RLS with optimum multiple adaptive forgetting factors for SoC and SoH estimation of Li-Ion battery

Rijanto, Estiko; Rozaqi, Latif; Nugroho, Asep; Kanarachos, Stratis

doi:10.1109/ica.2017.8068416

Cited by 17 publications

(11 citation statements)

References 15 publications

(21 reference statements)

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“…Other widely used algorithms in adaptive filtering are the Least Square-based ones [47][48][49][50][51]. A lot of attention has been given recently to these algorithms and especially to the Recursive Least Square (RLS) due to its simple implementation and accuracy.…”

Section: Least Square-based Filtersmentioning

confidence: 99%

“…This identification process and state estimation have been investigated in [52] where the importance of the battery model is clearly pointed out. The author in [48] indicates the high performances of an improved RLS-based algorithm, the Multi Adaptive Forgetting Factors RLS (MAFFRLS). In the MAFFRLS algorithm, the forgetting factor is optimized through Particle Swarm Optimization (PSO) algorithm to reach a higher accuracy parameter estimation.…”

Section: Least Square-based Filtersmentioning

confidence: 99%

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A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Noura

Boulon

Jemeï

2020

WEVJ

135

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To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

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Section: Least Square-based Filtersmentioning

confidence: 99%

Section: Least Square-based Filtersmentioning

confidence: 99%

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Noura

Boulon

Jemeï

2020

WEVJ

135

View full text Add to dashboard Cite

show abstract

“…Other widely used algorithms in adaptive filtering are the least square-based ones, specially the recursive least square (RLS) method because of its simple implementation and accuracy [42]. This method gives an accurate estimation of the parameters, directly linked to battery SoH [43].…”

Section: State Of the Art Challenges And Outlookmentioning

confidence: 99%

Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

Javadipour

Mehran

2021

IET Smart Grid

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Lithium-ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium-ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real-time simulation of the multi-physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real-time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi-physics model of the cell is developed in COMSOL modelling software, and real-time data fusion process is implemented on dSPACE Microlabbox real-time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Moreover, due to its fixed forgetting factor, the robustness of the system is poor when disturbed [ 24 ]. The least-square algorithm with a forgetting factor (FFRLS) adds a forgetting factor on the basis of RLS algorithm to solve the problem of data saturation [ 25 , 26 ]. Battery parameter identification based on RLS and SOC estimation algorithm based on EKF is widely used [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A Battery SOC Estimation Method Based on AFFRLS-EKF

Zhang

et al. 2021

Sensors

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The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares—extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

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RLS with optimum multiple adaptive forgetting factors for SoC and SoH estimation of Li-Ion battery

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 17 publications

References 15 publications

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges

Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

A Battery SOC Estimation Method Based on AFFRLS-EKF

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