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Within a public founded project test cell investigations were undertaken to identify parameters which predominantly influence the development of critical deposits in injection nozzles. A medium-duty diesel engine was operated in two different coking cycles with a zinc-free lubricant. One of the cycles is dominated by rated power, while the second includes a wide area of the operation range. During the experiments the temperatures at the nozzle tip, the geometries of the nozzle orifice and fuel properties were varied. For a detailed analysis of the deposits methods of electron microscopy were deployed. In the course of the project optical access to all areas in the nozzle was achieved. The experiments were evaluated by means of the monitoring of power output and fuel flow at rated power. The usage of a SEM (scanning electron microscope) and a TEM (transmission electron microscope) revealed images of the deposits with a magnification of up to 160 000.

The future worldwide emission regulations will request a drastic decrease of Diesel engine tailpipe emissions. Depending on the planned application and the real official regulations, a further strong decrease of engine out emissions is necessary, even though the utilized exhaust after-treatment systems are very powerful. To reduce NOx emissions internally, the external exhaust gas recirculation (EGR) is known as the most effective way. Due to the continuously increasing requirements regarding specific power, dynamic behavior and low emissions, future air path systems have to fulfill higher requirements and, consequently, become more and more complex, e.g. arrangements with a 2-stage turbo charging or 2-stage EGR system with different stages of cooling performance.

The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

Today, SULEV legislation represents the most stringent emission standard for vehicles with combustion engines, and it will be introduced starting by Model Year 2003. In order to meet such standards, even higher effort is required for the development of the exhaust gas emission concept of SI engines. Beyond a facelift of the combustion system, exhaust gas aftertreatment, and the engine management system, new approaches are striven for. The principle keys are well known: low HC feed gas, high thermal load for quick light-off, exhaust system with low heat capacity and highly effective exhaust gas aftertreatment.

A fuel cell based Auxiliary Power Unit (APU) represents a rather complex technical system consisting of different subsystems, components and low-level controllers. Particularly in the case of gasoline-fueled systems, a sophisticated supervisory control is needed to manage the sequential control and to achieve fault tolerant and fail-safe operation. In this paper, a state machine-based APU control concept is presented, offering a transparent and modular structure. In addition to a superior control system (top level supervisor) that manages the overall strategies and the interaction of all subsystems, each subsystem is equipped with its own subsystem control (second level supervisor). This controller is responsible for all subsystem specific issues. The APU control concept was implemented using Matlab®/Simulink® and applied on a rapid prototyping controller unit.

The new Thermo-Elasto-Hydro-Dynamic (TEHD) code developed by FEV, is designed to improve the predictability of journal bearing designs and thereby increase the reliability of safety factors in the development of highly loaded internal combustion engines. Advanced analysis tools are evaluated by their performance as well as by their ease of use. High performance means on the one hand: taking into account all the important characteristics, like bearing elasticity or cavitation effects, to mention only some major parameters for modern journal bearing analysis. On the other hand: an economic run-time behavior must be a key feature concerning usability of the TEHD-demands for daily development praxis. Ease of use means also, that the TEHD model can easily be used as a plug-in routine of an already existing software package that is well known to the development departments.

Future HD Diesel engine technology is facing a combination of both extremely low exhaust emission standards (US 2002/2004, EURO IV and later US 2007, EURO V) and new engine test procedures such as the European Transient Cycle (ETC) in Europe and the Not-to-Exceed Area (NTE) in the US). Customers furthermore require increased engine performance, improved efficiency, and long-term durability. In order to achieve all targets simultaneously, future HD Diesel engines must have improved fuel injection and combustion systems and utilize suitable technologies such as exhaust gas recirculation (EGR), variable geometry turbine turbocharger systems (VGT) and exhaust gas after-treatment systems. Future systems require precision controlled EGR in combination with a VGT-turbocharger during transient operation. This will require new strategies and calibration for the Electronic Engine Control Unit (ECU).

This paper focuses on start-up technology for fuel processing systems with special emphasis on gasoline fueled burners. Initially two different fuel processing systems, an autothermal reformer with preferential oxidation and a steam reformer with membrane, are introduced and their possible starting strategies are discussed. Energy consumption for preheating up to light-off temperature and the start-up time is estimated. Subsequently electrical preheating is compared with start-up burners and the different types of heat generation are rated with respect to the requirements on start-up systems. Preheating power for fuel cell propulsion systems necessarily reaches up to the magnitude of the electrical fuel cell power output. A gasoline fueled burner with thermal combustion has been build-up, which covers the required preheating power.

