Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM.

A significant amount of the literature is focused on converters that supply the required voltage with low input-current ripple to electronic devices. A hybrid converter that combines boost and Cuk converters was developed recently. This hybrid converter achieved a relatively low input-current ripple based on earlier strategies. This paper proposes a new model that simulates a hybrid power converter system using a unified evolutionary algorithm (EA). As part of this paper, we present an improved framework for a hybrid power converter. Moreover, a unified EA is developed to incorporate differential evolution (DE) and genetic algorithm (GA) properties in order to analyze the proposed modified hybrid power converter. This research describes a modified hybrid power converter that optimizes the zero-ripple duty cycle (D_Z) through the proposed algorithm for minimizing the input-current ripple. Based on our simulation results, comparing the proposed method with the baseline algorithm reveals that the proposed approach is significantly more efficient than the baseline algorithm and achieves the minimum input-current ripple in different gain values. In addition, we observe that the proposed algorithm performs better than the DE and GA algorithms in terms of obtaining low input-current ripple results. Therefore, the proposed hybrid algorithm is becoming more efficient with hybridization.