Design and Development of a Green Mobile Air Conditioning System for Electric Vehicles
Bibliographic record
Abstract
Electric Vehicles (EVs) have rapidly grown as a means for clean mobility, as they zero down tail pipe emission of greenhouse gases. Additionally, greenhouse gases such as Hydro-Fluoro-Carbon (HFCs) based refrigerants used in Mobile Air-Conditioning (MAC) are under global scrutiny for their high Global Warming Potential (GWP). To prevent earth environment to pass the climate tipping point that will be irreversible within human capacity, actions such as rapid phase down of high GWP rated HFCs under Kigali Amendment to Montreal Protocol are enacted. India being amongst signatory nations is now working to fast track phase-down use of high GWP refrigerant and transit to low GWP refrigerant options. Nearly half of national HFCs use and emissions are for manufacture and service MAC. Vehicle OEMs supplying to markets in developing countries (e.g. European nation and non-Article 5 Parties) have already phased out HFC-134a (GWP=1400) through alternate refrigerant solutions. The work presented here discusses a novel methodology to use sustainable low GWP refrigerant-based MACs in EVs. The existing MAC system operates through dual DX (Direct expansion) system cooling loop, one for cabin cooling requirement and another to ensure optimum cell temperature of high voltage battery. The refrigerant HFC-134a is not sustainable. To meet global commitments, alternative low GWP options such as R152a (GWP=124) can be considered. The superior thermal properties and low direct cost of R152a, makes it a suitable alternative to HFC-134a. The work also discusses development of intelligently controlled Secondary Loop-Mobile Air Conditioning (SL-MAC) system to address operational constraint with mildly flammable nature of R152a. In the proposed architecture, R152a is use as primary medium to produce refrigeration effect to cool the coolant by deploying chiller unit. The cooled coolant is then circulated in secondary loop comprising of two parallel cooling loops, to extract heat from cabin and HV battery systems. Suitable decision matrices have been consider to design the SLMAC configuration and components involved with it. Cooling performance have been compared basis the transition from DX system with HFC-134a to SLMAC system with R152a refrigerant, together with gap analysis and proposed solutions for bridging performance gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".