Abstract
The proposed BOECMS (Bi-Object Equivalent Consumption Minimization Strategy) possesses three key features: being real-time, causal and capable of fulfilling two objects, namely, (i) minimized fuel consumption, and (ii) the battery SOC (State Of Charge) steadily retained within a relatively narrow range. A HEV (Hybrid Electric Vehicle) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (ADvanced VehIcle SimulatOR) and Simulink at design stage. According to the computer simulation results, the degree of the improvement of fuel economy is up to 40.39 % in terms of 'MANHATTAN' drive cycle in comparison to the conventional pure ICE (Internal Combustion Engine) vehicles. Moreover, the experimental results by HIL (Hardware-In-the-Loop) are pretty close to the computer simulations undertaken by Simulink. This implies that the proposed energy management strategy, BOECMS, can be potentially applied to the real-world driving in the future.
| Original language | English |
|---|---|
| Title of host publication | 2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781538664575 |
| DOIs | |
| Publication status | Published - 2018 Aug 21 |
| Event | 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018 - Kitakyushu, Japan Duration: 2018 Apr 23 → 2018 Apr 25 |
Publication series
| Name | 2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018 |
|---|
Other
| Other | 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018 |
|---|---|
| Country/Territory | Japan |
| City | Kitakyushu |
| Period | 18-04-23 → 18-04-25 |
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Control and Optimization
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2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8443492 (2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Bi-object Energy Consumption Minimization Strategy for HEVs
AU - Huang, Yen Hsiang
AU - Tsai, Nan Chyuan
AU - Chiu, Hsin Lin
AU - Chen, Yu Chi
AU - Kuo, Chia Cheng
N1 - Funding Information: Figure 8. Engine operation points under “WVUSUB” driving cycle: (a) BOECMS, (b) rule-based, (c) conventional pure ICE vehicle. B. Experimental Results by Hardware-in-the-loop The effectiveness of BOECMS by Hardware-In-the-Loop experiments is quite consistent with the computer simulation results earlier undertaken by Simulink. In other words, the same advantages of BOECMS proposed by this paper are shown again in HIL experiments, namely, (i) the operation points of engine tend to be located near the high-efficiency zone, (ii) the battery SOC can be sustained within relatively narrow range, (iii) the fuel consumption is reduced by 23.29% in terms of SC03 driving cycle, in comparison to “Rule-based Strategy”, and (iv) the final SOC is converged to the initial SOC. They can be summarized by Fig. 9. It is concluded that the results of HIL experiments are pretty in accordance with those by computer simulations. Figure 9. Experimental result under “SC03” points, (b) battery SOC. driving cycle: (a) operation VI. CONCLUSIONS A combined forward/backward-facing BSG (Belt-Driven Starter Generator) HEV (Hybrid Electric Vehicle) model is built up and the effectiveness of proposed control strategy, BOECMS (Bi-object Equivalent Consumption Minimization Strategy), is verified by simulations and experiments. Intensive simulations results show that the proposed power allocation strategy can evidently improve fuel economy, compared with conventional pure ICE (Internal Combustion Engine) vehicles and the most popularly applied rival: Rule-based Strategy, in addition to the battery SOC (State Of Charge) being able to be steadily retained within a relatively narrow range: [0.4, 0.6]. According to the computer simulation results, the improvement degree of fuel economy is up to 40.39 % and 23.09 % in terms of “MANHATTAN” drive cycle in comparison to the conventional pure ICE vehicles and Rule-based Strategy respectively. In addition, the SOC can be retained within a relatively narrow range [0.4, 0.6], instead of fluctuating significantly. To be more practical, the corresponding HIL experimental test rig is successfully set up for verification upon the proposed power split strategy, BOECMS, for BSG HEVs. By HIL experiments, it is concluded that the proposed BOECMS is potentially applicable to the real-world drivings and especially beneficial