Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

Omariba, Zachary Bosire; Zhang, Lijun; Kang, Hanwen; Sun, Dongbai

doi:10.3390/wevj11030050

Cited by 20 publications

(12 citation statements)

References 53 publications

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“…In this case, the solution consists of a linear approximation of the nonlinear relation: converting a nonlinear problem into a linear one. Based on the extended Karman filter algorithm, the battery health assessment process determines the model's parameters following the initial health value [63]. The first step in estimating health status is creating a model.…”

Section: Battery State Of Health (Soh) Estimation: Model-based Methodsmentioning

confidence: 99%

State of Health Estimation Methods for Lithium-Ion Batteries

Nuroldayeva

Serik

Adair

et al. 2023

International Journal of Energy Research

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Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

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Section: Battery State Of Health (Soh) Estimation: Model-based Methodsmentioning

confidence: 99%

State of Health Estimation Methods for Lithium-Ion Batteries

Nuroldayeva

Serik

Adair

et al. 2023

International Journal of Energy Research

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“…Kalman filter-based methods (Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Dual EKF, etc.) have been widely used to estimate battery states and parameters [39][40][41][42]. In a series of three papers [43][44][45], the author investigates the SOC and parameter estimation of a lithium ion polymer battery pack using Kalman Filtering.…”

Section: Kalman-based Filtersmentioning

confidence: 99%

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

Noura

Boulon

Jemeï

2020

WEVJ

137

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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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“…Kalman filter-based methods (Kalman filter (KF), extended Kalman filter (EKF), Unscented Kalman filter (UKF), Dual EKF, etc.) have been successfully used to estimate battery SoH using different HIs [39][40][41]. 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].…”

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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Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

Cited by 20 publications

References 53 publications

State of Health Estimation Methods for Lithium-Ion Batteries

State of Health Estimation Methods for Lithium-Ion Batteries

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

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