the material and process variations cannot be avoided, the imbalance phenomena exits among the internal connected cells of the lithium-ion battery pack, 2 which makes the balance state evaluation to be necessary for power supply applications. 4 The existing modelling methods reported in literature so far cannot resolve the life shortening, thermal runaway and other issues in the lithium-ion battery packs for reliable power supply purposes. 4 The existing modelling methods reported in literature so far cannot resolve the life shortening, thermal runaway and other issues in the lithium-ion battery packs for reliable power supply purposes.…”

“…3 As a result, it is quite difficult to obtain accurate working state descriptions of the lithium-ion battery packs using the current power supply conditions. 4 The existing modelling methods reported in literature so far cannot resolve the life shortening, thermal runaway and other issues in the lithium-ion battery packs for reliable power supply purposes. 5 The existing research has carried out a great work by improving the safety level of the lithium-ion battery pack effectively.…”