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Computational finite element (FE) modeling of real world motor vehicle crashes (MVCs) is valuable for analyzing crash-induced injury patterns and mechanisms. Due to unavailability of detailed modern FE vehicle models, a simplified vehicle model (SVM) based on laser scans of fourteen modern vehicle interiors was used. A crash reconstruction algorithm was developed to semi-automatically tune the properties of the SVM to a particular vehicle make and model, and subsequently reconstruct a real world MVC using the tuned SVM. The required algorithm inputs are anthropomorphic test device position data, deceleration crash pulses from a specific New Car Assessment Program (NCAP) crash test, and vehicle interior property ranges. A series of automated geometric transformations and five LSDyna positioning simulations were performed to match the FE Hybrid III’s (HIII) position within the SVM to reported data. Once positioned, a baseline simulation using the crash test pulse was created.

This paper outlines a real-time hierarchical control allocation algorithm for multi-axle land vehicles with independent hub motor wheel drives. At the top level, the driver’s input such as pedal position or steering wheel position are interpreted into desired global state responses based on a reference model. Then, a locally linearized rigid body model is used to design a linear quadratic regulator that generates the desired global control efforts, i.e., the total tire forces and moments required track the desired state responses. At the lower level, an optimal control allocation algorithm coordinates the motor torques in such a manner that the forces generated at tire-road contacts produce the desired global control efforts under some physical constraints of the actuation and the tire/wheel dynamics. The performance of the proposed control system design is verified via simulation analysis of a 3-axle heavy vehicle with independent hub-motor drives.

Heavy-duty diesel engines are used in a wide range of applications. For varying operating environments, the engine and aftertreatment system must comply with the real-world emission legislation limits. Simultaneously, minimal fuel consumption and good drivability are crucial for economic competitiveness and usability. Meeting these requirements takes substantial development and calibration effort, and complying with regulations results in a trade-off between emissions and fuel consumption. TNO's Integrated Emission Management (IEM) strategy finds online, the cost-optimal point in this trade-off and is able to deal with variations in operating conditions, while complying with legislation limits. Based on the actual state of the engine and aftertreatment system, an optimal engine operating point is computed using a model-based optimal-control algorithm.

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc.

This paper presents payload estimation based on experimental friction coefficients identification. To estimate exact payload mass, dynamic mathematical model such as actuator dynamics and front linkage dynamics is derived by using Newton-Euler method. From the dynamic equation, nonlinear terms are analyzed and transformed. And a friction model is derived from the experiments with various conditions which have three states; boom joint angle, head and rod chamber pressures. It can identify friction coefficients and compensate friction forces. In addition, the accuracy of payload estimation system is verified through the field test.