A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

The fact that electric vehicles (EVs) are characterized by relatively short driving range not only signifies the importance of routing applications to compute energy efficient or optimal paths, but also underlines the necessity for realistic simulation models to estimate the energy consumption of EVs. To this end, the present paper introduces an accurate yet computationally efficient energy consumption model for EVs, based on generic high-level specifications and technical characteristics. The proposed model employs a dynamic approach to simulate the energy recuperation capability of the EV and takes into account motor overload conditions to represent the vehicle performance over highly demanding route sections. To validate the simulation model developed in this work, its output over nine typical driving cycles is compared to that of the Future Automotive Systems Technology Simulator (FASTSim), which is a simulation tool tested on the basis of real-world data from existing vehicles. The validation results show that the mean absolute error (MAE) of cumulative energy consumption is less than 45 W h on average, while the computation time to perform each driving cycle is of the order of tens of milliseconds, indicating that the developed model strikes a reasonable balance between efficacy of representation and computational efficiency. Comprehensive simulation results are presented in order to exemplify the key features of the model and analyze its output under specific highly aggressive driving cycles for road gradients ranging from −6% to 6%, in support of its usability as a practical solution for estimating the energy consumption in EV routing applications.     

Vehicle energy efficiency evaluation from well-to-wheel lifecycle perspective

Given a fundamental role of automobiles in human society, evaluation of vehicle energy efficiency is of utmost importance. Various reports have been published hitherto concerning well-to-wheel (WTW) fuel consumption at the vehicle operation phase. On the other hand, WTW energy consumption at other lifecycle phases has been scarcely integrated in the assessment of vehicle energy efficiency. Particularly, WTW energy consumption for material structure is significantly associated with fuel economy. As such, this paper firstly analyzes the lifecycle WTW vehicle energy efficiency from the perspective of both material structures at the manufacture phase and fuel consumption at the operation phase for conventional vehicle (CV), electric vehicle (EV), hybrid vehicle (HV) and fuel cell vehicle (FCV). Then, an expected transition of vehicle weight and energy consumption arising from material structural shift through the replacement of steel with aluminum is evaluated. Finally, the overall vehicle energy efficiency in Japan in 2020–2050 is projected. It is discovered that the inclusion of energy consumption for material structure has a significant impact on the determination of the vehicle energy efficiency, particularly for new generation vehicles. WTW analysis at the multiple lifecycle phases may be of use in establishing more comprehensive principles of vehicle energy efficiency.     

Assessing CO2 emissions of electric vehicles in Germany in 2030

Electric vehicles are often said to reduce carbon dioxide (CO₂) emissions. However, the results of current comparisons with conventional vehicles are not always in favor of electric vehicles. We outline that this is not only due to the different assumptions in the time of charging and the country-specific electricity generation mix, but also due to the applied assessment method. We, therefore, discuss four assessment methods (average annual electricity mix, average time-dependent electricity mix, marginal electricity mix, and balancing zero emissions) and analyze the corresponding CO₂ emissions for Germany in 2030 using an optimizing energy system model (PERSEUS-NET-TS). Furthermore, we distinguish between an uncontrolled (i.e. direct) charging and an optimized controlled charging strategy. For Germany, the different assessment methods lead to substantial discrepancies in CO₂ emissions for 2030 ranging from no emissions to about 0.55 kg/kWh_el (110 g/km). These emissions partly exceed the emissions from internal combustion engine vehicles. Furthermore, depending on the underlying power plant portfolio and the controlling objective, controlled charging might help to reduce CO₂ emissions and relieve the electricity grid. We therefore recommend to support controlled charging, to develop consistent methodologies to address key factors affecting CO₂ emissions by electric vehicles, and to implement efficient policy instruments which guarantee emission free mobility with electric vehicles agreed upon by researchers and policy makers.     

Globally energy-optimal speed planning for road vehicles on a given route

This paper provides a globally optimal solution to an important problem: given a real-world route, what is the most energy-efficient way to drive a vehicle from the origin to the destination within a certain period of time. Along the route, there may be multiple stop signs, traffic lights, turns and curved segments, roads with different grades and speed limits, and even leading vehicles with pre-known speed profiles. Most of such route information and features are actually constraints to the optimal vehicle speed control problem, but these constraints are described in two different domains. The most important concept in solving this problem is to convert the distance-domain route constraints to some time-domain state and input constraints that can be handled by optimization methods such as dynamic programming (DP). Multiple techniques including cost-to-go function interpolation and parallel computing are used to reduce the computation of DP and make the problem solvable within a reasonable amount of time on a personal computer.     

