Evidence map›Paper›PMID 39300192›Full record

ReviewCommunications engineering2024

Synergizing physics and machine learning for advanced battery management.

Manashita Borah, Qiao Wang, Scott Moura, Dirk Uwe Sauer, Weihan Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Integrated process-property modeling of YBaCommunications engineering · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Manashita Borah *Energy, Controls and Application Laboratory, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA. manashitaborah@berkeley.edu.
Qiao Wang *Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074, Aachen, Germany.
Scott MouraEnergy, Controls and Application Laboratory, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA.ORCID http://orcid.org/0000-0002-6393-4375
Dirk Uwe SauerCenter for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074, Aachen, Germany.ORCID http://orcid.org/0000-0002-5622-3591
Weihan LiCenter for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074, Aachen, Germany.ORCID http://orcid.org/0000-0002-2916-3968

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and reliable approaches. Our analysis emphasizes that the integration of physics and machine learning stands as a disruptive innovation in the development of emerging battery health and safety management technologies.

Identifiers

PMID39300192
PMCPMC11413374

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.