State Of Charge
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APEC focuses on the practical and applied aspects of the power electronics business. Not just a power designer’s conference, APEC has something of interest for anyone involved in power electronics including:- Equipment OEMs that use power supplies and converters in their equipment- Designers of power supplies, dc-dc converters, motor drives, uninterruptable power supplies, inverters and any other power electronic circuits, equipments and systems- Manufacturers and suppliers of components and assemblies used in power electronics- Manufacturing, quality and test engineers involved with power electronics equipment- Marketing, sales and anyone involved in the business of power electronic- Compliance engineers testing and qualifying power electronics equipment or equipment that uses power electronics
The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.
Batteries; charge/discharge; ultra-capacitors; flywheels; hybrid energy storage; fuel cells; auxiliary power; SoC and SoH; solar vehicles; Converters; rectifiers; inverters; motor drives; power semiconductors; EMI/EMC; generators; integrated starter/alternators; drive trains; electro-magnetic compatibility; power architectures; 42V PowerNet; X-by-wire; electric power steering; hydraulic powertrain; Active suspension; cruise controls; remote sensing; wireless sensors; vehicular networking; cooperative driving; intelligent & autonomous vehicles; active and passive safety; embedded operation; driver assistance; virtual/digital Power split; fault tolerance; energy management;driving pattern recognition; driver modelling; shifting control; Vehicular systems/components; CAD/CAE; virtual prototyping; driving cycle design; ecodriving; life cycle analysis; EV infrastructure; V2X; on board chargers; AC & DC infrastructure; fast, superfast, wireless, smart & conductive charging; Smart Grid
2018 10th International Conference on Communication Software and Networks (ICCSN 2018) will be held during July 6-9, 2018 in Chengdu, China. ICCSN 2018 is sponsored by University of Electronic Science and Technology of China, co-sponsored by 54th Institute, CETC, China, Science and Technology on Communication Networks Laboratory, supported by Guangdong University of Technology, China and AET Journal.
1. Ageing and life expectancy of HV insulation;2. Bio-dielectrics;3. Conduction and breakdown in dielectrics;4. Dielectric materials for electronics and photonics;5.Dielectric phenomena and applications;6.Dielectrics for superconducting applications;7.Eco-friendly dielectric materials;8.Electrical insulation in high voltage power equipment and cables;9.Electrical and water tree development and surface tracking;10.Gaseous electrical breakdown and discharges;11.HVDC insulation systems;12. Nano-technology and nano-dielectrics;13. New functional dielectrics for electrical systems;14. Partial discharges;15.Space charge and its effects;16. Surface and interfacial phenomena.
No periodicals are currently tagged "State Of Charge"
Advances in Battery Manufacturing, Service, and Management Systems, None
This chapter provides a state‐of‐the‐art review for Li‐ion battery diagnostics, prognostics, and uncertainty management. It illustrates battery models used for battery state‐of‐charge (SOC) and state‐of‐health (SOH) estimation and reviews various estimation algorithms. The chapter elaborates data‐driven prognostics for predicting the remaining useful life (RUL) of battery SOC and SOH. In particular, a Copula‐based sampling method is explained in detail for ...
IEEE Instrumentation & Measurement Magazine, 2005
We encounter mixed-signal system-on-a-chip (SOC) devices in our daily lives in a broad range of products. Consumer products like PDAs, automobiles, and appliances all contain microcontrollers, battery management, and power chips; these can be mixed-signal devices. We use broadband products such as set-top boxes, cable modems, DSL, and DVD players that contain mixed-signal devices. Wireless products, cordless phones, cellular phones, ...
Impedance Source Power Electronic Converters, None
This chapter addresses the quasi-Z-source inverters (qZSI) with battery paralleling to C2for a PV system as an example. The dynamic model and the control method, including battery energy management, PV power maximum power point tracking (MPPT), and grid-tie synchronization, are presented. Similar methods can be performed with the Z-source inverters (ZSI) or when battery connecting to C1. Example simulations are ...
Advances in Battery Manufacturing, Service, and Management Systems, None
This chapter deals with each of three components of battery health management (BHM): battery state estimation, battery prognostics, and decision making. The state‐of‐health (SOH) of batteries is a measurement that reflects the general condition of a battery and its ability to deliver the specified performance compared to a fresh battery. The chapter discusses how to determine the remaining useful life ...
2017 National Power Electronics Conference (NPEC), 2017
Multi-level inverters are preferred choice for PV application due to better harmonic profile and low dv/dt. Generally multilevel inverters for PV applications are fed with multiple sources and the load power distribution among these sources may not be equal for all operating conditions. This paper proposes an optimal distribution of PV panels and Batteries in multi level inverter for off ...
