Hosam K. Fathy

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

Energy storage in datacenters: what, where, and how much?

Energy storage - in the form of UPS units - in a datacenter has been primarily used to fail-over to diesel generators upon power outages. There has been recent interest in using these Energy Storage Devices (ESDs) for demand-response (DR) to either shift peak demand away from high tariff periods, or to shave demand allowing aggressive under-provisioning of the power infrastructure. All such prior work has only considered a single/specific type of ESD (typically re-chargeable lead-acid batteries), and has only employed them at a single level of the power delivery network. Continuing technological advances have provided us a plethora of competitive ESD options ranging from ultra-capacitors, to different kinds of batteries, flywheels and even compressed air-based storage. These ESDs offer very different trade-offs between their power and energy costs, densities, lifetimes, and energy efficiency, among other factors, suggesting that employing hybrid combinations of these may allow more effective DR than with a single technology. Furthermore, ESDs can be placed at different, and possibly multiple, levels of the power delivery hierarchy with different associated trade-offs. To our knowledge, no prior work has studied the extensive design space involving multiple ESD technology provisioning and placement options. This paper intends to fill this critical void, by presenting a theoretical framework for capturing important characteristics of different ESD technologies, the trade-offs of placing them at different levels of the power hierarchy, and quantifying the resulting cost-benefit trade-offs as a function of workload properties.

Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control

This paper develops techniques to design plug-in hybrid electric vehicle (PHEV) power management algorithms that optimally balance lithium-ion battery pack health and energy consumption cost. As such, this research is the first to utilize electrochemical battery models to optimize the power management in PHEVs. Daily trip length distributions are integrated into the problem using Markov chains with absorbing states. We capture battery aging by integrating two example degradation models: solid-electrolyte interphase (SEI) film formation and the “Ah-processed” model. This enables us to optimally tradeoff energy cost versus battery-health. We analyze this tradeoff to explore how optimal control strategies and physical battery system properties are related. Specifically, we find that the slope and convexity properties of the health degradation model profoundly impact the optimal charge depletion strategy. For example, solutions that balance energy cost and SEI layer growth aggressively deplete battery charge at high states-of-charge (SoCs), then blend engine and battery power at lower SoCs.

On the coupling between the plant and controller optimization problems

Examines plant and controller optimization problems. One can solve these problems sequentially, iteratively, using a nested (or bi-level) strategy, or simultaneously. Unlike the nested and simultaneous strategies, the sequential and iterative strategies fail to guarantee system-level optimality. This is because the plant and controller optimization problems are coupled. This coupling is introduced using a simple experiment. To prove it theoretically, the necessary conditions for combined plant and controller optimality are derived. These combined optimality conditions differ from the individual sets of necessary conditions for plant and controller optimality by a coupling term that reflects the plant designs influence on the plant dynamics and control input constraints.

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

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 models algebraic equations using quasi-linearization. This improves the models execution speed compared to solving the algebraic equations via optimization. Second, we reduce the models order by deriving a family of analytic Pade approximations to the models 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 models overall accuracy and simulation speed.

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

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

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 papers 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.

Review of hardware-in-the-loop simulation and its prospects in the automotive area

Hardware-in-the-loop (HIL) simulation is rapidly evolving from a control prototyping tool to a system modeling, simulation, and synthesis paradigm synergistically combining many advantages of both physical and virtual prototyping. This paper provides a brief overview of the key enablers and numerous applications of HIL simulation, focusing on its metamorphosis from a control validation tool into a system development paradigm. It then describes a state-of-the art engine-in-the-loop (EIL) simulation facility that highlights the use of HIL simulation for the system-level experimental evaluation of powertrain interactions and development of strategies for clean and efficient propulsion. The facility comprises a real diesel engine coupled to accurate real-time driver, driveline, and vehicle models through a highly responsive dynamometer. This enables the verification of both performance and fuel economy predictions of different conventional and hybrid powertrains. Furthermore, the facility can both replicate the highly dynamic interactions occurring within a real powertrain and measure their influence on transient emissions and visual signature through state-of-the-art instruments. The viability of this facility for integrated powertrain system development is demonstrated through a case study exploring the development of advanced High Mobility Multipurpose Wheeled Vehicle (HMMWV) powertrains.

Development of a Scaled Vehicle With Longitudinal Dynamics of an HMMWV for an ITS Testbed

This paper applies Buckinghams pi theorem to the problem of building a scaled car whose longitudinal and power-train dynamics are similar to those of a full-size high-mobility multipurpose wheeled vehicle (HMMWV). The scaled vehicle uses hardware-in-the-loop (HIL) simulation to capture some of the scaled HMMWV dynamics physically, and simulates the remaining dynamics onboard in real time. This is performed with the ultimate goal of testing cooperative collision avoidance algorithms on a testbed comprising a number of these scaled vehicles. Both simulation and experimental results demonstrate the validity of this HIL-based scaling approach.

Transport-Based Load Modeling and Sliding Mode Control of Plug-In Electric Vehicles for Robust Renewable Power Tracking

This paper develops a modeling and control paradigm for the aggregate charging dynamics of plug-in electric vehicles (PEVs). The central goal of the paper is to derive a control policy that can adapt the aggregate charging power of PEVs to highly intermittent renewable power. The key assumption here is that the grid is able to directly control the charging power of PEVs in real-time, through broadcasting a universal control signal. Using the transport-based load modeling principle, we develop a partial differential equation model for the collective charging of PEVs. We use real driving data to simulate the model and validate it against a PEV Monte Carlo simulation model. Adopting the sliding mode control theory, we then develop a robust output tracking controller for the system. The controller uses the real-time error between power supply and demand as the only measured signal, and attempts to suppress it despite the variation of the population of PEVs on the grid. We examine the performance of the controller using numerical simulations on a real wind power trajectory.