Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles
Open accessAbstract
Thermal management systems of passenger vehicles are fundamental to provide adequate cabin thermal comfort. However, for battery electric vehicles they can use a significant amount of battery energy and thus reduce the real driving range. Indeed, when heating or cooling the vehicle cabin the thermal management system can consume up to 84% of the battery capacity. This study proposes a model-based approach to design an energy-efficient control strategy for heating electric vehicles, considering the entire climate control system at different ambient conditions. Specifically, the study aims at reducing the energy demand of the compressor and water pumps when operating in heat pump mode. At this scope, the climate control system of the reference vehicle is modelled and validated, enabling a system efficiency analysis in different operating points. Based on the system performance assessment, the optimized operating strategy for the compressor and the water pumps is elaborated and the results show that the demand-based control achieves up to 34% energy reduction when compared to the standard control.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In BEVs, cabin heating energy demand must be supplied by the battery because less waste heat is available from the powertrain than in ICEVs, which adds to energy consumption and reduces range.
“Since electric powertrains have a higher efficiency than conventional ones, BEVs have less waste heat available for cabin heating than ICEVs.”
From Effect of Low Temperature on Electric Vehicle Range (2021) · cites this paper as [17]
Section 1.2 says BEVs lack the engine waste heat available to conventional ICE vehicles and must draw battery energy for heating, reducing range. It does not state that the difference is caused by electric powertrains’ higher efficiency, as the citing sentence asserts. No page number is available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 22 · Cited by 23
Research works printed but not linked: 26
- Thomas, C.E. Sustainable Transportation Options for the 21st Century and Beyond; Appendix A Range Limitations of Battery Electric Vehicles; Springer International Publishing: Cham, Switzerland, 2015; p. 119. [CrossRef] · DOI
- Franke, T.; Rauh, N.; Günther, M.; Trantow, M.; Krems, J.F. Which Factors Can Protect Against Range Stress in Everyday Usage of Battery Electric Vehicles? Toward Enhancing Sustainability of Electric Mobility Systems. Hum. Factors 2015, 58, 13-26. [CrossRef] [PubMed] · DOI
- Basciotti, D.; Dvorak, D.; Gellai, I. A Novel Methodology for Evaluating the Impact of Energy Efficiency Measures on the Cabin Thermal Comfort of Electric Vehicles. Energies 2020, 13, 3872. [CrossRef] · DOI
- Shete, K. Influence of Automotive Air Conditioning load on Fuel Economy of IC Engine Vehicles. Int. J. Sci. Eng. Res. 2015, 6, 1367-1372.
- Farrington, R.; Rugh, J. Impact of Vehicle Air Conditioning on Fuel Economy, Tailpipe Emissions, and Electric Vehicle Range. In Proceedings of the Earth Technologies Forum, Washington, DC, USA, 31 October 2000.
- Paffumi, E.; Otura, M.; Centurelli, M.; Casellas, R.; Brenner, A.; Jahn, S. Driving Range and Cabin Temperature Performances at Different Ambient Conditions in Support to the Design of a User-Centric Efficient Electric Vehicle: The QUIET Project. In Proceedings of the 14th Conference on Sustainable Development of Energy, Water and Environment Systems 2019, Dubrovnik, Croatia, 1-6 October 2019.
- Park, J.; Lee, M.; Kim, G.; Park, S.; Kim, J. Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH. Energies 2020, 13, 2138. [CrossRef] · DOI
- Maletić, F.; Hrgetić, M.; Deur, J. Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack. Energies 2020, 13, 540. [CrossRef] · DOI
- Ali, M.U.; Zafar, A.; Nengroo, S.H.; Hussain, S.; Park, G.-S.; Kim, H.-J. Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features. Energies 2019, 12, 4366. [CrossRef] · DOI
- Liang, K.; Zhang, Z.; Liu, P.; Wang, Z.; Jiang, S. Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles. Energies 2019, 12, 4772. [CrossRef] · DOI
- Chen, Y.; He, Y.; Li, Z.; Chen, L. A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries. Energies 2019, 12, 4376. [CrossRef] · DOI
- Barcellona, S.; Piegari, L. Effect of current on cycle aging of lithium ion batteries. J. Energy Storage 2020, 29, 101310. [CrossRef] · DOI
- Vatanparvar, K.; Faezi, S.; Burago, I.; Levorato, M.; Al Faruque, M.A. Extended Range Electric Vehicle with Driving Behavior Estimation in Energy Management. IEEE Trans. Smart Grid 2018, 10, 2959-2968. [CrossRef] · DOI
- Zhang, Z.; Li, W.; Shi, J.; Chen, J. A Study on Electric Vehicle Heat Pump Systems in Cold Climates. Energies 2016, 9, 881. [CrossRef] · DOI
- Rabl, B. Heat Pump Air Conditioning Systems for Optimized Energy Demand of Electric Vehicles. Comprehensive Energy Management-Safe Adaptation Predictive Control and Thermal Management. In SpringerBriefs in Applied Sciences and Technology; Graz University of Technology: Graz, Austria, 2018; pp. 81-92.
