Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors/actuators
| Autor: | Fotis Kopsaftopoulos, Purim Ladpli, Fu-Kuo Chang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Battery (electricity)
Materials science Guided wave testing Renewable Energy Sustainability and the Environment State of health Piezoelectric sensor 020209 energy Acoustics Energy Engineering and Power Technology 02 engineering and technology 021001 nanoscience & nanotechnology Signal Lithium-ion battery State of charge Transducer 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Physical and Theoretical Chemistry 0210 nano-technology |
| Zdroj: | Journal of Power Sources. 384:342-354 |
| ISSN: | 0378-7753 |
| DOI: | 10.1016/j.jpowsour.2018.02.056 |
| Popis: | This work presents the feasibility of monitoring state of charge (SoC) and state of health (SoH) of lithium-ion pouch batteries with acousto-ultrasonic guided waves. The guided waves are propagated and sensed using low-profile, built-in piezoelectric disc transducers that can be retrofitted onto off-the-shelf batteries. Both experimental and analytical studies are performed to understand the relationship between guided waves generated in a pitch-catch mode and battery SoC/SoH. The preliminary experiments on representative pouch cells show that the changes in time of flight (ToF) and signal amplitude (SA) resulting from shifts in the guided wave signals correlate strongly with the electrochemical charge-discharge cycling and aging. An analytical acoustic model is developed to simulate the variations in electrode moduli and densities during cycling, which correctly validates the absolute values and range of experimental ToF. It is further illustrated via a statistical study that ToF and SA can be used in a prediction model to accurately estimate SoC/SoH. Additionally, by using multiple sensors in a network configuration on the same battery, a significantly more reliable and accurate SoC/SoH prediction is achieved. The indicative results from this study can be extended to develop a unified guided-wave-based framework for SoC/SoH monitoring of many lithium-ion battery applications. |
| Databáze: | OpenAIRE |
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