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This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

This paper presents an energy-optimal plan for the allocation of a heterogeneous fleet of delivery vehicles in a dynamic network of multiple distribution centers and fulfillment centers. Each distribution center with a heterogeneous fleet of delivery vehicles is considered as a hub connected with the fulfillment centers through the routes as spokes. The goal is to minimize the overall energy consumption of the fleet while meeting the demand of each of the fulfillment centers. To achieve this goal, the problem is divided into two sub-problems that are solved in a hierarchical way. Firstly, for each spoke, the optimal number of vehicles to be allocated from each hub is determined. Secondly, given the number of allocated delivery vehicles from a hub for each spoke, the optimal selection of vehicle type from the available heterogeneous fleet at the hub is done for each of spokes based on the energy requirement and the energy efficiency of the spoke under consideration.

The majority of powertrain types considered important contributors to achieving the CO2 targets in the transportation sector employ a battery as an energy storage device. The need for batteries is hence expected to grow drastically with increasing market share of CO2-optimized powertrain concepts. The resulting huge pressure on the development of future electrochemical energy storage systems necessitates the application of advanced methodologies enabling a fast and cost-efficient concept definition and optimization process. This paper presents a model-based methodology for the optimization of BEV thermal management concept layouts and operation strategies targeting minimized energy consumption. Starting at the vehicle level, the proposed methodology combines appropriate representations of all primary powertrain components with 1D cooling and refrigerant circuit models and focuses on their interaction with the battery chemistry.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

The need to reduce carbon dioxide emissions from motor vehicles pushes the European Union towards drastic choices on future mobility. Despite this, the engines of the “future” have not yet been defined: the choice of engine type will undoubtedly depend on the type of application (journey length, availability of recharging/refueling facilities), practical availability of alternative fuels, and electricity to recharge the batteries. The electrification of vehicles (passenger and transportation cars) may be unsuitable for several aspects: the gravimetric energy density could be too low if the vehicle has to be lightweight, must achieve a high degree of autonomy, or needs a very short refueling time.

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.

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output.

For the NOx removal from diesel exhaust, the selective catalytic reduction (SCR) and lean NOx traps are established technologies. However, these procedures lack efficiency below 200 °C, which is of importance for city driving and cold start phases. Thus, the present paper deals with the development of a novel low-temperature deNOx strategy implying the catalytic NOx reduction by hydrogen. For the investigations, a highly active H2-deNOx catalyst, originally engineered for lean H2 combustion engines, was employed. This Pt-based catalyst reached peak NOx conversion of 95 % in synthetic diesel exhaust with N2 selectivities up to 80 %. Additionally, driving cycle tests on a diesel engine test bench were also performed to evaluate the H2-deNOx performance under practical conditions. For this purpose, a diesel oxidation catalyst, a diesel particulate filter and a H2 injection nozzle with mixing unit were placed upstream to the full size H2-deNOx catalyst.

Internal combustion engines are the primary transportation mover for today society and they will likely continue to be for decades to come. Hybridization is the most common solution to reduce the petrol-fuels consumption and to respect the new raw emission limits. The gasoline engines designed for running together with an electric motor need to have a very high thermal efficiency because they must work at high loads, where engine thermal efficiency is close to the maximum one. Therefore, the technical solutions bringing to thermal efficiency enhancement were adopted on HVs (Hybrid Vehicles) prior to conventional vehicles. In these days, these solutions are going to be adopted on conventional vehicles too. The purpose of this work was to trace development guidelines useful for engine designers, based on the target power and focused on the maximization of the engine thermal efficiency, following the engine rightsizing concept.

The paper deals with the reduction of pollutant emissions in urban areas by considering a Zero-Emission Zone (ZEZ) in which hybrid electric vehicles (HEVs) are allowed to be driven without using the internal combustion engine, as several cities have planned to realize in the next decades. Moreover, since vehicle connectivity has spread more and more in the last years, a vehicle-to-network (V2N) communication system has been taken into account to retrieve real-time navigation data from a map service provider and thus reconstructing the so-called electronic horizon, which is a reconstruction of the future conditions of the vehicle on the road ahead. The speed profile and the road slope are used as input for an on-board predictive control strategy of a plug-in HEV (PHEV). In particular, a dedicated algorithm predicts the amount of necessary energy to complete the city event in full-electric mode, giving a state of charge (SoC) target value.

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.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment have given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Lamborghini has recently invested in the development of a hybrid super sport car, due to performance and comfort reasons. Aventador series gearbox is an Independent Shift Rod gearbox with a single clutch and during gear shifts, as all the single clutch gearbox do, it generates a torque gap. To avoid the additional weight of a Dual Clutch Transmission, a 48V Electric Motor has been connected to the wheels, in a P3 configuration, to fill the torque gap, and to habilitate regenerative braking and electric boost functions.

This paper presents an optimization algorithm, based on discrete dynamic programming, that aims to find the optimal control inputs both for energy and thermal management control strategies of a Plug-in Hybrid Electric Vehicle, in order to minimize the energy consumption over a given driving mission. The chosen vehicle has a complex P1-P4 architecture, with two electrical machines on the front axle and an additional one directly coupled with the engine, on the rear axle. In the first section, the algorithm structure is presented, including the cost-function definition, the disturbances, the state variables and the control variables chosen for the optimal control problem formulation. The second section reports the simplified quasi-static analytical model of the powertrain, which has been used for backward optimization. For this purpose, only the vehicle longitudinal dynamics have been considered.

To meet the requirements in relation to pollutants, CO2-emissions, performances, comfort and costs for 2025 timeframe, many technology options for the powertrain, that plays a key role in the vehicle, are possible. Beside the central aspect of reducing standard cycle consumption levels and emissions, consumer demands are also growing with respect to comfort and functionality. In addition, there is also the challenge of finding cost efficient ways of integrating technologies into a broad range of vehicles with different levels of hybridization. High degrees of electrification simultaneously provide opportunities to reduce the technology content of the internal combustion engines (ICE), resulting in a cost balancing compromise between combustion engine and hybrid technology. The design and optimization of powertrain topologies, functionalities, and components require a complex development process.

This paper shows the influence of different battery charge management strategies on the fuel economy of a hybrid parallel axle-split vehicle in a real driving scenario, for a vehicle control system that has the additional possibility to split the torque between front and rear axles. The first section regards the validation of a self-developed Model in the Loop (MiL) environment of a P1-P4 plug-in hybrid electric car, using experimental data of a New European Driving Cycle test. In its original version, which is implemented on-board the vehicle, the energy management supervisor implements a heuristic, or rule-based, Energy Management Strategy (EMS). During this project, a different EMS has been developed, consisting of a sub-optimal control scheme called Equivalent Consumption Minimization Strategy (ECMS), explained in detail in the second section.

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting more attention in the automotive industry with the technology improvement and increasing focus on fuel economy. For EVs and HEVs, especially all-wheel drive (AWD) EVs with two electric motors powering front and rear axles separately, an accurate motor speed measurement through resolver is significant for vehicle performance and drivability requirement, subject to resolver faults including amplitude imbalance, quadrature imperfection and reference phase shift. This paper proposes a diagnostic scheme for the specific type of resolver fault, amplitude imbalance, in AWD EVs. Based on structural analysis, the vehicle structure is analyzed considering the vehicle architecture and the sensor setup. Different vehicle drive scenarios are studied for designing diagnostic decision logic. The residuals are designed in accordance with the results of structural analysis and the diagnostic decision logic.

Currently known hybrid systems are technically complex, cost-intensive and referring to this for many end-customers not available. Under this boundaries IAV has developed a cost-optimal concept of an efficient and modular powertrain platform for electric and hybrid vehicles. The system is based on one unity gear-set for up to three speeds, which enables seamless shifting with only one friction based clutch. With this platform powertrains can be realized by using a maximum number of carry over parts (COP) for electric vehicles as well as for hybrids. The derivable hybrid powertrains of the platform system are designed for 48V electric motors (EM) which enables the maximum cost potential in combination with the realized gear set and transmission technology. The real simple powertrain platform concept is furthermore scalable for different vehicle segments optionally with or without a hybrid option.

The recent advance in the development of various hybrid vehicle technologies comes along with the need of establishing optimal energy management strategies, in order to minimize both fuel economy and pollutant emissions, while taking into account an increasing number of state and control variables, depending on the adopted hybrid architecture. One of the objectives of this research was to establish benchmarking performance, in terms of fuel economy, for real time on-board management strategies, such as ECMS (Equivalent Consumption Minimization Strategy), whose structure has been implemented in a SIMULINK model for different hybrid vehicle concepts.