Comparative study on modeling and estimation of State of Charge in battery

Rahul, K; Ramprabhakar, J.; Shankar, Shobha

doi:10.1109/smarttechcon.2017.8358637

Cited by 14 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the definition of PIO, it can be designed as follows:

\dot{\hat{x}}=A\hat{x}+ Bu+{K}_p\left(y-\hat{y}\right)+{K}_{i2}w;

\dot{w}={K}_{i1}\left(y-\hat{y}\right),

where

w

in reality is known as the integral of the difference between actual and estimated terminal voltage

\left(y-\hat{y}\right)

, while vectors

{K}_p

{K}_{i1}

, and

{K}_{i2}

are the proportional and integral gains, respectively, and instead of the gain vector

L

of the Luenberger observer (Amir et al, 2018). The PIO is straightforward to install owing to its basic structure paired with great efficiency and accuracy when compared to other methods like KF or fuzzy logic (Rahul et al, 2017). Nevertheless, trial and error adjustment of the gains is required (Manthopoulos & Wang, 2020).…”

Section: Soc Estimation Methodsmentioning

confidence: 99%

“…where w in reality is known as the integral of the difference between actual and estimated terminal voltage y Àb y ð Þ, while vectors K p , K i1 , and K i2 are the proportional and integral gains, respectively, and instead of the gain vector L of the Luenberger observer (Amir et al, 2018). The PIO is straightforward to install owing to its basic structure paired with great efficiency and accuracy when compared to other methods like KF or fuzzy logic (Rahul et al, 2017). Nevertheless, trial and error adjustment of the gains is required (Manthopoulos & Wang, 2020).…”

Section: Observers and Controllersmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Based on the definition of PIO, it can be designed as follows:

\dot{\hat{x}}=A\hat{x}+ Bu+{K}_p\left(y-\hat{y}\right)+{K}_{i2}w;

\dot{w}={K}_{i1}\left(y-\hat{y}\right),

where

w

in reality is known as the integral of the difference between actual and estimated terminal voltage

\left(y-\hat{y}\right)

, while vectors

{K}_p

{K}_{i1}

, and

{K}_{i2}

are the proportional and integral gains, respectively, and instead of the gain vector

L

Section: Soc Estimation Methodsmentioning

confidence: 99%

Section: Observers and Controllersmentioning

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

“…Therefore, data-driven approaches are advantageous for estimation of systems for which mathematical models are unknown, have high uncertainty, cannot be modeled by empirical equations, or are not suitable for analysis [13]. Fuzzy logic controllers [14], support vector machines (SVM) [15], genetic algorithms (GA) [16], and neural network (NN) algorithms [17] are used in black-box models. Estimation accuracy is highly dependent on the training data set and training method.…”

mentioning

confidence: 99%

Development of a Matlab/Simulink Model for Monitoring Cell State-of-Health and State-of-Charge via Impedance of Lithium-Ion Battery Cells

Kim

Kowal

2022

Batteries

View full text Add to dashboard Cite

Lithium-ion battery cells not only show different behaviors depending on degradation and charging states, but also overcharge and overdischarge of cells shorten battery life and cause safety problems, thus studies aiming to provide an accurate state of a cell are required. Measurements of battery cell impedance are used for cell SoH and SoC estimation techniques, but it generally takes a long time for a cell in each state to be prepared and cell voltage response is measured when charging and discharging under each condition. This study introduces an electrical equivalent circuit model of lithium-ion cells developed in the MATLAB/Simulink environment. Cell SoC, SoH, temperature, and C-rate are considered for more accurate cell impedance prediction, and the simulation results are verified with the measurement results. The developed model is suitable for use in cell SoC and SoH monitoring studies by successfully outputting cell impedance through real-time prediction of cell voltage during discharge.

show abstract

V2G/G2V Bi-directional On-Board EV Charger Using Two Phase Interleaved DC-DC Converter

Kumar¹,

Deepa²

2022

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Comparative study on modeling and estimation of State of Charge in battery

Cited by 14 publications

References 9 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

Development of a Matlab/Simulink Model for Monitoring Cell State-of-Health and State-of-Charge via Impedance of Lithium-Ion Battery Cells

V2G/G2V Bi-directional On-Board EV Charger Using Two Phase Interleaved DC-DC Converter

Contact Info

Product

Resources

About