Networked control systems (NCSs) have been one of the main research focuses in academia as well as in industry for many decades and have become a multidisciplinary area. With these growing research trends, it is important to consolidate the latest knowledge and information to keep up with the research needs. In this paper, the NCS and its different forms are introduced and discussed. The beginning of this paper discusses the history and evolution of NCSs. The next part of this paper focuses on different fields and research arenas such as networking technology, network delay, network resource allocation, scheduling, network security in real-time NCSs, integration of components on a network, fault tolerance, etc. A brief literature survey and possible future direction concerning each topic is included.

Networked Control System: Overview and Research Trends

The electric vehicle (EV) technology addresses the issue of the reduction of carbon and greenhouse gas emissions. The concept of EVs focuses on the utilization of alternative energy resources. However, EV systems currently face challenges in energy storage systems (ESSs) with regard to their safety, size, cost, and overall management issues. In addition, hybridization of ESSs with advanced power electronic technologies has a significant influence on optimal power utilization to lead advanced EV technologies. This paper comprehensively reviews technologies of ESSs, its classifications, characteristics, constructions, electricity conversion, and evaluation processes with advantages and disadvantages for EV applications. Moreover, this paper discusses various classifications of ESS according to their energy formations, composition materials, and techniques on average power delivery over its capacity and overall efficiencies exhibited within their life expectancies. The rigorous review indicates that existing technologies for ESS can be used for EVs, but the optimum use of ESSs for efficient EV energy storage applications has not yet been achieved. This review highlights many factors, challenges, and problems for sustainable development of ESS technologies in next-generation EV applications. Thus, this review will widen the effort toward the development of economic and efficient ESSs with a longer lifetime for future EV uses.

Review of energy storage systems for electric vehicle applications: Issues and challenges

A critical review of battery thermal performance and liquid based battery thermal management

With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.

State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.

Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

As the number of nodes in large scale network control systems increases, allocating constant bandwidth to all the nodes is neither practical nor efficient in terms of quality of control because of the limited amount of available bandwidth. In order to resolve this issue of bandwidth management in a networked control system consisting of several control loops, dynamic methods for resource allocation should be used. Bandwidth allocation techniques like Large Error First (LEF) and some auction based techniques exist which have approached this problem from the perspective of efficient bandwidth usage, and from the perspective of performance improvement in the control loop. In this paper, we present a novel Behavioral Control (BC) based adaptive bandwidth allocation method in a fleet of Unmanned Ground Vehicles (UGV). The BC algorithm uses the UGV's speed and trajectory relative to the future path to allocate the available bandwidth. Simulation results have shown that the performance, in terms of the trajectory tracking error and tracking time, has improved up to 22% and 9% resp. when using the BC algorithm compared to existing bandwidth allocation algorithms.

Behavioral control based adaptive bandwidth allocation in a system of Unmanned Ground Vehicles

In network based path tracking control of systems with multiple unmanned ground vehicles (UGVs), performance can be affected by network constraints including time-varying network delays and bandwidth limitations. Network delay has previously been compensated for using smith predictor based techniques and gain scheduling to limit UGV motion. The predictive gain scheduler introduced in this paper uses the predicted trajectory and future path to maximize allowable travel while considering the network delay. On the other hand, bandwidth management has been approached by optimizing total bandwidth usage under performance constraints. However, by optimizing the performance of the UGVs under bandwidth constraints, the total networked control system performance increases.

Unnati Ojha

Papers

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

Behavioral control based adaptive bandwidth allocation in a system of Unmanned Ground Vehicles

Predictive control of multiple UGVs in a NCS with adaptive bandwidth allocation