https://hal-cea.archives-ouvertes.fr/cea-01791260/document

A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

Nanotechnology is the science of understanding matter and the control of matter at dimensions of 100 nm or less. Encompassing nanoscale science, engineering, and technology, nanotechnology involves imaging, measuring, modeling, and manipulation of matter at this level of precision. An important aspect of research in nanotechnology involves precision control and manipulation of devices and materials at a nanoscale, i.e., nanopositioning. Nanopositioners are precision mechatronic systems designed to move objects over a small range with a resolution down to a fraction of an atomic diameter. The desired attributes of a nanopositioner are extremely high resolution, accuracy, stability, and fast response. The key to successful nanopositioning is accurate position sensing and feedback control of the motion. This paper presents an overview of nanopositioning technologies and devices emphasizing the key role of advanced control techniques in improving precision, accuracy, and speed of operation of these systems.

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A Survey of Control Issues in Nanopositioning

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

It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.

Aggregate Flexibility of Thermostatically Controlled Loads

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

http://flyingv.ucsd.edu/smoura/pubs/JPS_ChgPatternOpt_InPress.pdf

Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity

This paper examines the problem of demand-side energy management in smart power grids through the setpoint control of aggregate thermostatic loads. This paper models these loads using a novel partial differential equation framework that builds on existing diffusion- and transport-based load modeling ideas in the literature. Both this partial differential equation (PDE) model and its finite-difference approximations are bilinear in the state and control variables. This key insight creates a unique opportunity for designing nonlinear load control algorithms with theoretically guaranteed Lyapunov stability properties. This paper's main contribution to the literature is the development of the bilinear PDE model and a sliding mode controller for the real-time management of thermostatic air conditioning loads. The proposed control scheme shows promising performance in adapting aggregate air conditioning loads to intermittent wind power.

Modeling and Control of Aggregate Air Conditioning Loads for Robust Renewable Power Management

This paper examines an electrochemistry-based lithium-ion battery model developed by Doyle, Fuller, and Newman. The paper makes this model more tractable and conducive to control design by making two main contributions to the literature. First, we adaptively solve the model's algebraic equations using quasi-linearization. This improves the model's execution speed compared to solving the algebraic equations via optimization. Second, we reduce the model's order by deriving a family of analytic Pade approximations to the model's spherical diffusion equations. The paper carefully compares these Pade approximations to other published methods for reducing spherical diffusion equations. Finally, the paper concludes with battery simulations showing the significant impact of the proposed model reduction approach on the battery model's overall accuracy and simulation speed.

https://iopscience.iop.org/article/10.1149/1.3519059/pdf

Reduction of an Electrochemistry-Based Li-Ion Battery Model via Quasi-Linearization and Padé Approximation

This paper examines the problem of using thermostat offset signals to directly control distributed air conditioning loads attached to the grid. The paper models these loads using a novel partial differential equation framework that builds on existing diffusion-based load modeling ideas in the literature. Both this PDE model and its finite-difference discretizations are bilinear in the state and control variables. This key insight creates a unique opportunity for designing nonlinear direct load control algorithms with theoretically guaranteed Lyapunov stability properties. The paper's main contribution to the literature is the development of the bilinear PDE model and Lyapunov-stable controller for real-time management of thermostatic air conditioning loads.

/pdf/modeling-and-control-insights-into-demand-side-energy-5e5cpofi14.pdf

Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads

Precision control of multiple-axis piezo-flexural stages used in a variety of scanning probe microscopy systems suffers not only from hysteresis nonlinearity, but also from parametric uncertainties and the cross-coupled motions of their axes. Motivated by these shortfalls, a Lyapunov-based control strategy is proposed in this article for simultaneous multiple-axis tracking control of piezo-flexural stages. A double-axis stage is considered for system analysis and controller validation. Hysteresis and coupling nonlinearities are studied through a number of experiments, and it is demonstrated that the widely used proportional-integral (PI) controller lacks accuracy in high-frequency tracking. Adopting the variable structure control method, a robust adaptive controller is then derived with its stability guaranteed through the Lyapunov criterion. It is shown that a parallelogram-type zone of attraction can be explicitly formed for the closed-loop system to which the error phase trajectory converges. Practical implementation of the controller demonstrates effective double-axis tracking control of the stage in the presence of hysteresis and coupling nonlinearities and despite parametric uncertainties for low-and high-frequency trajectories. Moreover, good agreements are achieved between the experiments and theoretical developments.

Saeid Bashash

Papers

Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity

Modeling and Control of Aggregate Air Conditioning Loads for Robust Renewable Power Management

Reduction of an Electrochemistry-Based Li-Ion Battery Model via Quasi-Linearization and Padé Approximation

Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads

Robust Adaptive Control of Coupled Parallel Piezo-Flexural Nanopositioning Stages