Low‐complexity
            <scp>state of charge</scp>
            and anode potential prediction for lithium‐ion batteries using a simplified electrochemical model‐based observer under variable load condition

Wu, Longxing; Pang, Hui; Geng, Yuanfei; Liu, Xiaofei; Li, Kai

doi:10.1002/er.7949

Cited by 54 publications

(25 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current mainstream SOC identification methods have the open circuit voltage approach, the coulomb counting approach, the data‐driven approach, and the model‐based estimation approach 7 . Commonly, battery models can be classified as: the data driven models (DDMs), 8 the electrochemical models (EMs), 9,10 and the equivalent circuit models (ECMs) 11 …”

Section: Introductionmentioning

confidence: 99%

“…5,6 1.1 | The classifications of SOC identification methods Current mainstream SOC identification methods have the open circuit voltage approach, the coulomb counting approach, the data-driven approach, and the model-based estimation approach. 7 Commonly, battery models can be classified as: the data driven models (DDMs), 8 the electrochemical models (EMs), 9,10 and the equivalent circuit models (ECMs). 11 Generally, the open circuit voltage approach provides the initial SOC for other lithium battery estimation methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Wang

2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary For a lithium battery state space model, to solve the unsuitable problems caused by the single‐rate (same rate) sampled state space model facing resistor‐capacity (R‐C) couples with different time scales, a dual‐rate sampled exponential weighted multiple innovation adaptive extended Kalman filter (AEKF) method is investigated. The details include: (a) By using dual‐rate sampled method, select the suitable sampled rate for their own time scale for different couples (R1‐C1 and R2‐C2) to establish dual‐rate sampled state space equations; (b) Considering noise variation, a recursive noise estimation based AEKF algorithm is adopted to realize adaptive correction of noise; (c) Considering the influences of present and past data, an AEKF algorithm based exponential weighted multiple innovation is studied. Experiment results show that the investigated method is clearly better than the classical single‐rate sampled method with high precision.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Wang

2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…The SOH is an important indicator to evaluate the aging state. Currently, the SOH estimation methods have been mainly divided into two categories: model-based methods and data-driven methods (Xiong et al, 2018;Liu F. et al, 2021;Khaleghi et al, 2022;Wu et al, 2022). Among them, the model-based methods mainly include electrochemical methods, empirical methods, and equivalent circuit methods.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the model-based methods mainly include electrochemical methods, empirical methods, and equivalent circuit methods. The aging model of the solid electrolyte interphase (SEI) layer is established for the electrochemical method (Tian et al, 2021;Wu et al, 2022). The electrochemical method can not only describe the complex electrochemical reaction but also provide an accurate aging model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State of health estimation for lithium-ion batteries based on incremental capacity analysis under slight overcharge voltage

Min

Wei²,

Wang³

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

Accurate and reliable estimation of state of health (SOH) for lithium-ion batteries under slight overcharge voltage cycling has great significance for battery management systems. In this study, commercial lithium-ion phosphate batteries are investigated under slight overcharge voltage cycling. The aging mechanism is discussed based on incremental capacity analysis and differential voltage analysis. Moreover, the syncretic health indicator is obtained from the incremental capacity curves based on principal component analysis. Specifically, the capacity retention and Coulombic efficiency are analyzed under slight overcharge voltage cycling. The incremental capacity peaks (i.e., peak B and peak C) are discussed to extract potential health indicators, and a syncretic health indicator is adopted based on principal component analysis. Finally, the Gaussian process regression is established for accuracy SOH estimation with a 95% confidence interval under small data of slight overcharge cycling. In comparison with the traditional methods, the proposed method exhibits higher accuracy with a 95% confidence interval, and the error is limited to 3%.

show abstract

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Zeng,

Wang,

Cao

et al. 2023

Circuit Theory & Apps

View full text Add to dashboard Cite

SummaryAccurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method.

show abstract

Low‐complexity state of charge and anode potential prediction for lithium‐ion batteries using a simplified electrochemical model‐based observer under variable load condition

Cited by 54 publications

References 55 publications

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

State of health estimation for lithium-ion batteries based on incremental capacity analysis under slight overcharge voltage

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Contact Info

Product

Resources

About