Methods of Measuring Regenerative Braking Efficiency in a Test Cycle

Liu, Zheng; Ortmann, Walter; Nefcy, Bernard; Colvin, Dan; Connolly, Francis T.

doi:10.4271/2017-01-1168

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Supercapacitors have a very high power density, allowing a higher charge rate than is achievable with a battery pack and thus more energy can be recouped during regenerative braking. However, they also have a very low energy density relative to batteries, making them only really suited to the minimal energy storage requirements of MHEV powertrains [17], [18]. Some of the advantages of the implementation of SC include fast charge and discharge time, large capacity, long cycle life and other characteristics.…”

Section: System With Supercapacitor Energy Storagementioning

confidence: 99%

Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Awadallah¹,

Tawadros²,

Walker³

et al. 2017

SAE Technical Paper Series

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Driven by stricter mandatory regulations on fuel economy improvement and emissions reduction, market penetration of electrified vehicles will increase in the next ten years. Within this growth, mild hybrid vehicles will become a leading sector. The high cost of hybrid electric vehicles (HEV) has somewhat limited their widespread adoption, especially in developing countries. Conversely, it is these countries that would benefit most from the environmental benefits of HEV technology. Compared to a full hybrid, plug-in hybrid, or electric vehicle, a mild hybrid system stands out due to its maximum benefit/cost ratio. As part of our ongoing project to develop a mild hybrid system for developing markets, we have previously investigated improvements in drive performance and efficiency using optimal gearshift strategies, as well as the incorporation of high power density supercapacitors. In this paper, the fuel and emissions of a baseline conventional vehicle and mild hybrid electric vehicle (MHEV) are compared. The objective of this analysis is to compare the fuel economy and Greenhouse Gas (GHG) emissions of the baseline and MHEV models, using low and highdensity traffic patterns chosen for their similarity to traffic density profiles of our target markets. Results demonstrate the benefits of a lower ongoing cost for the HEV architecture. These advantages include torque-hole filling between gear changes, increased fuel efficiency and performance.

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Section: System With Supercapacitor Energy Storagementioning

confidence: 99%

Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Awadallah¹,

Tawadros²,

Walker³

et al. 2017

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

“…Several metrics are developed in [12] for measuring regen braking efficiency in a hybrid electric vehicle. Overall efficiency is the ratio of battery energy to wheel energy, and powertrain efficiency is the ratio of battery energy to "driveshaft energy," the mechanical energy transferred to the driveshaft via the transmission output.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

“…Overall efficiency is the ratio of battery energy to wheel energy, and powertrain efficiency is the ratio of battery energy to "driveshaft energy," the mechanical energy transferred to the driveshaft via the transmission output. The latter efficiency measurement is important for measuring the efficiency of the electric powertrain because it excludes the energy absorbed by the friction brakes [12].…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

“…Furthermore, given the positive effect of DFCO on regen efficiency in HEVs, it may be productive to explore how a similar state in BEVs impacts energy consumption, either separate from or in conjunction with regen braking. Finally, discussion of braking-wheel to tractive-wheel efficiency in [11] and the study in [12] both provide a good basis for assessing the true efficiency of regen braking. The fact that only a portion of the energy that is regenerated into the battery pack during braking makes it back to the wheel as tractive energy is notable.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

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Energy Modeling of Deceleration Strategies for Electric Vehicles

Hom¹,

Nelson²

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power. Decel modes based on zero battery and motor power are also forward modeled. Multi-mode decel (using a low power decel mode with regenerative braking) is presented as a set of intermediate strategies. An example vehicle using these strategies is modeled in fixed-route simulations, and scoring is based on travel time, energy consumption, and bias towards minimizing one of those metrics. Regenerative braking has the lowest travel time, and coasting the lowest energy consumption, but such bias increases overall cost. Multi-mode strategies lower overall cost by balancing reductions in travel time and energy consumption. Model sensitivity to grade and accessory load fluctuation makes it adaptable to different vehicles and environments. Simulation results demonstrate how regen braking alternatives could be modeled to enhance connected and automated vehicle systems in battery electric vehicles.</div></div>

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“…The ratio that determines the recoverable energy by means of regenerative braking is general and applies to any driving cycle [32]. The regenerative braking energy efficiency η rb is defined by Equation (3) [33,34].…”

Section: Brake Modelmentioning

confidence: 99%

Intrinsic Characteristics of Forward Simulation Modeling Electric Vehicle for Energy Analysis

2022

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The forward method for modeling electric vehicles is one of the most suitable for estimating energy consumption in different imposed driving cycles. However, a detailed description of the methodology used for the development of electric vehicle models is necessary and is scarce in the current literature. To fill this gap, this study focuses on highlighting the intrinsic characteristics through a theoretical study with a mathematical model, complemented by demonstrative simulations in Matlab/Simulink. The results show that the forward method can be estimated more accurately based on the energy consumption of the electric vehicle. Moreover, this paper aims to be explicitly descriptive for the development of more complex electric vehicle models to incorporate real driving cycles, being able to size the drivetrain of the vehicle itself or develop ecological routes.

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Methods of Measuring Regenerative Braking Efficiency in a Test Cycle

Cited by 7 publications

References 5 publications

Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Energy Modeling of Deceleration Strategies for Electric Vehicles

Intrinsic Characteristics of Forward Simulation Modeling Electric Vehicle for Energy Analysis

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