APEC 2011- Methode Electronics at APEC 2011
APEC 2015: KeyTalks - Solid State Lighting
Being the Future of Innovation - Lorraine Martin - WIE ILC 2018
APEC 2011 State-Space Averaging: Past, Present and Future
ITEC 2014: Electrified Powertrain Vehicles: State of the Industry
Molecular Cellular Networks: A Non von Neumann Architecture for Molecular Electronics - Craig Lent: 2016 International Conference on Rebooting Computing
State-of-the-art Electrical Machines for Hybrid Electric Vehicles
The Josephson Effect: SQUIDs Then and Now: From SLUGS to Axions
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 7 of 7 - SQUID-based noise thermometers for sub-Kelvin thermometry
The Josephson Effect: The Josephson Volt
IEEE's Leah Jamieson focuses on Women Accelerating Change through Philanthropy - 2016 Women in Engineering Conference
State-of-the art techniques for advanced vehicle dynamics control & vehicle state estimation
Speaker Manu Bhardwaj - ETAP San Jose 2015
Catherine A. Novelli - Internet Inclusion: Global Connect Stakeholders Advancing Solutions, Washington DC, 2016
IEEE Magnetics Distinguished Lecture - Yoshichika Otani
2017 IEEE Donald O. Pederson Award in Solid-State Circuits: Takao Nishitani and John S. Thompson
APEC 2015: KeyTalks - How to Optimize Performance and Reliability of GaN Power Devices
TechFlash with Stefano Zanero - IEEE Young Professionals
IMS 2014: Out-of-Plane and Inline RF Switches based on Ge2Sb2Te5 Phase-Change Material
This chapter provides a state‐of‐the‐art review for Li‐ion battery diagnostics, prognostics, and uncertainty management. It illustrates battery models used for battery state‐of‐charge (SOC) and state‐of‐health (SOH) estimation and reviews various estimation algorithms. The chapter elaborates data‐driven prognostics for predicting the remaining useful life (RUL) of battery SOC and SOH. In particular, a Copula‐based sampling method is explained in detail for predicting the RUL of the capacity fade. The chapter describes various uncertainties in battery diagnostics and prognostics and a proposed framework is illustrated for managing the battery model parameter uncertainty and model uncertainty in a systematic manner. Battery models can be classified into two groups: electrochemical models and equivalent circuit models (ECMs). Five types of uncertainty play a key role for reliable estimation of the battery performances of interest and they can be classified as measurement uncertainty, algorithm uncertainty, environmental uncertainty, model parameter uncertainty, and model uncertainty.
We encounter mixed-signal system-on-a-chip (SOC) devices in our daily lives in a broad range of products. Consumer products like PDAs, automobiles, and appliances all contain microcontrollers, battery management, and power chips; these can be mixed-signal devices. We use broadband products such as set-top boxes, cable modems, DSL, and DVD players that contain mixed-signal devices. Wireless products, cordless phones, cellular phones, WLAN, Bluetooth, GPS receivers, and cable tuners also contain mixed-signal SOC devices. The content of the mixed-signal SOC device is characterized by different types of cores. They may be analog cores or digital cores. Many applications include mixed analog and digital cores such as digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). These devices can provide complete system functionality on a single chip. Figure 1 is an example of a multimedia SOC device.
This chapter addresses the quasi-Z-source inverters (qZSI) with battery paralleling to C2for a PV system as an example. The dynamic model and the control method, including battery energy management, PV power maximum power point tracking (MPPT), and grid-tie synchronization, are presented. Similar methods can be performed with the Z-source inverters (ZSI) or when battery connecting to C1. Example simulations are demonstrated in different cases of battery state of charge. The energy storage battery integrated ZSI/qZSI deals with power management among the renewable energy sources, grid, and battery in single-stage power conversion, without the extra dc-dc battery charging converter, providing simple topology and control. Its small-signal model was built to design the controller, especially battery current and energy management control. Simulation results were illustrated for different cases of battery state of charge. The solution is also inheritable to the other derived impedance topologies with battery paralleling to the capacitors.
