Unnati Ojha

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

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.

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

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.

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

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 UGVs 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.

Predictive constrained gain scheduling for UGV path tracking in a networked control system

This paper presents a predictive gain scheduler for path tracking control in a networked control system with variable delay. The controller uses the plant model to predict future position and find the amount of travel possible with the global path as a constraint. Based on variable network conditions and vehicle trajectorys curvature the vehicle is allowed to travel farther on the current control signal while the vehicle trajectory matches the path constraint. This method uses path specific characteristics to evaluate the effectiveness of each generated control signal. By scheduling the gain on the control signal the vehicle tracking performance is maintained with an increase in network delay. The tracking time is decreased compared to other methods since the proposed control method allows the controller to look ahead and thus evaluate predicted effect of each control signal before scaling it. The proposed method is compared with existing delay compensation methods through simulation.

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

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.

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

In this work, a non-uniform multi-rate control strategy is applied to a kind of Networked Control System (NCS) where a wireless path tracking control for an Unmanned Ground Vehicle (UGV) is carried out. The main aims of the proposed strategy are to face time-varying network-induced delays and to avoid packet disorder. A Markov chain-driven NCS scenario will be considered, where different network load situations, and consequently, different probability density functions for the network delay are assumed. In order to assure mean-square stability for the considered NCS, a decay-rate based sufficient condition is enunciated in terms of probabilistic Linear Matrix Inequalities (LMIs). Simulation results show better control performance, and more accurate path tracking, for the scheduled (delay-dependent) controller than for the non-scheduled one (i.e. the nominal controller when delays appear). Finally, the control strategy is validated on an experimental test-bed.

Realization and validation of Delay Tolerant Behavior Control based Adaptive Bandwidth Allocation for networked control system

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 limitation. Different static as well as dynamic bandwidth management strategies like Larger Error First (LEF), Rate Monotonic (RM), Behavior Control (BC) Based Allocation etc. have been proposed in the past to allocate bandwidth. However, these existing methods are not robust to network delay which is an important constraint in a Network Control System (NCS). The Delay Tolerant Behavior Control (DTBC) based Adaptive Bandwidth Allocation is one of the bandwidth allocation techniques that is robust to network delays[1]. In this paper, DTBC algorithm has been validated and compared to the performance of existing algorithms in an intelligent space environment. IEEE 802.15.4 standard wireless protocol was used for communication between the unmanned ground vehicles (UGVs) and the Supervisory Controller. A T-test was conducted and we found that at network delays of more than 200ms we can say with 99% confidence that DTBCs performance is better than the existing algorithms. Furthermore, using the slope of the linear fit calculated for each algorithms performance at different network delays, the performance of DTBC was found to be at least 25% to at most 57% better than the existing algorithms.

An analysis of Artificial Immune System and Genetic Algorithm in urban path planning

Evolutionary Algorithms like Genetic Algorithm (GA) and Artificial Immune System (AIS) are commonly used to find solutions to problems not suitable for traditional optimization approaches. In this study, we compare the results of AIS and GA for path-planning where the objective is to optimize the safety and the travelling distance. Since these algorithms are computationally intensive, we perform offline optimization to generate a list of suboptimal solutions. Results show that the performance of GA and AIS are similar in terms of convergence and optimality. Furthermore, an analysis of AIS revealed that the convergence rate is faster at higher separation threshold; however, the effects of maturity age and percentage of hypermutation had minimal effects in convergence of AIS. Using AIS, we were also able to produce several sub-optimal paths in the form of memory cells, which provide robustness to the optimal path subject to perturbations.

Analysis on the kalman filter performance in GPS/INS integration at different noise levels, sampling periods and curvatures

Kalman filters (KF) have been extensively used in the integration of Global Positioning System (GPS) and Inertial Navigation System (INS) data. Often, the GPS data is used as a benchmark to update the INS data. In this study, an analysis of integration of GPS data with INS data using an Extended Kalman filter is performed in terms of the filters performance with respect to the amount of noise in the GPS data and the sampling time of the vehicle position. The study further analyzes and compares the pattern of error at varying sampling periods in vehicle trajectories with high curvature path segments and low curvature path segments. Simulation results are presented at the end. The results show that the performance of the KF depends linearly on the amount of noise and sampling times. The relationship between the curvature of the road and the performance of the KF was found to be quadratic.

Gene libraries for a next generation warning system in Intelligent Transportation

Driver warning systems are the first step towards Intelligent Transportation System. There is a need for next generation warning systems that can integrate the information that is currently available in the vehicles with the information about the environment that the vehicles are operating in order to make more informed and accurate decisions. Integration of data from such different sources implies higher complexity of computation which is difficult to implement in real-time. Thus it is necessary to develop new methods that can integrate huge amount of data while meeting the hard real-time constraints of Intelligent Transportation Systems. In this paper, we introduce gene libraries that are based on the processes involved in gene expression. It is shown that gene libraries are capable of reducing the complexity of the problem by storing only the relevant information. A formulation for next generation warning system within the framework of gene libraries is proposed and simulations are presented that compare this approach with fuzzy inference system. Results show that gene library based approach is at least 23 times faster and 3.85 times more space efficient than fuzzy inference systems based approach.