Showing papers by "Giorgio Rizzoni published in 2010"

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

Abstract: This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.Copyright © 2010 by ASME

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

Abstract: 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.

A Rule-Based Strategy for a Series/Parallel Hybrid Electric Vehicle: An Approach Based on Dynamic Programming

Abstract: Dynamic programming (DP) provides the optimal global solution to the energy management problem for hybrid electric vehicles (HEVs), but needs complete a-priori knowledge of the driving cycle and has high computational requirements. This article presents a possible methodology to extract rules from the dynamic programming solution to design an implementable rulebased strategy. The case study considered is a series/parallel HEV, in which a clutch allows to switch from one configuration to another. The strategy works according to a two layer policy: the supervisory controller, which decides the powertrain configuration (either series or parallel), and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described. Then, the performance of the resulting rule-based strategy is studied and compared with thesolutiongivenbythedynamicprogramming, whichfunctions

Optimal control for Plug-in Hybrid Electric Vehicle applications

Abstract: 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 Pontryagin's minimum principle to produce a solution that can be implemented on-line. Simulation results are presented to illustrate the developed energy management strategy.

Nonlinear Fault Detection and Isolation for a Lithium-Ion Battery Management System

Abstract: Lithium-ion batteries are a growing source for electric power, but must be maintained within acceptable operating conditions to ensure efficiency and reliability. Therefore, a robust fault detection and isolation scheme is required that is sensitive enough to determine when sensor or actuator faults present a threat to the health of the battery. A scheme suitable for a hybrid electric vehicle battery application is presented in this work. The diagnostic problem is formulated as a nonlinear parity equation approach, but is modified for the considered application. Sliding mode observers are designed for input estimation, while the output voltage estimation is performed using an open loop model. The selection of optimal thresholds given a maximum allowable probability of error is also considered. An assessment of the design using real-world driving-cycle data leads to the conclusion that the estimation error of the observers determines a lower bound on the minimum detectable fault magnitude.Copyright © 2010 by ASME

Intelligent energy management in hybrid electric vehicles

Abstract: The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.

Statistical analysis of PHEV fleet data

Abstract: The added load that a PHEV (Plug-in Hybrid Electric Vehicle) fleet imposes on the existing electrical grid is of great concern to the electric utility industry. In this paper, analysis was done for a PHEV fleet which consists of 6 PHEVs that were instrumented using data loggers for a period of approximately one year. Systematic analysis using a clustering approach was carried out for the real world velocity profiles. A driving pattern recognition algorithm was developed based on the clustering of the results and Markov-chain model was used for the stochastic velocity generation for different driving patterns. The work of this paper is a part of a larger project in which a mass simulation of a neighborhood of PHEVs will be conducted based on statistical representations of key factors such as vehicle usage patterns, vehicle characteristics, and market penetration of PHEVs.

Design of an Extended-Range Electric Vehicle for the EcoCAR Challenge

Abstract: The EcoCAR Challenge team at The Ohio State University has designed a range-extending electric vehicle capable of 40 miles all-electric range via a 22 kWh lithium-ion battery pack, with range extension and limited parallel operation supplied by a 1.8 L dedicated E85 engine. This vehicle is designed to drastically reduce fuel consumption, with an estimated fuel economy of 89 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper documents the team’s control system development effort, starting with the vehicle architecture selection and specifying the powertrain configuration, explaining a detailed control system development process, summarizing the selected control hardware architecture at vehicle and component level, describing supervisory control algorithm design and implementation for fuel economy optimization and performance improvement, and concluding with the use of MIL and HIL techniques for system development and validation.Copyright © 2010 by ASME

A High Fidelity Lumped-Parameter Engine Model for Powertrain Control Design and Validation

Abstract: The paper presents a lumped-parameter modeling approach for the characterization of internal combustion engine systems dynamics, suitable for the design, analysis and calibration of powertrain control. Through an analysis of the deficiencies of traditional filling and emptying, crank angle resolved models, a novel approach is presented to calibrate the distributed effects of charge heating, backflow, and wave propagation dynamics within the context of a zero-dimensional (lumped) model. The resulting model is capable of predicting the cycle averaged values of volumetric efficiency and torque, as well as the crank angle resolved values of cylinder pressure and indicated torque in steady state and transient conditions within accuracy comparable to a 1D gas dynamic model, while maintaining a significantly reduced computational effort. The approach is applied to a 4-cylinder spark ignition engine with dual independent variable valve timing, and validated using simulation and experimental data. Validation results allow for an evaluation of the model’s high accuracy and low computation effort.Copyright © 2010 by ASME and General Motors

System design and optimization of the world's fastest hydrogen fuel cell vehicle

Abstract: The Buckeye Bullet 2 is the world's fastest hydrogen fuel cell electric vehicle, with a certified FIA record of 487.433 km/hr (302.877 mi/hr). This paper provides the basic details of the overall vehicle and focuses on the design, testing, and optimization of the propulsion system. A unique fuel cell system was designed, tested, and integrated to produce over 500 kW of power, more than twice of its original rating. A unique pressure control is required to run the cathode system at maximum pressure during the race, and to manage the transient pressure pulses that occur when the race vehicle manual transmission is shifted. This causes rapid changes in the consumption of reactants, leading to severe pressure spikes that were limited with a custom tuned pressure relief system for the anode and cathode.