This chapter deals with each of three components of battery health management (BHM): battery state estimation, battery prognostics, and decision making. The state‐of‐health (SOH) of batteries is a measurement that reflects the general condition of a battery and its ability to deliver the specified performance compared to a fresh battery. The chapter discusses how to determine the remaining useful life (RUL) of a battery. State estimation techniques, like the extended Kalman filter (EKF), have been applied for real‐time prediction of state‐of‐charge (SOC) and SOH of automotive batteries. A decision‐level fusion of data‐driven algorithms, like Autoregressive Integrated Moving Average (ARIMA) and neural networks, have been investigated for both diagnostics and prognostics. Particle filters (PFs) are a novel class of nonlinear filters that combine Bayesian learning techniques with importance sampling to provide good state tracking performance while keeping the computational load tractable.
Multi-level inverters are preferred choice for PV application due to better harmonic profile and low dv/dt. Generally multilevel inverters for PV applications are fed with multiple sources and the load power distribution among these sources may not be equal for all operating conditions. This paper proposes an optimal distribution of PV panels and Batteries in multi level inverter for off grid PV applications. In this paper, generalized equations are proposed for calculating the energy supplied during each voltage level for any multilevel inverter. In addition, generalized panel distribution scheme in NPC inverter for optimal utilization of sources is also presented. This generalized panel distribution is according to pre calculated load shared by individual sources. This scheme is validated for five-level conventional NPC inverter. The advantages of the scheme are presented by State of Charge (SOC) of batteries using Mat lab/Simulink.
This paper discusses a simulation platform for predicting the behavior of a battery system comprising two batteries, which can be parallelized in a controllable way. The model of the battery, the load and the parallelization algorithm is developed and simulated in MATLAB®Simulink environment. The simulation platform and the proposed parallelization algorithm are validated in a real gardening application. The simulation results prove to be useful for further investigation into the benefits of battery parallelization in terms of reduced battery aging and improved energy efficiency.
We propose complete technology-design-system co-optimization method in which power, performance, thermal, area and cost metrics are all simultaneously optimized from transistor to mobile SOC system level. This novel method, Unified Technology Optimization Platform using Integrated Analysis (UTOPIA), incorporates thermally limited performance, wafer process complexity and die area scaling model in addition to author's previous transistor-interconnect optimization method. Thermal model in UTOPIA evaluates/optimizes device and technology parameters not only for peak frequency but also for sustained performance after thermal throttling. Optimum N7 technology is selected using proposed UTOPIA method, showing significant overall gain over N10 technology.
The rapid development of battery energy storage technology has provided a new solution for integrating large scale renewable power generation; however, the accurate model of battery energy storage system (BESS) in multi-time scales has not been developed yet. The BESS model consists of battery model, power electronics interface model and controller model. There are still no certain math expressions about the parameters of battery model, the model of power electronics and controller are also different in different applications. Considering different applications in power system, the multi-time scales model of BESS should be established to take account of the transient, steady and dynamic state. In this paper, an equivalent modeling method of BESS was developed. First, the different equivalent circuit models of battery were summarized, according to the comparison, the thevenin equivalent circuit model was supposed as the effective model for power system application. So, taking into account the relations between the parameters such as current, internal resistance, and state of charge (SOC), the battery parameters were fitted based on experimental date. Then, the power electronics interface model including DC/AC and DC/DC converter were introduced, and the control method with different operations were proposed. Finally, the BESS model were built and verified on the platform of BESS experiment. The results show that the model can represent the multi-state characteristics of battery and has quick response.
Power battery is the heart of electric vehicles, and the accurate state of charge (SOC) estimation is crucial for the management of the power battery. This paper proposes an adaptive strong tracking unscented Kalman filter (ASTUKF) algorithm to estimate the SOC of lithium-ion battery. This method doesn't need to compute the Jacobian matrix compared with the traditional strong tracking filter. This algorithm can correct the SOC estimation error caused by the model error and update the noise covariance in real time, which improves the accuracy and robustness of the SOC estimation for lithium-ion battery. The experiments of LiFePO4 power battery are conducted to validate the accuracy and robustness of the proposed algorithm.
Two of the major heat sources in a high-performance automotive lithium-ion battery cell are parameterized in this study: Joule heat and entropy heat. Established electrochemical models are investigated and experiments are designed to acquire the relevant parameters such as open circuit voltage, entropy coefficient and internal impedance from ohmic losses and mass transport. It is shown that the irreversible joule heat and the reversible entropy heat has a similar magnitude at many operating points for the device tested. The strong influence of irreversible entropy heat has the potential to absorb all the joule heat in currents up to 135 A (C-rate of 13.5) charging and 66 A (6.6 C) discharge in a power optimized automotive lithiumion cell. It is also shown that, by including the entropy heat in a simple thermal model, the temperature error can be reduced down to 28 % and 44 % for under charging and discharging with high currents, respectively.
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