International audienceThis paper focuses on the hot forging of multi-material cladded work pieces using upsetting tests. Thecase study corresponds to gas metal arc welding cladding of a SS316L on a mild steel (C15). Experimentaltests and simulations using a slab model and the finite element method were performed using differenttemperatures and die/billet tribological conditions. As a result, a crack mode, specific to clad billets, wasobserved experimentally and can be predicted by the FE method using a Latham and Cockcroft criterion.The material distribution was well simulated by the FE method; in particular, the effects of the frictionat die/work piece interface on the crack occurrence, the material distribution and, to a lesser extent,the forging load are well predicted. However, the latter was underestimated, highlighting the fact thatthe effect of the dilution associated with the cladding process on the material behavior of the clad layercannot be neglected
The lithium-ion batteries of an electric vehicle belong to a high-voltage direct-current system. The high-voltage insulation performance of electric vehicles is very important for their safe operation. To solve the problems of slow response and the poor estimation accuracy of the insulation resistance under complex vehicle working conditions, a real-time insulation resistance detection method based on the variable forgetting factor least squares algorithm is proposed in this paper. Based on the low-frequency signal injection method and considering the influence of the Y capacitor, the corresponding circuit model and the mathematical model of the reflected wave voltage are established, and the mathematical model is linearized by a first-order Taylor expansion. By analyzing the influence of the forgetting factor on model parameter identification and setting appropriate shutdown criteria, the least squares algorithm with a variable forgetting factor is designed to quickly and accurately estimate the insulation resistance and Y capacitance. The experimental test results show that the proposed method can quickly track the changes in the insulation resistance and Y capacitance under the condition of noise interference and that the root mean square error of the estimation resistor is within 0.012.
INDEX TERMS electric vehicle (EV), embedded micro-control unit, insulation detection, lithium-ion batteries, variable forgetting factor recursive least squares (VFFRLS)
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