Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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This paper is an attempt to ascertain the role of the optical coherence tomography by measuring the retinal nerve fiber layer thickness and ganglion cell complex area to predict postoperative visual outcome after chiasmal decompression. 16 eyes scheduled for chiasmal decompression surgery were assessed before and 3 months after surgery with standard automated perimetry and OCT (optical coherence tomography). Preoperative RNFL (retinal nerve fibre layer) thickness and GCC (ganglion cell complex) area were compared with 20 normal control eyes. 13 cases were operated by microscopic assisted endoscopic endonasal transsphenoidal approach; the remaining 3 cases were operated transcranially. Spearman's correlation analysis was used to evaluate the relationship between preoperative RNFL thickness, GCC area, postoperative mean deviation and temporal visual field sensitivity (1/Lambert). Preoperative measurements of RNFL thickness and all GCC area were significantly reduced in the patients compared with normal control. 3 months postoperative evaluation showed improvement of the visual field, but reduction in global and sectorial RNFL thickness except for nasal sector. Moreover, absolute postoperative (not pre-post change) visual field parameters were significantly correlated to preoperative RNFL (P = 0.00399 for mean deviation, P = 0.0023 for temporal sensitivity), GCC thickness (P = 0.00736 for mean deviation, P = 0.0469 for temporal sensitivity), with FLV (focal loss value) (P = 0.0012 for mean deviation, P = 0.0021 for temporal sensitivity) showed a higher correlation. Reduced RNFL thickness mainly, and GCC area minimally, were associated with the worst visual field outcome. FLV is a new prognostic value.

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