Dominik Jöst

1.6k total citations · 1 hit paper
17 papers, 1.2k citations indexed

About

Dominik Jöst is a scholar working on Electrical and Electronic Engineering, Automotive Engineering and Control and Systems Engineering. According to data from OpenAlex, Dominik Jöst has authored 17 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 12 papers in Automotive Engineering and 3 papers in Control and Systems Engineering. Recurrent topics in Dominik Jöst's work include Advanced Battery Technologies Research (12 papers), Advancements in Battery Materials (9 papers) and Advanced Battery Materials and Technologies (8 papers). Dominik Jöst is often cited by papers focused on Advanced Battery Technologies Research (12 papers), Advancements in Battery Materials (9 papers) and Advanced Battery Materials and Technologies (8 papers). Dominik Jöst collaborates with scholars based in Germany, China and United States. Dominik Jöst's co-authors include Dirk Uwe Sauer, Weihan Li, Florian Ringbeck, Julia Badeda, Dominik Schulte, Fan Yue, Xuebing Han, Minggao Ouyang, Fabian Frie and Matthias Kuipers and has published in prestigious journals such as Journal of Power Sources, Applied Energy and ACS Energy Letters.

In The Last Decade

Dominik Jöst

16 papers receiving 1.2k citations

Hit Papers

Digital twin for battery ... 2020 2026 2022 2024 2020 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation
    2020 429 citations Weihan Li, Julia Badeda et al. Journal of Energy Storage profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dominik Jöst Germany 11 1.0k 1.0k 228 103 80 17 1.2k
Mattia Ricco Italy 17 1.1k 1.1× 1.4k 1.4× 498 2.2× 36 0.3× 46 0.6× 93 1.7k
Mince Li China 13 1.5k 1.4× 1.4k 1.4× 386 1.7× 119 1.2× 17 0.2× 23 1.7k
Daniel T. Gladwin United Kingdom 19 1.0k 1.0× 1.3k 1.3× 511 2.2× 37 0.4× 75 0.9× 110 1.6k
Zongchang Liu United States 8 472 0.5× 453 0.4× 235 1.0× 99 1.0× 42 0.5× 23 730
Akram Eddahech France 13 1.3k 1.3× 1.2k 1.2× 213 0.9× 160 1.6× 9 0.1× 21 1.4k
Xiaowei Zhao China 12 646 0.6× 553 0.5× 190 0.8× 60 0.6× 16 0.2× 27 769
Poria Fajri United States 19 787 0.8× 1.5k 1.4× 657 2.9× 28 0.3× 22 0.3× 92 1.7k
Aihua Tang China 21 1.4k 1.3× 1.1k 1.1× 375 1.6× 133 1.3× 9 0.1× 64 1.6k
Jiaqiang Tian China 15 1.7k 1.6× 1.5k 1.5× 511 2.2× 209 2.0× 12 0.1× 50 1.9k

Countries citing papers authored by Dominik Jöst

Since Specialization
Citations

This map shows the geographic impact of Dominik Jöst's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dominik Jöst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominik Jöst more than expected).

Fields of papers citing papers by Dominik Jöst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dominik Jöst. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dominik Jöst. The network helps show where Dominik Jöst may publish in the future.

Co-authorship network of co-authors of Dominik Jöst

This figure shows the co-authorship network connecting the top 25 collaborators of Dominik Jöst. A scholar is included among the top collaborators of Dominik Jöst based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dominik Jöst. Dominik Jöst is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Dobschinski, Jan, et al.. (2025). Predictability of the operating behaviour of different types of heat pump systems. IET conference proceedings.. 2024(16). 499–506.
2.
Jöst, Dominik, et al.. (2024). Towards robust state estimation for LFP batteries: Model-in-the-loop analysis with hysteresis modelling and perspectives for other chemistries. Journal of Energy Storage. 92. 112042–112042. 10 indexed citations
3.
Jöst, Dominik, et al.. (2023). Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy. Batteries & Supercaps. 6(8). 7 indexed citations
4.
Dechent, Philipp, et al.. (2023). Correlation of Health Indicators on Lithium‐Ion Batteries. Energy Technology. 11(7). 5 indexed citations
5.
Dechent, Philipp, Dominik Jöst, Yuliya Preger, et al.. (2021). ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature. ACS Energy Letters. 6(6). 2351–2355. 32 indexed citations
6.
Li, Weihan, Fan Yue, Florian Ringbeck, Dominik Jöst, & Dirk Uwe Sauer. (2021). Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression. Applied Energy. 306. 118114–118114. 60 indexed citations
7.
Li, Weihan, Jiawei Zhang, Florian Ringbeck, et al.. (2021). Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries. Journal of Power Sources. 506. 230034–230034. 102 indexed citations
8.
Jöst, Dominik, et al.. (2021). Zeitreihendaten eines Fahrzyklus-Alterungstests von 28 Hochenergie NCA/C+Si Rundzellen des Typs 18650. RWTH Publications (RWTH Aachen). 8 indexed citations
9.
Li, Weihan, et al.. (2021). Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence. Energy storage materials. 44. 557–570. 141 indexed citations
10.
Li, Weihan, Dominik Jöst, Florian Ringbeck, et al.. (2020). Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy. 269. 115104–115104. 170 indexed citations
11.
Li, Weihan, et al.. (2020). Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage. 30. 101557–101557. 429 indexed citations breakdown →
12.
Li, Weihan, Fan Yue, Florian Ringbeck, et al.. (2020). Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources. 476. 228534–228534. 209 indexed citations
13.
Jöst, Dominik, et al.. (2019). Identification of load dependent cell voltage model parameters from sparse input data using the Mixed Integer Distributed Ant Colony Optimization solver. Journal of Power Sources. 437. 226880–226880. 11 indexed citations
14.
15.
Jöst, Dominik, et al.. (2015). Dynamic dimensioning of frequency restoration reserve capacity based on quantile regression. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 7 indexed citations
16.
Rogge, Matthias, Dominik Jöst, & Dirk Uwe Sauer. (2015). Lithium-Titanate Cells for Fast Charging Battery Buses - Results of a Performance and Aging Analysis. 1 indexed citations
17.
Jöst, Dominik, et al.. (2014). A new method for day-ahead sizing of control reserve in Germany under a 100% renewable energy sources scenario. Electric Power Systems Research. 119. 485–491. 29 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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