Browsing by Author "Yen, Jieh-Hwang"

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A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions
(2016-01-01)
Dung, Lan-Rong
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Yuan, Hsiang-Fu
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Yen, Jieh-Hwang
;
She, Chien-Hua
;
Lee, Ming-Han
A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.
ILP-Based Algorithm for Lithium-Ion Battery Charging Profile
(2010)
Dung, Lan-Ron
;
Yen, Jieh-Hwang
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電控工程研究所
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Institute of Electrical and Control Engineering
A novel algorithm applying (0, 1)-integer linear programming is proposed to search the optimal charging profile for rechargeable Lithium-ion batteries. The analysis of charging efficiency and charging time is based on equally segmented battery state of charge (SoC) and the results are re-assembled into a charging profile by (0, 1)-integer linear programming. Experimental results show that the proposed algorithm achieves up to 18.25% and 21.38% charging time reduction in comparison to fast-and slow-charging specifications of a commercial product respectively, subject to a charging efficiency constraint at the same time.