Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors/actuators
By P. Ladpli, Fotis P. Kopsaftopoulos, Fu-Kuo Chang. 2018
194 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Polymers and Plastics
View Paper: https://www.semanticscholar.org/paper/f92de704930c5bc8cbd39b600c6fc7a810b1b165
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
- Kailong Liu
- Simona Onori
- Xianke Lin
- Maitane Berecibar
- Yi Li
- Xiaosong Hu
- Aoife M. Foley
- J. Van Mierlo
In Electrical and Electronic Engineering
- Kailong Liu
- Simona Onori
- Xianke Lin
- Maitane Berecibar
- Yi Li
- Xiaosong Hu
- Aoife M. Foley
- J. Van Mierlo
Citations in the corpus
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “[90] developed a built-in acoustic-ultrasonic guided wave technique to monitor battery SOH and discovered a non-linear correlation between the remaining battery capacity with the guided wave signal features.”
- Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, methodology): “For example, based on the built-in piezoelectric sensors [176], the electrochemical acoustic time of flight analysis can be done to capture the implicit correlation between waveform signal parameters, and battery SOC and SOH.”