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The current trend towards an increasing electrification of road vehicles brings to life a whole series of unprecedent design issues. Among these, the ageing process that affects the lifetime of lithium-ion based energy storage systems is of particular importance since it turns out to be extremely sensitive to the variation of battery operating conditions normally occurring especially in hybrid electric vehicles (HEVs). This paper aims at analyzing the impact of operating conditions on the predicted lifetime of a parallel-through-the-road plug-in HEV battery both from thermal and ageing perspectives. The retained HEV powertrain architecture is presented first and modeled, and the related energy management system is implemented. Dedicated numerical models are also discussed for the high-voltage battery pack that allow predicting its thermal behavior and cyclic ageing.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

When developing a new engine control strategy, some of the important issues are cost, resource minimization, and quality improvement. This paper outlines how a model based approach was used to develop an engine control strategy for an Extended Range Electric Vehicle (EREV). The outlined approach allowed the development team to minimize the required number of experiments and to complete much of the control development and calibration before implementing the control strategy in the vehicle. It will be shown how models of different fidelity, from map-based models, to mean value models, to 1-D gas dynamics models were generated and used to develop the engine control system. The application of real time capable models for Hardware-in-the-Loop testing will also be shown.

Sustainable and energy-efficient consumption is a main concern in contemporary society. Driven by more stringent international requirements, automobile manufacturers have shifted the focus of development into new technologies such as Hybrid Electric Vehicles (HEVs). These powertrains offer significant improvements in the efficiency of the propulsion system compared to conventional vehicles, but they also lead to higher complexities in the design process and in the control strategy. In order to obtain an optimum powertrain configuration, each component has to be laid out considering the best powertrain efficiency. With such a perspective, a simulation study was performed for the purpose of minimizing well-to-wheel CO2 emissions of a city car through electrification. Three different innovative systems, a Series Hybrid Electric Vehicle (SHEV), a Mixed Hybrid Electric Vehicle (MHEV) and a Battery Electric Vehicle (BEV) were compared to a conventional one.

H-due, a concept of light electric vehicle devoted to personal mobility, will here be presented. The main goals involve the creation of a low-noise, user-friendly, zero-emission vehicle, with a pleasant style. Its main features include high payload/vehicle mass ratio, electric energy supplied either by batteries or by fuel cell, innovative style aimed at transporting a driver and a passenger, chassis design to minimize cost of production, variable wheelbase. The paper presents the main ideas on which the vehicle design was based and summarizes the most important results obtained.

The paper focuses on the advantages of the diesel electric traction applied to military vehicles. In recent years electric cars developed mainly to reduce the dependence on fossil fuels and cut down the emissions. The reduction of fuel consumption, important for civil vehicles above all to reduce emissions and to lower costs, is important also for the military in order to increase vehicle autonomy. In addition, the interest for hybrid electric military vehicles is linked with vehicle packaging flexibility, on board power generation and stealth potential related to their abilities of silent movement. Among many possible layouts the optimum is considered to be hub mounted drive motors in each wheel [ 1 ]. This study shows the development of a demonstrator of an hybrid electric 4×4 military vehicle. It was carried out for a future extension of the technology to a 8×8 armoured vehicle.

H-ergo, a concept of light electric vehicle devoted to personal mobility, will here be presented. H-ergo is a low-noise, user-friendly, zero-emission vehicle, with a pleasant style. Its main features include high payload/vehicle mass ratio, electric energy supplied either by batteries or by fuel cell, ergonomic style in order to transport a driver or a person whit mobility problems, chassis design to minimize cost of production, variable wheelbase (through electric actuator). The paper presents the main ideas on which the vehicle design was based and summarizes the most important results obtained.

Electric scooters are suited to mobility in zones with environmental traffic limitations, and particularly for city centers with very poor room for parking. Aim of this paper is the illustration of the performance that can be obtained from a purposely designed electric scooter. The features of the main components of the scooter driveline: battery package, converter, motor and control will be described.

