The Ways Ahead
Green Energy and Technology, ISSN: 1865-3537, Page: 245-258
2022
- 4Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures4
- Readers4
Book Chapter Description
Although great efforts have been made in developing data science technology for benefitting full-lifespan management of Li-ion batteries, many knowledge gaps still exist. This chapter summarizes these challenges, future trends, and promising solutions to boost the development of data science solutions in the management of battery manufacturing, operation, and reutilization, respectively. This could further inform the selections of data science methodology and academic research agendas alike, thus boosting progress in data science-based battery full-lifespan management on different technology readiness levels.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128006981&origin=inward; http://dx.doi.org/10.1007/978-3-031-01340-9_7; https://link.springer.com/10.1007/978-3-031-01340-9_7; https://dx.doi.org/10.1007/978-3-031-01340-9_7; https://link.springer.com/chapter/10.1007/978-3-031-01340-9_7
Springer Science and Business Media LLC
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