Scopus
YÖKSİS Eşleşti
State Of Charge Estimation in Li-ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modelling Based on Operating States
IEEE Access · Ocak 2025
YÖKSİS Kayıtları
State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States
IEEE Access · 2025 SCI-Expanded
PROFESÖR HAYRİ ARABACI →
Makale Bilgileri
DergiIEEE Access
Yayın TarihiOcak 2025
Scopus ID2-s2.0-105011602430
Özet
In Li-ion battery applications, effective energy management relies heavily on accurate knowledge of the state of charge (SOC). As SOC cannot be directly measured, it must be estimated using several methods. Deep learning has emerged as one of the most widely used approaches. However, in cases where the input data exhibit limited variation over time and consist of low-dimensional features, deep learning models like convolutional neural network (CNN) and recurrent neural network (RNN) may tend toward overfitting. To address this, deep learning algorithms such as long short-term memory (LSTM) have been focused on for SOC prediction. Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. This variability negatively impacts the performance of conventional LSTM models. To overcome this limitation, this study proposes a parallel LSTM architecture composed of three distinct models, each tailored to a specific battery operating condition. Both the proposed and conventional models were evaluated using various standardized driving cycles. Mean absolute error, mean squared error, and boxplot analysis were employed for performance comparison. Across all metrics, the proposed method consistently outperformed the standard model. The best mean absolute error result was achieved with the proposed method, at 0.75% under the LA92 driving cycle. These results demonstrate the effectiveness of the proposed approach in accurately and reliably estimating SOC in dynamic battery applications.
Yazarlar (2)
1
Osman Ozer
ORCID: 0000-0001-7489-1721
2
Hayri Arabaci
Anahtar Kelimeler
data-driven model
deep learning
Li-ion batteries
long-short term memory
state of charge (SOC) estimation
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey