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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.

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.

Whiplash-associated disorder is one of the most common injuries from rear-impact crash scenarios. Knowing the injury mechanism is one of the keys in designing the seat to reduce the risk of injury. Due to the effects of variation, whiplash prevention is one of the most challenging safety-related topics in automotive industry. The test variation can originate from the dummy itself, seat components, materials, assembly tolerance, and as well as typical test setup variations. It is important to understand these variations and take them into account using Computer-Aided Engineering (CAE) analysis in order to identify how to reduce the risk of injury. In this paper, a robust methodology to predict occupant response from CAE simulations is developed by combining a Design of Experiment (DOE) with an Automated Process (AP). A Whiplash Variation Map (WVM) is developed to serve as a seat design aid.