本研究旨在開發細菌覓食演算法（Bacterial Foraging Algorithm, BFA）用於三動力複合動力車的能量管理/變速策略系統，並且真正應用硬體嵌入式系統(Hardware-In-The-Loop, HIL)進行即時(Real-Time)驗證驗算法之可行性。本研究中，使用HIL進行評估使用細菌覓食演算法(BFA)之三動力源複合動力車系統能量管理與變速策略控制。車輛子系統包括43 kW內燃機引擎、30 kW馬達、15 kW一體式啟動馬達和1.872 kW-h鋰電池，車重1368 kg。在能量管理系統，BFA能量管理控制，主要有三個步驟（1）趨化、（2）複製、（3）驅散。總疊代次數為30次，共有80個細菌進行最佳能量管理。
BFA與兩種控制策略進行NEDC行車型態之油耗比較：(1)規則庫管理(Rule base):有五種控制模式(系統準備、充電模式、電動模式、複合動力模式及延距模式)，根據工程師經驗設定模式切換時機；(2)最小等效油耗策略（Equivalent Consumption Minimization Strategy, ECMS）:搭配全域搜尋(Global Search Algorithm, GSA)將範圍內所有的可能解進行尋找，找出最小油耗時之動力分配比與變速策略。最後透過HIL模擬BFA於車輛控制單元(Vehicle Control Unit, VCU)Real-time之可行性與油耗效益驗證。基本規則庫、ECMS、BFA、Real-time，這四種情狀況在NEDC下的等效油耗：[538.9 g、209.6 g、248.9 g、253.6 g]，FTP-72等效油耗：[579.2 g、291 g、316.3 g、320.38 g]，再來是ECMS、BFA、Real-time三種狀況與基本規則庫相比在NEDC的能耗改善百分比是[61 %、53.8 %、52.9 %]，FTP-72下運行之能耗改善百分比是[49.7 %、45.3 %、44.6 %]。其中BFA與Real-time兩者在兩個行車型態中等效油耗改善度有高達98%的相似度，皆僅次於ECMS最佳解。未來將會實施於真實之三動力源e-CVT複合動力車輛。

The purpose of this study is to develop the bacterial foraging algorithm (BFA) by applying it to the energy management/gear shifting strategy system of a three-power-source hybrid powertrain. Furthermore, this study was practical in nature, as it used the real-time simulation Hardware-in-the-Loop (HIL) to verify the algorithm’s feasibility. This study employs HIL to assess the influence that using BFA will have on the energy management and gear shifting strategy control of a three-power-source hybrid powertrain. The vehicle weighs 1,368 kilograms and its subsystems include a 43kW internal combustion engine, 30kW motor, 15kW integrated starter generator, and a 1.872kW-h Ah lithium battery. There are three primary steps for the energy management system and BFA energy management control: 1) chemotaxis, 2) reproduction, and 3) elimination-dispersal. The overall number of iterations was 30, and 80 bacteria were used carry out optimal energy management.
BFA and two control strategies were used to carry out a comparison of fuel consumption with the NEDC (New European Driving Cycle) driving pattern. 1) Rule-based management: There are five control modes, which are system preparation, battery charging mode, electric mode, hybrid power mode, and extended range mode; the engineer used his experience to determine when to set and change modes. 2) Equivalent consumption minimization strategy (ECMS): By incorporating the global search algorithm (GSA), we searched for all the scope’s possibilities in order to find the most minimal fuel consumption for power distribution ratio and gear shifting strategy. At the end of the study, we used HIL to simulate the feasibility and verify fuel consumption benefits of BFA on vehicle control units (VCU) in real time. A basic rule base, ECMS, BFA, and real-time were the four conditions for the equivalent consumption with the NEDC driving pattern: 538.9g, 209.6g, 248.9g, and 253.6g were their respective values. The equivalent consumption values with a FTP-72 driving cycle were 579.2g, 291g, 316.3g, and 320.38g. ECMS, BFA, and real-time were compared with a basic rule base when using a NEDC driving pattern to determine percentage values for improvement in energy consumption: 61%, 53.8%, and 52.9%. Percentage values for improvement in energy consumption for a FTP-72 driving cycle were 49.7%, 45.3%, and 44.6%. The improvement in equivalent consumption values for BFA and real-time for the NEDC driving pattern and FTP-72 driving cycle were 98% similar, and they were only outperformed by ECMS, which was the optimal solution. In the future, this experiment will be used to test a three-power-source e-CVT hybrid-powered vehicle.

