This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.

/pdf/review-of-battery-charger-topologies-charging-power-levels-85yy2igx2w.pdf

Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles

Annals of physics

Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.

Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

/pdf/quantum-models-of-cognition-and-decision-hejlh57vgk.pdf

Quantum Models of Cognition and Decision

The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme.

Optimal Scheduling for Charging and Discharging of Electric Vehicles

Over the past few years, networked control systems (NCSs) have shown rapid progress and have indeed been very popular in terms of research as well as industrial applications. The issue of limited resources has been a fundamental problem in the translation of modern control techniques to NCS design. In order to address the aforesaid design challenge, an improved event-triggered adaptive backstepping control scheme is presented in this study for a class of uncertain NCSs with non-Lipschitz non-linearities under limited resources. Rather than a preselected constant threshold assumption, a well-designed and systematic triggering rule is derived based on the Lyapunov approach in order to satisfy bandwidth limitation and ensure system stability with acceptable transient performance. Relative to existing strategies in the literature, the proposed method leads to a substantially reduced number of transmissions with longer inter-event time. Thereby, the proposed algorithm exhibits more efficiency in resource utilisation. Simulation results on a networked control based robotic manipulator system illustrate the efficacy of the proposed adaptive scheme compared to benchmark control algorithms intended for a similar application.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cta.2018.6002

Improved event-triggered adaptive control of non-linear uncertain networked systems

A theoretical quantum brain model is proposed using a nonlinear Schroedinger wave equation. The model proposes that there exists a quantum process that mediates the collective response of a neural lattice (classical brain). The model is used to explain eye movements when tracking moving targets. Using a Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical brain, a wave packet is triggered in the quantum brain. This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of 'jumps' and 'rests'. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.

Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS.

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

This paper presents a new paradigm for stochastic filtering by modeling the unified response of a neural lattice using the Schroedinger wave equation. The model is based on a novel concept that a quantum object mediates the collective response of a neural lattice. The model is referred as recurrent quantum neural network (RQNN). The RQNN model has been simulated in two different ways. In one case the potential field of the Schroedinger wave equation is linearly modulated and in the other case the potential field of the Schroedinger wave equation is nonlinearly modulated. It is shown that the proposed quantum stochastic filter can efficiently denoise signals such as DC, sinusoid, amplitude modulated sinusoid and speech signals embedded in very high Gaussian and non-Gaussian noises. Performance of linearly modulated RQNN compares well with traditional techniques such as Kalman filter and wavelet filter. However, preliminary results show that nonlinearly modulated RQNN performs much better when compared with traditional techniques. For example, nonlinearly modulated RQNN model denoises a DC signal 1000 times more accurately in comparison to a traditional Kalman filter. The most important fact is that the proposed quantum stochastic filter does not make any assumption about the shape and nature of the signal and noise when denoising a signal. In a sense, the proposed quantum stochastic filter is a step forward towards intelligent filtering.

Quantum stochastic filtering

This paper deals with the design of fuzzy controller and observer for a Twin Rotor MIMO System (TRMS). Twin Rotor MIMO System is the prototype of a helicopter with two degrees of freedom which involves significant cross coupling between the main rotor and the tail rotor. A Takagi-Sugeno (T-S) fuzzy model is used to approximate the nonlinear dynamics of the system. With the derived T-S fuzzy model, a fuzzy controller is designed that guarantees not only stability but also satisfies the specified performance criteria of the closed-loop control system. A set of inequalities is formed based on the Lyapunov sufficient condition to ensure stability of the T-S fuzzy model using a parallel distributor fuzzy compensator. The controller gains are obtained by solving the set of inequalities. A fuzzy observer is also designed to estimate the states of the system.

Indrani Kar

Papers

Improved event-triggered adaptive control of non-linear uncertain networked systems

Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

Quantum stochastic filtering

T-S fuzzy model based controller and observer design for a Twin Rotor MIMO System