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In the near future engine emitted carbon dioxides (CO2) are going to be limited for all vehicle categories with respect to the Green House Gases (GHG) norms. To tackle this challenge, new concepts need to be developed. For this reason waste heat recovery (WHR) is a promising research field. For commercial vehicles the first phase of CO2 emission legislation will be introduced in the USA in 2014 and will be further tightened towards 2030. Besides the US, CO2 emission legislation for commercial engines will also be introduced in Europe in the near future. The demanded CO2 reduction calls for a better fuel economy which is also of interest for the end user, specifically for the owners of heavy duty diesel vehicles with high mileages. To meet these future legislation objectives, a waste heat recovery system is a beneficial solution of recovering wasted energies from different heat sources in the engine.

The emission legislations are becoming increasingly strict all over the world and India too has taken a big leap in this direction by signaling the migration from Bharat Stage 4 (BS 4) to BS 6 in the year 2020. This decision by the Indian government has provided the Indian automotive industry a new challenge to find the most optimal solution for this migration, with the existing BS 4 engines available in their portfolio. Indian market for the LCV segment is highly competitive and cost sensitive where the overall vehicle operation cost (vehicle cost + fluid consumption cost) is the most critical factor. The engine and after-treatment technology for BS 6 emission levels should consider the factors of minimizing the additional hardware cost as well as improving the fuel efficiency. Often both of which are inversely proportional. The presented study involves the optimization of after treatment component size, layout and various systems for NOx and PM reduction.

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.

The range of tasks in automotive electrical system development has clearly grown and now includes goals such as achieving efficiency requirements and complying with continuously reducing CO2 limits. Improvements in the vehicle electrical system, hereinafter referred to as the power net, are mandatory to face the challenges of increasing electrical energy consumption, new comfort and assistance functions, and further electrification. Novel power net topologies with dual batteries and dual voltages promise a significant increase in efficiency with moderate technological and financial effort. Depending on the vehicle segment, either an extension of established 12 V micro-hybrid technologies or 48 V mild hybridization is possible. Both technologies have the potential to reduce fuel consumption by implementing advanced stop/start and sailing functionalities.

Dynamic skip fire (DSF) has shown significant fuel economy improvement potential via reduction of pumping losses that generally affect throttled spark-ignition (SI) engines. In DSF operation, individual cylinders are fired on-demand near peak efficiency to satisfy driver torque demand. For vehicles with a downsized-boosted 4-cylinder engine, DSF can reduce fuel consumption by 8% in the WLTC (Class 3) drive cycle. The relatively low cost of cylinder deactivation hardware further improves the production value of DSF. Lean burn strategies in gasoline engines have also demonstrated significant fuel efficiency gains resulting from reduced pumping losses and improved thermodynamic characteristics, such as higher specific heat ratio and lower heat losses. Fuel-air mixture stratification is generally required to achieve stable combustion at low loads.

The interest in electric propulsion of vehicles has increased in recent years and is being discussed extensively by experts as well as the public. Up to now the driving range and the utilization of pure electric vehicles are still limited in comparison to conventional vehicles due to the limited capacity and the long charging times of today's batteries. This is a challenge to customer acceptance of a pure electric vehicle, even for a city car application. A Range Extender concept could achieve the desired customer acceptance, but should not impact the “electric driving” experience, and should not cause further significant increases in the manufacturing and purchasing cost. The V2 engine concept presented in this paper is particularly suited to a low cost, modular vehicle concept. Advantages regarding packaging can be realized with the use of two generators in combination with the V2 engine.

Despite the trend in increased prosperity, the Indian automotive market, which is traditionally dominated by highly cost-oriented producion, is very sensitive to the price of fuels and vehicles. Due to these very specific market demands, the U-LCV (ultra-light commercial vehicle) segment with single cylinder natural aspirated Diesel engines (typical sub 650 cc displacement) is gaining immense popularity in the recent years. By moving to 2016, with the announcement of leapfrogging directly to Bharat Stage VI (BS VI) emission legislation in India, and in addition to the mandatory application of Diesel particle filters (DPF), there will be a need to implement effective NOx aftertreament systems. Due to the very low power-to-weight ratio of these particular applications, the engine operation takes place under full load conditions in a significant portion of the test cycle.

Advanced powertrains for modern vehicles require the optimization of conventional combustion engines in combination with tailored electrification and vehicle connectivity strategies. The resulting systems and their control devices feature many degrees of freedom with a large number of available adjustment parameters. This obviously presents major challenges to the development of the corresponding powertrain control logics. Hence, the identification of an optimal system calibration is a non-trivial task. To address this situation, physics-based control approaches are evolving and successively replacing conventional map-based control strategies in order to handle more complex powertrain topologies. Physics-based control approaches enable a significant reduction in calibration effort, and also improve the control robustness.

Electrification is a key enabler to reduce emissions levels and noise in commercial vehicles. With electrification, Batteries are being used in commercial hybrid vehicles like city buses and trucks for kinetic energy recovery, boosting and electric driving. A battery management system monitors and controls multiple components of a battery system like cells, relays, sensors, actuators and high voltage loads to optimize the performance of a battery system. This paper deals with the development of modular control architecture for battery management systems in commercial vehicles. The key technical challenges for software development in commercial vehicles are growing complexity, rising number of functional requirements, safety, variant diversity, software quality requirements and reduced development costs. Software architecture is critical to handle some of these challenges early in the development process.

The decision to leapfrog from the Bharat Stage (BS) IV emission standards directly to the BS VI standards not only effects passenger and commercial vehicles but also India’s by far largest vehicle class, with regards to sales and production, the two-wheelers. The BS VI norm will not only tighten the emission standards, but it will also increase the required emission mileage level and upgrade the On-Board Diagnostic (OBD) requirements, also by introducing In-Use Monitor Performance Ratio (IUMPR) standards. While OBD was already introduced for passenger and commercial vehicles with BS IV in 2010, OBD will be then newly introduced for two-wheelers. The OBD system monitors the vehicle’s in-use emission performance, informs the driver via the malfunction indication light (MIL) on the dashboard in case of an emission relevant failure, standardises the diagnostic code handling and regulates a standardised access to the electronic control units (ECUs) for maintenance and inspection purposes.

Within a project of the Research Association for Combustion Engines e.V., different measures for rising the temperature of exhaust gas aftertreatment components of both a passenger car and an industrial/commercial vehicle engine were investigated on a test bench as well as in simulation. With the passenger car diesel engine and different catalyst configurations, the potential of internal and external heating measures was evaluated. The configuration consisting of a NOx storage catalyst (NSC) and a diesel particulate filter (DPF) illustrates the potential of an electrically heated NSC. The exhaust aftertreatment system consisting of a diesel oxidation catalyst (DOC) and a DPF shows in simulation how variable valve timing in combination with electric heated DOC can be used to increase the exhaust gas temperature and thus fulfill the EU6 emission limits.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.