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

Shahverdi, Masood; Mazzola, Michael S.; Grice, Quintin; Doude, Matthew

doi:10.1109/tte.2015.2508000

Cited by 40 publications

(18 citation statements)

References 28 publications

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“…Those experiments aim at providing the passive voltage and current profile of the battery while the vehicle is running. Even if the sampling frequency for this test looks small (only 20 Hz), previous literature [66][67][68][69] supports that this sampling frequency is adequate for estimating the battery impedance for automotive applications. Furthermore, the identified battery bandwidth for The car has been run through repetition of many HWFET (highway) and UDDS (urban) drive cycles from full battery charge (respectively, 96% and 100% of SoC) to its complete depletion (5% of SoC) on a dynamometer.…”

Section: Experimental Protocolmentioning

confidence: 78%

Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter

et al. 2018

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Estimation of a lithium battery electrical impedance can provide relevant information regarding its characteristics. Currently, electrochemical impedance spectroscopy (EIS) constitutes the most recognized and accepted method. Although highly precise and robust, EIS is usually performed during laboratory testing and is not suitable for any on-board application, such as in battery electric vehicles (BEVs) because it is an instrumentally and computationally heavy method. To address this issue and on-line system applications, this manuscript describes, as a main contribution, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal. This method has been tested on the same battery with different passive voltage and current profile and has been validated by achieving similar results. Compared to the original idea presented in the published conference paper, this manuscript explains, in detail, the previously developed method of transforming the battery impedance from the frequency domain to time domain. Moreover, this impedance measurement is used to estimate more robustly the battery state of charge (SoC) through Kalman filters. In the original published conference paper, only an extended Kalman filter (EKF) was applied. However, in this manuscript, an EKF and an unscented Kalman filter (UKF) are used and their performances are compared.

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Section: Experimental Protocolmentioning

confidence: 78%

Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter

et al. 2018

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“…Batteries and supercapacitors [22] are two of the most well-known types of ESS components in the HEV literature. The ESS optimization can be accomplish through sizing and powertrain components costs optimization [23].…”

Section: Quasi-static Model Of a Hybrid Electric Refuse Collectiomentioning

confidence: 99%

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Cortez¹,

Moreno-Eguilaz²,

Soriano³

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-This paper presents a new methodology to estimate the fuel economy in a hybrid electric refuse collector vehicle (RCV). This methodology is based on determinating the fuel consumption in a conventional RCV using real routes, through a quasi-static model that incorporates a mathematical modeling of each component of the powertrain, including the longitudinal dynamic of the conventional RCV. Moreover, a classification of different operational modes of a conventional RCV using real routes is proposed to determinate the required energy that must be provided by the hybrid electric counterpart. The optimal sizing of the energy storage system for the hybrid electric powertrain is presented in order to estimate the fuel consumption by using a real route. The error in the estimation of fuel consumption in a conventional RCV is less than 1.5% in the worst case scenario. The energy storage system sizing allows a 8.62% reduction in the hybrid electric refuse collector vehicle's fuel consumption, by considering 10% of hybridization.

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“…To reduce fuel consumption, greenhouse gasses (GHG) emission and battery aging, researchers have formerly investigated new power management designs [1,2] as the main technology for future electrified vehicles. Various on-line and off-line control strategies have been examined for enhancing the efficiency of power distribution in-between onboard power/energy sources.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

Sockeel

Shi

Shahverdi

et al. 2018

WEVJ

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Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

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

Cited by 40 publications

References 28 publications

Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter

Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

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