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Neural Network-Based Battery State-of-Charge Estimation Enhanced by Frequency-Optimized Excitation Signals | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 شهریور 1405 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2026.12582.909 | ||
| نویسندگان | ||
| Ramazan Havangi* ؛ Fatemeh Karimi | ||
| Faculty of Electrical Engineering and Computer, University of Birjand, Birjand, Iran. | ||
| تاریخ دریافت: 17 فروردین 1405، تاریخ بازنگری: 29 تیر 1405، تاریخ پذیرش: 05 مرداد 1405 | ||
| چکیده | ||
| Background and Objectives: Accurate state-of-charge (SOC) estimation is essential for improving the performance, reliability, and lifetime of lithium-ion battery systems. Although neural-network-based methods have demonstrated promising SOC estimation capability, the influence of excitation signal frequency characteristics on estimation performance has not been systematically investigated. This study aims to address this gap by examining how the frequency content of excitation signals influences the SOC estimation performance of NNs. Methods: An Amplitude Pseudo-Random Binary Sequence (APRBS) signal was applied as the excitation input to the battery system. Fast Fourier Transform (FFT) analysis was then conducted to assess the frequency content of the APRBS signal. The relationship between the frequency components of the APRBS input and the SOC estimation error of the NN was systematically investigated. Results: The results indicate that increasing the frequency bandwidth of the APRBS input signal significantly improves the SOC estimation accuracy of the neural network, particularly during the initial stages of operation. However, beyond a certain bandwidth threshold, the improvement becomes marginal, indicating the existence of an optimal frequency range that provides the best trade-off between excitation richness and estimation performance. Furthermore, a correlation was identified between the extracted frequency components and the Bode diagram of the battery system, providing valuable insights into the underlying dynamic characteristics of the battery. Conclusion: The findings demonstrate that the frequency characteristics of input signals play a critical role in SOC estimation accuracy when using NNs. Identifying an optimal frequency bandwidth not only improves estimation performance but also enhances understanding of battery dynamics. This work introduces a novel perspective for optimizing SOC estimation through the integration of signal processing and machine learning, laying the groundwork for future advancements in battery management systems. | ||
| کلیدواژهها | ||
| Battery State-of-Charge Estimation؛ Excitation Signal Optimization؛ Neural Network Optimization؛ Frequency Response Analysis؛ Battery Management Systems. | ||
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