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Summary: Reliable operation and control of battery packs can lead to increasing applications of batteries as energy sources for mobile power systems such as electric/hybrid electric aircraft. If the operation of a battery pack is controlled and monitored thoroughly, the safety in the battery system of an electrified aircraft can be guaranteed. The battery model has many applications in battery management systems such as battery performance analysis and fault detection. To achieve an accurate fault diagnosis for electric aircraft, an intelligent fault detection scheme within an accurate battery cell model is required. In this study, an adaptive lithium‐ion battery model is proposed in which models' parameters are estimated by a supervised machine learning paradigm. This adaptive battery model is developed based on a second order equivalent circuit model, which has a good representation of lithium‐ion batteries dynamics. Comparative verification experiments show good accuracy and robustness of the machine learning‐based parameter estimator lead to an accurate battery model with an average error less than 0.4%. Moreover, to see the effectiveness of this machine learning‐based model in fault detection applications, a model‐based fault diagnosis scheme is developed. Finally, the analysis of fault diagnosis tests under different test conditions proves that the proposed adaptive battery model can significantly improve the fault diagnosis accuracy of batteries. [ABSTRACT FROM AUTHOR]

Summary: The charge, discharge, and total energy efficiencies of lithium‐ion batteries (LIBs) are formulated based on the irreversible heat generated in LIBs, and the basics of the energy efficiency map of these batteries are established. This map consists of several constant energy efficiency curves in a graph, where the x‐axis is the battery capacity and the y‐axis is the battery charge/discharge rate (C‐rate). In order to introduce the energy efficiency map, the efficiency maps of typical LIB families with graphite/LiCoO2, graphite/LiFePO4, and graphite/LiMn2O4 anode/cathode are generated and illustrated in this paper. The methods of usage and applications of the developed efficiency map are also described. To show the application of the efficiency map, the effects of fast charging, nominal capacity, and chemistry of typical LIB families on their energy efficiency are studied using the generated maps. It is shown how energy saving can be achieved via energy efficiency maps. Overall, the energy efficiency map is introduced as a useful tool for engineers and researchers to choose LIBs with higher energy efficiency for any targeted applications. The developed map can be also used by energy systems designers to obtain accurate efficiency of LIBs when they incorporate these batteries into their energy systems. Energy efficiency map for Li‐ion batteries (LIBs) is introduced.Determination of energy efficiency is important for LIBs operating at ≥C/2.This map can be used for selection of LIBs with higher efficiency.This map can be used to find the LIB efficiency when it is incorporated into an energy system.This map can be used to study effects of fast charging, capacity, C‐rate, and chemistry of LIBs. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries are well known as an efficient energy storage solution for plug-in hybrid electric vehicles (PHEVs). However, performance and state of health of these batteries strictly depends on the usage scenario including operating temperature, power demand profile, and control strategy imposed by the battery management system. Also, in PHEVs equipped with electric climate control systems, climate control loads are imposed as additional loads on the battery, which results in a reduced all-electric range (AER) and increased battery capacity degradation. In this paper, vehicle AER, and fuel economy and life degradation of an aftermarket LiFePO4 Li-ion battery cell are studied for a PHEV under several usage scenarios. Each scenario consists of a series and parallel PHEV powertrain layout developed in Autonomie software, climate condition, that is, hot and cold weather, and a daily driving and charging profile. For simulations, models of battery performance, heat generation, and degradation developed based on experimental results are integrated with a thermal vehicle cabin model. Impact of climate control loads and battery thermal preconditioning are incorporated in the simulations. It is observed that climate control loads significantly affect the AER (up to 20%), fuel economy (up to 65%), and battery degradation (up to 25%). On the other hand, thermal preconditioning could be used to reduce these impacts. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A design method is presented based on pinch technology and exergy analysis to reduce heat transfer irreversibility of the feedwater heaters network in steam power plants. In order to show the effects of this method, an extensive study was performed on four steam power plants. The results show that applying this method can decrease the fuel consumption and the condenser load. It also increases the boiler, the feedwater heaters network, and the turbine exergetic efficiencies. On the whole, the results show that applying this method, with a target pinch temperature of 3°C, increases the cycle 2nd law efficiency 0.3–1.3% and the fossil fuel consumption decreases about 64 × 106kg annually for 8000 operating hours per year of the studied steam power plants. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]