Vincenzo Marano

Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles

Plug-in hybrid electric vehicles (PHEVs) are currently recognized as a promising solution for reducing fuel consumption and emissions due to the ability of storing energy through direct connection to the electric grid. Such benefits can be achieved only with a supervisory energy management strategy that optimizes the energy utilization of the vehicle. This control problem is particularly challenging for PHEVs due to the possibility of depleting the battery during usage and the vehicle-to-grid interaction during recharge. This paper proposes a model-based control approach for PHEV energy management that is based on minimizing the overall CO2 emissions produced-directly and indirectly-from vehicle utilization. A supervisory energy manager is formulated as a global optimal control problem and then cast into a local problem by applying the Pontryagins minimum principle. The proposed controller is implemented in an energy-based simulator of a prototype PHEV and validated on experimental data. A simulation study is conducted to calibrate the control parameters and to investigate the influence of vehicle usage conditions, environmental factors, and geographic scenarios on the PHEV performance using a large database of regulatory and “real-world” driving profiles.

Lithium-ion batteries life estimation for plug-in hybrid electric vehicles

This paper deals with life estimation of lithium batteries for plug-in hybrid electric vehicles (PHEVs). An aging model, based on the concept of accumulated charge throughput, has been developed to estimate battery life under “real world” driving cycles (custom driving cycles based on driving statistics). The objective is to determine the “damage” on the life related to each driving pattern to determine equivalent miles/years. Results indicates that Lithium-ion batteries appear to be 10 year/150,000 mile capable, provided that they are not overcharged, nor consistently operated at high temperatures, nor in charge sustaining mode at a very low state of charge.

Effects of different PHEV control strategies on vehicle performance

Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of todays era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence, many efforts are being concentrated on innovative systems for transportation that could replace petroleum with cleaner fuel, i.e. electricity from the power grid. The use of plug-in hybrid electric vehicles (PHEVs) can become a very important change in this direction, since such vehicles could benefit from the increasing availability of renewable energy. PHEVs requires new control and energy management algorithms, that are crucial for vehicle performance. This paper deals with evaluation of two modes, Electric Vehicle (EV) mode and Blended mode, for plug-in hybrid electric vehicles and their comparison with conventional and hybrid electric vehicle performance.

Study of PEV Charging on Residential Distribution Transformer Life

Due to the characteristics of electric power generation, transmission and distribution in the U.S., experts have identified local distribution as a likely part of the chain to be adversely affected by unregulated PEV (Plug-in Electric Vehicle) charging. This paper presents a study performed to assess the impact of PEV charging on a local residential distribution transformer.

Energy management for plug-in hybrid electric vehicles using equivalent consumption minimisation strategy

One strategy to minimise petroleum fuel consumption of a Plug-in Hybrid Electric Vehicle (PHEV) is to attain the lowest admissible battery State of Charge (SOC) at the end of driving cycle while following an optimal SOC profile. The challenge of an optimisation algorithm is to find this optimal profile by using least future information about the power demand. An application of Equivalent Consumption Minimisation Strategy (ECMS) for PHEV is presented in this paper and benchmarked against the dynamic programming (DP) for information requirement and optimality. The optimality is assessed in simulation by considering petroleum fuel economy and deviation of the optimal SOC profile from a reference profile for different driving scenarios and battery sizes. Results show that for longer distances and larger battery sizes, ECMS and DP provide similar fuel economy and SOC profiles. A sensitivity analysis with respect to driving distance is presented at the end of the paper.

Energy and economic evaluation of PHEVs and their interaction with renewable energy sources and the power grid

Strong dependency on crude oil in most areas of modern transportation coupled with increased demand for electric power generation lead to a significant consumption of fossil fuel resources over many decades. Homes and cars represent the biggest personal impact on the increasing energy demand, global warming and air quality; furthermore, electric power utilities spend a tremendous amount of capacity to continuously balance supply and demand across the grid or provide backup electricity during outages and peak demand periods. As a consequence, research is quickly moving towards interconnected renewable energy based systems for transportation and residential/commercial buildings. Thus, this paper deals with an energy and economic evaluation of Plug-in Hybrid Electric Vehicles (PHEVs) and their interaction with the power grid and the energy market. A multi-configurable personal eco-system with a plug-in hybrid vehicle is modeled. The model uses a set of data for the State of Ohio, including cost of energy, potential photovoltaic capacity, wind patterns and government regulations and incentives. The PHEV can draw electricity either from the power grid or from a personal eco-system consisting of a hybrid wind/photovoltaic generating system. Simulations are carried out starting from hourly local load demand, wind speed data, approximate solar radiation, energy market and state regulations. Various configurations and various available contracts for buying/selling energy from/to the grid are analyzed and compared. Results show the potential for reduction of energy cost, pollutant and dependency on the grid, along with substantial economic benefits.

A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications

This paper presents a new approach to life estimation for lithium-ion batteries used in plug-in hybrid electric vehicles (PHEVs) applications. A new framework for battery life estimation is developed which investigates the effects of two primary factors of battery life reduction in PHEVs applications, namely, depth of discharge (DOD) and temperature (Tbatt), under typical driving conditions, driving habits, and average commute time of typical user over a year. This framework, whose development is built upon a weighted ampere-hour throughput model of the battery, is based on the novel concept of severity factor map which captures and quantifies the battery damage caused by different operating conditions. The proposed methodology can be a suitable tool to estimate battery life in terms of miles/year on-board of the vehicle.

Optimal control for Plug-in Hybrid Electric Vehicle applications

Plug-In Hybrid Electric Vehicles (PHEVs) are a promising solution to reduce fuel consumption and emissions, due to their capability of storing energy in the battery through direct connection to the grid. In order to achieve the highest benefits from this technology, a suitable energy management strategy that optimizes the vehicle energy efficiency must be defined. The present work proposes a supervisory controller for PHEVs, which explicitly accounts for the on-board electricity consumption during vehicle operations. The approach is based on the formulation of an optimal control problem that is solved by the Pontryagins minimum principle to produce a solution that can be implemented on-line. Simulation results are presented to illustrate the developed energy management strategy.

Real-time energy management and sensitivity study for hybrid electric vehicles

This paper presents a real-time energy management algorithm for hybrid electrical vehicles (HEV). The proposed approach features a practical structure and manageable computation complexity for real-time implementation. It adopts a Model Predictive Control framework and utilizes the information attainable from Intelligent Transportation Systems (ITS) to establish a prediction based real-time controller structure. Simulations have been conducted with a Matlab/Simulink based vehicle model to assess the optimality of the algorithm, in comparison with existing control approaches. For real-time HEV control algorithms, ITS based driving prediction is an essential component. It is important to investigate the impact of the accuracy of ITS information on HEV energy consumption. In this work, we study the the effect of noises and errors in the velocity profile prediction under different control approaches. The sensitivity of the HEV energy use is investigated based on real driving data. The results provide better understanding of the need in driving profile prediction in real-time HEV control.

PHEV fleet data collection and analysis

This paper deals with a project related to Plug-in Hybrid Electric Vehicles fleet studies. The project is part of a broader research consortium underway at Ohio State Universitys Center for Automotive Research called SMART@CAR. Main goals are to create and maintain a database containing all charging and duty cycle data collected from a growing PHEV fleet. Real-world data provided by these vehicles will be collected, archived, organized and analyzed. The availability of real world data will also help estimate the effects of PHEV penetration on the utility energy sales, generation capacity, the transmission grid, market economics, and environmental emissions.