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.

Method and apparatus for measuring the voltage and charge of a battery

Abstract: Method and apparatus for measuring the voltage and state of charge of a secondary battery The voltage and state of charge values are taken by a circuit every four (4) seconds by reading voltage, waiting two (2) seconds, reading current, waiting two (2) seconds, and repeating the voltage readings and the current readings The circuit includes a 14-bit microprocessor, CMOS analog switches, and two operational amplifiers The voltage and current readings are taken from dual-sloped converters, and the values determined by software algorithms in the microprocessor

Energy Management in Smart Distribution Systems With Vehicle-to-Grid Integrated Microgrids

Abstract: In modern electric power systems, plug-in electric vehicle (PEV) with vehicle-to-grid (V2G) potential are becoming reliable and flexible resources for energy balancing under varying energy supply and demand scenarios. In this evolving paradigm, designing energy management strategies for feasible and cost-effective utilisation of V2G is one of the several challenges faced by the utility operators and regulators. This paper proposes two energy management strategies to effectively utilize V2G potential of PEVs in managing energy imbalances in grid-connected microgrids. The contributions of this paper are in twofold. First, it proposes a novel bidding strategy for PEVs offering V2G by including the projected battery degradation cost to integrate them into microgrid operation. Second, two energy management strategies are proposed for inclusion of V2G into the microgrid operation based on the forecast accuracy on energy supply and demand, and market prices. The proposed V2G integration strategies are implemented using a multi-agent system developed in Java agent development framework and applied to a microgrid case study system. The simulation results and their analysis show that V2G can be used to maximum depth of discharge levels if the electricity price variation is high and battery cost of PEVs is low.

Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework

Abstract: The battery is a key component in Plug-in Electric Vehicles (PEVs) whose degradation should be considered in vehicle modeling and if the battery pack is to be used in a Vehicle to Grid (V2G) smart grid studies. Several researchers have proposed different methodologies for PEV batteries degradation modeling from various aspects. Most of the battery degradation literature consists of empirical-based studies with results extracted from experimental tests in laboratories. As such, the results have been presented in non-formulated forms and are of less effectiveness for smart grid researchers. Furthermore, the impact of battery degradation in V2G smart grid have not been examined in smart grid studies. This paper reviews and compares different battery technologies focusing on Lithium-ion batteries which dominant in today and future vehicle applications. After that the most prominent degradation models are assessed, the effects of degradation factors on battery performance are examined. The literature shows that the degradation causes can be categorized into two groups namely calendar ageing and cycling ageing. Generally, the calendar ageing is influenced by temperature, time, and state of charge, while the cycling ageing is influenced by cycle number, charge rate and depth of discharge. Finally, in this work a conceptual framework for battery degradation modeling is proposed that can be easily used in smart grid studies, without necessarily requiring a detailed understanding of fundamental electrochemical processes. The proposed framework considers not only the battery degradation modeling, but also that of other related components in a smart grid.

Battery health management system for electric UAVs

Abstract: This paper presents a novel battery health management system for electric UAVs (unmanned aerial vehicles) based on a Bayesian inference driven prognostic framework. The aim is to be able to predict the end-of-discharge (EOD) event that indicates that the battery pack has run out of charge for any given flight of an electric UAV platform. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This problem is more pronounced in battery powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements and a dead stick condition (battery shut off in flight) can have catastrophic consequences. Since UAVs deployments are relatively new, there is a lack of statistically significant flight data to motivate data-driven approaches. Consequently, we have developed a detailed discharge model for the batteries used and used it in a Bayesian inference based filtering (Particle Filtering) technique to generate remaining useful life (RUL) distributions for a given discharge. The results section presents the validation of this approach in hardware-in-the-loop tests.12

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Abstract: State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.