to the lifespan of both EM (Electric Motor) and ICE. ACKNOWLEDGMENT This research was partially supported by Yen Tjing Ling Industrial Development Foundation (Taiwan) for the research grant. The authors would like to express their appreciations. REFERENCES [1] L. Guzzella, and A. Sciarretta, “Vehicle propulsion systems, Springer-Verlag Berlin Heidelberg,” Heidelberg, Germany, 2007. [2] J.P. Trovão, P.G. Pereirinha, H.M. Jorge, and C.H. Antunes, “A multi-level energy management system for multi-source electric vehicles-An integrated rule-based meta-heuristic approach,” Applied Energy, vol. 105, pp. 304-318, May 2013. [3] S.H. Liu, C.Q, Du, F.W. Yan, J. Wang, Z. Li, and Y. Luo, “A rule-based energy management strategy for a new BSG hybrid electric vehicle,” 2012 Third Global Congress on Intelligent Systems, Wuhan, China, pp. 209-212, 2012. [4] Y. Hu, W.M. Li, H. Xu, and G.Q. Xu, “An online learning control strategy for hybrid electric vehicle based on fuzzy Q-learning,” Energies, vol. 8, pp. 11167-11186, 2015. [5] I . Arsie, C. Pianese, G. Rizzo, and M. Santoro, “A model for the energy management in a parallel hybrid vehicle,” 2001 3rd International Conference on Control and Diagnostics in Automotive Applications, Genova, Italy, 2001. [6] A.H. Wang, and W.Z. Yang, “Design of energy management strategy in hybrid vehicles by evolutionary fuzzy system part I: fuzzy logic controller development,” 2006 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006. [7] A.H. Wang, and W.Z. Yang, “Design of energy management strategy in hybrid electric vehicles by evolutionary fuzzy system part II: tuning fuzzy controller by genetic algorithms,” 2006 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006. [8] Q. Li, W.R. Chen, Y.K. Li, S.K. Liu, and J. Huang, “Energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle based on fuzzy logic,” Electrical Power and Energy Systems, vol. 43, pp. 514-525, 2012. [9] L.M. Niu, H.Y. Yang, and Y.H. Zhang, “Intelligent HEV fuzzy logic control strategy based on identification and prediction of drive cycle and driving trend,” World Journal of Engineering and Technology, vol. 3, pp. 215-226, 2015. [10] M. Marx, X. Shen, and D. Soffker, A data-driven online identification and control optimization approach applied to a hybrid electric powertrain system, IFAC Proceedings, vol. 45, pp. 153-158, 2012. [11] J. Park, Z.H. Chen, and Y.L. Murphey, “Intelligent vehicle power management through neural learning,” 2010 International Joint Conference on Neural Networks, Barcelona, Spain, pp. 1-7, 2010. [12] D. Bianchi, L. Rolando, L. Serrao, S. Onori, G. Rizzoni, N. Al-Khayat, T.M. Hsieh, and P. Kang, “A rule-based strategy for a series/parallel hybrid electric vehicle: An approach based on dynamic programming,” 2010 ASME Dynamic Systems and Control Conference, Cambridge, Massachusetts, USA, 2010. [13] Y.B. Yu, Q.N. Wang, H.T. Min, P.Y. Wang, and C.G. Hao, “Control strategy optimization using dynamic programming method for synergic electric system on hybrid electric vehicle,” Natural Science, vol. 1, pp. 222-228, 2009. [14] O. Sundström, D. Ambühl, and L. Guzzella, “On implementation of dynamic programming for optimal control problems with final state constraints,” Oil & Gas Science and Technology-Revue d'IFP Energies nouvelles, vol. 65, pp. 91-102, 2010. [15] J.X. Fan, J.Y. Zhang, and T.L. Shen, “Map-based power-split strategy design with predictive performance optimization for parallel hybrid electric vehicles,” Energies, vol. 8, pp. 9946-9968, 2015. [16] D. Kum, H. Peng, and N.K. Bucknor, “Supervisory control of parallel hybrid electric vehicles for fuel and emission reduction,” Journal of Dynamic Systems, Measurement, and Control, vol. 133, p. 061010, 2011. [17] C. Musardo, G. Rizzoni, Y. Guezennec, and B. Staccia, “A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management,” European Journal of Control, vol. 11, pp. 509-524, 2005. [18] H. He, C. Sun, and X. Zhang, “A method for identification of driving patterns in hybrid electric vehicles based on a LVQ neural network,” Energies, vol. 5, pp. 3363-3380, Sep. 2012. [19] J. Wang, Q.N. Wang, X.H. Zeng, P.Y. Wang, and J.N. Wang, “Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles,” International Journal of Automotive Technology, vol. 16, pp. 685-695, 2015. Reviewer Comments -----------------------REVIEW 1 --------------------- Overall evaluation: 1 (weak accept) -----------Overall evaluation ----------- This paper proposes an energy consumption minimization strategy with two objectives: (i) minimized fuel consumption, and (ii) the battery SOC (State Of Charge) steadily retained within a relatively narrow range. The paper is well-written. However, reviewer has following comments: 1) Lot of research is already available to minimize fuel consumption and SOC of the battery. Reviewer is wondering about the innovation in the proposed energy consumption minimization strategy. It will be better if authors could state few bullet points stating its contributions. 2) Authors state that the proposed BOECMS possesses a feature of being causal. What does it mean in this paper? 3) Pictures given in the paper are not very clear. Some text is not readable. It should be improved. 4) Conclusions of the paper is missing. It is advisable to add conclusion of the paper. 5) Chapter V and VI have the same name. -----------------------REVIEW 2 --------------------- Overall evaluation: 2 (accept) -----------Overall evaluation ----------- This paper proposes a Bi-object optimization taking account of both minimizing fuel consumption of ICE and retaining SOC of batteries. According to simulation results, the proposed optimization achieved the 40% improvement of the fuel economy. This improvement was validated by experiment using Hardware-In-the-Loop. Technical writing in English is also good. This paper incorporates several optimization algorithm including Genetic Algorithm, Learning Vector Quantization Neural Network, and Fuzzy Logic Algorithm. However, it is not clearly described what exactly each algorithm calculates or optimizes. Also, some figures are a bit difficult to read, for example, Figure. 2. Please updates these figures. Publisher Copyright: © 2018 IEEE.
PY - 2018/8/21
Y1 - 2018/8/21
N2 - The proposed BOECMS (Bi-Object Equivalent Consumption Minimization Strategy) possesses three key features: being real-time, causal and capable of fulfilling two objects, namely, (i) minimized fuel consumption, and (ii) the battery SOC (State Of Charge) steadily retained within a relatively narrow range. A HEV (Hybrid Electric Vehicle) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (ADvanced VehIcle SimulatOR) and Simulink at design stage. According to the computer simulation results, the degree of the improvement of fuel economy is up to 40.39 % in terms of 'MANHATTAN' drive cycle in comparison to the conventional pure ICE (Internal Combustion Engine) vehicles. Moreover, the experimental results by HIL (Hardware-In-the-Loop) are pretty close to the computer simulations undertaken by Simulink. This implies that the proposed energy management strategy, BOECMS, can be potentially applied to the real-world driving in the future.
AB - The proposed BOECMS (Bi-Object Equivalent Consumption Minimization Strategy) possesses three key features: being real-time, causal and capable of fulfilling two objects, namely, (i) minimized fuel consumption, and (ii) the battery SOC (State Of Charge) steadily retained within a relatively narrow range. A HEV (Hybrid Electric Vehicle) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (ADvanced VehIcle SimulatOR) and Simulink at design stage. According to the computer simulation results, the degree of the improvement of fuel economy is up to 40.39 % in terms of 'MANHATTAN' drive cycle in comparison to the conventional pure ICE (Internal Combustion Engine) vehicles. Moreover, the experimental results by HIL (Hardware-In-the-Loop) are pretty close to the computer simulations undertaken by Simulink. This implies that the proposed energy management strategy, BOECMS, can be potentially applied to the real-world driving in the future.
UR - http://www.scopus.com/inward/record.url?scp=85053630260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053630260&partnerID=8YFLogxK
U2 - 10.1109/EPECS.2018.8443492
DO - 10.1109/EPECS.2018.8443492
M3 - Conference contribution
AN - SCOPUS:85053630260
SN - 9781538664575
T3 - 2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018
BT - 2018 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Electric Power and Energy Conversion Systems, EPECS 2018
Y2 - 23 April 2018 through 25 April 2018
ER -