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Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's 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. In this paper two PHEV architectures are considered: through road parallel plug-in hybrid and series plug-in hybrid. Similar models have been developed to evaluate vehicle performance for conventional and hybrid architectures. Both PHEV architectures are analyzed with two different modes- EV and Blended; a modified version of ECMS (Equivalent Consumption Minimization Strategy) is used for both algorithms. Various standard as well as custom designed driving cycles are used in this analysis. The paper provides quantitative analysis of the control algorithms to analyze their effects on fuel economy, use of electric energy, cost of operation, etc.; these results are compared with the simulations for hybrid and conventional vehicles. Some important relationships between fuel economy, design architectures and control strategies are shown and can be useful in the design of the optimal control algorithms for PHEVs. As shown in the results, the control problem for PHEVs is not limited to fuel economy but it also involves external factors, such as price of electricity, energy market and regulations, charging - availability, battery life issues, etc

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A novel integrated robust control synthesis methodology is presented here which combines a traditional sensitivity theory with relatively new advancements in bilinear matrix inequality (BMI) constrained optimization problems. The proposed methodology is demonstrated using a numerical example of integrated control design problem for combine harvester header linkage. The integrated design methodology presented is compared with a traditional sequential design method and the results show that the proposed methodology provides a viable alternative for robust controller synthesis and often times offers even a better performance than competing methods.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.\ud Two control algorithms - ECMS (Equivalent Consumption Minimization Strategy) and DP (dynamic programming) - are considered in this paper to optimize the power split between electrical and mechanical energy sources. The performance obtained using dynamic programming as global optimal energy management strategy for a PHEV is used as benchmark for evaluating on-board implementable control strategy - ECMS. The ECMS is used to design two control modes - EV and Blended. The model of a PHEV version of a Chevrolet Equinox fueled by bio-diesel B20 has been developed in the Matlab/Simulink environment. A Chevrolet Equinox was hybridized at The Center of Automotive Research (CAR), at The Ohio State University as part of Challenge-X competition; the vehicle was used to validate the components of the Simulink model

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