Rakesh Patil

Characterizing naturalistic driving patterns for Plug-in Hybrid Electric Vehicle analysis

While much of the previous research relies on Federal Driving Schedules originally developed for emission certification tests of conventional vehicles, consumer acceptance and market penetration will depend on PHEV performance under realistic driving conditions. Therefore, characterizing the actual driving is essential for PHEV design and control studies, and for establishing realistic forecasts pertaining to vehicle energy consumption and charging requirements. To achieve this goal, we analyze naturalistic driving data generated in Field Operational Tests (FOT) of passenger vehicles in Southeast Michigan. The FOT were originally conceived for evaluating driver interaction with advanced safety systems, but the databases are rich with information pertaining to vehicle energy. After the initial statistical analysis of the vehicle speed histories, the naturalistic driving schedules are used as input to the PHEV computer simulation to predict energy usage as a function of trip length. The highest specific energy, i.e. energy per mile, is critical for battery and motor sizing. As an illustration of the impact of actual driving, the low-energy and high-energy driving patterns would require PHEV20 battery sizes of 6.12 kWh and 13.6 kWh, respectively. This is determined assuming that the minimum state of charge (SOC) is 40%. In addition, the naturalistic driving databases are mined for information about vehicle resting time, i.e. time spent at typical locations during the 24-hour period. The locations include “home”, “work”, “large-business” such as a large retail store, and “small business”, such as a gas station, and finally “residential” other than home. The characterization of vehicle daily missions supports analysis of charging schedules, as it indicates times spent at given locations as well as the likely battery SOC at the time of arrival.

A Framework for the Integrated Optimization of Charging and Power Management in Plug-in Hybrid Electric Vehicles

This paper develops a dynamic programming (DP)-based framework for simultaneously optimizing the charging and power management of a plug-in hybrid electric vehicle (PHEV). These two optimal control problems relate to activities of the PHEV on the electric grid (i.e., charging) and on the road (i.e., power management). The proposed framework solves these two problems simultaneously to avoid loss of optimality resulting from solving them separately. The framework furnishes optimal trajectories of PHEV states and control inputs over a 24-h period. We demonstrate the framework for 24-h scenarios with two driving trips and different power grid generation mixes. The results show that addressing the aforementioned optimization problems simultaneously can elucidate valuable insights. For example, for the chosen daily driving scenario, grid generation mixes, and optimization objective, it is shown that it is not always optimal to completely charge a battery before each driving trip. In addition, reduction in CO2 resulting from the synergistic interaction of PHEVs with an electric grid containing a significant amount of wind power is studied. The main contribution of this paper to the literature is a framework that makes it possible to evaluate tradeoffs between charging and on-road power management decisions.

Design Optimization of a Series Plug-in Hybrid Electric Vehicle for Real-World Driving Conditions

This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS). This is a finding of high relevance for forecasting technology diffusion, consumer acceptance, and impact of PHEVs on power grid. These differences increase progressively with desired AER due to increasing energy/mile usage of real world driving with distance.

Impact of Naturalistic Driving Patterns on PHEV Performance and System Design

The paper investigates the impact of the drive cycle choice on the Plug-in Hybrid Electric Vehicle (PHEV) design, and particularly the selection of component sizes. Models of representative Power-Split and Series PHEVs have been built and validated first. Then, the performance and energy/power usage metrics were obtained by simulating the vehicle behavior over realworld (naturalistic) drive cycles recorded during Field Operational Tests in South East Michigan. The PHEV performance predictions obtained with real-world driving cycles are in stark contrast to the results obtained by using a sequence of repeated federal drive cycles. Longer commutes require much higher peak power and consume much greater amount of energy per mile than EPA UDDS or HWFET cycle. The second part of the paper investigates the sensitivities of the PHEV attributes, such as the charge depleting range and the fuel economy in the charge sustaining mode, to component size variations. The results provide quantitative guidance pertaining to design decisions in the context of driving patterns.

Attitude Dynamics of Rigid Bodies in the Vicinity of the Lagrangian Points

I N THE last 30 years, several sun-monitoring spacecraft have orbited around the sun–Earth L1 point, and several space observatories will be placed near the sun–EarthL2 in the next decade. Although a large body of literature exists on the orbital dynamics of spacecraft in the restricted three-body problem, very few have explored the attitude dynamics of a rigid Lagrangian-point spacecraft. Kane and Marsh [1] considered the attitude dynamics of an axial symmetric satellite that is rotating about its axis of symmetry, which is normal to the primary bodies’ orbital plane. Robinson [2] first studied the attitude dynamics of a dumbbell satellite located at a triangular point. The same author [3] later determined the equilibrium attitudes of an arbitrary shaped satellite located at a collinear point or at a triangular point and constructed a linear stability diagram about one of the equilibrium configurations. Misra andBellerose [4] studied the librational dynamics of a tethered satellite located at the Earth–moon Lagrangian points and obtained the libration frequencies. The rigid spacecraft is assumed to be held at the Lagrangian points in all of these studies. In practice, however, collinear point satellites are not located directly at the Lagrangian point but in periodic orbits around the point. This note extends the work done by Robinson [3] and Misra and Bellerose [4] and addresses the question of how the translational motions of the spacecraft affect its attitude dynamics. The attitude dynamics of the satellite are studied while it is in a planar Lyapunov orbit and its three-dimensional counterpart. A triangular point spacecraft was previously demonstrated to have two equilibrium configurations, and linear stability diagrams about both equilibrium configurations for a L4 spacecraft are also presented. II. Problem Formulation

