Simulation based study of battery electric vehicle performance in real world cycles

Dhand, Aditya; Pullen, Keith

doi:10.1504/ijehv.2013.059372

Cited by 7 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This case as well any other case where the clutch A is not engaged, would usually take place at vehicle speeds below 10-15 kph. This situation is similar to the case with the base BEV 8 where the EM does not do brake energy recuperation below 10 kph.…”

Section:   supporting

confidence: 66%

“…The initial SOC of the battery for the simulations is taken to be between 85% and 90% since, above 90%, regenerative braking is not allowed in the base BEV. 8 The simulations are run in the steady state at a temperature of 25°C. These conditions also represent the best case for the BEV since, at higher or lower temperatures or at lower SOCs, the performance of the battery is expected to be poorer.…”

Section: Resultsmentioning

confidence: 99%

“…The clutch A is used to provide the CVT with the gear neutral ability. The paper 8 shows the baseline BEV for the presented HV. Besides the FW, CVT and clutches, the one main difference is that the EM in the HV is a downsized version (by about 37%) of the one in the base BEV.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal energy management for a flywheel-assisted battery electric vehicle

Dhand

Pullen

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractBattery electric vehicles (BEV) are crucial to the reduction of dependence on fossil fuels and for moving towards a zero emission transport system. Though BEV technology has been rapidly improving, the limited driving range and high cost are significant impediments to the popularity of electric vehicles. The battery is the main element which affects the range and cost of the vehicle. The batteries can provide either high power or high energy but not both. Hybridization of the energy source is one of the methods to improve the energy efficiency of the vehicle, which would involve combining a high energy battery with a high power source. High speed flywheels (FW) have attractive properties and low cost potential which makes them excellent secondary energy storage devices to be used in hybrid and electric vehicles. They are utilized to load level the battery so as to protect it from peak loads and enhance its capacity and life. The flywheel is coupled to the drive line with a continuous variable transmission (CVT). This paper presents the optimal energy management strategy (EMS) for a mechanically connected flywheel assisted BEV (FWBEV) powertrain. The optimization problem is complex due to factors such as the small storage capacity of flywheel, kinematic constraints and slipping of clutches. Dynamic programming is used to calculate the optimal control strategy for torque distribution during operation in real world driving cycles. The results show significant potential for reduction of energy consumption in extra-urban and highway cycles, while reducing battery peak loads during all cycles. The results give a benchmark of the energy saving potential for such a powertrain and insights into how a real sub-optimal controller can be designed.

show abstract

Section:   supporting

confidence: 66%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal energy management for a flywheel-assisted battery electric vehicle

Dhand

Pullen

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

show abstract

“…They consequently are not suitable for exploring consistent multidimensional design spaces. Different simulation models for BEV design and analysis have been proposed (Belingardi et al, 2018;Dhand and Pullen, 2013;Guezennec et al, 2012;Zhou, 2017). On the other hand, simplified analytical models require significantly lower computational time and can thus be implemented in recursive calculation.…”

Section: Introductionmentioning

confidence: 99%

Comparing battery electric vehicle powertrains through rapid component sizing

Anselma

Belingardi

2019

IJEHV

View full text Add to dashboard Cite

The current transition from internal combustion engine vehicles to battery electric vehicles (BEVs) requires the development and implementation of novel design methodologies. This paper introduces an original design procedure for battery electric light vehicle powertrains based on rapid brute force optimisation. At first, a large design space is generated by sweeping the relevant design parameters, specifically the motor size, the number of gears and the transmission ratios. Analytical modelling for electric vehicle powertrains is then presented and combined with lookup tables for the actual system components. The feasible candidate designs are identified by introducing evaluation criteria for the transmission ratios according to the vehicle and electric machine data considered. Subsequently, these candidate designs are evaluated in terms of both launching performance and energy consumption by adopting an efficiency based gear-shifting strategy. Optimal designs can finally be graphically identified through a Pareto frontier analysis as effectively demonstrated in the case of four actual vehicles of different classes. The methodology introduced efficiently addresses the non-linear multidimensional optimisation problem of electric vehicle powertrain design.

show abstract

“…The energy consumption is dependent on vehicle characteristics and some characteristics are influenced by the weather conditions (Neaimeh et al, 2013;Nordelöf et al, 2014). There are two methodologies for determining the energy consumption: building statistical models based on real world driving measurements (Shankar and Marco, 2013;De Cauwer et al, 2015;Rodgers et al, 2013) and creating vehicle models based on physics (Dhand and Pullen, 2013;Wu et al, 2015). Using real-world measurements can result in more realistic values for the energy consumption calculation, but it relies on a high number of real-world measurements and cannot be directly coupled with vehicle parameters and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Battery electric vehicle energy consumption modelling for range estimation

Wang

Besselink

Nijmeijer

2017

IJEHV

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Simulation based study of battery electric vehicle performance in real world cycles

Cited by 7 publications

References 13 publications

Optimal energy management for a flywheel-assisted battery electric vehicle

Optimal energy management for a flywheel-assisted battery electric vehicle

Comparing battery electric vehicle powertrains through rapid component sizing

Battery electric vehicle energy consumption modelling for range estimation

Contact Info

Product

Resources

About