skip to main content
research-article

Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles

Published:05 July 2018Publication History
Skip Abstract Section

Abstract

Electric Vehicles (EV) as a zero-emission means of transportation encounter challenges in battery design that cause a range anxieties for the drivers. Besides the electric motor, the Heating, Ventilation, and Air Conditioning (HVAC) system is another major contributor to the power consumption that may influence the EV battery lifetime and driving range. In the state-of-the-art methodologies for battery management systems, the battery performance is monitored and improved. While in the automotive climate control, the passenger’s thermal comfort is the main objective. Hence, the influence of the HVAC power on the battery behavior for the purpose of jointly optimized battery management and climate control has not been considered. In this article, we propose an automotive climate control methodology that is aware of the battery behavior and performance, while maintaining the passenger’s thermal comfort. In our methodology, battery parameters and cabin temperature are modeled and estimated, and the HVAC utilization is optimized and adjusted with respect to the electric motor and HVAC power requests. Therefore, the battery stress reduces, while the cabin temperature is maintained by predicting and optimizing the system states in the near-future. We have implemented our methodology and compared its performance to the state-of-the-art in terms of battery lifetime improvement and energy consumption reduction. We have also conducted experiments and analyses to explore multiple control window sizes, drive profiles, ambient temperatures, and modeling error rates in the methodology. It is shown that our battery-aware climate control can extend the battery lifetime by up to 13.2% and reduce the energy consumption by up to 14.4%.

