SoC Estimation Lithium Polymer Battery Based on Equivalent Circuit Model and Extended Kalman Filter

Anggraeni, Dewi; Sudiarto, Budi; Subhan, Achmad; Chasanah, Nurul; Santosa, Cahya Edi; Suryanti, Desti Ika; Prabowo, Gunawan Setyo; Priambodo, Purnomo Sidi

doi:10.1109/aceee56193.2022.9851867

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A BMS has indicators that can determine the condition of a battery, namely, the state of charge (SoC), the state of health (SoH), the state of function (SoF), and the state of temperature (SoT) [5]. The SoC is one of the essential parameters offered by a BMS, and reflects a battery's remaining capacity [6]. SoC is determined according to the battery model.…”

Section: Theoretical Reviewmentioning

confidence: 99%

Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition

Anggraeni,

Sudiarto,

Fitrianingsih

et al. 2023

WEVJ

Self Cite

View full text Add to dashboard Cite

The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size (M) greatly affects the accuracy of SoC estimation in both methods. M provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC.

show abstract

Section: Theoretical Reviewmentioning

confidence: 99%