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
Press Enter · cited answer in seconds

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].

📑 Cite this page

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.

APA 4ort.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 prompt According 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)

Canonical URL: https://4ort.xyz/entity/adaptive-kalman-filter-and-self-designed-early-stopping-strategy-optimized-convolutional-neural-network-for-state-of-ene · Last refreshed: