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Applications of rechargeable batteries have recently expanded from small information technology (IT) devices to a wide range of other industrial sectors, including vehicles, rolling stocks, and energy storage system (ESS), as a part of efforts to reduce greenhouse gas emissions and enhance convenience. The capacity of rechargeable batteries adopted in individual products is meanwhile increasing and the price of the batteries in such products has become an important factor in determining the product price. In the case of electric vehicles, the price of batteries has increased to more than 40% of the total product cost. In response, various battery management technologies are being studied to increase the service life of products with large-capacity batteries and reduce maintenance costs. In this paper, a charging algorithm to increase the service life of batteries is proposed. The proposed charging algorithm controls charging current in anticipation of heating inside the battery while the battery is being charged. The validity of the proposed charging algorithm is verified through an experiment to compare charging cycles using high-capacity type lithium-ion cells and high-power type lithium-ion cells.

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This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

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In this study, the thermal behavior of a 1S18P battery pack is examined based on the power demand during train propulsion between two stations. The proposed thermal prediction model is classified into Joules heating with equivalent resistance, reversible heat, and heat dissipation. The equivalent resistances are determined by 5% of the state of charge intervals using the hybrid pulse power characterization test. The power demand profile during train propulsion between two stations is provided by the Korea Railroad Research Institute. An experiment is conducted to examine the 1S18P battery pack thermal behavior during the propulsion between two stations. A comparison of the simulation and experiment results validated the proposed thermal model. Electronics 2020, 9, 447 2 of 13 electrochemical thermal model require chemical parameters which are hard to examine. Therefore, a simplified electro-thermal model was introduced and classified the heat generation term into reversible heat and Joule heat. An electrochemical reaction (including polarization and entropy change) generates reversible heat. Joule heating occurs due to the resistance during the transfer of ions and electrons. A simplified thermal model of a lithium-ion battery was examined by Onda et al. [9]. Equivalent resistances are obtained by state of charge (SOC) intervals and employed for Joule heating. Additionally, entropy change is measured at different SOCs and applied to reversible heat [14]. Tables 1 and 2 are definitions and descriptions of abbreviation and symbols used in this paper.