Masood Shahverdi

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

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High bandwidth energy storage devices for HEV/EV energy storage system

1.125-kWh battery, plus an 80-

Active gate drive solutions for improving SiC JFET switching dynamics

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

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.

Li-Ion Battery Pack and Applications

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.

Passive tracking of the electrochemical impedance of a hybrid electric vehicle battery and state of charge estimation through an extended Kalman filter

In a HEV or EV, analysis of the energy storage system (ESS) is often based on standard drive cycles sampled at a 1 Hz rate. The switching harmonics caused by the motor drive are well-known source of high-frequency harmonics on the ESS since perfect de-coupling using the capacitors native to the motor drive is not possible, but additional current harmonics flow between the 0.5 Hz (Nyquist frequency of sampling frequency) and the switching frequency because of the transient nature of load in automotive applications. This digest quantifies these harmonic currents, sizes high-bandwidth energy storage devices (ESDs) for ESS, and compares power loss reduction using measured RMS currents.

Sensitivity analysis of the battery model for model predictive control implemented into a plug-in hybrid electric vehicle

Active and Non-Active Gate Drives (AGDs and NAGDs) are known for managing switching characteristics of silicon (Si) and silicon carbide (SiC) power semiconductors. As SiC adoption has grown, the need for intelligent gate drives which are capable of managing the dynamics associated with the fast-switching characteristics of these devices has become apparent. To propose a solution for managing driven and post-driven dynamic behavior, this paper first studies the most recent AGD solutions for silicon power semiconductors with focus on closed loop schemes. The study is continued by reviewing available AGD and NAGD solutions for silicon carbide power semiconductors concentrating on the SiC JFET. Oscillatory modes which can be observed in application circuits based on SiC devices are discussed, and an AGD design example is proposed for improving the final dynamic response of such circuits. The active gate drive design example is constructed, and both simulated and empirical results are shown to substantially reduce the occurrence of natural and forced oscillations at turn-off of the SiC JFET.

Modular microgrid unit (MMGU) specifications for a pumped-storage application

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.