Pareto Front Analysis of the Objective Function in Model Predictive Control Based Power Management System of a Plug-in Hybrid Electric Vehicle

Sockeel, Nicolas; Shi, Jian; Shahverdi, Masood; Mazzola, Michael S.

doi:10.1109/itec.2018.8449957

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…The data can be gathered by the vehicle navigation system and sensors or by a mathematical model. A series PHEV in charge-sustaining mode controlled by a MPC is studied in [135][136][137] with the aging model from [97]. The sensitivity results highlight the trade-off between accuracy and calculation time of different battery model fidelities for different objectives [136] and the need for a suitable vehicle mass approximation by the controller [135].…”

Section: Model Predictive Controlmentioning

confidence: 99%

A Review on Aging-Aware System Simulation for Plug-In Hybrids

Frambach

Liedtke

Dechent

et al. 2022

IEEE Trans. Transp. Electrific.

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The lithium-ion battery is a vital powertrain component in plug-in hybrid electric vehicles (PHEVs). The fuel reduction potential and cost-effectiveness of these vehicles depend on the sizing of the powertrain components as well as on their utilization, which is defined by the energy management system (EMS). The battery is affected by power and capacity reduction over the lifetime of the vehicle, which needs to be considered during the design process to ensure the performance goals throughout the vehicle lifetime. Current literature regarding battery aging usually contains experimental results, which are not transformed into a useful aging model for system simulations. Consequently, battery aging is often neglected, which is why this paper intends to help researchers understand the degradation process of batteries in PHEVs and consider this in their simulation and dimensioning process. First, PHEV powertrain topologies and components are presented. Afterward, battery degradation mechanisms and recent findings are explained, followed by appropriate modeling approaches for different simulation targets. Finally, current aging-aware EMS literature is systematically reviewed and the integration of the aging models is analyzed, so researchers in system simulation areas can improve their powertrain models.

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Section: Model Predictive Controlmentioning

confidence: 99%

A Review on Aging-Aware System Simulation for Plug-In Hybrids

Frambach

Liedtke

Dechent

et al. 2022

IEEE Trans. Transp. Electrific.

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show abstract

“…Once the structure of the cost function was defined, we should proceed to make a sweeping of the weights to determine the Pareto front that allows choosing those that allow reduction of the power delivered by the fuel cell in order to reduce hydrogen consumption, as proposed in [55]. In addition, the one that allows a smoother variation of the SOC of the battery to preserve its useful life should be selected.…”

Section: Coefficient Sweep For Badcmentioning

confidence: 99%

Optimal Sizing of Storage Elements for a Vehicle Based on Fuel Cells, Supercapacitors, and Batteries

2019

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To achieve a vehicle-efficient energy management system, an architecture composed of a PEM fuel cell as the main energy source and a hybrid storage system based on battery banks and supercapacitors is proposed. This paper introduces a methodology for the optimal component sizing aiming at minimizing the total cost, achieving a cheaper system that can achieve the requirements of the speed profiles. The chosen vehicle is an urban transport bus, which must meet the Buenos Aires Driving Cycle, and the Manhattan Driving Cycle. The combination of batteries and supercapacitors allows a better response to the vehicle’s power demand, since it combines the high energy density of the batteries with the high power density of the supercapacitors, allowing the best absorption of energy coming from braking. In this way, we address the rapid changes in power without reducing the global efficiency of the system. Optimum use of storage systems and fuel cell is analyzed through dynamic programming.

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“…The vehicle shown in Figure 3 has been already described in [62][63][64][65]. It is a series PHEV and its powertrain is composed of an electric generator, an Energy Storage System (ESS) made of a lithium iron phosphate battery, and an electric motor connected to a DC bus.…”

Section: Experimental Protocolmentioning

confidence: 99%

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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Pareto Front Analysis of the Objective Function in Model Predictive Control Based Power Management System of a Plug-in Hybrid Electric Vehicle

Cited by 11 publications

References 27 publications

A Review on Aging-Aware System Simulation for Plug-In Hybrids

A Review on Aging-Aware System Simulation for Plug-In Hybrids

Optimal Sizing of Storage Elements for a Vehicle Based on Fuel Cells, Supercapacitors, and Batteries

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

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