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Current issue

ELEKTRO 6/2020 was released on June 6th 2020. Its digital version will be available on June 24th 2020.

Topic: Electrical machines, drives and power electronics, electromobility

Main Article
New traction power supply technology 25 kV/50 Hz (part 1)

SVĚTLO (Light) 2/2020 was released on March 6th 2020. Its digital version will be available immediately.

Market, business, enterprise
BOOBA in new showroom, which surpassed all expectations
Discourse with Technology of Capital city Prague chairman of management

Day light
Diagram of overshadow for 21st march
Modern methods of gaining dates for processing lighting technology assessment

AI techniques used to improve battery health and safety

6. 4. 2020 | University of Cambridge | www.cam.ac.uk

Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This misses important features that indicate battery health. Tracking the many processes that are happening within the battery requires new ways of probing batteries in action, as well as new algorithms that can detect subtle signals as they are charged and discharged.

Machine learning for better batteries

The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.

Read more at University of Cambridge

Image Credit: University of Cambridge

-jk-