Comparative study of curve determination methods for incremental capacity analysis and state of health estimation of lithium-ion battery

He, Jiangtao; Bian, Xiaolei; Liu, Longcheng; Wei, Zhongbao; Yan, Fengjun

doi:10.1016/j.est.2020.101400

Cited by 48 publications

(22 citation statements)

References 37 publications

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“… 158 The voltage–capacity model-based features of interest extracting optimized the IC fitting accuracy, robustness to aging and the computing cost, allowing real-time applications. 159 A universal approach named the level evaluation analysis is proposed for calculating differentiations in data to derivate different types of differential curves (ICA/DVA/DTV). 160 Due to measurement noise, by cross-validation, the robust cubic smoothing spline method on producing IC curves is superior over typical filters that require tuning on window size usually by trial & error.…”

Section: Digital Management For Batterymentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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Section: Digital Management For Batterymentioning

confidence: 99%

Towards interactional management for power batteries of electric vehicles

Xie

et al. 2023

RSC Adv.

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“…Furthermore, He et al. [27] conducted a comparative study with respect to the ICA‐based methods and analysed the application boundary. The results from these studies demonstrate that the ICA is an effective method for battery aging monitoring.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, the ICA has attracted increasing attention and several studies have provided the ICA-based schemes to realize battery aging monitoring [24][25][26]. Furthermore, He et al [27] conducted a comparative study with respect to the ICA-based methods and analysed the application boundary. The results from these studies demonstrate that the ICA is an effective method for battery aging monitoring.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels

Wang

et al. 2021

IET Intelligent Trans Sys

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Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real-world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.

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“…Traditionally, when using differential capacity plots, researchers have drawn conclusions based on an arbitrarily chosen subset of cycles and reported mainly qualitative claims on how peaks shift during cycling, due to the difficulties in analyzing the full amount of data produced in the differential capacity plots. Additionally, although it is known that peak shapes and areas correlate to important electrochemical events, only a few papers report using peak deconvolution as a method to interpret dQ/dV plots (Aihara et al, 2016;Bian, Liu, & Yan, 2019;He, Bian, Liu, Wei, & Yan, 2020;Huang, 2019;Torai et al, 2016). Further, there does not exist any standardized method for peak deconvolution of differential capacity plots.…”

Section: Statement Of Needmentioning

confidence: 99%