Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 10 min ago
Peer-reviewedCC BY 4.0Batteries

Data-Driven Battery State Estimation for E-Mobility Applications: Challenges, Recent Advances, and Future Directions

  • Aaruththiran Manoharan1
  • Fazal Ur Rehman1,2
  • Aydin Emre Utku3
  • Mehmet Emre Aslakci3
  • Kun Qian1
  • Kasper Mayntz Paasch1
  • Thomas Ebel1
  • Henrik Andersen1
  1. 1Centre for Industrial Electronics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark
  2. 2Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy
  3. 3MEAtec Mühendislik Ltd., 06800 Ankara, Turkey
Open at sourceDOICite

Abstract

Data-driven battery state estimation has been widely studied in recent years to enable safe, reliable, and lifetime-aware battery management in e-mobility applications, where accurate estimation is essential under highly dynamic operating conditions. With several recent reviews focusing on model architectures and methodological developments, a critical assessment of practical feasibility, real-world deployability, and application-specific challenges in e-mobility is needed. This review therefore provides a challenge-oriented assessment of recent data-driven methods for battery state estimation, with particular emphasis on practical deployment in e-mobility systems. Beyond summarising existing approaches, it assesses how recent studies address key deployment barriers. These barriers include data dependency, limited ground-truth health labels, and fragmented datasets. Further challenges concern feature complexity and interpretability, robustness under domain shifts, embedded-BMS constraints, and cell-to-pack scalability.Recentresearch increasingly focuses on battery health analytics, reflecting the importance of ageing-aware battery management in e-mobility. Based on the gaps identified, future research directions are outlined to support the development of safe, reliable, interpretable, and deployment-ready battery state estimation for next-generation e-mobility systems.

Related

  1. Advances in Shear Thickening Fluids: Challenges, Opportunities, and Future Directions
    Ashish Garg, Pulak Gupta, Nitin Kumar +1
    ChemRxivPreprintCC BYGeneral chemistryChemRxiv
  2. Recent Advances in Nanocomposite Hydrogel Sensors for Healthcare Applications: Fabrication Techniques, Multifunctionality and Challenges
    Md M. Hossain, Tarikul Islam, Mehedi H. Chaion +6
    Nanomaterials (Basel, Switzerland)Peer-reviewedCC BYFull textMaterials & polymersEurope PMC
  3. Unlocking the potential of silver-copper bimetallic nanoparticles: a review of recent advances, applications and future directions
    Noor Ul Islam, Maaz Ahmad, Habib Ullah +9
    Journal of Saudi Chemical SocietyPeer-reviewedCC BY-NC-NDFull text5 citedMaterials & polymersOpenAlex
  4. Advances in catalytic hydrogen storage materials: progress, breakthroughs, and challenges toward scalable applications.
    Pan Shulin, Walid Tahri, Rashid Khan +1
    Frontiers in chemistryPeer-reviewedCC BYFull textMaterials & polymersEurope PMCPMC
  5. Waste-Derived Sustainable Nanomaterials: Comprehensive Review of Synthesis Advances, Applications and Translational Challenges
    Mahima Yadav, Jason Hodge, Terrence J. Piva +6
    NanomaterialsPeer-reviewedCC BY8 citedMaterials & polymersOpenAlex

Cite

BibTeXRIS
BibTeX
@article{manoharan2026datadriven,
  title = {{Data-Driven Battery State Estimation for E-Mobility Applications: Challenges, Recent Advances, and Future Directions}},
  author = {Aaruththiran Manoharan and Fazal Ur Rehman and Aydin Emre Utku and Mehmet Emre Aslakci and Kun Qian and Kasper Mayntz Paasch and Thomas Ebel and Henrik Andersen},
  journal = {Batteries},
  year = {2026},
  month = nov,
  doi = {10.3390/batteries12110475},
  url = {https://www.mdpi.com/2313-0105/12/11/475},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Data-Driven Battery State Estimation for E-Mobility Applications: Challenges, Recent Advances, and Future Directions
AU  - Aaruththiran Manoharan
AU  - Fazal Ur Rehman
AU  - Aydin Emre Utku
AU  - Mehmet Emre Aslakci
AU  - Kun Qian
AU  - Kasper Mayntz Paasch
AU  - Thomas Ebel
AU  - Henrik Andersen
PY  - 2026
DA  - 2026/11/06
JO  - Batteries
DO  - 10.3390/batteries12110475
UR  - https://www.mdpi.com/2313-0105/12/11/475
AB  - Data-driven battery state estimation has been widely studied in recent years to enable safe, reliable, and lifetime-aware battery management in e-mobility applications, where accurate estimation is essential under highly dynamic operating conditions. With several recent reviews focusing on model architectures and methodological developments, a critical assessment of practical feasibility, real-world deployability, and application-specific challenges in e-mobility is needed. This review therefore provides a challenge-oriented assessment of recent data-driven methods for battery state estimation, with particular emphasis on practical deployment in e-mobility systems. Beyond summarising existing approaches, it assesses how recent studies address key deployment barriers. These barriers include data dependency, limited ground-truth health labels, and fragmented datasets. Further challenges concern feature complexity and interpretability, robustness under domain shifts, embedded-BMS constraints, and cell-to-pack scalability.Recentresearch increasingly focuses on battery health analytics, reflecting the importance of ageing-aware battery management in e-mobility. Based on the gaps identified, future research directions are outlined to support the development of safe, reliable, interpretable, and deployment-ready battery state estimation for next-generation e-mobility systems.
SN  - 2313-0105
ER  -