Development of a Simulation Model to Analyze the Effect of Thermal Management on Battery Life

Yuksel, Tugce; Michalek, Jeremy J.

doi:10.4271/2012-01-0671

Cited by 21 publications

(5 citation statements)

References 18 publications

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“…Past studies have shown that life cycle plug-in electric vehicle (PEV) emissions depend heavily on the assumed electricity grid mix [1][2][3][4][5][6][7], driving patterns (including drive cycle and distance) [8][9][10] and climate (including ambient temperature) [7,11]. These factors vary regionally, so PEV emissions implications also vary regionally.…”

Section: Introductionmentioning

confidence: 99%

Effect of regional grid mix, driving patterns and climate on the comparative carbon footprint of gasoline and plug-in electric vehicles in the United States

Yuksel

Tamayao

Hendrickson

et al. 2016

Environ. Res. Lett.

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We compare life cycle greenhouse gas (GHG) emissions from several light-duty passenger gasoline and plug-in electric vehicles (PEVs) across US counties by accounting for regional differences due to marginal grid mix, ambient temperature, patterns of vehicle miles traveled (VMT), and driving conditions (city versus highway). We find that PEVs can have larger or smaller carbon footprints than gasoline vehicles, depending on these regional factors and the specific vehicle models being compared. The Nissan Leaf battery electric vehicle has a smaller carbon footprint than the most efficient gasoline vehicle (the Toyota Prius) in the urban counties of California, Texas and Florida, whereas the Prius has a smaller carbon footprint in the Midwest and the South. The Leaf is lower emitting than the Mazda 3 conventional gasoline vehicle in most urban counties, but the Mazda 3 is lower emitting in rural Midwest counties. The Chevrolet Volt plug-in hybrid electric vehicle has a larger carbon footprint than the Prius throughout the continental US, though the Volt has a smaller carbon footprint than the Mazda 3 in many urban counties. Regional grid mix, temperature, driving conditions, and vehicle model all have substantial implications for identifying which technology has the lowest carbon footprint, whereas regional patterns of VMT have a much smaller effect. Given the variation in relative GHG implications, it is unlikely that blunt policy instruments that favor specific technology categories can ensure emission reductions universally.

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Section: Introductionmentioning

confidence: 99%

Effect of regional grid mix, driving patterns and climate on the comparative carbon footprint of gasoline and plug-in electric vehicles in the United States

Yuksel

Tamayao

Hendrickson

et al. 2016

Environ. Res. Lett.

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“…Their effects on the total battery capacity loss are normally cumulative [17]. As a result, it is possible to consider them separately.…”

Section: Battery Cycle-life Modelmentioning

confidence: 99%

Integrated Online Energy and Battery Life Management for Hybrid Long Haulage Truck

Pham

Bosch

Kessels

et al. 2014

2014 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Battery lifetime management plays an important role for successful commercializing hybrid electric vehicles. This paper aims at integrating the battery lifetime management into the energy management system of a heavy-duty hybrid electric truck. The developed strategy called Integrated Energy Management (IEM) is real-time implementable, and able to guarantee the battery lifetime requirement while allowing appropriate HEV operations. Moreover, by utilizing feedforward control, the IEM strategy handles the trade-off between the cost (for battery aging) and the benefit (from fuel reduction) when recovering regenerative braking energy in the battery: braking energy is absorbed without harming the battery life too much. The feedforward controller uses a (dynamic) battery lifetime model and the angular engine speed, power demand as well as the battery temperature. Simulations illustrate the capability of the IEM strategy in assuring the battery lifetime requirement and the benefit from using the proposed feedforward control algorithm.

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“…An equivalent circuit model considering the effect of the temperature was introduced in [8]. As observed in [9], the effect of the temperature on the available capacity of a battery can be modeled as an Arrhenius type of equation. The effect of temperature on the terminal voltage was modeled in [10], which adds an offset to the open circuit voltage (OCV) in the equivalent electrical circuit.…”

Section: ) Battery Modelingmentioning

confidence: 99%

A framework with temperature-aware accuracy levels for battery modeling from datasheets

Petricca

Shin

Bocca

et al. 2013

2013 23rd International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)

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Many solutions for battery modeling have been proposed in the literature; they usually rely on the definition of a generic model template (e.g., a circuit equivalent), which is then populated using data obtained from direct measurements on actual devices. This approach incurs in high costs (for the instrumentation) and long characterization times.In this work we present a modeling framework that is an extension of [1], and which allows to automatically build battery models using only data reported in the battery datasheets, without requiring any measurement. Specifically, we extend the existing methodology by including temperature dependence in the battery behavior. The flexibility and the accuracy of the proposed strategy are demonstrated on some sample batteries that provide temperature-dependent information in their datasheets.

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Development of a Simulation Model to Analyze the Effect of Thermal Management on Battery Life

Cited by 21 publications

References 18 publications

Effect of regional grid mix, driving patterns and climate on the comparative carbon footprint of gasoline and plug-in electric vehicles in the United States

Effect of regional grid mix, driving patterns and climate on the comparative carbon footprint of gasoline and plug-in electric vehicles in the United States

Integrated Online Energy and Battery Life Management for Hybrid Long Haulage Truck

A framework with temperature-aware accuracy levels for battery modeling from datasheets

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