Abstract
This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV’s efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach.
The work described in this paper is supported by the Innovation Technology Fund of the Hong Kong Special Administrative Region (GHP/011/05).
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© 2007 Springer Berlin Heidelberg
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Wang, Z., Xu, G., Li, W., Xu, Y. (2007). Driving Load Forecasting Using Cascade Neural Networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_121
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DOI: https://doi.org/10.1007/978-3-540-72395-0_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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