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Accuracy of efficiency maps for permanent magnet synchronous machines depends on loss and torque calculations. Losses that occur in the stator or rotor core material as well as copper loss that occurs in the winding at high speeds are non-linear, and can be predicted by Finite Element Analysis (FEA) precisely. Though prediction is precise with FEA, the technique introduces substantial computational overhead. Replacing FEA with analytical estimates of the losses expedites loss modeling, but reduces precision of loss estimates, making efficiency calculations less precise as well. The prediction method proposed in this paper potentially offers accuracy similar to that attained using FEA while reducing the computational overhead typically required.

Minimizing lithium ion battery aging and maximizing overall system efficiency are key engineering design objectives for Plug-in Electric Hybrid Vehicles (PHEVs). To quantitatively optimize the aging and system efficiency, an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) based algorithm is implemented within vehicle simulation code. Battery charge and discharge cycling is modeled using equivalent circuit modeling techniques where circuit parameters are updated based on estimated aging effects. These aging effects are predicted through a so-called Single Particle Model (SPM) wherein particle interactions are neglected, and Solid Electrolyte Interface (SEI) layer aging is predicted for graphite anode. The aging model in this study is calibrated against available battery aging data for similar batteries. Steady state capacity fade map under given environmental conditions and various battery states of charge and current levels are predicted.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

Due to ever-tightening CO2 regulations on passenger vehicles, it is necessary to find novel methods to improve powertrain system efficiency. These increases in efficiency should generally be cost effective so that the customer perceives that they add value. One approach for improving system efficiency has been the use of thermal energy management. For example, changing the flow of, or reusing “waste” heat from the powertrain to improve efficiency. Due to the interactions involved with thermal management, a system level approach is useful for exploring, selecting, and developing alternative solutions. It provides a structured approach to augment the right kind of synergies between subsystems and mitigate unintended consequences. However, one challenge with using these approaches early in a program is having appropriate metrics for assessing key aspects of the system behaviors.

This paper introduces a methodology to calculate confidence bounds for a normal and Weibull distribution using Monte Carlo pivotal statistics. As an example, a ready-to-use lookup table to calculate one-sided lower confidence bounds is established and demonstrated for normal and Weibull distributions. The concept of one-sided lower tolerance limits for a normal distribution was first introduced by G. J. Lieberman in 1958 (later modified by Link in 1985 and Wei in 2012), and has been widely used in the automotive industry because of the easy-to-use lookup tables. Monte Carlo simulation methods presented here are more accurate as they eliminate assumptions and approximations inherent in existing approaches by using random experiments. This developed methodology can be used to generate confidence bounds for any parametric distribution. The ready-to-use table for the one-sided lower tolerance limits for a Weibull distribution is presented.