Intelligent prognostics for battery health monitoring based on sample entropy
By Achmad Widodo, Min-Chan Shim, Wahyu Caesarendra, Bo-Suk Yang. Expert systems with applications, 2011
381 citations recorded by the source corpus.
Subfields: Automotive Engineering, Biomedical Engineering, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/8bbcaa082a7321a17b79e7cad6a0c290e8a38113
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Xiaosong Hu
- Michael G. Pecht
- Yong Guan
- Lifeng Wu
- Xiaohui Fu
- Jiuchun Jiang
- Chunting Chris Mi
- Wenxin Huang
In Biomedical Engineering
In Cognitive Neuroscience
Citations in the corpus
- Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies (2016, methodology): “[29] predicted the RUL of the battery using the RVM algorithm and found that the long-term prediction performance is poor and not suitable for direct RUL prediction.”
- Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, methodology): “A sample entropy-based capacity estimator for a lithium-ion battery was synthesized in [42].”
- A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation (2015, methodology): “Alternate applications of machine learning approaches for battery state estimation include particle swarm optimization [46], Gaussian process regression [47], recurrent neural fuzzy systems [48], genetic algorithm [49], sample entropy based approaches [50,51], the naive Bayes model [52,53] and other”
- A review on fault mechanism and diagnosis approach for Li-Ion batteries (2015, methodology): “Other powerful signal processing tools such as impulse response [52], PDF [53], sliding mode observer [54] and support vector machine and relevance vector machine [55, 56] were applied in fault diagnosis for Li-ion batteries and made certain progress.”
- A review on lithium-ion battery ageing mechanisms and estimations for automotive applications (2013, background): “The most used indicator in the literature is the State Of Health (SOH) which is generally de ned by [71, 72]: SOH(t) = nominal capacity at t intial capacity (%) Other SOH de nitions can be made through the End Of Life criterion (EOL) [73] but it is still a proportion of battery remaining capacity.”