Dynamic EV Battery Health Recommendations
Author:
Affiliation:
1. Deggendorf Institute of Technology, Deggendorf, Germany
Funder
Horizon 2020 Framework Programme
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3208903.3213896
Reference29 articles.
1. Issam Baghdadi Olivier Briat Jean-Yves Delétage Philippe Gyan and Jean-Michel Vinassa. 2016. Lithium battery aging model based on Dakin's degradation approach. Journal of Power Sources 325 (sep 2016) 273--285. Issam Baghdadi Olivier Briat Jean-Yves Delétage Philippe Gyan and Jean-Michel Vinassa. 2016. Lithium battery aging model based on Dakin's degradation approach. Journal of Power Sources 325 (sep 2016) 273--285.
2. Nejmeddine Bouchhima Matthias Gossen Sascha Schulte and Kai Peter Birke. 2018. Lifetime of self-reconfigurable batteries compared with conventional batteries. Journal of Energy Storage 15 (feb 2018) 400--407. Nejmeddine Bouchhima Matthias Gossen Sascha Schulte and Kai Peter Birke. 2018. Lifetime of self-reconfigurable batteries compared with conventional batteries. Journal of Energy Storage 15 (feb 2018) 400--407.
3. Chin-Yao Chang Punit Tulpule Giorgio Rizzoni Wei Zhang and Xinyu Du. 2017. A probabilistic approach for prognosis of battery pack aging. Journal of Power Sources 347 (apr 2017) 57--68. Chin-Yao Chang Punit Tulpule Giorgio Rizzoni Wei Zhang and Xinyu Du. 2017. A probabilistic approach for prognosis of battery pack aging. Journal of Power Sources 347 (apr 2017) 57--68.
4. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
5. C. Dudézert Y. Reynier J.-M. Duffault and S. Franger. 2016. Fatigue damage approach applied to Li-ion batteries ageing characterization. Materials Science and Engineering: B 213 (nov 2016) 177--189. C. Dudézert Y. Reynier J.-M. Duffault and S. Franger. 2016. Fatigue damage approach applied to Li-ion batteries ageing characterization. Materials Science and Engineering: B 213 (nov 2016) 177--189.
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