For techniques used to estimate battery state of charge (SOC) based on equivalent electric circuit models (ECMs), the battery equivalent model parameters are affected by factors such as SOC, temperature, battery aging, leading to SOC estimation error. Therefore, it is necessary to accurately identify these parameters. Updating battery model parameters constantly also known as online parameter identification can effectively solve this issue. In this paper, we propose a novel strategy based on the sunflower optimization algorithm (SFO) to identify battery model parameters and predict the output voltage in real-time. The identification accuracy has been confirmed using empirical data obtained from CALCE battery group (the center for advanced life cycle engineering) performed on the Samsung (INR 18650 20R) battery cell under one electric vehicle (EV) cycle protocol named dynamic stress test. Comparative analysis of SFO and AFRRLS (adaptive forgetting factor of recursive least squares) is carried out to prove the efficiency of the proposed algorithm. Results show that the calibrated model using SFO has superiority compared with AFFRLS algorithm to simulate the dynamic voltage behavior of a lithium-ion battery in EV application.

These days, the researches on permanent magnetic synchronous motors (PMSMs) as mechanical power sources have obtained more and more attention. the popular strategies applied to control the machine speed presents higher standards for torque response speed and accuracy of the PMSMs control system. To follow the reference motor speed as quick as possible, a sliding mode (SM) motor speed controller that can decouple q-axis and d-axis currents is proposed Moreover, To mitigate the well-known chattering phenomenon caused by the discontinuous term in a steady state of the conventional sliding mode control, a Fuzzy logic algorithm is introduced. The proposed Fuzzy-SMC performance is tested in simulation using MATLAB/Simulink environment. To eliminate the chattering phenomenon a combination of the command in sliding mode and the fuzzy logic is adopted. The proposed method also can guarantee the robust control of PMSMs under model parameters (resistance, inductance) and load torque variations. Based on the obtained results, it is clear that a fuzzy sliding mode controller can perform better than the conventional PI controller in terms of rising time, overshoot, settling time, and steady-state error. The effectiveness of the combined Fuzzy-SM Controller also can guarantee the robust control of PMSMs and shows that this command did not depend on machine parameters (resistance, inductance) comparing to other existing commands and the chattering effect is reduced using this proposed method.