Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries

Zheng, Ling; Zhu, Jianguo; Lu, Dylan Dah-Chuan; Wang, Guoxiu; He, Tingting

doi:10.1016/j.energy.2018.03.023

Cited by 196 publications

(63 citation statements)

References 42 publications

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“…3 The effective implementation was established with the charge replacement, in which the extended Kalman filtering algorithm was introduced and realized. 9 The incremental capacity and differential voltage were analyzed for the state-of-charge estimation of the lithium-ion batteries, 10 and a new multi-time-scale filter was proposed to obtain the state of energy and the state of power values. 5 A systematic and reliable state-of-charge estimation method was proposed for the lithium-ion battery pack by using the comprehensive state evaluation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Wang

Fernandez

Xie

et al. 2019

Energy Science & Engineering

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A novel real-time state monitoring method is proposed to realize the real-time energy management of the lithium-ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factorunscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved.Meanwhile, the recursive calculation is derived by using the real-time measured factors, according to which the state-of-charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state-of-charge estimation accuracy is 98.34% under the complex current charge-discharge working conditions. Meanwhile, the effective closed-circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium-ion battery packs. K E Y W O R D Scomplex current variation, Kalman filter, lithium-ion battery, state monitoring, unscented transform, weight coefficient | 3039 WANG et Al.

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Section: Introductionmentioning

confidence: 99%

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Wang

Fernandez

Xie

et al. 2019

Energy Science & Engineering

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“…Nevertheless, data-driven methods are often subject to certain drawbacks, such as data saturation and specific input requirement. And the reliability of data-driven methods strongly depends on the scope of training data, which severely hinders their application in BMS.The experimental techniques [21][22][23][24][25][26][27][28][29][30][31] are based on electrochemical principles and can be grouped into ex-situ and in-situ methods. Common ex-situ methods, such as scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and X-ray diffractometry (XRD), apply physicochemical and electrochemical invasive techniques to study the battery cell internally.…”

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confidence: 99%

“…In addition, the voltage plateau of V-Q curve is flat and easily subject to measurement noise, rendering difficulty for extracting dQ/dV from raw data. Hence, the reported research on IC analysis for LIB health assessment are mainly focused on either the degradation mechanisms [26,27] or the design of robust algorithms to obtain an improved IC curve [23][24][25][28][29][30][31], often confined to a specific battery cell under a given load profile. There has been scarce published work on applying IC analysis to study impacts of diverse usage patterns on battery capacity fade, which is a realistic and highly challenging problem in practical automotive applications.…”

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confidence: 99%

Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Huang

2019

Batteries

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Aging assessment is critical for lithium-ion batteries (LIBs) as the technology of choice for energy storage in electrified vehicles (EVs). Existing research is mainly focused on either increasing modeling precision or improving algorithm efficiency, while the significance of data applied for aging assessment has been largely overlooked. Moreover, reported studies are mostly confined to a specific condition without considering the impacts of diverse usage patterns on battery aging, which is practically challenging and can greatly affect battery degradation. This paper addresses these issues through incremental capacity (IC) analysis, which can both utilize data directly available from on-board sensors and interpret degradations from a physics-based perspective. Through IC analysis, the optimal health feature (HF) and the state of charge (SOC)-based optimal data profile for battery aging assessment have been identified. Four stress factors, i.e., depth-of-discharge (DOD), charging C-rate, operating mode, and temperature, have been selected to jointly characterize diverse usage patterns. Impact analysis of different stress factors through the optimal HF with the SOC-based optimal data profile from aging campaign experiments have generated practical guidance on usage patterns to improve battery health monitoring and lifetime control strategies.Batteries 2019, 5, 59 2 of 16 fitting. The simplicity and superiority in real-time computation have given rise to their wide applications in BMS [9]. Yet the lack of physical representation makes it unable to interpret battery degradation mechanisms and ultimately suffers from insufficient estimation accuracy. In contrast, the physics-based approach is built upon electrochemical models (EChMs) to gain insights into the internal physicochemical processes related with aging. However, the high modeling precision comes at the price of demanding computation resources, given that the governing equations of EChMs are mostly partial differential equations (PDEs) [11], which are not amendable for BMS application. Despite multiple model order reduction (MOR) techniques [14] to reduce the computational complexity with various introduced approximations, the balance between modeling precision and computation efficiency still faces severe challenges.The data-driven methods [17][18][19][20] have attracted tremendous attention in LIB aging research owing to their flexibilities from the model-free feature. The independence from battery physical properties makes it feasible for the application of various machine learning methods, including support vector machines (SVM) [17], artificial neural networks (ANN) [18], relevance vector machines (RVM) [19], and particle swarm optimization [20], etc., to develop mathematical descriptions of battery degradation behaviors through statistical learning from a large amount of data. Nevertheless, data-driven methods are often subject to certain drawbacks, such as data saturation and specific input requirement. And the reliability of data-driven methods stron...

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“…Therefore, the associated battery management system (BMS) is required, in which the realtime condition monitoring and security control should be performed to ensure its safety, power, and durability. 10 Therefore, the packing equivalent modeling and state estimation method are the key to suppress the safety threat to the LIB packs, and it is of great significance to improve its energy conversion efficiency and extending its service life. 3 The LIB pack belongs to a complex electrochemical system, which is different from general industrial control objects.…”

mentioning

confidence: 99%

“…9 It is very difficult to extract and express the feature information mathematically. 10 Therefore, the packing equivalent modeling and state estimation method are the key to suppress the safety threat to the LIB packs, and it is of great significance to improve its energy conversion efficiency and extending its service life.…”

mentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries

Cited by 196 publications

References 42 publications

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions

Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

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