Pinak Tulpule

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.

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.

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.

Hybrid large scale system model for a DC microgrid

A microgrid power system with multiple energy sources and loads is considered in this paper. Such microgrids are common due to the needs of distributed generation, renewable energy, and hybrid power sources. The system under study consists of a large number of power converters operating over a wide range of voltages and currents, interconnected via a distribution network. Stability analysis and supervisory control design requires a good model of the system that considers different operations within the microgrid, such as voltage/current levels, bidirectional power flows, and on/off switching of the power converters. In this paper, a state variable modeling approach is presented to develop a hybrid large-scale system model of the microgrid. State variable models of individual converters linearized at different operating points are the building blocks of the model. A large-scale interconnected system model is developed for each feasible interconnection of the linearized models of the converters. The switching model, which is a combination of state based and input based switching events between these large- scale system models, is developed using hybrid system theory. The modeling approach is applied to two example systems consisting of DC-DC converters and a DC bus. The hybrid large scale system models are compared with circuit simulations to show the validity of the modeling process.

Optimality Assessment of Equivalent Consumption Minimization Strategy for PHEV Applications

This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy — Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy — Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC’. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.Copyright

The role of ITS in PHEV performance improvement

Driving patterns have great impact on fuel economy or power split control decisions of PHEV (Plug-in Hybrid Electric Vehicle) energy management. In this paper, a statistical approach was used to analyze real world velocity profiles to gather traffic information such as average speed, speed limits, segment length, etc. A Markov chain model was developed to make use of such information for generation of random velocity profiles that are representative of real world driving scenarios. The velocity profiles generated using the Markov chain models are used to calculate vehicle fuel economy by means of a validated through the road parallel PHEV model and ECMS (Equivalent Consumption Minimization Strategy) control strategy. The end goal of the research is to find mathematical, statistical or heuristic relationships between road events and the performance of PHEV energy management.

Vehicle electrification: Implications on generation and distribution network

There is perhaps no better symbol of the twenty-first century than the automobile. It is the dominant means of transport aspired to throughout the world. However, as demand for mobility continues to rise around the world, environmental and energy problems are rapidly making transportation as we know it unsustainable for our society. Thus, the role of the automobile in the future needs to be rigorously re-examined. Vehicles are becoming part of a much bigger “energy network”, wherein communication and optimization play a key role. In addition to driving pattern information, to optimize vehicle performance, the knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions, and economics. Therefore this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control. This paper focuses on the use of electricity as a transportation energy source, and outlines how new and existing technologies could change work and driving patterns resulting in a different mix of vehicles and in a communication architecture serving as the backbone of the interaction between vehicles and utility grid.