Systematic mixed adaptive observer and EKF approach to estimate SOC and SOH of lithium–ion battery

Gholizadeh, Mehdi; Yazdizadeh, Alireza

doi:10.1049/iet-est.2019.0033

Cited by 45 publications

(37 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BMS estimates the state of charge (SoC) and state of health (SoH) of the connected Li-ion cells within a battery pack, and uses this estimation to perform actions such as cell charge balancing, to mitigate overcharging and deep discharging; accelerated ageing; and permanent damage [13][14][15]. Many methods have been studied for the estimation of the SoC and SoH, including extended Kalman filter techniques [16,17], such as the splice Kalman filter algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an in situ QAM‐based Power Line Communication system for lithium‐ion batteries

Koshkouei

Kampert

Moore

et al. 2021

IET Electrical Syst in Trans

View full text Add to dashboard Cite

Power Line Communication (PLC) is used to transmit high-fidelity data on internal cell characteristics from within instrumented cells to an external Battery Management System (BMS). Using PLC is beneficial, as it avoids the need for a complex and heavyweight wiring harness within a battery. The use of advanced modulation, such as Quadrature Amplitude Modulation (QAM), is considered here. The existing experimental results of lithium-ion cell impedance characteristics for frequencies of 100 kHz-200 MHz are exploited in order to create a realistic battery model. This model is used to determine the effectiveness and optimal properties of PLC with QAM, as a means of in situ battery communication for Battery Electric Vehicles (BEVs) in combination with a real-world dynamic drive profile. Simulations reveal that the performance of the PLC system is heavily dependent on the selected carrier frequency due to the significant changes in reactance and internal resistance of the lithium-ion cells tested. Furthermore, cells placed in parallel display a decreased performance compared with cells in series. The results highlight that the optimal carrier frequency for in situ QAM-based PLC for a lithium-ion battery system is 30 MHz, and that additional signal conditioning is required for 4-QAM and higher modulation orders.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of an in situ QAM‐based Power Line Communication system for lithium‐ion batteries

Koshkouei

Kampert

Moore

et al. 2021

IET Electrical Syst in Trans

View full text Add to dashboard Cite

show abstract

“…Due to the highly nonlinear characteristics of lithium-ion batteries during operation, it is difficult for a single KF algorithm to meet the system's requirements [84], which limits the application of KF in the actual operation scenarios of lithium-ion batteries. In order to meet the requirements of high accuracy and reliability of SOH estimation and prediction for lithium-ion batteries, several improved KF algorithms such as unscented Kalman filtering (UKF) [85] and extended Kalman filter (EKF) [86] have been proposed successfully. A double extended Kalman filter (DEKF) joint estimation algorithm combined with the equivalent circuit model was proposed by [87], which is applicable for lithium-ion battery application scenarios by comparison with a single KF.…”

Section: Statistical Filtering Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

101

View full text Add to dashboard Cite

Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

show abstract

“…Beyond impedance spectroscopy, examples of model-based approaches to SoH estimation include Kalman filtering [23,24] or, more recently, generalised approaches using a fuzzy c-regression model incorporating particle swarm optimisation [25], which has seen applied in SoH estimation for a NiMH battery [26]. Further, filtering-based methods include [27,28] for decoupled estimations of SoC and SoH, employing a recursive least-squares-based SoH estimator with online-identified ECM and with SoC estimation using a Kalman filter and H-infinity filter, respectively.…”

Section: Eis and Model-based State Of Health Estimationmentioning

confidence: 99%

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rastegarpanah

Hathaway

2021

Energies

View full text Add to dashboard Cite

The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

show abstract

Systematic mixed adaptive observer and EKF approach to estimate SOC and SOH of lithium–ion battery

Cited by 45 publications

References 45 publications

Evaluation of an in situ QAM‐based Power Line Communication system for lithium‐ion batteries

Evaluation of an in situ QAM‐based Power Line Communication system for lithium‐ion batteries

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Contact Info

Product

Resources

About