Gopi Vinod Avvari

Battery charging optimization for OCV-resistance equivalent circuit model

In this paper, we present a closed-form solution to the problem of optimally charging a Li-ion battery. The objective function is considered as a combination of two cost functions: time-to-charge (TTC) and energy losses (EL). For the case where cost function is a combination of TTC and EL, the optimal charging strategy is a Constant Current-Constant Voltage (CC-CV) policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery. The case where the cost function is a weighted sum of TTC, EL and a temperature rise index (TRI) is also considered and an analytical solution for the problem is derived. This analytical solution can be approximated by a CC-CV with the value of current in the CC stage being a function of ratio of weighting on TTC and EL, resistance of the battery and the effective thermal resistance. The effects of weights in the objective function on the optimal charging profile is discussed and the behavior of different kinds of commercial batteries are analyzed.

Robust battery fuel gauge algorithm development, part 0: Normalized OCV modeling approach

The open circuit voltage (OCV) characterization of Li-ion batteries as it applies to battery fuel gauging (BFG) in portable applications is considered in this paper. Accurate knowledge of the nonlinear relationship between the OCV and the state of charge (SOC) is required for adaptive SOC tracking during battery usage. BFG in portable applications requires this OCV-SOC characterization to be defined with a minimum number of parameters. With the help of OCV characterization data collected from 34 battery cells each at 16 different temperatures ranging from -25°C to 50°C, we present a novel normalized OCV modeling approach that dramatically reduces the number of OCV-SOC parameters and as a result simplifies and generalizes the BFG across temperatures and aging.

Proactive decision support for dynamic assignment and routing of Unmanned Aerial Systems

Unmanned Aerial System (UAS) missions are executed by teams of operators with highly specialized training and roles; however, the task demands on each operator are highly variable, often resulting in uneven workloads among operators and sometimes in mishaps. Therefore, there is a need to develop anticipative and effective decision support algorithms that permit the evaluation of courses of action (COAs), while assuring that operators are attending to the right task at the right time and that task demands do not exceed the operators cognitive capabilities in dynamic multi-mission environments. Motivated by the need to assist UAS operators in efficiently managing their workloads, this paper develops algorithms for the dynamic scheduling of UAS tasks by providing efficient COA recommendations in an unobtrusive manner. The dynamic scheduling of a set of UASs to search for targets with varying rewards is an NP-hard problem. We model this problem as an extension to the open vehicle routing problem (OVRP). Extensions to OVRP include risk propensity of human decision making, task deadlines, and multiple vehicle types. UAS operators would benefit greatly from the COA recommendations and the algorithms proposed in this paper by (a) enhancing rapid planning and re-planning capabilities; (b) proactive allocation of UASs, while balancing operator workloads; and (c) adapting plans as new targets of opportunity appear or information is updated about a target and/or UAS. The proposed algorithms are embedded in the Supervisory Control Operations User Testbed (SCOUTTM), an experimental paradigm developed by the Naval Research Laboratory-Washington DC.

Robust battery fuel gauge algorithm development, part 3: State of charge tracking

In this paper, we present a novel SOC tracking algorithm for Li-ion batteries. The proposed approach employs a voltage drop model that avoid the need for modeling the hysteresis effect in the battery. Our proposed model results in a novel reduced order (single state) filtering for SOC tracking where no additional variables need to be tracked regardless of the level of complexity of the battery equivalent model. We identify the presence of correlated noise that has been so far ignored in the literature and use this for improved SOC tracking. The proposed approach performs within 1% or better SOC tracking accuracy based on both simulated as well as HIL evaluations.

