Abstract
This dissertation proposes power management of optimal control scheme for hybrid energy storage system (HESS) like super capacitor and (SCAP) battery in electric vehicles. The proposed technique is a parallel performance of both the random decision forest (RDF) and krill herd optimization (KHO), and thus, it is called as KHO–RDF method. The main objective is to minimize the difference between the actual and reference power in the battery and SCAP. Here, the HESS framework comprises of two sections: (1) figuring the SCAP reference voltage dependent on load dynamics. (2) Maximizing the power flow through HESS. The reference voltage of SCAP by evaluating real-time load dynamics is computed at first, i.e., the vehicle dynamic, motor characteristics, regenerative braking systems and driving conditions. Furthermore, at the same time the magnitude variety of battery power was minimized and the power loss will occur. The input parameters of SCAP are load current, battery current and state of charge. In proposed technique, possible control signals dataset of HESS is fused to produce KHO. By utilizing the practiced dataset of KHO, the RDF is trained and predicts the optimal parameters of HESS. Moreover, the proposed technique advances the SCAP voltage, battery current magnitude, battery current varieties and battery power. With the proposed approach, the parameter of HESS is optimized and it provides certain solutions. The proposed technique is executed in Matrix Laboratory (MATLAB)/Simulink working platform. By using the comparison analysis with the existing procedures, the performance of the HESS is surveyed.
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References
Abu Arqub O, AL-Smadi M, Momani S, Hayat T (2015) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20:3283–3302. https://doi.org/10.1007/s00500-015-1707-4
Ahmadi S, Bathaee S, Hosseinpour A (2018) Improving fuel economy and performance of a fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy. Energy Convers Manag 160:74–84
Arqub O, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput. https://doi.org/10.1007/s00500-020-04687-0
Arqub O, Al-Smadi M, Momani S, Hayat T (2016) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191–7206
Barsali S, Ceraolo M (2002) Dynamical models of lead-acid batteries: implementation issues. IEEE Trans Energy Convers 17:16–23
Beiranvand V, Hare W, Lucet Y (2017) Best practices for comparing optimization algorithms. Optim Eng 18:815–848
Borhan H, Vahidi A, Phillips A, Kuang M, Kolmanovsky I, Di Cairano S (2012) MPC-based energy management of a power-split hybrid electric vehicle. IEEE Trans Control Syst Technol 20:593–603
Byeon G, Yoon T, Oh S, Jang G (2013) Energy management strategy of the DC distribution system in buildings using the EV service model. IEEE Trans Power Electron 28:1544–1554
Camara M, Gualous H, Gustin F, Berthon A, Dakyo B (2010) DC/DC converter design for supercapacitor and battery power management in hybrid vehicle applications—polynomial control strategy. IEEE Trans Ind Electron 57:587–597
Carter CA, Hall P (2012) Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle. IEEE Trans Veh Technol 61:1526–1533
Chen Z, Mi C, Xu J, Gong X, You C (2014) Energy management for a power-split plug-in hybrid electric vehicle based on dynamic programming and neural networks. IEEE Trans Veh Technol 63:1567–1580
Choi M, Lee J, Seo S (2014) Real-time optimization for power management systems of a battery/supercapacitor hybrid energy storage system in electric vehicles. IEEE Trans Veh Technol 63:3600–3611
Domínguez-Navarro J, Dufo-López R, Yusta-Loyo J, Artal-Sevil J, Bernal-Agustín J (2019) Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems. Int J Electr Power Energy Syst 105:46–58
Dusmez S, Khaligh A (2014) A supervisory power-splitting approach for a new ultracapacitor-battery vehicle deploying two propulsion machines. IEEE Trans Ind Inf 10:1960–1971
Gandomi A, Alavi A (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845
Gao S, Chau K, Liu C, Wu D, Chan C (2014) Integrated energy management of plug-in electric vehicles in power grid with renewables. IEEE Trans Veh Technol 63:3019–3027
Garcia P, Fernandez L, Garcia C, Jurado F (2010) Energy management system of fuel-cell-battery hybrid tramway. IEEE Trans Ind Electron 57:4013–4023
Jin RX, Yang M, Xu M (2009) Power management for fuel-cell power system cold start. IEEE Trans Power Electron 24:2391–2395
Kaur K, Kumar N, Singh M (2018) Coordinated power control of electric vehicles for grid frequency support: MILP-based hierarchical control design. IEEE Trans Smart Grid 10:3364–3373
