Matthew Doude

Pareto Front of Energy Storage Size and Series HEV Fuel Economy Using Bandwidth-Based Control Strategy

The hybrid electric vehicle (HEV) offers superior fuel economy (FE) compared to the conventional counterpart; yet, it is costlier. To optimize an HEV design, both energy storage cost-one of the major contributors in overall cost-and FE are often set as objective functions in optimization processes. The techniques applied to manage the power flow between powertrain energy sources and sinks significantly affect the results of this optimization. In this paper, an advanced bandwidth-based control strategy teamed up with a duty ratio control strategy is applied and a Pareto Frontier, including energy storage system (ESS) costs and FEs, for a series HEV (SHEV) with limited all-electric-mode is introduced. The bandwidth-based control allows the SHEVs advantage in engine efficiency management to be extended to the lighter ESS as compared to the ESS sizes in vehicles available in the market. To produce the Pareto Frontier, a vehicle model created in the powertrain modeling environment Autonomie is customized and used in parallel-mode multiobjective genetic algorithm (MOGA) optimization. It is noted that the approach proposed here is not claimed to be a global optimal solution but instead is an improvement over typical solutions used in real vehicles based on constraints such as the need to implement on a practical real-time controller.

Bandwidth-Based Control Strategy for a Series HEV With Light Energy Storage System

The twin goals of maximizing fuel economy (FE) and improving consumer acceptance by reducing the cost of the energy storage system (ESS) in a series hybrid electric vehicle (SHEV) powertrain is addressed here by using energy storage as a means for filtering drive-cycle power demands on the engine, rather than an energy source for supplying an all-electric drive mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full-range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed that a 4.5-kWh lithium-ion battery pack was still required. In this paper, a sports-car-class SHEV was studied, where the challenge is to reduce the size of the ESS even more because the available space allocation is only one fourth that of the mid-size vehicle. In this paper, a controller is developed, which allows a hybridized SHEV to be realized with a light ESS. The controller includes a duty-cycling feature that manages the engine performance in multiple efficient regions and a bandwidth-limited (BWL) proportional controller feature that limits low-bandwidth battery current. The performance of the controller has been validated for a SHEV powertrain model with an 80-

A hybrid electric vehicle with minimal energy storage system

\text{V}_{\mathrm{dc}}

MPC-based power management system for a plug-in hybrid electric vehicle for relaxing battery cycling

1.125-kWh battery, plus an 80-

Design methodology for a range-extended PHEV

\text{V}_{\mathrm{dc}}

Powertrain Analysis and Computational Environment (PACE) for Multi-Physics Simulations Using High Performance Computing

46.4-F ultracapacitor module using a customized Autonomie vehicle model. The results show that the combined FE of the new design is increased by 13%, compared with the corresponding FE in the equivalent conventional vehicle. Additional FE improvement is possible by reducing the engine size further to reflect the average power demand imposed by real drive cycles.

A Kinematic Modeling Framework for Prediction of Instantaneous Status of Towing Vehicle Systems

A series HEV with a minimal or “light” ESS and a small engine is examined with the objective of maximizing fuel economy with little all-electric range. The motivation is the re-examination of assumptions about consumer adoption of electric vehicles, with the goal to consider lowest cost of ownership without compromising range. Lowest cost of ownership is indicated by maximizing fuel economy with a small energy storage system and a minimal displacement engine sized for average, rather than peak power. A minimal ESS is found via a parametric study for maximum fuel economy and empirically validated on a chassis dynamometer.

Near Automatic Translation of Autonomie-Based Power Train Architectures for Multi-Physics Simulations Using High Performance Computing

Power management strategies affect fuel economy, emission as well as other key parameters such as durability of power-train components. Different off-line and real time optimal control approaches are applied for developing power management strategies while the real-time control seems more attractive in the sense that it can be implemented and directly applied for controlling power flow in a real vehicle. One promising example of this type is the Model Predictive Control (MPC)-based algorithm where a utility function is optimized while system constraints are validated all in real time. MPC-based algorithms have been applied by developing simulation and test bench-based experimental works, but the authors have not seen a report of implementing a MPC-based algorithm in a real vehicle in the literature. In this manuscript, a real-time MPC-based algorithm is developed for implementation in a reference sport class series plug-in hybrid electric vehicle under construction and performance results are compared with engine duty ratio (thermostat) control algorithm. The results show almost identical fuel consumptions in both cases while the relaxed battery cycling is observed with MPC-based strategy which shows the possibility of extended battery life time.

Mississippi State University EcoCAR 2 Final Technical Report

The range-extended PHEV is typically designed to operate in an all-electric mode where the batteries are initially charged from the electric power grid. Unlike the pure EV, the vehicles range is not limited by the battery capacity as an engine and generator are incorporated to sustain the battery once the state of charge has dropped to a minimal level. The effectiveness of the concept in terms of fuel economy and reduced emissions is dependent upon the optimal selection of components and a control algorithm that is appropriate for a specific drive cycle. A design methodology is presented that first provides guidelines for the development of the VTS and the various trade-offs that must be considered. A rigorous procedure is then presented for the design and later validation of the configuration and control algorithm using hardware-in-the-loop (HIL).