Transparent Electronic Skin Device Based on Microstructured Silver Nanowire Electrode

Transparent, flexible electronic skin holds a wide range of applications in robotics, humanmachine interfaces, artificial intelligence, prosthetics, and health monitoring. Silver nanowire are mechanically flexible and robust, which exhibit great potential in transparent and electricconducting thin film. Herein, we report on a silver-nanowire spray-coating and electrodemicrostructure replicating strategy to construct a transparent, flexible, and sensitive electronic skin device. The electronic skin device shows highly sensitive piezo-capacitance response to pressure. It is found that micropatterning the surface of dielectric layer polyurethane elastomer by replicating from microstructures of natural-existing surfaces such as lotus leaf, silk, and frosted glass can greatly enhance the piezo-capacitance performance of the device. The microstructured pressure sensors based on silver nanowire exhibit good transparency, excellent flexibility, wide pressure detection range （0-150 kPa）, and high sensitivity （1.28 kPa-1）.

Failure Prediction Modeling of Lithium Ion Battery toward Distributed Parameter Estimation

Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.