References

  1. Mohammad Abdullah Al Faruque and Korosh Vatanparvar. 2016. Modeling, analysis, and optimization of electric vehicle HVAC systems. In Proceedings of the Asia and South Pacific Design Automation Conference (ASP-DAC’16). 1--6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Maral Amir and Tony Givargis. 2017. HES machine: Harmonic equivalent state machine modeling for cyber-physical systems. In Procedings of International High Level Design Validation and Test Workshop (HLDVT’17). 31--38.Google ScholarGoogle ScholarCross RefCross Ref
  3. Maral Amir and Tony Givargis. 2017. Hybrid state machine model for fast model predictive control: Application to path tracking. In Proceedings of the International Conference on Design (ICCAD’17). 185--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Chakraborty, M. A. Al Faruque, W. Chang, D. Goswami, M. Wolf, and Q. Zhu. 2016. Automotive cyber-physical systems: A tutorial introduction. IEEE Design Test 33, 4 (2016), 92--108.Google ScholarGoogle ScholarCross RefCross Ref
  5. S. Chakraborty, M. Lukasiewycz, C. Buckl, S. Fahmy, P. Leteinturier, and H. Adlkofer. 2012. Embedded systems and software challenges in electric vehicles. In Proceedings of the Conference on Design Automation and Test in Europe (DATE’12). 424--429. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Ka Wai Eric Cheng, B. P. Divakar, Hongjie Wu, Kai Ding, and Ho Fai Ho. 2011. Battery-management system (BMS) and soc development for electrical vehicles. IEEE Trans. Vehic. Technol. 60, 1 (2011), 76--88.Google ScholarGoogle ScholarCross RefCross Ref
  7. Dennis Doerffel and Suleiman Abu Sharkh. 2006. A critical review of using the Peukert equation for determining the remaining capacity of lead-acid and lithium-ion batteries. J. Power Sources 155, 2 (2006), 395--400.Google ScholarGoogle ScholarCross RefCross Ref
  8. Ulrich Eberle and Rittmar von Helmolt. 2010. Sustainable transportation based on electric vehicle concepts: A brief overview. Energy Environ. Sci. 3, 6 (2010), 689--699.Google ScholarGoogle ScholarCross RefCross Ref
  9. United States Environmental Protection Agency EPA. 2017. Test drive cycles. Retrieved from www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules.Google ScholarGoogle Scholar
  10. Google. 2015. The Google API web services. Retrieved from developers.google.com/maps/documentation/webservices.Google ScholarGoogle Scholar
  11. Mehdi Maasoumy Haghighi. 2013. Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber Physical System Approach. University of California, Berkeley.Google ScholarGoogle Scholar
  12. John G. Hayes, R. Pedro R. de Oliveira, Sean Vaughan, and Michael G. Egan. 2011. Simplified electric vehicle power train models and range estimation. In Proceedings of the IEEE Vehicle Power and Propulsion Conference. 1--5.Google ScholarGoogle Scholar
  13. K. David Huang, Sheng Chung Tzeng, Tzer Ming Jeng, and Wing Ding Chiang. 2006. Air-conditioning system of an intelligent vehicle-cabin. Appl. Energy 83, 6 (2006), 545--557.Google ScholarGoogle ScholarCross RefCross Ref
  14. B. S. K. K. Ibrahim, M. A. N. Aziah, S. Ahmad, R. Akmeliawati, H. M. I. Nizam, A. G. A. Muthalif et al. 2012. Fuzzy-based temperature and humidity control for HVAC of electric vehicle. Procedia Eng. 41 (2012), 904--910.Google ScholarGoogle ScholarCross RefCross Ref
  15. Anthony Kelman and Francesco Borrelli. 2011. Bilinear model predictive control of a HVAC system using sequential quadratic programming. In Proceedings of the International Federation of Automatic Control World Congress. 9869--9874.Google ScholarGoogle ScholarCross RefCross Ref
  16. Hahnsang Kim and Kang G. Shin. 2009. Scheduling of battery charge, discharge, and rest. In Proceedings of the 30th IEEE Real-Time Systems Symposium (RTSS’09). 13--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. D. Knibbs, R. J. De Dear, and S. E. Atkinson. 2009. Field study of air change and flow rate in six automobiles. Indoor Air 19, 4 (2009), 303--313.Google ScholarGoogle ScholarCross RefCross Ref
  18. Kosmas Knoedler, Jochen Steinmann, Sylvain Laversanne, Stephen Jones, Arno Huss, Emre Kural, David Sanchez, Oliver Bringmann, and Jochen Zimmermann. 2012. Optimal energy management and recovery for FEV. In Proceedings of the Conference on Design Automation and Test in Europe (DATE’12). 683--684. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Stéphanie Lefèvre, Dizan Vasquez, and Christian Laugier. 2014. A survey on motion prediction and risk assessment for intelligent vehicles. Robomech. J. 1, 1 (2014), 1.Google ScholarGoogle ScholarCross RefCross Ref
  20. Thomas Levermore and others. 2014. A review of driver modelling. In Proceedings of the UKACC International Conference on Control (CONTROL’14). 296--300.Google ScholarGoogle Scholar
  21. Xue Lin, Paul Bogdan, Naehyuck Chang, and Massoud Pedram. 2015. Machine learning-based energy management in a hybrid electric vehicle to minimize total operating cost. Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD’15). 627--634. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Manuel Lorenz, Dusan Fiala, Markus Spinnler, and Thomas Sattelmayer. 2014. A coupled numerical model to predict heat transfer and passenger thermal comfort in vehicle cabins. SAE Technical Paper.Google ScholarGoogle Scholar
  23. L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang. 2013. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226 (2013), 272--288.Google ScholarGoogle ScholarCross RefCross Ref
  24. Martin Lukasiewycz and Sebastian Steinhorst. 2013. System architecture and software design for electric vehicles. In Proceedings of the Design Automation Conference (DAC’13). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Srdjan M. Lukic, Jian Cao, Ramesh C. Bansal, Fernando Rodriguez, and Ali Emadi. 2008. Energy storage systems for automotive applications. IEEE Trans. Industr. Electron. 55, 6 (2008), 2258--2267.Google ScholarGoogle ScholarCross RefCross Ref
  26. Srdjan M. Lukic and Ali Emadi. 2004. Effects of drivetrain hybridization on fuel economy and dynamic performance of parallel hybrid electric vehicles. IEEE Trans. Vehicu. Technol. 53, 2 (2004), 385--389.Google ScholarGoogle ScholarCross RefCross Ref
  27. MathWorks. 2015. MATLAB, Simulink. Retrieved from www.mathworks.com.Google ScholarGoogle Scholar
