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Hydrogen-based fuel cell electric vehicles are a promising alternative to pure battery electric vehicles (BEV) in heavy-duty (HD) truck applications, due to lower weight penalty on the cargo mass, a higher range, and a lower refueling time. The overall drivetrain optimization (including battery and fuel cell sizing) requires an efficient and robust energy management concept, capable of exploiting the maximum system fuel saving potential, while considering critical component health metrics. In recent years, the Equivalent Consumption Minimization Strategy (ECMS) has demonstrated its capability to meet those requirements when applied to passenger car hybrid powertrains. In a traditional implementation, the ECMS-based control policy is typically calculated a-priori, based on steady state operating conditions. The solutions are then implemented as look up tables in the final dynamic model.

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost. With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication.

In recent years, electrification of vehicle powertrains has become more mainstream to meet regulatory fuel economy and emissions requirements. Amongst the many challenges involved with powertrain electrification, developing supervisory controls and energy management of hybrid electric vehicle powertrains involves significant challenges due to multiple power sources involved. Optimizing energy management for a hybrid electric vehicle largely involves two sets of tasks: component level or low-level control task and supervisory level or high-level control task. In addition to complexity within powertrain controls, advanced driver assistance systems and the associated chassis controls are also continuing to become more complex. However, opportunities exist to optimize energy management when a cohesive interaction between chassis and powertrain controls can be realized.

The recent automotive industry trend towards electrification has created new challenges for NVH engineers. These challenges stem from new powertrain architectures and their complex interactions, the governing control strategies which aim to optimize energy management, and new unmasked sources of excitation. Additionally, vehicle manufacturers are attempting to reduce hardware testing in order to rapidly satisfy increasing production demand and to minimize its costs. Hence, to meet the above-mentioned challenges up front in the development process of Hybrid Electrical Vehicles (HEVs) while balancing competing design objectives of drivability, durability and NVH, a simulation-led design and optimization is required. NVH problems are often the result of mechanisms that originate through complex interactions between different physical domains (flow, electromagnetic, structural/mechanical, control logic, etc.) and the assembly of individual components into a complete system.

One promising solution for increasing vehicle fuel economy, while still maintaining long-range driving capability, is the plug-in hybrid electric vehicle (PHEV). A PHEV is a hybrid electric vehicle (HEV) whose rechargeable energy source can be recharged from an external power source, making it a combination of an electric vehicle and a traditional hybrid vehicle. A PHEV is capable of operating as an electric vehicle until the battery is almost depleted, at which point the on-board internal combustion engine turns on, and generates power to meet the vehicle demands. When the vehicle is not in use, the battery can be recharged from an external energy source, once again allowing electric driving. A series of models is presented which simulate various powertrain architectures of PHEVs. To objectively evaluate the effect of powertrain architecture on fuel economy, the models were run according to the latest test procedures and all fuel economy values were utility factor weighted.

One promising solution for increasing vehicle fuel economy, while still maintaining long-range driving capability, is the plug-in hybrid electric vehicle (PHEV). A PHEV is a hybrid electric vehicle (HEV) whose rechargeable energy source can be recharged from an external power source, making it a combination of an electric vehicle and a traditional hybrid vehicle. A PHEV is capable of operating as an electric vehicle until the battery is almost depleted, at which point the on-board internal combustion engine turns on, and generates power to meet the vehicle demands. When the vehicle is not in use, the battery can be recharged from an external energy source, once again allowing electric driving. A series of models is presented which simulate various powertrain architectures of PHEVs. To objectively evaluate the effect of powertrain architecture on fuel economy, the models were run according to the latest test procedures and all fuel economy values were utility factor weighted.