Khaligh A, Li Z (2010) 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 Veh Technol 59:2806–2814
Khodayar M, Wu L, Li Z (2013) Electric vehicle mobility in transmission-constrained hourly power generation scheduling. IEEE Trans Smart Grid 4:779–788
Kouchachvili L, Yaïci W, Entchev E (2018) Hybrid battery/supercapacitor energy storage system for the electric vehicles. J Power Sources 374:237–248
Lee HD, Sul S-K (1998) Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle. IEEE Trans Ind Electron 45:625–632
Lin C-C, Peng H, Grizzle J, Kang J-M (2003) Power management strategy for a parallel hybrid electric truck. IEEE Trans Control Syst Technol 11:839–849
Murphey Y (2013) Intelligent hybrid vehicle power control—part II: online intelligent energy management. IEEE Trans Veh Technol 62:69–79
Murphey Y, Park J, Chen Z, Kuang M, Masrur M, Phillips A (2012) Intelligent hybrid vehicle power control—part I: machine learning of optimal vehicle power. IEEE Trans Veh Technol 61:3519–3530
Ramachandran B, Srivastava S, Cartes D (2013) Intelligent power management in micro grids with EV penetration. Expert Syst Appl 40:6631–6640
Roy R, Ghoshal D (2017) Adaptive second-order sliding-mode controller for shank-foot orthosis system. Int J Control 92:1580–1589
Roy R, Ghoshal D (2019a) Grey wolf optimization-based second order sliding mode control for inchworm robot. Robotica 37:1–19
Roy R, Ghoshal D (2019b) A novel adaptive second-order sliding mode controller for autonomous underwater vehicles. Adapt Behav 27:1–16
Roy R, Ghoshal D (2019c) Advanced heavy water reactor control with the aid of adaptive second-order sliding mode controller. Eng Comput 37(4):1237–1259
Santucci A, Sorniotti A, Lekakou C (2014) Power split strategies for hybrid energy storage systems for vehicular applications. J Power Sources 258:395–407
Schouten N, Salman M, Kheir N (2002) Fuzzy logic control for parallel hybrid vehicles. IEEE Trans Control Syst Technol 10:460–468
Shen J, Khaligh A (2015) A supervisory energy management control strategy in a battery/ultracapacitor hybrid energy storage system. IEEE Trans Transp Electr 1:223–231
Simjee F, Chou P (2008) Efficient charging of supercapacitors for extended lifetime of wireless sensor nodes. IEEE Trans Power Electron 23:1526–1536
Smith A (2010) Image segmentation scale parameter optimization and land cover classification using the random forest algorithm. J Spat Sci 55:69–79
Solero L, Lidozzi A, Pomilio J (2005) Design of multiple-input power converter for hybrid vehicles. IEEE Trans Power Electron 20:1007–1016
Tate E, Boyd S (2000) Finding ultimate limits of performance for hybrid electric vehicles. SAE technical paper series
Thounthong P, Raël S, Davat B (2009) Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications. J Power Sources 193:376–385
Uebel S, Murgovski N, Tempelhahn C, Baker B (2018) Optimal energy management and velocity control of hybrid electric vehicles. IEEE Trans Veh Technol 67:327–337
Uzunoglu M, Alam M (2008) Modeling and analysis of an FC/UC hybrid vehicular power system using a novel-wavelet-based load sharing algorithm. IEEE Trans Energy Convers 23:263–272
Xie S, Hu X, Liu T, Qi S, Lang K, Li H (2019) Predictive vehicle-following power management for plug-in hybrid electric vehicles. Energy 166:701–714
Xiong R, Cao J, Yu Q (2018) Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl Energy 211:538–548
Yan X, Patterson D (2001) Novel power management for high performance and cost reduction in an electric vehicle. Renew Energy 22:177–183
Zheng C, Li W, Liang Q (2018) An energy management strategy of hybrid energy storage systems for electric vehicle applications. IEEE Trans Sustain Energy 9:1880–1888
Zhou D, Al-Durra A, Matraji I, Ravey A, Gao F (2018) Online energy management strategy of fuel cell hybrid electric vehicles: a fractional-order extremum seeking method. IEEE Trans Ind Electron 65:6787–6799
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The author Mrs. Aruna Ponnupandian declares that she has no conflict of interest. The author Dr. Vasan Prabhu Veeramani declares that he has no conflict of interest.
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Ponnupandian, A., Veeramani, V.P. Optimal design for SCAP/battery power management applied in electric vehicle (EV) applications: a KHO–RDF technique. Soft Comput 24, 17247–17263 (2020). https://doi.org/10.1007/s00500-020-05016-1
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DOI: https://doi.org/10.1007/s00500-020-05016-1