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Tighter emission limits are discussed and established around the world to improve quality of the air we breathe. In order to control global warming, authorities ask for lower CO2 emissions from combustion engines. Lots of efforts are done to reduce engine out emissions and/or reduce remaining by suitable after treatment systems. Watlow, among others, a manufacturer of high accurate, active temperature sensor ExactSense™, wanted to understand if temperature sensor accuracy can have an influence on fuel consumption (FC). For this purpose a numerical approach was chosen where several non-road driving cycles (NRTCs) were simulated with the data base of a typical Stage IV heavy duty diesel engine. The engine is equipped with an exhaust gas after treatment system consisting of a DOC, CDPF and an SCR. In this work scope, the investigations shall be restricted to the FC benefits obtained in the active and passive DPF regeneration.

Exhaust emission reduction and improvements in energy consumption will continuously determine future developments of on-road and off-road engines. Fuel flexibility by substituting Diesel with Natural Gas is becoming increasingly important. To meet these future requirements engines will get more complex. Additional and more advanced accessory systems for waste heat recovery (WHR), gaseous fuel supply, exhaust after-treatment and controls will be added to the base engine. This additional complexity will increase package size, weight and cost of the complete powertrain. Another critical element in future engine development is the optimization of the base engine. Fundamental questions are how much the base engine can contribute to meet the future exhaust emission standards, including CO2 and how much of the incremental size, weight and cost of the additional accessories can be compensated by optimizing the base engine.

With the increasing popularity of seamless gear changing and smooth driving experience along with the need for high fuel efficiency, transmission system development has rapidly increased in complexity. So too has transmission control software while quality requirements are high and time-to-market is short. As a result, extensive testing and documentation along with quick and efficient development methods are required. FEV responds to these challenges by developing and integrating a transmission software product line with an automated verification and validation process according to the concept of Continuous Integration (CI). Hence, the following paper outlines a software architecture called “PERSIST” where complexity is reduced by a modular architecture approach. Additionally, modularity enables testability and tracking of quality defects to their root cause.

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.

PlugIn Hybrid Electric Vehicles (PHEV) offer the opportunity to experience electric driving without the risk of vehicle break-down due to a low battery charge state. Thus, PHEV's represent an attractive means of meeting future CO2-legislation. PHEV batteries must fulfill a divergent list of requirements: on the one hand, the battery must supply sufficient energy to ensure it can be driven an appropriate distance in EV-mode. On the other hand, even with a low state-of-charge (SOC), the battery must supply sufficient power to assist the engine in vehicle acceleration or to recuperate on deceleration. This leads to a compromise in terms of cell selection. Fundamentally, high energy cells cannot provide high charge and discharge rates and high power cells cannot provide sufficient energy.

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.

One of the challenges faced when using Li-ion batteries in electric vehicles is to keep the cell temperatures below a given threshold. Mathematical modeling would indeed be an efficient tool to test virtually this requirement and accelerate the battery product lifecycle. Moreover, temperature predicting models could potentially be used on-board to decrease the limitations associated with sensor based temperature feedbacks. Accordingly, we present a complete modeling procedure which was used to calculate the cell temperatures during a given electric vehicle trip. The procedure includes a simple vehicle dynamics model, an equivalent circuit battery model, and a 3D finite element thermal model. Model parameters were identified from measurements taken during constant current and pulse current discharge tests. The cell temperatures corresponding to an actual electric vehicle trip were calculated and compared with measured values.

The performance of high voltage batteries is the key factor for further success of electric vehicles. The primary areas for battery development include high voltage (HV) and functional safety, maximum power and usable energy, battery life, packaging and weight reduction. This paper explains the development of the HV battery and the battery management system for the FEV Liona fleet, a retrofit of a pure electric powertrain into a FIAT 500. The multi-disciplinary process used to develop this program includes electrical, mechanical and functional aspects. The layout of the electrical system includes cell selection, layout of modules and the interconnection of twelve modules to a battery pack. The mechanical design of mounting the battery under the floor addresses the housing issues regarding robustness and sealing, the packaging into the vehicle as well as the positioning of the HV components inside the battery.

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.

With the increasing application of the lithium ion battery technology in automotive industry, development processes and validation methods for the battery management system (BMS) have drawn more and more attentions. One fundamental function of the BMS is to continuously estimate the battery’s state-of-charge (SOC) and state-of-health (SOH) to guarantee a safe and efficient operation of the battery system. For SOC as well as SOH estimations of a BMS, there are certain non-ideal situations in a real vehicle environment such as measurement inaccuracies, variation of cell characteristics over time, etc. which will influence the outcome of battery state estimation in a negative way. Quantifying such influence factors demands extensive measurements. Therefore, we have developed a model-in-the-loop (MIL) environment which is able to simulate the operating conditions that a BMS will encounter in a vehicle.

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.

Upcoming motor vehicle emission regulations, such as California's LEVIII, continue to tighten emission limitations in diesel vehicles. These increasingly challenging emission requirements will be met by improving the combustion process (reducing engine-out emissions), as well as improving the exhaust gas aftertreatment efficiency. Furthermore, intricate On-Board Diagnostics (OBD) systems are required to properly diagnose and meet OBD regulation requirements for complex aftertreatment systems. Under these conditions, current monitoring strategies are unable to guarantee reliable detection of partially failed systems. Additionally, new OBD regulations require aftertreatment systems to be diagnosed as a whole. This paper covers potential OBD strategies for LEVIII aftertreatment concepts with regard to regulation compliance and robustness, while striving to use existing sensor concepts.

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.