Identification and Energy Management for Next Generation Vehicles

Neurodye公司与工业合作伙伴amerigon，Inc.合作，计划向电动和混合动力汽车发起新的攻势。Neurodye之前的工作已经开发出识别和智能控制的方法，使用人工神经网络(ANN)，这种网络非常适合与车辆系统一起工作。这项工作的一部分(由美国国家科学基金会资助的ECS 9216530“通过南卡罗来纳大学使用智能控制减少污染和提高燃油经济性”)导致了一种使用前馈神经网络识别一类非线性动态系统的合理方案。要解决的主要问题是将Neurodye识别方法应用于电能管理系统(EMS)；EMS的目标是通过预算功率需求来优化行驶里程，同时考虑安全性、可驾性和乘客舒适性。这项工作的扩展是为混合动力汽车(燃油发动机加电池)提供基于人工神经网络的控制，同时考虑到燃油经济性、尾气排放和效率。

In cooperation with industrial partner Amerigon, Inc., NeuroDyne, Inc., plans a new thrust toward electric and hybrid vehicles. Previous work by NeuroDyne has developed methods of identification and intelligent control, using artificial neural networks (ANN) which are well adapted to work with vehicle systems. Part of this effort (supported by NSF Grant ECS 9216530 `Reducing Pollution and Increasing Fuel Economy Using Intelligent Control` through the University of South Carolina) resulted in a rational scheme for identifying, with feedforward neural networks, a class of nonlinear dynamic systems. The major problem to be addressed is to apply the NeuroDyne identification methods to an electric energy management system (EMS); the goal of the EMS is to optimize driving range by budgeting power demands, while taking into account safety, driveability and passenger comfort. An extension of this work is to provide ANN-based controls for hybrid automobiles (fuel-burning engine plus batteries) taking into account fuel economy, exhaust emissions, and efficiency.