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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.

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.

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.

Designing automobile brake systems is generally complex and time consuming. Indeed, the brake system integrates several components and has to satisfy numerous conflicting government regulations. Due to these constraints, designing an optimal configuration is not easy. This paper consequently proposes a simple, intuitive and automated methodology that enables rapid optimal design of light vehicle hydraulic brake systems. Firstly, the system is modeled through cascaded analytical equations for each component. A large design space is then generated by varying the operational parameters of each component in its specific reasonable range. The system components under consideration include the brake pedal, the master cylinder, the vacuum-assisted booster, the brake line and the brake pistons. Successful system configurations are identified by implementing the requirements of the two most relevant safety homologation standards for light vehicle brake systems (US and EU legislations).

Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS.