The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.

The rational inattention model has recently attracted much attention as a promising candidate to model bounded rationality in the research field of decision-making and game theory. However, in contrast to this energetic promotion of the theoretical works, empirical verification of the validity of the RI model has not progressed much. Furthermore, to our knowledge, the central assumption of the RI model, that the amount of mutual information obtained from signals adequately represents the cognitive cost of information, has not been tested from a neuroscientific perspective. The purpose of the present study was to test whether the amount of mutual information adequately represents the cognitive cost of information from a neuroscientific perspective. We proposed a sequential investment task, in which the two main models of RI can be treated simultaneously in a more realistic experimental environment. We used a model-fitting approach to analyze the subjective information cost, and compared the model parameters representing the information cost with the concentration of oxidized hemoglobin in the brain blood. Our results showed that the cost parameter λ of the stochastic choice type model, which fits the behavioral data of the present experiment better than the Kalman filter type model, was significantly positively correlated with the activation status of the rostral prefrontal cortex and dorsolateral prefrontal cortex. The cognitive cost represented by the amount of mutual information employed in the RI model is consistent with the activation of brain regions associated with cognitive cost, and, thus, indirectly supports the assumption of the RI model.

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The exploration versus exploitation dilemma is a critical issue in human information acquisition and sequential belief formation, and the multi-armed bandit problem has been widely used to address it. Because of its high descriptive accuracy, the SGU model, which combines SoftMax type probabilistic selection, Gaussian process regression type belief updating, and upper confidence interval type evaluation, has attracted much attention. However, this model assumes that the analyst has access to the returns from people’s choices, but in many realistic tasks, this assumption cannot be made because only choices are observable. Moreover, many of the returns are subjective. The authors introduce a new model-fitting method that overcomes this barrier and evaluates its performance using data sets derived from agent-based simulations and real consumer data. This approach has the potential to significantly broaden the range of issues to which the SGU model can be applied.