In this paper a hybrid algorithm combining Neural Networks and Equivalent Consumption minimization strategy (ECMS) is presented for energy management in parallel hybrid electric vehicles. This hybrid algorithm is divided into parts, in first part the selection of mode from the five possible modes i.e. motor only mode (mode 1), engine only mode (mode 2), engine + motor mode (mode 3), charging mode (mode 4) and regenerative mode (mode 5) is done by neural networks. Neural networks itself do not provide optimal result for fuel consumption, so to obtain better solution equivalent consumption minimization strategy is employed in MODE 3 in second part of the hybrid algorithm. European drive cycle UN/ECE Extra-Urban driving cycle (part 2) has been used for testing the hybrid algorithm. The results obtained from hybrid algorithm have been compared with results obtained from control algorithm like neural networks only, fuzzy only and rule based. This hybrid algorithm shows better fuel economy as compared to the results obtained from control algorithms like neural, fuzzy and rule based. This hybrid algorithm can be used for both online and offline scenario.
Parallel electric hybrid vehicles, equivalent consumption minimization strategy, neural networks, hybrid algorithm, fuel consumption.
Vikas Gupta, Energy Management in Parallel Hybrid Electric Vehicles Combining Neural Networks and Equivalent Consumption Minimization Strategy, HCTL Open International Journal of Technology Innovations and Research, Volume 10, July 2014, ISSN: 2321-1814, ISBN: 978-1-62951-619-6.