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.

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and - independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$ . Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation- based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29 % compared to the adaptive rule- based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real- time applications in hybrid electric vehicles.

The role of sustainability has made it a vital point to measure companies’ financial performances and sustainability practices along the overall supply chain. Logistics service providers (LSPs) are among the supply chain actors that need to consider sustainability practices to present a better sustainable service. Therefore, we studied LSPs in Egypt, as Egypt has set sustainability goals in the logistics sector to be achieved by 2030. This research proposes a new sustainable logistics service quality (SLSQ) scale through reviewing the literature on sustainable service quality (SSQ) and logistics service quality (LSQ). While additional semi-structured interviews were conducted with LSP companies in Egypt to formulate the SLSQ scale for this specific region, the Q-sorting technique was used to confirm SLSQ measurements. Hence, we present a scale to evaluate Egypt’s overall LSPs’ service quality. Our research thereby contributes to the theory by proposing a new framework that measures the SLSQ in LSP companies in emerging countries and to the practice by tying the framework to accompany Egyptian law. The results are discussed against previous literature and concluded by showing limitations and potential future research avenues.

In light of increasing demands on decarbonized transportation systems, it became increasingly necessary to meet performance and environmental requirements for electromobility. Major challenges of electrified transportation can be pointed out as: rapid degradation of batteries, limited driving range and performance, and charging scheduling within infrastructural capabilities. Intelligent power management systems (I-PMS), providing optimal energy handling decisions for electrified drivelines, have been increasingly the focus of electrification efforts, owing to their significant potential to tackle the aforementioned challenges despite of existing technological limitations. This contributions presents a comparative evaluation of recent advances in predictive and cognitive PMS with a particular focus on problem formulation, solving techniques, and practical examples of each method. Moreover, the evolving role of I-PMS in light of emerging intelligent transportation systems (ITS) and connected vehicles technologies (CVT) are discussed, to point out the ability of I-PMS to bridge the gap towards smart electromobility. This comprehensive overview of I-PMS aims at providing useful insights into the pros and cons of each method and puts forward potential outcomes for electrified transportation.

Battery management systems in electric/hybrid vehicles are entirely based on accurate and reliable state estimation techniques. Consideration of combined effects of temperature, internal resistance rise, and capacity loss is essential to accurately estimate the current state of charge (SOC) of the battery. Most of the existing algorithms fail to capture the combined effects of the aforementioned variables. Moreover, an analysis of the inter-dependencies between the states and parameters related to the driving conditions is required. The main contribution of this paper is to introduce a hybrid and adaptive method of SOC estimation which captures the effects of initial SOC deviation and changes in SOC due to loss in capacity and rise in internal resistance. Modeling parameters related to the change in SOC, temperature, and capacity loss are continuously updated for the implemented battery model, whereby the equivalent circuit can be tuned to recent state of the battery. Simulation results are verified with experimental data from literature to prove the efficiency of the approach in terms of real-time applicability and accuracy.

Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability.