Battery thermal management system (BTMS) is a hot research area for electric vehicles (EVs). Common BTMS schemes include air cooling, liquid cooling, and phase-change materials (PCMs). Air cooling BTMS is widely used in EVs because of its simplicity, high efficiency, and low cost. However, past air cooling BTMS research focused on inlet flow, air channel design, and battery layout. Few studies have focused on improving the heat transfer efficiency of battery packs. This paper aimed to improve the heat transfer efficiency of air cooling BTMS by using herringbone fins. Both inline and staggered arrangements of lithium-ion cells were considered. Moreover, the effects of transverse pitch, longitudinal pitch, fin height, fin number, and inlet velocity were examined. Installation of herringbone fins with optimal values of parameters caused a decrease in average temperature of cells by 3.687 K in the inline arrangement and 4.15 K in the staggered arrangement. Furthermore, a significant improvement in temperature uniformity was also observed. The simulation results will be helpful for the design of air cooling BTMS.

Air‐cooling‐based battery thermal management system (BTMS) is a research hotspot for electric vehicles because of lower cost and simpler design. Past research works have immensely concentrated on the enhancement of heat removal from Li‐ion battery, but the minimum consideration has been given to minimize the parasitic power. In this paper, a novel procedure is proposed to predict the operating parameters (inlet velocity, working time of fan, and range of heat generation rates) of air‐cooling design for minimizing parasitic power and ensuring the battery temperature do not exceed the upper threshold limit simultaneously. Based on findings via computational fluid dynamics analysis, an empirical model of air‐cooling BTMS is further developed using an evolutionary approach of model selection criteria approximated genetic programming (MSC‐GP). The model is then optimized to determine operating parameters of air cooling which causes minimum parasitic power while keeping the average temperature of battery cells within the limit. Further, sensitivity analysis and parameter interaction (2D and 3D) analysis is also performed to study the effect and identify the contribution of various operating parameters on parasitic power. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only. Finally, the optimal values of operating parameters for various heat generation rate per cell (function of discharge rate) are obtained. The optimized parasitic power was observed to be nonlinearly increasing with heat generation rate. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However, parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only.

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Recovery of the vital metals from spent batteries using bioleaching is one of the commonly used method for recycling of spent batteries. In this study, a statistical based automated neural network approach is proposed for determination of optimum input parameters values in bioleaching of zinc‐manganese batteries. Experiments are performed to measure the recovery of zinc and manganese based on the input parameters such as energy substrates concentration, pH control of bioleaching media, incubating temperature, and pulp density. It was found that the proposed model based metal extraction models precisely estimated the yields of zinc and manganese with higher values of coefficient of determination of 0.94. Based on global sensitivity analysis, it was found that for the extraction of zinc, the most contributing parameters are pulp density and pH while for extraction of Mn the most contributing parameters are pulp density and incubating temperature. The optimum parameter values for maximum recovery of zinc and maximum recovery of manganese are determined using optimization method of simulated annealing. The optimum parameter values obtained for maximum recovery of Zn metal are as substrates concentration 32 g/L, pH 1.9 to 2.0, incubating temperature 30°C, pulp density 10%, and substrates concentration 32 g/L, pH 2.0, incubating temperature 35°C, pulp density 8% for maximum recovery of Mn.

Efficient design of Battery Thermal Management Systems (BTMS) plays an important role in enhancing performance, life, and safety of Electric vehicles (EV). This paper aims at designing and optimizing cold plate based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of outermost channel are taken as design parameters. Evaluating influence and optimization of design parameters by repeated CFD calculations are time consuming. To tackle this, effect of design parameters is studied by using surrogate modelling. Optimized design variables should ensure perfect balance between certain conflicting goals, namely cooling efficiency, BTMS power consumption (parasitic power) and size of the battery. So, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson Sampling Efficient Multi-Objective Optimization (TSEMO) algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

This research article attempts to design Inverse Dynamics Controller (IDC) to execute the motion tracking of Stewart Platform. In the presence of modeling uncertainties and external disturbances, the closed-loop dynamic equation of IDC with fixed gains becomes nonlinear and configuration-dependent, which compromises the motion tracking accuracy. Further, both modeling uncertainties and external disturbances are unavoidable in real life conditions. To tackle this issue, this article proposes a novel control algorithm by combining IDC with Feed-Forward Artificial Neural Network (FF-ANN) trained using PSO. The proposed modified control algorithm offers superior motion tracking accuracy in comparison with traditional IDC.

Summary The performance, lifetime, and safety of electric vehicles (EVs) are highly reliant on the battery's capacity and temperature. Battery thermal management system (BTMS) involves techniques to control the battery's thermal behavior by maintaining temperature and temperature uniformity within an optimal range. Air cooling BTMS is frequently used in EVs due to its design simplicity and cost‐effectiveness. Only limited works have utilized passive convective heat transfer enhancement strategies for air‐cooled BTMS, especially fin‐based structures for cylindrical lithium‐ion battery packs. Present work introduces a novel design by incorporating the interrupted fins into a conventionally air‐cooled BTMS. The proposed design offers superior heat transfer efficiency as well as ease of manufacturing when compared to the existent herringbone fin design used in prior air‐cooled BTMS literature. Results indicate that the average temperature of the cells and maximum temperature difference were reduced by 5.646 and 7.958 K, respectively, by incorporating an optimal uniform interrupted fin arrangement. The average temperature of the cells was reduced by 0.479 K when the interrupted fins were added to increase the area, compared to 0.088 K for an equivalent area enhancement in a conventional fin. The uniform interrupted fin improved the temperature uniformity by 5.05%, but the area enhanced conventional fin deteriorated it by 1.59%. The non‐uniform interrupted fin arrangement, resulted in an improvement of 4.47%, over uniform interrupted fins, in the battery pack's temperature uniformity.