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A wavelet-based power management system is proposed in this paper with a combination of the battery and ultracapacitor (UC) hybrid energy storage system (HESS). The wavelet filter serves as a frequency-based filter for distributing the power between the battery and UC. In order to determine the optimal level of wavelet decomposition as well as the optimal activation power of the wavelet controller, an optimization procedure is established. The proposed frequency-based power management system moderates the usage of battery current, consequently improving its lifetime. Compared with the conventional threshold-based power management systems, the proposed system has the advantage of enhanced battery and UC power management. A LiFePO 4 battery is considered and its life loss is modeled. As a case study, an electric motorcycle is evaluated in the federal test procedure (FTP) driving cycle. Compared with a conventional energy storage system (ESS) and a state of available power (SoP) management systems, the results show an improvement for the battery lifetime by 115% and 3%, respectively. The number of battery replacements is increased, and the energy recovery is improved. The 10-year overall costs of the proposed HESS strategy using wavelet are 1500 dollars lower, compared with the ESS.

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In this paper, a new idea for designing the control strategy for the energy management of hybrid powertrains based on the driving cycle type is presented. Here, every instance of an unknown driving cycle is considered to be similar to the reference driving cycles using similarity weights. To determine the control output in the unknown driving cycle, the weights are applied to a linear combination of the optimal control decisions generated in each of the reference driving cycles. The weights which are between zero and one are determined using a fuzzy driving cycle identification agent based on the comparison of preselected driving features. The simulation studies in seven different driving cycles show that, while all driving patterns in every driving cycle are considered for the generation of energy management by online implementation of the proposed intelligent control strategy, some driving patterns would be eliminated by using a non-fuzzy identification agent. This leads to a significant reduction in the fuel consumption of the hybrid powertrain utilized with the fuzzy identification agent in some driving cycles in comparison with those without the use of non-fuzzy driving cycle identification. In addition, in some driving cycles, the intelligent control strategy has a performance close to that for the offline optimized control strategy.