The commercial success of electric vehicles (EVs) relies heavily on the presence of high-efficiency charging stations. This paper provides an overview and a comprehensive performance comparison of the present status and future implementation plans for DC fast charging infrastructures and converter topologies. The paper also discusses critical consequences of DC fast charging stations on the AC grid. Different power converter topologies for DC fast charging are presented, compared, and evaluated, based on the power level requirements, efficiency, cost, and technical performance specifications. The paper focuses specifically on Level-3 DC fast charging converter topologies and their performance comparison. Finally, the paper presents a detailed well-towheels (WTW) analysis from an energy-efficiency standpoint. The most important part of this analysis focuses on the effect of usage of various charging levels and charger topologies on the all-important plug-to-battery (P2B) energy-efficiency within the overall context of WTW energy cycle efficiency.
Summary
Energy storage systems have been in the spotlight for the past decade as they offer tangible solutions to the ever‐growing pollution problem faced by the planet. These storage systems, primarily lithium‐ion based, power most of the mobile devices and electric vehicles (EVs). Substantial efforts are being made to electrify every mode of transportation to combat climate change. Accurate state‐of‐charge (SOC) and state‐of‐health (SOH) assessment of lithium‐ion batteries play an important role for determining the available range in EVs. Research in this area of capacity estimation demands extensive curated battery parameter data such as voltage, current, and temperature. Performing repeated experiments to collect these data is expensive and tedious. Also, lack of diversity in open access datasets has limited research in this area. This paper introduces a generative adversarial network (GAN)‐based approach for data augmentation. This technique enables expansion of sparse datasets in‐turn enhancing the learning capability of Neural Networks used for SOC/SOH estimation. A time series GAN is used in the present work to produce synthetic data. This technique was evaluated on two publicly available battery parameter datasets to test its effectiveness. A Kullback‐Leibler Divergence value of 0.2317 and 1.0572 was obtained for the battery dataset obtained from NASA prognostics repository and Oxford battery degradation dataset, respectively. The contributions of the paper include: (a) synthetic time‐series data augmentation of battery parameters, (b) high‐fidelity diverse data generation of battery profile data such as voltage, current, temperature, and SOC.
The electric vehicle (EV) industry has seen extraordinary growth in the last few years. This is primarily due to an ever increasing awareness of the detrimental environmental effects of fossil fuel powered vehicles and availability of inexpensive lithium-ion batteries (LIBs). In order to safely deploy these LIBs in electric vehicles, certain battery states need to be constantly monitored to ensure safe and healthy operation. The use of machine learning to estimate battery states such as state-of-charge and state-of-health have become an extremely active area of research. However, limited availability of open-source diverse datasets has stifled the growth of this field, and is a problem largely ignored in the literature. In this work, we propose a novel method of timeseries battery data augmentation using deep neural networks. We introduce and analyze the method of using two neural networks working together to alternatively produce synthetic charging and discharging battery profiles. One model produces battery charging profiles, and another produces battery discharging profiles. The proposed approach is evaluated using few public battery datasets to illustrate its effectiveness, and our results show the efficacy of this approach to solve the challenges of limited battery data. We also test this approach on dynamic electric vehicle drive cycles as well.
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