Modified extended Kalman filtering algorithm for precise voltage and state-of-charge estimations of rechargeable batteries

Yang, Fan; Shi, Dongliang; Lam, Kwok-ho

doi:10.1016/j.est.2022.105831

Cited by 16 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance adoption of EKF can be achieved through a joint algorithm such as dual KF. However, the nonlinear errors necessitate the optimization of the filter itself (F. Yang, Shi, et al, 2022).…”

Section: Soc Estimation Methodsmentioning

confidence: 99%

A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs.This article is categorized under: Emerging Technologies > Energy Storage Emerging Technologies > Digitalization Energy and Power Systems > Distributed Generation

show abstract

Section: Soc Estimation Methodsmentioning

confidence: 99%

A review of battery state of charge estimation and management systems: Models and future prospective

Hussein,

Aghmadi,

Abdelrahman

et al. 2024

WIREs Energy & Environment

View full text Add to dashboard Cite

show abstract

“…The extended Kalman filter algorithm can not only handle with the problems of linear system, but also solve some problems of nonlinear system. The Kalman filter algorithm theory is a recursive method used to deal with the problem of discrete data linear filtering, it consists of the state equation and the observation equation (Yang et al, 2022), which can be expressed as…”

Section: Extended Kalman Filter Algorithmmentioning

confidence: 99%

“…The battery management system (BMS) can be used to utilize the storage capacity rationally and monitor the working state precisely of the battery pack systems (Hao et al, 2018;Iurilli et al, 2019). The State of charge (SOC) estimation precisely in BMS not only improve the power transmission performance and increase the safety of the battery, but also decrease the over-charging and over-discharging and prolong the using life of the battery (Dang et al, 2022;Yang et al, 2022). Up to now, the determination of lithium-ion battery model is also needed to be studied.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with above algorithm, the extended Kalman filter algorithm uses the first order Taylor expansion to obtain the nonlinear function. This algorithm plays an important role in the SOC estimation of battery due to the accuracy and effectiveness (Chin et al, 2018;Yang et al, 2022). Firstly, the initial value estimation of lithium-ion battery using extended Kalman filter algorithm can significantly reduce the estimation error.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm

Xie

Wei

Bo³

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Due to excellent power and energy density, low self-discharge and long life, lithium-ion battery plays an important role in many fields. Directed against the complexity of above noises and the strong sensitivity of the common Kalman filter algorithm to noises, the state of charge estimation of lithium-ion battery based on extended Kalman filter algorithm is investigated in this paper. Based on the second-order resistor-capacitance equivalent circuit model, the battery model parameters are identified using the MATLAB/Simulink software. A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the individual battery are tested. The simulation and experimental results of terminal voltage for lithium-ion battery is compared to verify the effectiveness of this method. In addition, the general applicability of state of charge estimation algorithm for the battery pack is explored. The ampere-hour integral method combined with the battery modeling is used to estimate the state of charge of lithium-ion battery. The comparison of extended Kalman filter algorithm between experimental results and simulation estimated results is obtained to verify the accuracy. The extended Kalman filter algorithm proposed in this study not only establishes the theoretical basis for the condition monitoring but also provides the safe guarantee for the engineering application of lithium-ion battery.

show abstract

Real-time state-of-charge estimation for rechargeable batteries based on in-situ ultrasound-based battery health monitoring and extended Kalman filtering model

Yang,

Mao,

Zhang

et al. 2025

Applied Energy

View full text Add to dashboard Cite

Modified extended Kalman filtering algorithm for precise voltage and state-of-charge estimations of rechargeable batteries

Cited by 16 publications

References 43 publications

A review of battery state of charge estimation and management systems: Models and future prospective

A review of battery state of charge estimation and management systems: Models and future prospective

State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm

Real-time state-of-charge estimation for rechargeable batteries based on in-situ ultrasound-based battery health monitoring and extended Kalman filtering model

Contact Info

Product

Resources

About