- Ozbek, M.; Gyorog, T.; Hunemorder, W.; Kohli, V.; Caldevilla, A.; Savi, A.; Vitali, D.; Biglia, M.; Nakagawa, M. A compact and efficient water-to-water heat-pump system for electric vehicles. In Proceedings of the 1st International FEV Conference: Zero C02 Mobility, Aachen, Germany, 9-10 November 2017.
- Lajunen, A. Energy Efficiency and Performance of Cabin Thermal Management in Electric Vehicles. SAE Tech. Pap. 2017. [CrossRef] · DOI
- Weustenfeld, T.A.; Bauer-Kugelmann, W.; Menken, J.C.; Strasser, K.; Köhler, J. Heat flow rate based thermal management for electric vehicles using a secondary loop heating and cooling system. In Proceedings of the Vehicle Thermal Management Systems Conference and Exhibition, Nottingham, UK, 10-13 May 2015.
- Suh, I.-S.; Lee, M.; Kim, J.; Oh, S.T.; Won, J.-P. Design and experimental analysis of an efficient hvac (heating ventilation air-conditioning) system on an electric bus with dynamic on-road wireless charging. Energy 2015, 81, 262-273. [CrossRef] · DOI
- Leighton, D. Combined Fluid Loop Thermal Management for Electric Drive Vehicle Range Improvement. SAE Int. J. Passeng. Cars Mech. Syst. 2015, 8, 711-720. [CrossRef] · DOI
- Kitanoski, F.; Hofer, A. A contribution to energy optimal thermal management for vehicles. In Proceedings of the IFAC Symposium on Advances in Automotive Control, Munich, Germany, 14 July 2010; pp. 87-92.
- Fischer, T.; Götz, F.; Berg, L.F.; Kollmeier, H.-P.; Gauterin, F. Model-based development of a Holistic Thermal Management System for an Electric Car with a High Temperature Fuel Cell Range Extender. In Proceedings of the 11th International Modelica Conference, Versailles, France, 21-23 September 2015; pp. 127-133.
- De Nunzio, G.; Sciaretta, A.; Steiner, A.; Mladek, A. Thermal Management Optimization of a Heat-Pump-Based HVAC System for Cabin Conditioning in Electric Vehicles. In Proceedings of the International Conference on Ecological Vehicles and Renewable Energies, Monte-Carlo, Monaco, 10-12 April 2018.
- Hosoz, M.; Direk, M. Performance evaluation of an integrated automotive air conditioning and heat pump system. Energy Convers. Manag. 2006, 47, 545-559. [CrossRef] · DOI
- Al Faruque, M.A.; Vatanparvar, K. Modeling, analysis, and optimization of Electric Vehicle HVAC systems. In Proceedings of the 21st Asia and South Pacific Design Automation Conference (ASP-DAC), Macau, China, 25-28 January 2016.
- Lee, H.; Hwang, Y.; Song, I.; Jang, K. Transient thermal model of passenger car's cabin and implementation to saturation cycle with alternative working fluids. Energy 2015, 90, 1859-1868. [CrossRef] · DOI · Paper page
- Authors
- Dvorak, D., Basciotti, D., & Gellai, I.
- Affiliations
- AIT Austrian Institute of Technology GmbH (AT)
- Corresponding author
- Dvorak, Dominik
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2020-10-19
- Volume
- 13
- Issue
- 20
- Pages
- 5440
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W3092995307
- Cited by
- 23
- References
- 22
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 4
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 (769826)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Refrigeration and Air Conditioning Technologies
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Keywords
- heat pumps, battery electric vehicles, cabin thermal management, compressor control, water pump control, energy efficiency, cabin heating, driving range
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
