State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

Real-Time Battery Thermal Management for Electric Vehicles

Abstract: Electric vehicles (EVs) are powered by a large number of battery cells, requiring an effective battery management system (BMS) to maintain the battery cells in an operational condition while providing the necessary power efficiently. Temperature is one of the most important factors for battery operation, and existing BMSes have thus employed simple thermal management policies so as to prevent battery cells from very high and low temperatures which may likely cause their explosion and malfunction, respectively. In this paper, we study thermo-physical characteristics of battery cells, and design a battery thermal management system that achieves efficiency and reliability by active thermal controls.We first analyze the effect of a temperature change on the basic operation of a battery cell. We then show how it can cause thermal and general problems, e.g., a thermal runaway that results in explosion of battery cells, and unbalanced state of charge (SoC) that degrades battery cells' performance. Based on this understanding of thermal behavior, we finally develop temperature-control approaches which are then used to design a battery thermal management system. Our simulation results demonstrate that the proposed thermal management system improves battery performance by up to 58.4% without compromising reliability over the existing simple thermal management.

High-Efficiency Adaptive-Current Charging Strategy for Electric Vehicles Considering Variation of Internal Resistance of Lithium-Ion Battery

Abstract: This paper outlines a battery charging strategy to reduce charging losses in a lithium-ion battery for electric vehicles. The proposed charging strategy utilizes an adaptive current profile based on variations of the battery internal resistance as a function of the state of charge and the charge rate. To address the problem of finding the optimal current set for the proposed strategy, an evolutionary algorithm, which is a type of stochastic approach, is applied. A strategy for selecting the optimal number of charging intervals is also presented to reach a compromise between loss reduction and computational burden. Experimental results obtained using 34-Ah lithium-nickel-manganese-cobalt-oxide battery cells have proved that the proposed charging strategy decreases the charging loss of the battery cell by 40.1% compared with a conventional constant-current charging strategy. Furthermore, a 3.3-kW on-board charger prototype has been built to investigate the total loss reduction in an electric vehicle charging system that includes a 12-kWh battery pack. The proposed adaptive-current charging strategy reduces the total charging losses including both battery loss and charger loss of electric vehicles by 7.2%, 11.2%, and 21.2% in charging systems with power ratings of 3.3 kW, 6.6 kW, and 13.2 kW, respectively. These improvements can have the same effect as increasing the charger efficiency from a minimum of 0.50% to a maximum of 3.72% in our analysis range of 3.3–52.8 kW.