Search Results

The lightweight technology takes an important role in electric vehicle(EV) energy conservation domain, as lighter vehicle means less energy consumed under same condition. In this paper, the typical energy requirement in an NEDC cycle is investigated, and the relationship between lightweight rate and energy consumption reduction effectiveness is given. The benefit of lightweight to EV come from the less battery cost because of less energy requirement. For EVs, with less battery cost, a certain lightweight rate can be obtained with less total cost. On the other hand, if lightweight rate is very high, the battery cost won't be able to cover the lightweight cost. Besides, the relationship between driving range and battery capacity is discussed in this paper. It is found that there is a limitation of EV driving range, which is determined by the battery energy density.

Hybrid electric vehicle (HEV) is regarded as an important technology in solving the energy and environment crisis. In this paper, the HEV technology applied in passenger cars by major automotive OEMs such as Toyota, Honda, GM, Ford, Volkswagen, BMW are investigated. The configuration diagrams for each OEM are presented. Based on the architecture analysis, a classification is done according to similar structures and performances. Furthermore, a cost estimation methodology for HEV is presented based on the preliminary tear-down research done by Environment Protection Agency (EPA). Meanwhile, the logarithmic relationship between fuel consumption (FC) reduction and degree of hybridization (DOH) is discovered by investigating 30 different hybrid cars. Combining the cost estimation and relation between FC&DOH, the hybridization cost for cars to meet the FC regulations can be calculated.

Artificial intelligence systems are highly accepted as a technology to offer an alternative way to tackle complex and non-linear problems. They can learn from data, and they are able to handle noisy and incomplete data. Once trained, they can perform prediction and generalization at high speed. The aim of the present study is to propose a novel approach utilizing the adaptive neuro-fuzzy inference system (ANFIS) and the fuzzy clustering method for automotive ride performance estimation. This study investigated the relationship between the automotive ride performance and relative parameters including speed, spring stiffness, damper coefficients, ratios of sprung and unsprung mass. A Takagi-Sugeno fuzzy inference system associated with artificial neuro network was employed. The C-mean fuzzy clustering method was used for grouping the data and identifying membership functions.