Development and validation of energy demand uncertainty model for electric city buses

Abstract The prediction of electric city bus energy demand is crucial in order to estimate operating costs and to size components such as the battery and charging systems. Unfortunately, there are unpredictable dynamic factors that can cause variation in the energy demand, particularly concerning driver choices and traffic levels. The impact of these factors on energy demand has been difficult to study since fast computing sufficiently accurate dynamic simulation models have been missing, properly quantified in terms of relevant inputs which contribute to energy demand. The objective is to develop and validate a novel electric city bus model for computing the energy demand, to study the nature and impact of various input factors. The developed equation-based model predicted real-world electric city bus energy consumption within 0.1% error. The most crucial unmeasurable input factors were the driven bus route, the number of stops, the elevation profile, the traffic level and the driving style. This understanding can be used to specify routes and stops for a given electric bus battery capacity. Worst-case scenarios are also necessary for electric bus sizing analysis. The best- and worst-case levels of the crucial factors were identified and with them synthetic best- and worst-case speed profiles were generated to demonstrate their effect to the energy demand. While the measured nominal consumption was 0.70 kWh/km, the computed range of variation was between 0.19 kWh/km and 1.34 kWh/km. For design sizing purposes, an electric city bus can have a broad range of possible energy consumption rates due to mission condition variations.

[1]  Zhiwu Li,et al.  The Optimal Road Grade Design for Minimizing Ground Vehicle Energy Consumption , 2017 .

[2]  Nick Tyler,et al.  Reducing non-collision injuries aboard buses: Passenger balance whilst walking on the lower deck , 2018 .

[3]  Wenshuo Wang,et al.  A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing , 2016 .

[4]  John Miles,et al.  Electric Buses: Lessons to be Learnt from the Milton Keynes Demonstration Project , 2015 .

[5]  Roger A. Dougal,et al.  Dynamic lithium-ion battery model for system simulation , 2002 .

[6]  David Baglee,et al.  The effect of driving style on electric vehicle performance, economy and perception , 2012 .

[7]  Angela Sanguinetti,et al.  The many reasons your mileage may vary: Toward a unifying typology of eco-driving behaviors , 2017 .

[8]  Wen-Tai Lai.  The effects of eco-driving motivation, knowledge and reward intervention on fuel efficiency , 2015 .

[9]  Alexander Kunith,et al.  Electrification of a City Bus Network: An Optimization Model for Cost-Effective Placing of Charging Infrastructure and Battery Sizing of Fast Charging Electric Bus Systems , 2016 .

[10]  D. Sauer,et al.  Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries , 2014 .

[11]  R. Spotnitz.  Simulation of capacity fade in lithium-ion batteries , 2003 .

[12]  Konstantinos N. Genikomsakis,et al.  A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications , 2017 .

[13]  Xinkai Wu,et al.  Electric vehicles’ energy consumption measurement and estimation , 2015 .

[14]  João de Abreu e Silva,et al.  Influential vectors in fuel consumption by an urban bus operator: Bus route, driver behavior or vehicle type? , 2015 .

[15]  Leonard Evans.  Driver Behavior Effects on Fuel Consumption in Urban Driving , 1979 .

[16]  Shengbo Zhang.  A review on electrolyte additives for lithium-ion batteries , 2006 .

[17]  Bo Bergman,et al.  Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement , 2006, Qual. Reliab. Eng. Int..

[18]  Antti Lajunen,et al.  Energy consumption and cost-benefit analysis of hybrid and electric city buses , 2014 .

[19]  José Eugenio Naranjo,et al.  Modeling the Driving Behavior of Electric Vehicles Using Smartphones and Neural Networks , 2014, IEEE Intelligent Transportation Systems Magazine.

[20]  Anita Graser,et al.  Sensitivity analysis for energy demand estimation of electric vehicles , 2016 .

[21]  Volker Pickert,et al.  Electrified hydraulic power steering system in hybrid electric heavy trucks. , 2014 .

[22]  Claudia Pahl-Wostl,et al.  Toward a Relational Concept of Uncertainty: about Knowing Too Little, Knowing Too Differently, and Accepting Not to Know , 2008 .

[23]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[24]  Sebastien Glaser,et al.  Fast computing and approximate fuel consumption modeling for Internal Combustion Engine passenger cars , 2017 .

[25]  Kari Tammi,et al.  Experimental validation of electric bus powertrain model under city driving cycles , 2017 .

[26]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[27]  Genichi Taguchi,et al.  Introduction to quality engineering.... , 2014 .

[28]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[29]  Hesham Rakha,et al.  Power-based electric vehicle energy consumption model: Model development and validation , 2016 .

[30]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[31]  Hongjie Ma,et al.  Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses , 2015 .