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There have been increased investments in cleaner technologies and adoption of a voluntarily limit on transportation emissions by the global firms to handle the environmental concerns of supply chains and to increase demand for finished goods. Consequences are the reduction in net profit for the firm. To address this trade-off between the net profit and environmental concerns, the formulation and optimization of a compact model are needed. Development of these models requires a thorough understanding of the nature of the impact of three inputs (investment coefficient, penalty per unit emission and customer's emission elasticity) on production emissions and net profit. Past studies revealed that a compact model comprising the interactive effect of these inputs on the production emissions and net profit is not yet formulated. Therefore, this study illustrates the development of an evolutionary framework of an advanced multi-gene genetic programming in the formulation of functional expressions for the net profit and production emissions based on the three inputs (investment coefficient, penalty per unit emission and customer's emission elasticity) of the monopolist firm. The sensitivity and parametric based 2-D analysis determine the relationships and found that the penalty per unit emission is dominant input for reducing emissions and maintaining net profit simultaneously. The contribution of this work lies in designing of an evolutionary framework in the development of empirical explicit expressions, which can easily be optimized analytically to keep production emissions and net profit balanced.

Battery thermal management system (BTMS) has significant impacts on the performance of electric vehicles (EVs). In this research, a computational fluid dynamics (CFD) coupled multi-objective optimization framework is proposed to improve the thermal performance of the battery pack having metal separators. CFD is utilized to study the thermal and fluid dynamics performance of the designed battery pack. Input parameters include inlet air temperature, thermal conductivity of coolant, thermal conductivity of metal separator, and diameter of heat dissipation hole. Five vital output parameters are maximum temperature, average temperature, temperature standard deviation (TSD), maximum pressure, and volume of the pack. The support vector machine (SVM) model is used to replace the real output parameters of the battery pack. Sensitivity analysis results indicate that the diameter of heat dissipation hole is the main factor affecting the volume of the structure and the pressure drop, while the inlet air temperature has significant influence on the battery pack thermal behavior. The cooling efficiency and the uniformity of temperature distribution are mainly determined by the inlet air temperature. The decrease of inlet air temperature could lead to a rise of temperature standard deviation. The nondominated sorting genetic algorithm-II (NSGA-II) is taken to acquire the optimum set of input parameters. The obtained optimal scheme of battery pack can improve the cooling efficiency as well as reducing the volume cost and the energy consumption of the cooling system while such design may result in a higher level of nonuniformity of the temperature and pressure distribution.

This research aims to evaluate the influence of different 3D metals (Fe, Co, and Ni) substituted to Mn on the electrochemical performance of P2-NaxMe1/3Mn2/3O2 material, which was synthesized by the coprecipitation process followed by calcination at high temperature. X-ray diffraction (XRD) results revealed that the synthesized Mn-rich materials possessed a P2-type structure with a negligible amount of oxide impurities. The materials possessed their typical cyclic voltammogram and charge-discharge profiles; indeed, a high reversible redox reaction was obtained by NaxCo1/3Mn2/3O2 sample. Both NaxCo1/3Mn2/3O2 and NaxFe1/3Mn2/3O2 provided a high specific capacity of above 140 mAh·g−1; however, the former showed better cycling performance with 83% capacity retention after 50 cycles at C/10 and high rate capability. Meanwhile, the Ni-sub NaxNi1/3Mn2/3O2 exhibited excellent cycling stability but a low specific capacity of 110 mAh·g−1 and inferior rate capability. The diffusion coefficient of Na+ ions into the structure tended to decrease with a depth of discharge; those values were in the range of 10−10–10−9 cm2·s−1 and 10−11–10−10 cm2·s−1 in the solid solution region and biphasic region, respectively.

It is predicted that by 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries embedded in hundreds in packs used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. This motivates the notion of facilitating research on establishing a model which can detect and predict the state of battery based on parameters enable to be measured, such as voltage and stack stress. Thus, the present work proposes a stack stress-coupled-artificial intelligence approach for analyzing the residual energy (remaining) in the batteries. Experiments are designed and performed to verify the fundamentals. A robust model is formulated based on artificial intelligence approach of genetic programming. The findings in the study can provide an optimized recycling strategy for spent batteries by accurately predicting the state of battery based on stack stress.