A framework for the integrated optimization of charging and power management in plug-in hybrid electric vehicles

This paper develops a dynamic programming (DP) based framework for simultaneously optimizing the charging and power management of a plug-in hybrid electric vehicle (PHEV). These two optimal control problems relate to activities of the PHEV on the electric grid (i.e., charging) and on the road (i.e., power management). The proposed framework solves these two problems simultaneously in order to avoid the loss of optimality resulting from solving them separately. The framework furnishes optimal trajectories of the PHEV states and control inputs over a 24-hour period. We demonstrate the framework for 24-hour scenarios with two driving trips and different power grid generation mixes. The results show that addressing the above optimization problems simultaneously can elucidate valuable insights for certain combinations of daily driving scenarios, grid generation mixes, and optimization objectives. For example, in one of the cases considered, the grid produces higher CO2 per unit energy between 3AM and 8AM. This causes the optimal PHEV state and control input trajectory to refrain from completely charging the PHEVs battery in the early morning, and judiciously combine electricity and gasoline while driving. The papers main contribution to the literature is a framework that makes it possible to evaluate tradeoffs such as this one.

Computationally Efficient Combined Plant Design and Controller Optimization Using a Coupling Measure

This paper presents a novel approach to the optimization of a dynamic systems design and control. Traditionally, these problems have been solved either sequentially or in a combined manner. We propose a novel approach that uses a previously derived coupling measure to quantify the impact of plant design variables on optimal control cost. This proposed approach has two key advantages. First, because the coupling term quantifies the gradient of the control optimization objective with respect to plant design variables, the approach ensures combined plant/control optimality. Second, because the coupling term equals the integral of optimal control co-states multiplied by static gradient terms that can be computed a priori, the proposed approach is computationally attractive. We illustrate this approach using an example cantilever beam structural design and vibration control problem. The results show significant computational cost improvements compared to traditional combined plant/control optimization. This reduction in computational cost becomes more pronounced as the number of plant design variables increases.

Computationally Efficient Combined Design and Control Optimization using a Coupling Measure

Abstract This paper presents a novel approach to the optimization of a dynamic systems design and control. Traditionally, these problems have been solved either sequentially or in a combined manner. We propose a novel approach that uses a previously-derived coupling measure to quantify the impact of plant design variables on optimal control cost. This proposed approach has two key advantages. First, because the coupling term quantifies the gradient of the control optimization objective with respect to plant design variables, the approach ensures combined plant/control optimality. Second, because the coupling term equals the integral of optimal control co-states multiplied by static gradient terms that can be computed a priori , the proposed approach is computationally attractive. We illustrate this approach using an example cantilever beam structural design and vibration control problem. The results show significant computational cost improvements compared to traditional combined plant/control optimization. This reduction in computational cost becomes more pronounced as the number of plant design variables increases.

COMPARISON OF OPTIMAL SUPERVISORY CONTROL STRATEGIES FOR A SERIES PLUG-IN HYBRID ELECTRIC VEHICLE POWERTRAIN

This paper uses dynamic programming to compare the optimal fuel and electricity costs associated with two supervisory control strategies from the plug-in hybrid electric vehicle (PHEV) literature. One strategy blends fuel and electricity for propulsion throughout the useful range of battery state of charge (SOC), while the second strategy switches from all-electric to blended operation at a predefined SOC threshold. Both strategies are optimized for a series PHEV powertrain using deterministic dynamic programming (DDP) to ensure a fair comparison. The DDP algorithm is implemented in a novel manner using a backward-looking powertrain model instead of forward-looking models used in previous research. The paper’s primary conclusion is that there is no significant difference in the performance of the two control strategies for the series PHEV considered. This result contrasts sharply with previous results for parallel and power-split PHEVs, and is examined for different relative fuel and electricity prices and trip lengths.Copyright

Optimal sensor and actuator deployment for HVAC control system design

This paper studies control-theory-inspired optimal sensor and actuator deployment to improve the temperature monitoring and control performance of HVAC (heating, ventilation and air conditioning) systems in buildings. The deployment strategies are based on maximizing observability- and controllability-based metrics such as the respective Gramians. Our solution approach has the following benefits compared to previous work. First, an analytical, closed-form solution is developed. Second, the computational cost is low to ensure practical use of our approach. The effectiveness of our deployment strategies is demonstrated via simulation examples and by calculating different metrics of relevance.