Electrochemomechanical coupled behaviors of deformation and failure in electrode materials for lithium-ion batteries

The growing demands of lithium-ion batteries with high energy density motivate the development of high-capacity electrode materials. The critical issue in the commercial application of these electrodes is electrochemomechanical degradation accompanied with the large volume change, built-in stress, and fracture during lithiation and delithiation. The strong and complex couplings between mechanics and electrochemistry have been extensively studied in recent years. The multi-directional couplings, e.g.,(de)lithiation-induced effects and stress-regulated effects, require cooperation in the interdisciplinary fields and advance the theoretical and computational models. In this review, we focus on the recent work with topics in the electrochemomechanical couplings of deformation and fracture of conventional and alloying electrodes through experimental characterization, theoretical and computational models. Based on the point of view from mechanics, the strategies for alleviating the degradation are also discussed, with particular perspectives for component-interaction patterns in the composite electrodes. With interdisciplinary principles, comprehensive understanding of the electrochemomechanical coupled mechanism is expected to provide feasible solutions for low-cost, high-capacity, high-safety and durable electrodes for lithium-ion batteries.

An artificial neural network model for multi-flexoelectric actuation of Plates

Flexoelectric effect can be used to design actuators to control engi-neering structures including beams,plates,and shells.Multiple flexo-electric actuators method has the advantage of less stress concentration and better control effect,but the mode-dependent optimal actuator locations could influence the flexoelectric actuation effect significantly.In this work,a neural network model is established to study the optimal combinations of multiple flexoelectric actuators on a rectangular plate.In the physical model,an atomic force micro-scope(AFM)probe was employed to generate an electric field gradient in the flexoelectric patch,so that flexoelectric control force and moment can be obtained.Multiple flexoelectric actuators on the plate was considered.Case studies showed that the flexoelectricity induced stress mainly concentrate near the probe,the size and shape of the flexoelectric patch have limited effect on the actuation,hence,only the actuator positions were choosing as the input of the ANN model.Using the prediction of the neural network model,the driving effect of a large number of actuators at different positions can be quickly obtained,and the optimal position of the actuator can be analyzed more accurately.