Adaptive State of Power Estimation Using Fuzzy Logic Real Driving Patterns Recognition Based on Error-Effort Tradeoff

State of Power (SOP) is essential for evaluating a battery’s charging and discharging efficiency and predicting its performance under various conditions. Accurate SOP estimation requires a flexible model that considers battery constraints while optimizing computational efficiency. This research explores the balance between computational complexity and estimation accuracy in Adaptive State of Power (ASOP) modeling, using the Sportivity Index (SI) from Fuzzy Driving Pattern Recognition (FDPR). We examine how driving patterns and cell temperatures impact SOP estimation, considering accuracy and processing loads. We employ an Equivalent Circuit Model (ECM) that incorporates data on current, voltage, and temperature to enhance accuracy across diverse conditions. The ECM switches between polynomial adaptive models and battery architecture through Hybrid Adaptive Battery Parameter Estimates (HABPE). Fuzzy logic and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used to extract driving patterns and balance error with effort. Using experimental data from Panasonic lithium-ion batteries and the LA92 driving cycle, SOP is estimated considering time and error criteria. Results indicate an 80% improvement in battery parameter estimation over polynomial models, a 30% reduction in processing time, and a 20% overall improvement in ASOP estimation compared to classical SOP methods.