Supporting Multi-objective Decision Making Within a Supervisory Control Environment

This paper discusses decision making challenges involved in the management of multiple unmanned vehicles within a dynamic mission environment. Given the increased likelihood of this new supervisory control paradigm, the authors developed the Supervisory Control Operations User Testbed SCOUT. A brief overview of SCOUT will be provided, followed by a summary of initial research conducted within the testbed which demonstrates how eye tracking measurements can be utilized to assess workload and predict situation awareness. Subsequent discussion will address challenges associated with dynamic decision making under uncertainty, with respect to multiple asset allocation. Techniques for measuring the accuracy of these decisions as well as assessing operator risk throughout the mission will also be presented. The paper concludes with discussion of how these new decision making metrics can be used to drive decision aids and compares decision making performance and risk bias under varying levels of task load.

Robust battery fuel gauge algorithm development, part 1: Online parameter estimation

In this paper, we present a novel voltage drop model for battery SOC tracking and develop a robust, realtime approach for model parameter estimation. The proposed model avoids the need to model hysteresis voltage that hard to model and estimate in practical applications. Another advantage of the proposed voltage drop model is that the parameters of the model is estimated linearly, regardless of the model complexity, i.e., number of RC elements considered in the model. We identify the presence of correlated noise that has been so far ignored in the literature and use it to enhance the accuracy of model identification. The proposed parameter approach enables a robust initialization/re-initialization strategy for continuous operation of the battery fuel gauge (BFG). The performance of the online parameter estimation scheme was evaluated through objective measures.

Robust battery fuel gauge algorithm development, part 2: Online battery-capacity estimation

In this paper we present an approach for robust, real time capacity estimation in Li-ion batteries. The proposed capacity estimation scheme has the following novel features: it employes total least squares (TLS) estimation in order to account for uncertainties in both model and the observations in capacity estimation. The TLS method can adaptively track changes in battery capacity. We propose a second approach to estimate battery capacity by exploiting rest states in the battery. This approach is devised to minimize the effect of hysteresis in capacity estimation. Finally, we propose a novel approach for optimally fusing capacity estimates obtained through different methods. We demonstrate the performance of the algorithm through objective experiments.

Integrated battery fuel gauge and optimal charger

Contemporary Battery Management Systems (BMS) form an essential part of a wide range of devices, such as portable electronics, mobiles, personal digital assistants (PDAs), hybrid and electric vehicles, and aerospace equipment. In this paper, we propose a novel closed-loop integrated BMS consisting of a battery fuel gauge (BFG) and optimal charging algorithms (OCAs). The integrated system not only performs accurate estimation of the state of the batteries, such as the state of charge (SOC), the slate of health (SOH) and the remaining useful lire (RliL), but also computes the optimal charging current based on trade-offs between cycle lire, lime-to-charge (TTC), energy losses (EL), and a temperature rise index (TRI). The stale estimates from the battery fuel gauge are used in the optimal charging problem, and the charging constitutes one of the discrete stales (e.g., charge, discharge, rest) of a BMS. The system has been tested ou a number of commercially available batteries.

A Multiobjective Path-Planning Algorithm With Time Windows for Asset Routing in a Dynamic Weather-Impacted Environment

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.

Dynamic asset allocation for counter-smuggling operations under disconnected, intermittent and low-bandwidth environment

Counter-smuggling operations constitute a high priority national security mission since drug-trafficking not only involves many criminals, but can also be a source of financing for many illicit activities such as narco-terrorism and arms trafficking. The counter-smuggling mission involves surveillance operations (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend suspects). Potential smuggling activity is represented in the form of color-coded heat maps built using intelligence and meteorological and oceanographic information, which are interpreted in the form of probability of activity (PoA) surfaces. The PoA surfaces constitute the “sufficient statistics” for the asset allocation and scheduling processes. However, in the case of disconnected, intermittent, and low-bandwidth environments, the problem of allocating resources becomes very challenging as PoA information is unavailable or is not up to date. In this paper, we propose to utilize flow (historic PoA)-based surfaces, which provide cues on where the smugglers may have traversed in the past. Using the flow surfaces, we allocate the surveillance and interdiction assets to best thwart potential smuggling activities. We further evaluate the quality of our solution in terms of the number of targets interdicted and the amount of contraband seized.