Identification and Energy Management for Next Generation Vehicles

下一代车辆的识别和能源管理

• 批准号: 9461400
• 负责人: Donald Sofge
• 金额: $ 6.5万
• 依托单位: NeuroDyne Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-02-01 至 1995-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9461400&HistoricalAwards=false
• 关键词: Identification; Energy; Management; Next; Generation

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.

NeuroDyne, Inc. 与工业合作伙伴 Amerigon, Inc. 合作，计划向电动和混合动力汽车领域注入新的动力。 NeuroDyne 之前的工作已经开发了识别和智能控制方法，使用非常适合与车辆系统配合使用的人工神经网络 (ANN)。这项工作的一部分（由南卡罗来纳大学 NSF Grant ECS 9216530“利用智能控制减少污染和提高燃油经济性”的支持）产生了一个合理的方案，用于通过前馈神经网络识别一类非线性动态系统。需要解决的主要问题是将NeuroDyne识别方法应用于电能管理系统（EMS）； EMS 的目标是通过预算电力需求来优化行驶里程，同时考虑安全性、驾驶性能和乘客舒适度。这项工作的延伸是为混合动力汽车（燃油发动机加电池）提供基于人工神经网络的控制，同时考虑燃油经济性、废气排放和效率。