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A model-based methodology is presented, which allows the estimation of the characteristic phases of diesel combustion using a semi-physical model approach combined with state and parameter estimation through extended Kalman filtering. The physical relation between the fuel injection and the characteristic diesel combustion phases, such as premixed, diffusive combustion and burn-out, are modeled separately by linear dynamic transfer functions formulated in crank angle frequency domain and transformed into state space representation. The resulting state variables are the released burning energy and its derivatives of each combustion phase. Associated crank angle constants determine the dynamics of the combustion phases and represent the rate parameters to be estimated. By incorporating further physical assumptions regarding the fuel path and air–fuel-mixing dynamics, the combustion phase parameters are estimated online for each working cycle. Cylinder pressure signals and online combustion analysis are used to determine the burn rate of the diesel engine at the test bench. Investigations have shown that the estimated rate parameters depend on the current engine operation point. They are estimated during measurements and stored in lookup tables through an online-learning method based on a fast recursive least squares estimation algorithm.

The transfer of the forces and torques between the tire and the road surface is fundamental for all longitudinal and lateral motions of a vehicle. The physical effects of the force transfer depend on local properties at the contact patch. The amount of forces is a function of the frictional properties between the tire and the road surface.

Vehicle dynamics is concerned with the movements of the vehicle on a road surface. The stationary and dynamic behavior is based on the forces on the vehicle via the tires, gravitation, and aerodynamics. These forces act on the body, the tires, and the wheels.

Based on the book parts II “Modeling of Drive Dynamics” and III “Dynamic Control of Chassis Components” control-oriented advanced driver assistance systems (ADAS) are briefly described in this chapter.

The derived mathematical models of vehicle dynamics are mostly based on theoretical physical models and contain parameters \(\boldsymbol{\theta }\) which are partially known or not known at all. Therefore, they have to be determined experimentally by measurements of input and output signals and parameter identification or parameter-estimation methods have to be applied; see Fig. 10.1.

The application of parameter-estimation methods to determine the unknown vehicle model parameters is treated for a selection of dynamic and static vehicle or vehicle component models. Herewith, the methods of Sect. 10.1 are used, either in continuous or discrete time, in non-recursive or recursive form. In general, one tries to use series on-board sensors only depending on the standard equipment of the vehicle, (Robert Bosch GmbH 2018). However, in some cases also additional sensors are applied which are used for research vehicles. Compare the discussion of available sensors and measurement systems discussed in the introduction to Chap 10.

Automotive safety is one of the high-priority issues in the design of vehicles, construction, and equipment to minimize the occurrence and consequence of accidents. The improvements in automobile and roadway design have steadily reduced injury and death rates in developed countries. This positive development was supported by measures of passive and active safety systems.

Because of the increasing complexity of vehicle control functions, a unified structure eases the understanding and development. Therefore, this chapter summarizes vehicle control structures and the model-based design workflow with different simulation tools.

The basic behavior of suspension systems, their relevance for driving comfort and safety, their components, and linear and nonlinear mathematical models for passive suspensions were treated in Chap. 8. It was shown that ride quality and driving safety are conflicting properties and that an adaptation to changing road and load conditions can be reached with active suspensions. Therefore, this chapter considers semi-active as well as active hydraulic suspensions. A further topic is the indirect tire pressure monitoring based on wheel velocity and suspension sensors.

In the frame of vehicle electrification and electronic management of the chassis and the powertrain, the structures of the electrical and electronic architecture have a significant influence on the realization of functions. Therefore, this chapter briefly considers the types of networks, the communication networks and bus systems, and the software structure of the electronic control units (ECU).

Advanced driver-assistance systems (ADAS) support the driver during driving or parking. They are pretended to alert, automate, adapt, and enhance vehicle systems to improve safety, comfort, and driving.

The determination of not or not precisely measurable driving state variables as velocity over ground, slip angle, yaw, rolling, and pitch angle can be reconstructed by state estimation methods with kinematic or dynamic vehicle models. The estimation of some parameters may also be integrated in a state estimation procedure.

Roads are usually built from elements of circles, clothoids with intermediate connections, and straight lines; see RAA (2008). Curves have curvatures which develop steadily, such that driving without lateral jerk is possible.

The roll and pitch behavior is already included in the basic dynamic equations ( 7.3.4)–( 7.3.8) of the two-track models; see also Fig. 7.17. In the following, the roll and pitch dynamic behavior is considered separately by neglecting the couplings between the motions around the roll and pitch axis. The resulting models are required to obtain simplified models and as a basis for the estimation of the various parameters.

The driver assistance systems described in Chaps. 17 (TCS, ESC, LKA) and 19 (ACC) assist and control the vehicle in special cases and the driver performs continuously the driving task. These driver assistance systems (DAS, ADAS) are dedicated to level 1 of the automatic driving degrees in Table 18.1. For vehicles with partial automation, level 2, tasks of longitudinal and lateral driving are automatic (ACC-FR, LKA, LKC) but the driver has to be attentive all the time, to monitor the correct functioning, and to perform all remaining tasks.