The scarcity of aging data and the inconsistency of aging trends during the second life lithium-ion batteries (SL-LIBs) echelon utilization process make state-of-health (SOH) estimation challenging, hindering accurate monitoring when deployed as energy storage in power systems. To address this issue, a cross-domain physical information network (CDPIN) method is proposed to estimate SOH of SL-LIBs. First, an internal parameter inference module is proposed to address the difficulty in directly and accurately measuring the internal state information of SL-LIBs, supervising inference of aging parameters with discretized degradation physical mechanism. Second, a memory-assisted domain alignment mechanism is proposed to address feature distribution differences caused by inconsistent aging trends. It enhances the unified representation capability of aging trends through the best aging feature replay strategy. Further, a framework for fine-tuning the SOH estimation model is proposed to address the distribution shift between SL-LIBs and the source domain, dynamically adjusting parameters through degradation process modeling feedback. CDPIN utilizes widely available first life lithium-ion battery data as the source domain, overcoming the limitation of SL-LIBs data scarcity for SOH monitoring during echelon utilization. Finally, the effectiveness of the proposed method is verified using actual aging data and a semiphysical hardware platform.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30- minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, due to the tiny size, it is inconvenient for users to interact with a device using conventional methods, especially for text entry. Although some methods have been proposed to handle this problem, they have different limitations and are not applicable to many existing mobile devices. As a result, we take the first step to propose a digits-entry system, i.e., AcouDigits, in which digits can be entered in the air using a finger without taking help from any additional hardware. We implement AcouDigits on two commercial devices and conduct experiments to evaluate its performance in recognizing ten basic digits. Experimental results show that AcouDigits can achieve average accuracies of 91.7% and 87.4% in recognizing basic digits and 26 English alphabets, respectively.