PEMFC Flow-Field & Stack Design Patent Snapshot
The retrieved corpus for PEMFC flow-field and stack design is extremely small, with UTC Power Corp holding the dominant position among the ranked filers. Activity is concentrated in the H01M fuel-cell class, with a secondary digital-computing signal from Chinese university applicants exploring simulation and AI-assisted design.
UTC Power Corp leads a very small retrieved corpus
UTC Power Corp ranks first among all ranked applicants, accounting for the largest single block of patent records in this corpus. Xi’an Jiaotong University and Hebei University of Technology each contribute one patent record and share the second rank.
The top five filers collectively represent the entirety of the top ranked applicants visible in this query’ combined total — a concentration figure that reflects the very limited size of the retrieved set rather than a mature, crowded field. The tier gap between UTC Power Corp and the two universities is pronounced.
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 1 | UTC Power Corporation | 4 | |
| 2 | Xi’an Jiaotong University | 1 | |
| 3 | Hebei University of Technology | 1 |
UTC Power Corp’s position, anchored entirely in H01M 8 (fuel cells), indicates a hardware-first orientation toward actual stack and flow-field hardware claims, while the two university entrants are focused on computational and simulation subclasses under G06F, suggesting a divergence between industrial and academic filing strategies.
The most recent filings in 2025 and 2026 are subject to standard publication lag and should be treated as under-counted; the corpus as a whole is too small to support robust trend inference. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Minimal historical activity with a late-period signal in simulation methods
The annual filing trend and technology composition together reveal a corpus that is both numerically small and technologically mixed, spanning hardware fuel-cell claims and computational design tools.
Annual filing trend
Recorded filings show zero activity across 2017–2024, with single records appearing in 2025 and 2026. Given standard publication lag of 18–24 months, the 2025 and 2026 entries are almost certainly under-counted; no meaningful trend inference can be drawn from this data alone.
↗ Hover for values · click a bar to ask EurekaTechnology composition
H01M (batteries, cells, and fuel cells) is the visible IPC class with the largest share of patent records, confirming core fuel-cell hardware coverage. G06F (electric digital data processing) is the next most represented class, reflecting simulation, AI-model, and computational-chemistry subclasses that appear in the university filings — a signal that computational design methods are entering the space alongside traditional hardware claims.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
Degasified PEM fuel cell system
A system and method are provided for managing water coolant in a PEM fuel cell system’s (10) coolant circuit (14). Gas-liquid separating apparatus (26) serves to efficiently transport liquid coolant containing gases, and to separate gases from the liquid coolant. The liquid coolant having the gases removed therefrom is then circulated through the liquid… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | Degasified PEM fuel cell system | 33 |
| 2 | Degasified PEM fuel cell system | 24 |
| 3 | 质子交换膜燃料电池（PEMFC）高电流密度性能预测方法、系统、设备及介质 | 5 |
| 4 | Degasified PEM fuel cell system | 3 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
UTC Power Corp
UTC Power Corp holds 4 patent records in this corpus, all classified under H01M 8 (fuel cells), indicating an exclusive focus on core stack and fuel-cell hardware claims. Applicant momentum data is not available for this corpus, so trajectory inference is not possible. The degasified PEM fuel cell system appears repeatedly among the most-cited records, suggesting this is the anchor asset in the portfolio.
patent records: 4Xi’an Jiaotong University
Xi’an Jiaotong University contributes 1 patent record, focused entirely on G06F subclasses covering electric digital data processing — specifically simulation and data-analysis methods applicable to PEMFC performance prediction. This computational orientation distinguishes the university’s approach from UTC Power Corp’s hardware claims. Applicant momentum data is not available for this corpus.
patent records: 1Frequently asked questions
Within the retrieved corpus, UTC Power Corp holds the largest number of patent records, ahead of Xi’an Jiaotong University and Hebei University of Technology, which each contribute one record.
China and the United States each account for two patent records in this corpus. Australia and WIPO (PCT) each contribute one record, indicating some international filing activity.
H01M (batteries, cells, and fuel cells) is the most represented IPC class, confirming that core fuel-cell hardware is the primary subject matter. G06F (electric digital data processing) is the next most common class, driven by simulation and AI-assisted design filings from university applicants.
No co-applicant filings are present in the retrieved corpus. UTC Power Corp and both universities file independently. This may reflect the small size of the corpus rather than a broader absence of collaboration in the field.
Records titled ‘Degasified PEM fuel cell system’ appear multiple times among the most-cited entries, with citation counts of 33, 24, and 3 respectively. A PEMFC high-current-density performance prediction record also appears with 5 citations.
G06N (AI and machine-learning models) and G16C (computational chemistry) each appear only once in the corpus, representing relatively sparse coverage compared to the visible H01M and G06F classes. These could be worth monitoring as computational design methods mature, though the whitespace algorithm returned no validated candidates for this corpus.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
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