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Springback compensation studies on a few selected auto panels from the hot selling Chrysler 300C are presented with details. LS-DYNA® is used to predict the springback behavior and to perform the iterative compensation optimization. Details of simulation parameters using LS-DYNA® to improve the prediction accuracy are discussed. An iterative compensation algorithm is also discussed with details. Four compensation examples with simulation predictions and actual panel measurement results are included to demonstrate the effectiveness of LS-DYNA® predictions. An aluminum hood inner and a high strength steel roof bow are compensated, constructed and machined based on simulation predictions. The measurements on actual tryout panels are then compared with simulation predictions and good correlations were achieved. Iterative compensation studies are also done on the aluminum hood inner and the aluminum deck lid inner to demonstrate the effectiveness of LS-DYNA® compensation algorithm.

Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance (R0) are essential for efficient battery monitoring, which is an important part of the battery management system. SOC provides information regarding the instantaneous status of the battery system, while Qmax is a key indicator of the long-term State of Health (SOH) of the cell, which represents the abilities of a battery to store energy and retain charge over extended periods. In addition, the internal resistance is also required to predict the peak available power. Traditional methods use complex models and look-up tables that have high computation requirements and are thus unsuitable for online applications. In this paper, we propose a simple method for simultaneous SOC, Qmax and internal resistance estimation based on a second-order equivalent circuit model (ECM).

In all-electric aircraft, electromechanical actuators (EMAs) will be used to replace hydraulic actuators. Due to the highly transient mission profiles of the aircraft operation, thermal management of EMAs is a significant issue. In this paper, we study the heat problem of the control and drive units of EMAs, and build a model to calculate and simulate the power loss and heat generation in the driver board. The driver unit consists of a power inverter, a capacitor, a power dissipating resistor and a control circuit. The power loss of each component is studied. The heat loss in the power inverter comes mainly from the power switches: IGBTs. The on-state loss is proportional to the current of the motor, and the switching loss is determined by the switching frequency as well as current.