Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning.

Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)-a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis-with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures-the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

Publisher's Note: Direct Imaging of Dynamic Glassy Behavior in a Strained Manganite Film [Phys. Rev. Lett. 115, 265701 (2015)].

This corrects the article DOI: 10.1103/PhysRevLett.115.265701.

Direct Imaging of Dynamic Glassy Behavior in a Strained Manganite Film.

Complex many-body interaction in perovskite manganites gives rise to a strong competition between ferromagnetic metallic and charge-ordered phases with nanoscale electronic inhomogeneity and glassy behaviors. Investigating this glassy state requires high-resolution imaging techniques with sufficient sensitivity and stability. Here, we present the results of a near-field microwave microscope imaging on the strain-driven glassy state in a manganite film. The high contrast between the two electrically distinct phases allows direct visualization of the phase separation. The low-temperature microscopic configurations differ upon cooling with different thermal histories. At sufficiently high temperatures, we observe switching between the two phases in either direction. The dynamic switching, however, stops below the glass transition temperature. Compared with the magnetization data, the phase separation was microscopically frozen, while spin relaxation was found in a short period of time.

Repulsively driven frequency-increased-generators for durable energy harvesting from ultra-low frequency vibration.

An electromagnetic kinetic energy harvester has been developed, which can convert ultra-low-frequency motion and vibration energy into electrical power. This harvester employs a two-stage vibratory structure to collect low-frequency kinetic energy and effectively transfer it into electric power by using a pair of high-frequency resonant generators. Non-contact magnetic repulsive force is herein utilized for the 1st-stage sliding vibrator to drive the 2nd-stage resonators into frequency-up-conversion resonance. The non-contact actuation is helpful for durable and long-life working of the device. The prototyped device is fabricated and the design is well confirmed by experimental test. The harvester can be well operated at the frequency as low as 0.25 Hz. Under driving acceleration of 1 g at 0.5 Hz, the miniaturized harvester can generate a peak power of 4.42 mW and an average power of 158 µW.