[1] 陳玉蕙，“臺灣電動車產業需求面商業模式”，中央大學企業管理所，碩士論文，2013年1月。
[2] 蕭睿緒，“環保節能趨勢之臺灣電動車推廣策略─以台中市為例”，國立彰化師範大學環境暨觀光遊憩所，碩士論文， 2013年6月。
[3] K.C. Bayindir, et al. “A comprehensive overview of hybrid electric vehicle: Powertrain configurations, powertrain control techniques and electronic control units,” Energy Conversion and Management., vol. 52, no. 2, pp. 1305-1313, 2011.
[4] 宋德洤、黃永慧，電動車發展趨勢下機電整合與關鍵零組件商機與產 業布局策略， 工研院產經中心，2003。
[5] 董又銘，“三動力源複合式動力車模擬與性能分析”，中華民國第十九屆車輛工程學術研討會，桃園創新技術學院機械工程系，台灣，2014。
[6] E. Karden, et al. “Energy storage devices for future hybrid electric vehicles,” Journal of power sources., vol.168, no.1, pp. 2-11, 2007.
[7] B. Propfe, et al. “Market penetration analysis of electric vehicles in the German passenger car market towards 2030,” International Journal of Hydrogen Energy., vol. 38, no.13, pp. 5201-5208, 2013.
[8] M.A. Hannan, F.A. Azidin, and A. Mohamed. “Hybrid electric vehicles and their challenges: A review,” Renewable and Sustain Energy Reviews., vol. 29, pp. 135-150, 2015.
[9] Y.H. Hung, and C.H. Wu, “An integrated optimization approach for a hybrid energy system in electric vehicles,” Applied Energy., vol. 98, pp. 479-90, 2012.
[10] J.L. Torres, R. Gonzalez, and A. Gimenez, L. Lopez, “Energy management strategy for plug-in hybrid electric vehicles, A comparative study,” Applied Energy., vol. 113, pp. 816-24, 2014.
[11] 解潘祥，“複合電動車輛動力系統介紹”，機械工業雜誌，第224卷， 2001。
[12] J.P. Ribau, C.M. Silva, and J.M. Sousa, “Efficiency, cost and life cycle CO 2 optimization of fuel cell hybrid and plug-in hybrid urban buses,” Applied Energy., vol. 129, pp. 320-335, 2014.
[13] Y.H. Hung, and C.H. Wu, “A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs,” Applied Energy., vol. 139, pp. 260-271, 2013.
[14] P. Zhang, F. Yan, and C. Du, “A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics,” Renewable and Sustain Energy Reviews., vol. 48, pp.88-104, 2015.
[15] L. Tan, F. Lin, and H. Wang, “Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows,” Neurocomputing., vol. 151, pp. 1208-1215, 2015.
[16] M. Wikstrom, L. Hansson, P. Alvfors, “Socio-technical experiences from electric vehicle utilisation in commercial fleets,” Appl. Energy., vol. 123, pp. 82-93,2014.
[17] A. Khaligh, and Z. Li, “Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: State of the art,” IEEE Trans. Vehicular Tech., vol. 59, no. 6, pp. 2806-14,2010.
[18] T. Katrašnik, “Impact of vehicle propulsion electrification on Well-to-Wheel CO2 emissions of a medium duty truck,” Appl. Energy., vol. 108, pp. 236-47, 2013.
[19] Y.H. Hung, and C.H. Wu, “An integrated optimization approach for a hybrid energy system in electric vehicles,” Appl. Energy., vol. 98, pp. 479-90, 2012.
[20] N.C. Onat, M. Kucukvar, and O. Tatari, “Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States,” Appl. Energy., vol. 150, pp. 36-49, 2015.
[21] H. Yoo, S.K. Sul, Y. Park, and J. Jeong, “System integration and power-flow management for a series hybrid electric vehicle using supercapacitors and batteries,” IEEE Trans., vol. 44, no. 1, pp. 108–114, 2008
[22] G. Ren, G. Ma, and N. Cong, “Review of electrical energy storage system for vehicular applications,” Renewable and Sustainable Energy Reviews., vol. 41, pp. 225-236, 2015.
[23] K.Ç. Bayindir, M.A. Gözüküçük, and A. Teke. “A comprehensive overview of hybrid electric vehicle: Powertrain configurations, powertrain control techniques and electronic control units,” Energy Conversion and Management., vol. 52, no 2, pp. 1305-1313, 2011.
[24] J.L. Torres, R. Gonzalez, A. Gimenez, and L. Lopez , “Energy management strategy for plug-in hybrid electric vehicles. A comparative study,” Appl. Energy., vol. 113, pp. 816-24, 2014.
[25] N.J. Schouten, M.A. Salmanb, and N.A. Kheir, “Energy management strategies for parallel hybrid vehicles using fuzzy logic,” Control Engineering Practice., vol. 11, pp. 171–7, 2003.
[26] B.C. Chen, Y.Y. Wu, and H.C. Tsai, “Design and analysis of power management strategy for range extended electric vehicle using dynamic programming,” Appl. Energy., vol. 113, pp. 1764-74, 2014.
