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Environmental-friendliness is in the focus of customers and has become a dominant driver for powertrain electrification. FEV has developed the TwinRex hybrid system to match lowest CO2-targets and real life fuel efficiency at affordable costs. A simplified transmission enables full electric driving as well as series and parallel hybrid operation modes. The internal combustion engine (ICE) is dedicated to these hybrid modes. ICE-Technology investment focuses on high efficiency in relevant operation zones, while simplifying technology is sufficient for operation zones of lower interest in this highly electrified environment. Two general engine concepts, a turbocharged and a naturally aspirated version are compared in detail. The turbocharged 1.5 l 3-cylinder engine version achieves a peak efficiency of 43 % at stoichiometric combustion. The naturally aspirated engine version achieves 42.5 % efficiency with a bigger cylinder displacement. Due to the higher torque of the turbocharged engine, the electric machines, inverters and the battery can become smaller, making the hybrid powertrain with the turbocharged engine not only more efficient, but also cheaper. Insight is also given on how such a turbocharged 3-cylinder engine can achieve best NVH performance and how the TwinRex system supports achievement of the expected Post-Euro 6 emission legislation.

In this paper, the optimization of a power management strategy of a fuel cell/battery/supercapacitor hybrid vehicular system is investigated, both offline and in real time. Two offline optimization algorithms, namely, dynamic programming and Pontryagin's minimum principle, are first compared. The offline optimum is used as a benchmark when designing a real-time strategy, which is an inevitable step since the offline optimum is not real-time capable and is oriented only toward minimizing hydrogen consumption, which may result in the unnecessary overloading of the battery. The design and optimization of the real-time strategy makes use of a multiobjective genetic algorithm while taking into account, apart from hydrogen consumption, other important factors, such as the slow dynamics of the fuel cell system and minimizing the battery power burden. As a result, the real-time strategy is found to consume slightly more hydrogen than the offline optimum; however, it dramatically improves system durability.

In the last decade, the development of cleaner vehicles with higher fuel economy and lower emissions is becoming a mainstream in automotive industry because of the aggravation of oil crisis, public concerns on environmental protection, and stringent regulations on CO2 and pollutant emissions. Thus, the automotive industry regards hybridization and electrification of powertrain as a solution to reduce fuel consumption and pollutant emissions.

To address the global climate change concerns, challenging goals for the reduction of anthropogenic greenhouse gas emissions have been set as per the 2015 Paris Agreement of the United Nations Framework Convention on Climate Change [1.1]. One major contribution towards achieving this goal, is expected to come from fuel economy improvements in the field of transportation. In addition to the already established legislative regulations for the fleet CO2 emissions/fuel consumption for Light- and Heavy- Duty vehicles in the United States, China and Japan [1.2, 1.3, 1.4], the European Commission presented a two-stage legislative proposal setting CO2 standards for Heavy- Duty truck applications in May 2018 [1.5].

Fuel cell hybrid vehicles offer a high-efficiency and low-emission substitute for their internal combustion engine counterparts. The hybridization significantly improves the fuel economy of the vehicle; however, exploiting the hybridization requires a well-designed power management strategy that optimally shares the power demand between the power sources. This paper deals with the optimization of power management strategy of a fuel cell/battery hybrid vehicle, both off-line and in real-time. A new formulation of the optimization problem for the real-time strategy is presented. The new approach allows the optimization of the controller over a set of driving cycles at once, which improves the robustness of the designed strategy. The real-time optimization is applied to two forms of real-time controllers: a PI controller based on Pontryagin's Minimum Principle with three parameters and a fuzzy controller with ten parameters. The results show that the PI controller can outperform the fuzzy controller, even though it has fewer parameters. The real-time controllers are designed by simulation and then validated by experiment.