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The perceived quality of automotive closures (flushness and margin) is strongly affected by flanging and hemming of the outer panels and assembly respectively. To improve the quality of closures, the traditional hardware approach needs significant amount of time and costly die re-cuts and trials with prototype panels. Thus, such approach may delay the vehicle program and increase the overall investment cost. The proposed CAE methodology provides upfront design guidance to dies and panels, reduces time and increases cost savings associated with flanging and hemming while improving overall quality of the closures. In this proposed approach, as a first step, analytical formulae and design of experiments (DOE) are followed to estimate magnitude of design parameters of panels and dies as the upfront design guidance.

This paper proposes a novel allocation-based control method for four-wheel independently actuated electric vehicles. In the proposed method, both actuator dynamics and input/output constraints are fully taken into consideration in the control design. First, the actuators are modeled as first-order dynamic systems with delay. Then, the control allocation is formulated as an optimization problem, with the primary objective of minimizing errors between the actual and desired control outputs. Other objectives include minimizing the power consumption and the slew rate of the actuator outputs. As a result, this leads to frequency-dependent allocation that reflects the bandwidth of each actuator. To solve the optimization problem, an efficient numerical algorithm is employed. Finally the proposed control allocation method is implemented to control a four-wheel independently actuated electric vehicle.

This paper proposed a novel fault-tolerant control method based on control allocation via dynamic constrained optimization for electric vehicles with XBW systems. The total vehicle control command is first derived based on interpretation on driver's intention as a set of desired vehicle body forces, which is further dynamically distributed to the control command of each actuator among vehicle four corners. A dynamic constrained optimization method is proposed with the cost function set to be a linear combination of multiple control objectives, such that the control allocation problem is transformed into a linear programming formulation. An analytical yet explicit solution is then derived, which not only provides a systematic approach in handling the actuation faults, but also is efficient and real-time feasible for in-vehicle implementation. The simulation results show that the proposed method is valid and effective in maintaining vehicle operation as expected even with faults.