This paper presents a novel strategy for the control of the motor torques of a rear wheel drive electric vehicle with the objective of improving the lap time of the vehicle around a racetrack. The control strategy is based upon increasing the size of the friction circle by implementing torque vectoring and tire slip control. A two-level nested control strategy is used for the motor torque control. While the outer level is responsible for computing the desired corrective torque vectoring yaw moment, the inner level controls the motor torques to realize the desired corrective torque vectoring yaw moment while simultaneously controlling the wheel longitudinal slip. The performance of the developed controller is analyzed by simulating laps around a racetrack with a non-linear multi-body vehicle model and a professional human racing driver controller setting.

Plug-in Hybrid Electric Vehicles (PHEVs) are one of the main technology options for reducing vehicle CO2 emissions and helping vehicle manufacturers (OEMs) to meet the CO2 targets set by different Governments from all around the world. In Europe OEMs have introduced a number of PHEV models to meet their CO2 target of 95 g/km for passenger cars set for the year 2021. Fuel consumption (FC) and CO2 emissions from PHEVs, however, strongly depend on the way they are used and on the frequency with which their battery is charged by the user. Studies have indeed revealed that in real life, with poor charging behavior from users, PHEV FC is equivalent to that of conventional vehicles, and in some cases higher, due to the increased mass and the need to keep the battery at a certain charging level.

Future exploration missions, including human missions to the Moon and Mars, are expected to have increasingly demanding operational requirements. Generating electrical power, and also maintaining a specific thermal environment, are both critical capabilities for any mission. In the case of exploration, both a wide range of mission types (robotic, human, ISRU etc.) and a variety of environments exist: from interplanetary space, to the shadow of a lunar crater, to the attenuated and red-shifted lighting on the Martian surface, power requirements must be met. This objective could be met with different technologies. The choice is dictated by the operating conditions and the different types of mission. TAS-I is historically mainly involved in missions related to the space exploration with the presence of astronauts. A typical example is the exploration of the Moon with the installation on the Moon surface of a base inclusive of pressurized habitats and rovers.

With electric vehicles becoming more and more popular, the classic “general purpose” vehicle concept is changing to a “dedicated vehicle” concept. Light trucks for goods delivery in cities are one of the examples. The European vehicle category L7e fits perfectly in the low power, low weight vehicle requirements for an electric light truck for goods delivery. However, the safety requirements of this vehicle category are very low and their occupants are highly exposed to injuries in the event of a collision. The European Commission co-funded project OPTIBODY (Optimized Structural components and add-ons to improve passive safety in new Electric Light Trucks and Vans) is developing a new structural concept based on a chassis, a cabin a several add-ons. The add-ons will provide improved protection in case of frontal, side and rear impact.

As technology evolves, the number of sensors and available data on vehicles grow exponentially. In this context, it is essential to use sensors for monitoring key components, increasing safety and reliability, and gathering data useful for mechanical dimensioning and control systems. This paper presents an application of strain-gauged bolts on brake calipers fixation of two electric vehicles. With this approach it was possible to evaluate the loads applied to the brake pads fixation zone and correlate them with braking behavior, therefore gaining insights on braking conditions and system state for an improved braking function control. The goal of the study is analyzing the strengths and limitations of the method and proposing developments to deploy it in real applications. This is particularly important and novel for electric vehicles, where powertrains can create positive/negative torques and generate complex interactions with braking system.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

Monitoring and preserving state-of-health of high-voltage battery packs in electrified road vehicles currently represents an open and growing research topic. When predicting high-voltage battery lifetime, most current literature assumes a uniform temperature distribution among the different cells of the pack. Nevertheless, temperature has been demonstrated having a key impact on cell lifetime, and different cells of the same battery pack typically exhibit different temperature profiles over time, e.g. due to their position within the pack. Following these considerations, this paper aims at assessing the effect of temperature distribution on the predicted lifetime of cells belonging to the same battery pack. To this end, a throughput-based numerical cell ageing model is firstly selected due to its reasonable compromise between accuracy and computational efficiency.

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency.

Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller.