State of Charge Estimation for Lithium-Ion Batteries Using Optimized Model Based on Optimal HPPC Conditions Created Using Taguchi Method and Multi-Objective Optimization

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the infl...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 20; p. 9245
Main Authors Sungur, Bilal, Kaleli, Alirıza
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2024
Subjects
Accuracy
Algorithms
Automobile sales
Design of experiments
Efficiency
Electric vehicles
equivalent circuit model
extended Kalman filter
Li-ion battery
Lithium
Mathematical optimization
Mean square errors
Methods
Optimization techniques
optimized HPPC profile
Parameter identification
Taguchi method
Taguchi methods
Temperature
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Summary:This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14209245