Lithium-ion batteries, due to their high energy and power density characteristics, are suitable for applications such as portable electronic devices, renewable energy systems, and electric vehicles. Since the charging method can impact the performance and cycle life of lithium-ion batteries, the development of high-quality charging strategies is essential. Efficient charging strategies need to possess advantages such as high charging efficiency, low battery temperature rise, short charging times, and an extended battery lifespan. The challenges of charging algorithms encompass battery performance variation, temperature management, charging rate control, battery state estimation, and consideration of diverse charging requirements. Effective charging algorithms must strike a balance within these challenging conditions to ensure the battery’s longevity, high efficiency, and safety. This paper introduces and investigates five charging methods for implementation. These five charging methods include three different constant current–constant voltage charging methods with different cut-off voltage values, the constant loss–constant voltage charging method, and the constant power–constant voltage charging method. This paper will implement and compare the performance of the aforementioned five charging methods, including charging efficiency, battery temperature rise, charging time, and cycle life count, providing experimental data to enable users to choose a charging method more efficiently.

Effective charging techniques must consider factors such as charging efficiency, lifecycle, charging time (CT), and battery temperature. Currently, most charging strategies primarily focus on CT and charging losses (CL), overlooking the crucial influence of battery temperature on battery life. Therefore, this study proposes a constant temperature–constant voltage (CT-CV) charging method based on minimizing energy losses. The charging process is primarily divided into three stages. In the first stage, a constant current (CC) charging is implemented using a 2C rate that aims to expedite battery charging but may result in a rapid temperature increase. The second stage involves constant temperature charging, where the charging current is regulated based on battery temperature feedback using a PID controller to maintain a stable battery temperature. The third stage is constant voltage (CV) charging, where a fixed current is applied continuously until the current drops below the charging cutoff current. After completion of the charging process, the charging time can be calculated, and charging losses can be determined by incorporating the battery equivalent circuit model (ECM). To determine the optimal transition time, the paper employs Coulomb counting and the battery ECM, considering both CT and losses to simulate the transition time with minimal CL. This approach achieves optimization of transition points by establishing ECM, measuring internal impedance of the battery, and simulating various charging scenarios, and eliminates the need for multiple actual experiments. Experimental results show that the charging time (CT) should be reduced and the maximum temperature rise (TR) should be reduced under the same average TR condition of the proposed method. At the same CT, the average TR and the maximum TR should both decrease. The charging method proposed in this study exhibits the following advantages: (1) simultaneous consideration of the battery’s equivalent circuit model and charging time; (2) the achieved transition point demonstrates characteristics of minimized charging losses; (3) eliminates the need for multiple experimental iterations.

This paper presents a novel adaptive moving window forgetting factor method specifically designed for the decoupled recursive least squares approach applied to the second-order equivalent circuit model of batteries, The proposed technique adjusts weights using historical and current modeling errors, reducing complexity and improving parameter accuracy. To further improve its efficiency, the jellyfish search algorithm was employed to fine-tune the hyperparameters. Three distinct load profiles—the federal urban driving schedule, the dynamic stress test, and the supplemental federal test procedure driving schedule for United States —were selected to validate the effectiveness of the proposed algorithms. Experimental results show that the proposed method reduces the mean absolute error of terminal voltage by up to 74.3%, with an average improvement of 34.6% over the fixed forgetting factor method. Furthermore, it outperforms the sliding window difference forgetting factor method by up to 52.6%, with an average gain of 27.8%. Lastly, the method demonstrates up to 42.6% improvement and an average enhancement of 26.0% 20 over the original adaptive moving window forgetting factor method. In comparison to the aforementioned three methods, the proposed approach shows the capability to reduce mean absolute error of state of charge by up to 40.5%, 23.0%, and 7.6%, respectively