A Robust Online Parameter Identification Method for Lithium-Ion Battery Model Under Asynchronous Sampling and Noise Interference

Cui, Zhongrui; Cui, Naxin; Wang, Chunyu; Li, Changlong; Zhang, Chenghui

doi:10.1109/tie.2020.3028799

Cited by 26 publications

(8 citation statements)

References 36 publications

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“…In order to use the ROEMs to predict the RUL, unknown parameters must also be determined, such as lithium concentration on the electrodes, lithium concentration of the electrolyte, the resistance of the SEI layer, and solid/electrolyte fractions. Therefore, online parameters identification techniques are developed, such as improved recursive least squares [140], and Kalmen filters [141]. Once the battery parameters are identified, the RUL of the battery can be predicted using the ODEs.…”

Section: ) On-board Electric-based Rul Prediction Methodsmentioning

confidence: 99%

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

et al. 2022

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According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles' fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged on the idea that reducing fuel consumption is benefits the environment and human health. One of the ideas for reducing fuel usage is deploying hybrid electric vehicles (HEVs) and electric vehicles (EVs) using renewable energy as alternatives to gasoline. One of the main issues with EV batteries is that over operational time the battery health degrades and ultimately becomes unsafe to use. It is crucial that safety issues be addressed by researchers and battery manufacturers. Assessing and predicting battery health has been a high-priority research topic to attempt to mitigate the danger introduced by EV batteries. Although various techniques have been developed to estimate and predict the battery's state of health, they do not cover all degradation scenarios that may affect the battery's lifetime. In addition, the models used in estimating and predicting the battery's lifetime need to be improved to provide a more accurate battery health state and guarantee battery safety while in use by an EV. Even though all types of EV batteries face similar issues, this paper focuses on Li-ion EV batteries. The main objectives of this paper are 1) to present various Li-ion battery models that are used to mimic battery dynamic behaviors, 2) to discuss the degradation factors that cause the battery lifespan to be degraded and to become unsafe, 3) to provide a review of the estimation and prediction techniques used for Li-ion battery state-of-health (SOH) and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations, and 4) to provide recommendations for improving battery lifetime estimation. This paper represents a concise source of information for battery community researchers to help expedite beneficial and practical outcomes to improve EV battery safety. INDEX TERMSLithium-ion (Li-ion) batteries, State of health (SOH), Remaining useful life (RUL), Electric vehicles (EVs), battery models, and Battery aging.I. INTRODUCTION E Nvironmental issues caused by Gasoline-powered vehicles are a challenge to automotive manufacturers worldwide. Global energy policies that stress low-carbon emissions require the transforming and upgrading vehicles to use environmentally-friendly renewable energy sources. Lithium-ion (Li-ion) batteries are commonly used as an energy source in electric vehicles (EVs) and hybrid electric vehicles (HEVs) [1, 2] due to their relatively high energy density as well as their fast charging and low self-discharge rate [3]. As a point of clarification, in this paper, we refer to a single battery cell as a battery, whereas an EV's entire battery is referred to as a battery pack. Batteries' performance degrades over time and usage due ...

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Section: ) On-board Electric-based Rul Prediction Methodsmentioning

confidence: 99%

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

et al. 2022

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“…Figure 6 represents the scheme of the proposed online cell screening algorithm. The parameters required in the proposed algorithm are assumed to be provided as follows: i t and v t are on-board measurement parameters, and R s is calculated as the ohmic relation between the change in i t and v t [28][29][30][31][32][33][34]; C Ah is obtained through the online SOH estimation algorithm as described in [35][36][37][38]. OCV is obtained by feeding the online SOC estimation results into the SOC-to-OCV look-up table [39][40][41][42][43][44][45][46][47][48].…”

Section: Methodology Of Proposed Online Cell Screening Algorithmmentioning

confidence: 99%

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

2022

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In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.

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“…It is tedious and costly to calibrate the parameters at every moment during the use of the battery [88][89][90][91][92][93][94][95][96]. Besides the high cost of online parameter identification and the high identification failure rate, the results of online parameter identification are required to verify and compare with offline parameters [97][98][99][100]. Therefore, it is widely accepted to establish a battery model offline to simulate the behavior of the battery before charging and discharging [97,[101][102][103][104].…”

Section: Battery Modelsmentioning

confidence: 99%

Scientometric research and critical analysis of battery state-of-charge estimation

Yang¹,

Shi²,

Mao³

et al. 2023

Journal of Energy Storage

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A Robust Online Parameter Identification Method for Lithium-Ion Battery Model Under Asynchronous Sampling and Noise Interference

Cited by 26 publications

References 36 publications

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

Scientometric research and critical analysis of battery state-of-charge estimation

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