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In an aircraft, Electrical Energy Storage Systems (EESS) are used as support to other sources in few mission phases in order to ensure the energy availability. They are also used as electrical smoothing devices in order to guarantee the required levels of reliability, stability and quality for an embedded electrical network. This paper deals with the association of two EESS: supercapacitors and secondary battery, which exhibit complementary properties. In this paper, a sizing method for both EESS is developed by taking into account their hybridization and their characteristics (such as capacity or depth-of-discharge) so as to minimize the global storage system weight. Moreover, an energy management based on a frequency approach is implemented to dispatch the power between all the sources. The influence of this management on the sizing is studied. Indeed the cut-off frequency of the low-pass filter is used as a setting parameter of the sizing algorithm. Finally, the sizing validity is assessed and discussed according to temperature constraints. Although battery performances are reduced at low temperature, the sizing determined with the algorithm at 20 °C is still valid on all the temperature range thanks to an adaptation of the energy management parameter. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy-logic approach to the energy management of an embedded fuel-cell system. The main objective is to study one of the most technological bolts to be overcome for embedded fuel-cell systems: Their energy optimization. To reach this aim, experimentally validated models of a low-power 5-kW proton-exchange-membrane fuel cell and its most power- hungry ancillary (the fuel-cell air-supply system) are described. All simulation results have been performed using Matlab/Simulink environment. Two fuzzy controllers will be proposed for both the internal air-supply control loop and for the whole-embedded energy-management strategy. [ABSTRACT FROM AUTHOR]

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy sources (RES) are being deployed in the power system to meet the energy demand. The microgrid concept (AC, DC) is introduced, in which distributed energy resources (DERs), the energy storage system (ESS) and loads are interconnected. DC microgrids are appreciated due to their high efficiency and reliability performance. Despite its significant growth, the DC microgrid is still relatively novel in terms of grid architecture and control systems. In this context, an energy management system (EMS) is essential for the optimal use of DERs in secure, reliable, and intelligent ways. Therefore, this paper strives to shed light on DC microgrid architecture, control structure, and EMS. With an extensive literature survey on EMSs' role, different methods and strategies related to microgrid energy management are covered in this article. More attention is centered on the EMS for DC microgrids in terms of size and cost optimization. A very concise analysis of multiple optimization methods and techniques has been presented exclusively for residential applications. [ABSTRACT FROM AUTHOR]

Abstract Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mis-predictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). Highlights • A comprehensive review on existing driving prediction techniques is conducted. • A driving-scenario-based comparative study on prediction approaches is presented. • Several prediction accuracy and robustness improving strategies are discussed. • Bottlenecks and future developing trends of prediction techniques are proposed. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is utilized to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43 % and 7.30 % in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123 ms, less than the sampling time interval 1s, proving its potential of real-time applications. • A cost-optimization energy management is devised for postal-delivery FCEVs. • A data-driven dual-loop spatial-domain battery SoC reference estimator is devised. • Both energy consumption and power source durability are accounted. • A comprehensive evaluation of power-allocation and operating costs is conducted. [ABSTRACT FROM AUTHOR]

• A cost-minimization energy management is designed for fuel cell electric vehicles. • Both hydrogen consumption and energy source degradations are incorporated. • Dynamic programming is used to solve nonlinear optimization problems. • Vehicular cost and powertrain durability under multiple factors are analyzed. Fuel cell electric vehicles are widely deemed as the promising technology in sustainable transportation field, yet the high ownership cost makes them far from competitive in contemporary auto market. To maximize the economic potential of fuel cell/battery-based hybrid electric vehicles, this paper proposes a real-time cost-minimization energy management strategy to mitigate the vehicle's operating cost. Specifically, the proposed strategy is realized via model predictive control, wherein both hydrogen consumption and energy source degradations are incorporated in the multi-objective cost function. Assisted by the forecasted speed, dynamic programming is leveraged to derive the optimal power-splitting decision over each receding horizon. Thereafter, the performance discrepancy of the proposed strategy is analyzed under different affecting factors, including battery state-of-charge regulation coefficient, discrete resolution of optimization solver, speed prediction approaches and length of prediction horizon. Lastly, a comparative study is conducted to validate the effectiveness of the proposed strategy, where the proposed strategy can respectively reduce the operating cost and prolong the fuel cell lifetime by 14.17% and 8.48% in average versus a rule-based benchmark. Moreover, the online computation time per step of the proposed strategy is averaged at 266.26 ms, less than the sampling time interval 1 s, thereby verifying its real-time practicality. [ABSTRACT FROM AUTHOR]

• A predictive energy management is devised for fuel-cell/battery-based PHEVs. • The quality of speed prediction is enhanced by online-learning technique. • A time-efficient battery state-of-charge reference planning approach is designed. • MPC is employed to achieve the real-time multi-criteria control framework. • The enhancement on fuel efficiency and fuel cell durability is verified. As one of promising solutions towards future cleaner transportation, fuel cell electric vehicles have been widely regarded as an attractive technology in both academia and industry. To enhance the vehicle's operation efficiency, this paper proposes a multi-criteria power allocation strategy for a fuel cell/battery-based plug-in hybrid electric vehicle. Firstly, an adaptive online-learning enhanced Markov velocity-forecast approach is proposed. Its predictive behaviors can be adjusted accordingly under various driving scenarios through the real-time-identified transition probability matrices. Subsequently, based only on the previewed trip duration information and the speed prediction results, a state-of-charge (SOC) reference planning approach is designed to guide the allocation of battery energy. Combining with the velocity-forecast results and the reference SoC, model predictive control derives the optimal power-allocation decision through minimizing the multi-purpose objective function in a finite time horizon. It has been verified that (1) the presented power allocation strategy can reduce over 12.05% H2 consumption and over 94.40% fuel cell power spikes against the commonly used Charge-Depleting/Charge-Sustaining strategy; (2) despite the existence of mission time estimation errors, the presented control strategy could still bring performance enhancement over the benchmark strategy, thus demonstrating its feasibility for real-world implementations. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles. [Copyright &y& Elsevier]

A reliable power distribution strategy is of great significance towards the performance enhancement of fuel cell electric vehicles. In this work, a novel model predictive control-based energy management is developed for a fuel cell based light-duty range-extended hybrid electric vehicle. To fulfill the model predictive control framework, a cooperative speed forecasting method based on Markov Chain and fuzzy C-means clustering technique is proposed, which contains multiple predictive sub-models for handling different driving patterns. The final prediction results are obtained by synthesizing the forecasted speed profiles from all sub-models with the quantified fuzzy membership degrees. Besides, an adaptive battery State-of-Charge reference generator is built, which can regulate the SoC depleting rates against various power requirements. Combined with the forecasted speed and SoC reference, the desirable control actions are derived through minimizing the performance index over each finite time horizon. As a result, under the realistic urban-based postal-delivery mission profiles, the proposed strategy can achieve over 3.79% equivalent hydrogen consumption saving and over 40.04% fuel cell power dynamics decrement against benchmark strategy. Moreover, the presented predictive energy management is robust to certain level of trip duration estimation errors, further indicating its suitability for real applications. • A fuzzy C-means clustering enhanced Markov Chain velocity predictor is proposed. • A time-efficient battery state-of-charge reference generator is presented. • A multi-criteria energy management is devised using model predictive control. • The performance improvement on fuel economy and fuel cell durability is verified. [ABSTRACT FROM AUTHOR]