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The overall efficiency of hybrid electric vehicles largely depends on the design and application of its energy management system (EMS). Despite the load coordination when operating the system in a hybrid mode, the EMS accounts for state changes between the different driving modes. Whether a transition between pure electric driving and internal combustion engine (ICE) powered driving is beneficial depends, among others, on the respective operation point, the route ahead as well as on the energetic expense for the engine start itself. The latter results from a complex interaction of the powertrain components and has a tremendous impact on the efficiency and quality of EMSs. Optimization based methods such as dynamic programming serve as benchmark for the design process of rule based control strategies. In case no energetic expenses are assigned to a state change, the resulting EMS suffers from being sub-optimal regarding the fuel consumption.

The driving comfort is an important factor for buying decisions. For the interior noise of battery electric vehicles (BEV) high frequency tonal orders are characteristic. They can for example be caused by the gearbox or the electric drive and strongly influence the perception and rating of the interior noise by the customer. In this contribution methods for measuring, analyzing and predicting the excitation by the dynamic torque of the electric drive are presented. The dynamic torque of the electric drive up to 3.5 kHz is measured on a component test bench with the help of high frequency, high precision torque transducer. The analysis of the results for the order of interest shows a good correlation with the acoustic measurements inside the corresponding vehicle. In addition an experimental and numerical modal analysis of the rotor of the electric drive are performed.

In this paper a rule-based energy management for parallel hybrid electric vehicles (HEVs) is presented, which is based on the principles describing the optimal control behavior. Therefore we first show the general relations that can be used to describe the optimal limit of electric driving as well as the optimal torque split among the two propulsion systems. Subsequently these relations are employed to derive maps, which represent the optimal behavior depending on several input parameters. These maps are then used as inputs for the rules in the proposed energy management. This not only makes it possible to automatically calibrate the rule-based controller but also gives the optimal control in every driving situation. Given it is not fuel-efficient to turn the internal combustion engine (ICE) on or off for short intervals, it is further shown how this approach allows to adjust the established limit for electric driving by additional rules.

An accurate model to predict the formation of fogging and defogging which occurs for low windshield temperatures is helpful for designing the air-conditioning system in a car. Using a multiphase flow approach and additional user-defined functions within the commercial CFD-software STAR-CCM+, a model which is able to calculate the amount of water droplets on the windshield from condensation and which causes the fogging is set up. Different parameters like relative humidity, air temperature, mass flow rate and droplet distributions are considered. Because of the condition of the windshield's surface, the condensation occurs as tiny droplets with different sizes. The distribution of these very small droplets must be obtained to estimate numerically the heat transfer coefficient during the condensation process to predict the defogging time.

Electric cars are getting popular more and more and the expectations of the customers are very challenging. Concerning comfort, the situation is clear: customers want an electric car to be quiet and without any annoying noise from the powertrain. To develop an electric powertrain with a minimum noise level and minimized whining it is necessary to have an accurate CAE-simulation and precise criteria to assess whining noise. Based on the experience with electric powertrains in research cars the CAE-modelling was improved and a new ‘whining intensity factor’ was acquired for the development of Daimler's electric cars. The results are a very low noise level and a minimized whining noise, nearly not noticeable giving a comfortable sound to the customers of the smart electric drive and the B-Class Electric Drive.

Aerodynamic design and thermal management are some of the most important tasks when developing new concepts for the flow around tractor-trailers. Today, both experimental and numerical studies are an integral part of the aerodynamic and thermal design processes. A variety of studies have been conducted how the aerodynamic design reduces the drag coefficient for fuel efficiency as well as for the construction of radiators to provide cooling on tractor-trailers. However, only a few studies cover the combined effect of the aerodynamic and thermal design on the air temperature of the under hood flow [8, 13, 16, 17, 20]. The objective of this study is to analyze the heat transfer through forced convection for a scaled Cab-over-Engine (CoE) tractor-trailer model with under hood flow. Different design concepts are compared to provide low under hood air temperature and efficient cooling of the sub components.

Digital or virtual prototyping by means of a multibody simulation model (MBS) is a standard part of the automotive design process. A high-fidelity model is built and often correlated against test data to increase its accuracy. Once built the MBS model can then be used for high fidelity analysis in ride comfort, handling as well as durability. Next to the MBS model, current industry practice is to develop a reduced degree of freedom model for the design and validation of control or intelligent systems. The models used in the control system design are required to execute in hardware-in-the-loop (HIL) simulations where it is necessary to run real-time. The reason for the creation of the reduced degree of freedom models so far has been that the high-fidelity or off-line model does not execute fast enough to be used in an HIL simulation.

In modern vehicles the architecture of electronics is growing more and more complex because both the number of electronic functions – e.g. implemented as software modules – as well as the level of networking between electronic control units (ECUs) is steadily increasing. This complexity leads to greater propagation of failure symptoms, and diagnosing the causes of failure becomes a new challenge. Diagnostics aims at detecting failures such as defect sensors or faulty communication messages. It is subdivided into diagnosis algorithms on an ECU and algorithms running offboard, e.g. on a diagnostic tester. These algorithms have to complement each other in the best possible way. While in the past the diagnosis algorithm was developed late in the development process, nowadays there are efforts to start the development of such algorithms earlier – at least in parallel to developing a new feature itself. This would allow developers to verify the diagnosis algorithms in early design stages.