Home ›
Entities
› academia
› Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment
Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment
Research article (Journal of Energy Storage, 2024) · cited 32× · AI/ML
Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment
Summary
Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment is a scholarly article[1].
Key Facts
Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment. Retrieved May 24, 2026, from https://4ort.xyz/entity/adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene
MLA“Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene.
BibTeX@misc{4ortxyz_adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment}}, year = {2026}, url = {https://4ort.xyz/entity/adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment — https://4ort.xyz/entity/adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene (retrieved 2026-05-24)