High temperature direct double side cooled inverter module for hybrid electric vehicle application

In this paper a novel approach to the design and fabrication of a high temperature inverter module for hybrid electrical vehicles is presented. Firstly, SiC power electronic devices are considered in place of the conventional Si devices. Use of SiC raises the maximum practical operating junction temperature to well over 200°C, giving much greater thermal headroom between the chips and the coolant. In the first fabrication, a SiC Schottky barrier diode (SBD) replaces the Si pin diode and is paired with a Si-IGBT. Secondly, double-sided cooling is employed, in which the semiconductor chips are sandwiched between two substrate tiles. The tiles provide electrical connections to the top and the bottom of the chips, thus replacing the conventional wire bonded interconnect. Each tile assembly supports two IGBTs and two SBDs in a half-bridge configuration. Both sides of the assembly are cooled directly using a high-performance liquid impingement system. Specific features of the design ensure that thermo-mechanical stresses are controlled so as to achieve long thermal cycling life. A prototype 10 kW inverter module is described incorporating three half-bridge sandwich assemblies, gate drives, dc-link capacitance and two heat-exchangers. This achieves a volumetric power density of 30W/cm3.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.