Exploring low carbon transition pathways for the UK road transport sector

This paper uses an analytical framework of multi-level and multi-phase perspectives to explore low carbon transition pathways for the UK road transport system. The work draws on the impact made by the national (UK) and regional (EU) low carbon policy instruments on the UK road sector. The results show that the transformation pathway, which is at the take-off phase on a large scale, is the only fully active pathway. The transformation is mainly characterized by the adoption of biofuel blends and hybrid electric vehicles, as well as niche technologies. For the emergence of an ideal low carbon road system in the UK, it is shown that the transformation pathway is insufficient and the likely pathway sequence to full decarbonization will be transformation-substitution-de-alignment/re-alignment. However, the dynamics that can favour a smooth process of this sequence will demand a range of active niche technologies and strong government intervention.     

Assessing driving pattern factors for the specific energy use of electric vehicles: A factor analysis approach from case study data of the Mitsubishi i–MiEV minicar

Battery electric vehicles (BEVs) promise to contribute to the achievement of a more sustainable transport system. In order to estimate energetic efficiency potentials while taking into account operating conditions, insights on the factors of energy use are required. The driving pattern, i.e. the characteristics of the driving profile, is expected to affect the vehicles’ energy use to a great extent. This paper investigates whether the driving pattern parameters that have proved to be relevant for the fuel consumption of ICVs also apply to BEVs. In consequence, we analyse correlations between driving pattern factors and the specific energy use of BEVs. In order to record driving and energy data, four commercially used battery electric minicars were equipped with tracking devices. The resulting dataset contains 42 vehicle months. The driving pattern is described in 45 parameters that are calculated for segments of the logged driving profiles. Exploratory factor analysis is applied to reduce the large number of parameters into a smaller number of independent factors. Six independent driving pattern factors are identified. Suitable correlation coefficients are calculated to check for dependencies with energy use. The most significant correlations were found for the intensity of acceleration/deceleration, as well as for the oscillation factor. Our results could be used to inform further studies where driving pattern factors for ICVs and BEVs are directly compared. Also, results can be used to develop specific driving school training programs to learn to drive BEVs in an energy efficient manner.     

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.     

A three-level framework for performance-based railway timetabling

The performance of railway operations depends highly on the quality of the railway timetable. In particular for dense railway networks it can be challenging to obtain a stable robust conflict-free and energy-efficient timetable with acceptable infrastructure occupation and short journey times. This paper presents a performance-based railway timetabling framework integrating timetable construction and evaluation on three levels: microscopic, macroscopic, and a corridor fine-tuning level, where each performance indicator is optimized or evaluated at the appropriate level. A modular implementation of the three-level framework is presented and demonstrated on a case study on the Dutch railway network illustrating the feasibility of this approach to achieve the highest timetabling design level.     

Experimental testing and simulations of speed variations impact on fuel consumption of conventional gasoline passenger cars

Speed variations are considered as an alternative for reducing fuel consumption during the use phase of passenger cars. It explores vehicle engine operating zones with lower fuel consumption, thus making possible a reduction in fuel consumption when compared to constant speed operation. In this paper, we present an evaluation of two conditions of speed variations: 50–70 km/h and 90–110 km/h using numerical simulations and controlled tests. The controlled tests performed on a test track by a professional pilot show that a reduction in fuel consumption is achievable with a conventional gasoline passenger car, with no adaptations for realizing speed variations. Numerical simulations based on a backward quasi-static powertrain model are used to evaluate the potential of speed variations for reducing fuel consumption in other speed variation conditions. When deceleration is performed with gear in neutral position, simulations show that speed variations are always correlated to a lower fuel consumption. This was suspected through previous numerical tests or evaluation on test bench but not in controlled tests conditions.     

Operational profiles of ships in Norwegian waters: An activity-based approach to assess the benefits of hybrid and electric propulsion

Various regulations are imposed on shipping to increase energy efficiency and reduce environmental impacts. Alternative fuels and power systems are among the solutions for compliance with these regulations. The power system of a ship may not operate optimally because of the diversity of the operational profile during its lifetime. This article uses an activity-based approach and big data from the Automatic Identification System (AIS) to study the operational profiles of eight ship types operating in Norwegian waters around mainland Norway in 2016. The aim is to identify ship types that can benefit from electric and hybrid propulsion through analysis of their operational profiles. Close to shore, the operational profiles of various ship types are similar, and all ships spend a great proportion of their time with lower loads. As the distance from shore increases, the operational profiles of various ship types follow distinct trends. Among the considered ship types, reefers spend more operational time close to the diesel engine design condition. On the other hand, offshore and passenger ships show the most dynamic operational profiles and spend a large percentage of their operational time with a partial load, away from diesel engine design conditions. Such ships can benefit from hybridisation, diesel-electric propulsion, and other electric concepts, such as batteries and fuel cells. Another option is to downsize diesel engines for better operation while fuel cells and batteries supply peak and partial loads. Operational profiles are plotted and details of the approach are presented in the article.