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Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.

Owe to their high electrical energy density, lithium-ion batteries are the most employed technologies in electrified vehicles, whose market share is growing very fast. As a matter of fact, their thermal management is of crucial importance to keep the operating temperature within an appropriate range, as this might greatly affect performance and durability of such devices. Heat generation during cyclic charge and discharge processes, occurring during a vehicle mission, may cause critical temperature variations and, therefore, a suitable thermal management is indispensable. This is particularly true for fuel cell hybrid electric vehicles, where the battery undergoes more severe thermal stresses than in battery electric vehicles, due to higher operating C-rates.

Reducing computational time has become a critical issue in recent years, particularly in the transportation field, where the complexity of scenarios demands lightweight controllers to run large simulations and gather results to study different behaviors. This study proposes two novel formulations of the Optimal Control Problem (OCP) for the Energy Management System of a Plug-in Hybrid Electric Vehicle (PHEV) and compares their performance with a benchmark found in the literature. Dynamic Programming was chosen as the optimization algorithm to solve the OCP in a Matlab environment, using the DynaProg toolbox. The objective is to address the optimality of the fuel economy solution and computational time. In order to improve the computational efficiency of the algorithm, an existing formulation from the literature was modified, which originally utilized three control inputs.

To reduce carbon emissions and mitigate traffic congestion in urban environments, new innovative transportation concepts are required. While public transportation covers certain segments, it cannot supply all possible routes, use cases, and preferences and hence, other solutions are needed as well. Urban drive missions are not typically calling for huge powers or even large energy capacities. In the vehicle design, this should be shown as rightsizing. It is not only the powertrain that should be rightsized but also the vehicle physical dimensions, to enable, e.g., convenient maneuvering. Furthermore, due to the variety of options (walking, biking, scooters, public transportation etc.), one might need a personal vehicle only occasionally, and therefore, a vehicle with shared and multipurpose capabilities would be an asset. Lastly, since small urban vehicles are considered unsafe, improving the safety and general confidence on small vehicles is vital for the market penetration.

To encourage electric mobility, governments may pay part of the price of the new car. An economic evaluation of this incentive program is proposed. The economic value results from a comparison between the with-incentive scenario and the without-incentive counterfactual scenario. Four contributions are considered: the change in consumers’ welfare, the government expenditure, the change in the discounted costs of energy consumption for the portion that is purchased abroad, and the change in the discounted costs of well-to-wheel greenhouse gas emissions. A logit model is used to represent choices between differentiated cars and the consumption of all other goods in the economy (the numéraire). The car market in Italy in 2019 had incentives for all-electric and plug-in hybrid electric cars. In a worst case where no consumer scraps the old car the economic value is negative. The economic value is positive in the best case where all scrap.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

The port-logistic industry has a significant impact on the urban environment nearby ports and on the surrounding coastal areas. This is due to the use of large auxiliary power systems on ships operating during port stays, as well as to the employment of a number of fossil fuel powered road vehicles required for port operations. The environmental impact related to the use of these vehicles is twofold: on one hand, they contribute directly to port emissions by fuel consumption; on the other hand, they require some of the ship auxiliary systems to operate intensively, such as the ventilation system, which must operate to remove the pollutants produced by the vehicle engines inside the ship. The pathway to achieve decarbonization and mitigation of energy use in ports involves therefore the adoption of alternative and cleaner technology solutions for the propulsion systems of such port vehicles.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

The electrification of utility vehicles represents a promising solution to reduce the emissions in the urban context. Differently from traditional vehicles, they operate intermittently and generally follow routine driving cycles. In this paper, we model a 15-kW electric utility vehicle, adopting a backward-looking approach, widely used in literature to estimate the range of electric cars. The model requires a limited number of data, either supplied by the vehicle manufacturer or found in literature, as in case of the induction motor/generator efficiency and of the battery Peukert coefficient. The model can be used to assess the possibility of the vehicle to complete an assigned mission, as well as to optimize the vehicle’s design and architecture. The model is validated on GPS data obtained through an experimental campaign where the electric utility vehicle was driven to depletion considering different routes, including the effect of slopes.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

Lithium ion batteries are the most promising candidates for electric and hybrid electric vehicles, owe to their ability to store higher electrical energy. As a matter of fact, in automotive applications, these batteries undergo frequent and fast charge and discharge processes, which are associated to internal heat generation, which in turns causes temperature increase. Thermal management is therefore crucial to keep temperature in an appropriate level for safe operation and battery wear prevention. In a recent work authors have already demonstrated the capabilities of a coupled lattice Boltzmann-Finite Volume Method to deal with thermal transient of a three-dimensional air-cooled Li-ion battery at different discharging rates and Reynolds numbers. Here, in order to improve discharge thermal capabilities and reduce temperature levels of the battery itself, a layer of porous medium is placed in contact with the battery so to replace a continuum solid aluminum layer.

Proposed solutions for electric vehicles range from the simple single-motor drive coupled to one axle through a mechanical differential, to more complex solutions, such as four in-wheel motors, which ask for electronic torque vectoring. Main reasons for having more than one electric machine are: reduction of the rated power of each motor, which most likely leads to simplification and cost reduction of all the electric drive components; increased reliability of the overall traction system, enhancing fault tolerance ability; increase of the degrees of freedom which allows for control strategy optimization and efficiency improvement. In particular, electrical machines efficiency generally peaks at around 75% of load and this usually leads to machine downsizing to avoid operation in low efficiency regions.

Battery electric vehicles (BEVs) are considered one of the most promising solution to improve the sustainability of the transportation sector aiming at a progressive reduction of the dependence on fossil fuels and the associated local pollutants and CO2 emissions. Presently, the major technological obstacle to a large scale diffusion of BEVs, is the fairly low range, typically less than 300 km, as compared to classical gasoline and diesel engines. This limit becomes even more critical if the electric vehicle is operated in severe weather conditions, due to the additional energy consumption required by the cabin heating, ventilating, and air-conditioning (HVAC). The adoption of vapor-compression cycle, either in heat pump or refrigerator configuration, represents the state-of-the-art technology for HVAC systems in vehicles. Such devices typically employ an expansion valve to abruptly reduce the pressure causing the flash evaporation of the working fluid.

Due to their ability to store higher electrical energy, lithium ion batteries are the most promising candidates for electric and hybrid electric vehicles, whose market share is growing fast. Heat generation during charge and discharge processes, frequently undergone by these batteries, causes temperature increase and thermal management is indispensable to keep temperature in an appropriate level. In this paper, a coupled Lattice Boltzmann-Finite Volume model for the three-dimensional transient thermal analysis of an air-cooled Li-ion battery module is presented. As it has already been successfully used to deal with several fluid-dynamics problems, the Lattice Boltzmann method is selected for its simpler boundary condition implementation and complete parallel computing, which make this approach promising for such applications.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.