[27] Z. Yuan, L. Teng, S. Fengchun, and H. Peng, “Comparative study of dynamic programming and Pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle,” Energy., vol. 6, no. 4, pp. 2305-2318, 2013.
[28] M. Montazeri-Gh, A. Poursamad, B. Ghalichi, “Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles,” Journal of the Franklin Institute., vol. 343, pp. 420–35, 2006.
[29] J. Liu, and H. Peng, “Modeling and control of a power-split hybrid vehicle,” IEEE Trans Control Syst Tech., vol. 16, pp. 1242-1251, 2008.
[30] G. Paganelli, Y. Guezennec, G. Rizzoni, “Optimizing control strategy for hybrid fuel cell vehicle,” SAE Paper., no. 2002-01-0102, 2002.
[31] S.J. Moura, H.K. Fathy, D.S. Callaway, and J.L. Stein, “A stochastic optimal control approach for power management in plug-in hybrid electric vehicles,” IEEE Trans Control Syst Tech., vol. 19, no.3 pp. 545-555, 2011.
[32] J. Kennedy, “Particle swarm optimization,” Proc IEEE Int Neural Netw Conf., Perth, WA, Australia, Dec. 1995, pp. 1942-1958.
[33] M. Clerc, and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans Evol Comput., vol. 6, pp. 58-73, 2002.
[34] MathWorks, from https://www.mathworks.com/matlabcentral/fileexchange/35801-evolution-strategies--es-, 2012.
[35] J.B. Oliveira, J. Boaventura-Cunha, P.M. Oliveira, H. Freire, “A swarm intelligence-based tuning method for the sliding mode generalized predictive contro”, ISA Trans., vol. 53, pp. 1501-1515, 2014.
[36] A. Alfi, and H. Modares, “System identification and control using adaptive particle swarm optimization,” Appl Math Model., vol. 35, pp. 1210-1221, 2011.
[37] F.J. Lin, Chen SY, Teng LT, Chu H , “Recurrent functional-link-based fuzzy neural network controller with improved particle swarm optimization for a linear synchronous motor drive,” IEEE Trans Magn., vol. 45, pp. 3151-3165, 2009.
[38] S. Guo, C. Dang, and X. Liao, “Joint opportunistic power and rate allocation for wireless ad hoc networks: an adaptive particle swarm optimization approach,” J Netw Comput Appl., vol. 34, pp. 1353-1365, 2011.
[39] Y.Y. Hong, F.J. Lin, S.Y. Chen, Y.C. Lin, and F.Y. Hsu, “A novel adaptive elite-based particle swarm optimization applied to VAR optimization in electric power systems,” Math Probl Eng., vol. 2014, pp. 14, 2014.
[40] C.S. Yi, Y.H. Hung, and C.H. Wu. “An Integrated Optimal Energy Management/Gear-Shifting Strategy for an Electric Continuously Variable Transmission Hybrid Powertrain Using Bacterial Foraging Algorithm,” Mathematical Problems in Engineering., vol. 2016, pp. 15, 2016.
[41] P.L. Shih ,Y.H Hung, S.Y. Chen, and C.H. Wu, “Bacterial foraging algorithm for the optimal on-line energy management of a three-power-source hybrid powertrain,” International Conference on Innovation Communication and Engineering., Xi’an, China, 2016, pp.130.
[42] D. Ramaswamy, R. McGee, S. Sivashankar, A. Deshpande, J. Allen, K. Rzemien, and W. Stuart, “A case study in hardware-in-the-loop testing: Development of an ECU for a hybrid electric vehicle,” SAE Technical Paper., no. 2004-01-0303, 2004.
[43] H.K. Fathy, Z.S. Filipi, J. Hagena, and J.L Stein, , “Review of hardware-in-the-loop simulation and its prospects in the automotive area. In Defense and Security Symposium,”nternational Society for Optics and Photonics, pp. 62280E-62280E, 2006.
[44] 董又銘，“三動力源複合式動力車之最佳化能量管理與模式切換”，國立臺灣師範大學，碩士論文，2015。
[45] S. Y. Chen, Y.H. Hung, and C.H. Wu. "An Integrated Optimal Energy Management/Gear-Shifting Strategy for an Electric Continuously Variable Transmission Hybrid Powertrain Using Bacterial Foraging Algorithm." Mathematical Problems in Engineering 2016 (2016).
[46] P. Pisu, and G. Rizzoni, “A comparative study of supervisory control strategies for hybrid electric vehicles.” IEEE Transactions on, Control Systems Technology 15(3): 506-518,2007.
[47] K.M. Passion, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control System Magazine., vol. 22, no. 3 , pp. 52-67, 2002.