Abstract
Battery health prognosis and monitoring require the information of the available battery capacity that Tian et al. (2021) proposes to acquire from a partial 10-min charging curve via a deep neural network.
Highlights
Battery health prognosis and monitoring require the information of the available battery capacity that Tian et al (2021) proposes to acquire from a partial 10-min charging curve via a deep neural network
The societal and regulatory changes are trending the development of carbon-emissions-free transport systems where lithium-ion (Li-ion) battery technologies are dominating as the main energy storage system
To predict and monitor the state of health (SoH) of a battery is identified usually by health indicators extracted from a constant current charge and/or discharge curves that require a break of operation
Summary
Battery health prognosis and monitoring require the information of the available battery capacity that Tian et al (2021) proposes to acquire from a partial 10-min charging curve via a deep neural network. To predict and monitor the state of health (SoH) of a battery is identified usually by health indicators extracted from a constant current charge and/or discharge curves that require a break of operation.
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