  28. Alan Millner. 2010. Modeling lithium ion battery degradation in electric vehicles. In Proceedings of the IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply. 349--356.Google ScholarGoogle ScholarCross RefCross Ref
  29. Ross Montgomery and Robert McDowall. 2008. Fundamentals of HVAC control systems. Elsevier, 348 pages.Google ScholarGoogle Scholar
  30. Jeremy Neubauer and Eric Wood. 2014. The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility. J. Power Sources 257 (2014), 12--20.Google ScholarGoogle ScholarCross RefCross Ref
  31. Maria Nilsson. 2011. Electric vehicles: The phenomenon of range anxiety. ELVIRE Report. ELVIRE Consortium. http://e-mobility-nsr.eu/fileadmin/user_upload/downloads/info-pool/the_phenomenon_of_range_anxiety_elvire.pdf.Google ScholarGoogle Scholar
  32. Nissan. 2015. Nissan Leaf S Spec. Retrieved from www.nissanusa.com.Google ScholarGoogle Scholar
  33. Shuo Pang, Jay Farrell, Jie Du, and Matthew Barth. 2001. Battery state-of-charge estimation. In Proceedings of the American Control Conference. 1644--1649.Google ScholarGoogle Scholar
  34. Sangyoung Park, Younghyun Kim, and Naehyuck Chang. 2013. Hybrid energy storage systems and battery management for electric vehicles. In Proceedings of the Design Automation Conference (DAC’13). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Massimo Petricca, Donghwa Shin, Alberto Bocca, Alberto Macii et al. 2014. Automated generation of battery aging models from datasheets. In Proceedings of the International Conference on Computer Design (ICCD’14). 483--488.Google ScholarGoogle Scholar
  36. Konrad Reif. 2014. Fundamentals of automotive and engine technology. Bosch Profess. Auto. Info. Springer.Google ScholarGoogle Scholar
  37. Donghwa Shin, Enrico Macii, and Massimo Poncino. 2014. Statistical battery models and variation-aware battery management. In Proceedings of the Design Automation Conference (DAC’14). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. SIEMENS. 2015. LMS Imagine.Lab Amesim. Retrieved from www.plm.automation.siemens.com/en_us/products/lms/imagine-lab.Google ScholarGoogle Scholar
  39. Tesla Motors Inc. Tesla. 2015. Tesla Model S Spec. Retrieved from www.teslamotors.com.Google ScholarGoogle Scholar
  40. Kohei Umezu and Hideto Noyama. 2010. Air-conditioning system for electric vehicles (i-MiEV). In Proceedings of the SAE Automotive Alternate Refrigerant Systems Symposium.Google ScholarGoogle Scholar
  41. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2015. Battery lifetime-aware automotive climate control for electric vehicles. In Proceedings of the 52nd Annual Design Automation Conference (DAC’15). Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2016. Eco-friendly automotive climate control and navigation system for electric vehicles. In Proceedings of the International Conference on Cyber-Physical Systems (ICCPS’16). 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2016. OTEM: Optimized thermal and energy management for hybrid electrical energy storage in electric vehicles. In Proceedings of the Conference on Design, Automation & Test in Europe (DATE’16). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2017. ACQUA: Adaptive and cooperative quality-aware control for automotive cyber-physical systems. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD’17). 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2017. Electric vehicle optimized charge and drive management. ACM Trans. Design Auto. Electron. Syst. 23, 1 (2017), Article No. 3, 1–25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2017. Path to eco-driving: Electric vehicle HVAC and route joint optimization. IEEE Design Test (2017).Google ScholarGoogle Scholar
  47. Korosh Vatanparvar, Sina Faezi, Igor Burago, Marco Levorato, and Mohammad Abdullah Al Faruque. 2017. Driving behavior modeling and estimation for battery optimization in electric vehicles: Work-in-progress. Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion, Article 15 (2017), 15:1--15:2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Korosh Vatanparvar, Sina Faezi, Igor Burago, Marco Levorato, and Mohammad Abdullah Al Faruque. 2018. Extended range electric vehicle with driving behavior estimation in energy management. IEEE Trans. Smart Grid (2018).Google ScholarGoogle Scholar
  49. Korosh Vatanparvar, Jiang Wan, and Mohammad Abdullah Al Faruque. 2015. Battery-aware energy-optimal electric vehicle driving management. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED’15). 353--358.Google ScholarGoogle ScholarCross RefCross Ref
  50. John Wang, Ping Liu, Jocelyn Hicks-Garner, Elena Sherman, Souren Soukiazian, Mark Verbrugge, Harshad Tataria, James Musser, and Peter Finamore. 2011. Cycle-life model for graphite-LiFePO4 cells. J. Power Sources 196, 8 (2011), 3942–3948.Google ScholarGoogle ScholarCross RefCross Ref
  51. Yan Wang, Jianmin Jiang, and Tingting Mu. 2013. Context-aware and energy-driven route optimization for fully electric vehicles via crowdsourcing. IEEE Trans. Intell. Transport. Syst. 14, 3 (2013), 1331--1345. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Yanzhi Wang, Xue Lin, Massoud Pedram, and Naehyuck Chang. 2015. Joint automatic control of the powertrain and auxiliary systems to enhance the electromobility in hybrid electric vehicles. In Proceedings of the Design Automation Conference (DAC’15). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Yanzhi Wang, Xue Lin, Qing Xie, Naehyuck Chang, and Massoud Pedram. 2014. Minimizing state-of-health degradation in hybrid electrical energy storage systems with arbitrary source and load profiles. In Proceedings of the Conference on Design Automation and Test in Europe (DATE’14). 1--4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Qing Xie, Yanzhi Wang et al. 2013. Charge allocation in hybrid electrical energy storage systems. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 32, 7 (2013), 1003--1016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Mounir Zeraoulia, Mohamed El Hachemi Benbouzid, and Demba Diallo. 2006. Electric motor drive selection issues for HEV propulsion systems: A comparative study. IEEE Trans. Vehic. Technol. 55, 6 (2006), 1756--1764.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Design and Analysis of Battery-Aware Automotive Climate Control for Electric Vehicles

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!