Cyclic fluctuations of the in-cylinder processes in a Direct Injection Spark Ignition (DISI) engine may strongly affect the engine operation causing misfires or variations in the indicated mean effective pressure (imep). Particularly misfires prevent compliance with current or future exhaust emission legislations. Nevertheless, the origin of cyclic fluctuations is not well understood since fluctuations of in-cylinder air flow, fuel injection and wall interaction have to be considered. This paper focusses on a detailed experimental analysis of the origin of cyclic fluctuations in a DISI engine with an air guided combustion process by means of advanced Laser Induced Exciplex Fluorescence (LIEF) measurements. It reveals that cycle-to-cycle variations primarily originate from the air/fuel ratio at the spark plug.

A main focus of development in modern SI engine technology is variable valve timing, which implies a high potential of improvement regarding fuel consumption and emissions. Variable opening, period and lift of inlet and outlet valves enable numerous possibilities to alter gas exchange and combustion. However, this additional variability generates special demands on the calibration process of specific engine control devices, particularly under cold start and warm-up conditions. This paper presents procedures, based on Hardware-in-the-Loop (HiL) simulation, to support the classical calibration task efficiently. An existing approach is extended, such that a virtual combustion engine is available including additional valve timing variability. Engine models based purely on physical first principles are often not capable of real time execution. However, the definition of initial parameters for the ECU requires a model with both real time capability and sufficient accuracy.

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.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

As compared with other batteries, lithium-ion batteries are featured by high power density, long service life, high energy density, environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for electric and hybrid electric vehicles (EVs and HEVs) have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, cost and uniformity, imposes limitations on the wide application of lithium-ion batteries in the EVs and HEVs. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and through the use of management of battery management system. Battery state of health (SOH) monitoring has become a crucial challenge in EVs and HEVs research, as SOH significantly affects the overall vehicle performance and life cycle. This paper presents both cycling and calendar aging at high and low temperatures.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

A new method is presented for developing proving ground durability tests for electrified vehicle (xEV) drivetrains. For xEVs, the durability tests must be able to capture the added driveline torques due to regenerative braking, customer battery charge/recharge usage, and battery power degradation over time. In terms of xEV duty cycle test schedule development, using actual vehicle data from specific proving ground test events might prove to be costly and time consuming. Furthermore, newer electrified prototype vehicles might not be available for trackside testing while still undergoing the design stage. The new method presented here aims to reduce the complexities of xEV proving ground duty cycle development with the use of a high-fidelity vehicle dynamics simulation model for electrified powertrains.

As centralization of automotive E/E (Electrical and/or Electronic) architectures becomes reality for future vehicles, it is crucial that existing assets be reused in the most efficient and effective manner. We report on our experience developing a new centralized E/E architecture for a propulsion domain, and migrating the corresponding propulsion elements of an existing decentralized, CAN-based architecture to a prototype of the centralized propulsion domain. Our migration adopts automotive Ethernet and supporting standards as a next-generation communications backbone technology; a next-generation computation platform from automotive supplier NXP; and a new automotive virtualization solution from OpenSynergy. We discuss aspects of legacy software reuse and adaptation; modification of vehicle HiL simulation models used in testing; existing vendor tool support; and implications arising from functional safety and the ISO 26262 standard.

Modern automotive industry field is recently moving to more electrification level, so the presence of Battery Electric Vehicles (BEVs) is constantly increasing, along with charging technology evolution. Typically, BEVs do not use a significant portion of their battery’s capacity in day-to-day travel, which means their most valuable asset, the battery, sits idle during most of its life. Vehicle to Load (V2L) feature enables the transfer of energy from vehicle to the external loads (like utility tools, dryer, camping equipment or any other electrical appliance) which is connected to the power socket present in the Power Panel to perform AC Discharging. V2L technology lets consumers get more energy from a vehicle, even when it is turned off, improving consumer appeal. Bottomline, consumers can use this on-board Power Panel like a normal portable generator.

Battery terminal voltage modelling is crucial for various applications, including electric vehicles, renewable energy systems, and portable electronics. Terminal voltage models are used to determine how a battery will respond under load and can be used to calculate run-time, power capability, and heat generation and as a component of state estimation approaches, such as for state of charge. Previous studies have shown better voltage modelling accuracy for long short-term memory (LSTM) recurrent neural networks than other traditional methods (e.g., equivalent circuit and electrochemical models). This study presents two new approaches – sequence training and data shuffling – to improve LSTM battery voltage models further, making them an even better candidate for the high-accuracy modelling of lithium-ion batteries. Because the LSTM memory captures information from past time steps, it must typically be trained using one series of continuous data.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.