A combined experimental-numerical framework for residual energy determination in spent lithium-ion battery packs

The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum

Optimization of process conditions for maximum metal recovery from spent zinc‐manganese Batteries: Illustration of Statistical based Automated Neural Network approach

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 1.9‐2.0, 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

Echelon Utilization of Retired Power Lithium-Ion Batteries: Challenges and Prospects

The explosion of electric vehicles (EVs) has triggered massive growth in power lithium-ion batteries (LIBs). The primary issue that follows is how to dispose of such large-scale retired LIBs. The echelon utilization of retired LIBs is gradually occupying a research hotspot. Solving the issue of echelon utilization of large-scale retired power LIBs brings not only huge economic but also produces rich environmental benefits. This study systematically examines the current challenges of the cascade utilization of retired power LIBs and prospectively points out broad prospects. Firstly, the treatments of retired power LIBs are introduced, and the performance evaluation methods and sorting and regrouping methods of retired power LIBs are comprehensively reviewed for echelon utilization. Then, the problems faced by the scenario planning and economic research of the echelon utilization of retired power LIBs are analyzed, and value propositions are put forward. Secondly, this study summarizes the technical challenges faced by echelon utilization in terms of security, performance evaluation methods, supply and demand chain construction, regulations, and certifications. Finally, the future research prospects of echelon utilization are discussed. In the foreseeable future, technologies such as standardization, cloud technology, and blockchain are urgently needed to maximize the industrialization of the echelon utilization of retired power LIBs