CLC number: TP13
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-11-15
Cited: 0
Clicked: 7223
Citations: Bibtex RefMan EndNote GB/T7714
Ming-xin Kang, Jin-wu Gao. Design of an eco-gearshift control strategy under a logic system framework[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 340-350.
@article{title="Design of an eco-gearshift control strategy under a logic system framework",
author="Ming-xin Kang, Jin-wu Gao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="2",
pages="340-350",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900459"
}
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%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900459
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T1 - Design of an eco-gearshift control strategy under a logic system framework
A1 - Ming-xin Kang
A1 - Jin-wu Gao
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%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900459
Abstract: Good access to traffic information provides enormous potential for automotive powertrain control. We propose a logical control approach for the gearshift strategy, aimed at improving the fuel efficiency of vehicles. The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions; therefore, it offers opportunities for the design of a gearshift control scheme. Due to the discrete characteristics of a gearshift, the control design of the gearshift strategy can be formulated under a logic system framework. To this end, vehicle dynamics are discretized with several logic states, and then modeled as a logic system with the Markov process model. The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online. Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.
[1]Bender FA, Kaszynski M, Sawodny O, 2013. Drive cycle prediction and energy management optimization for hybrid hydraulic vehicles. IEEE Trans Veh Technol, 62(8): 3581-3592.
[2]Chen SQ, Wu YH, Macauley M, et al., 2019. Monostability and bistability of Boolean networks using semi-tensor products. IEEE Trans Contr Netw Syst, 6(4):1379-1390.
[3]Chen ZY, Xiong R, Wang C, et al., 2017. An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle. Appl Energy, 185:1663-1672.
[4]Cheng DZ, 2005. Controllability of switched bilinear systems. IEEE Trans Autom Contr, 50(4):511-515.
[5]Cheng DZ, Qi HS, 2010. A linear representation of dynamics of Boolean networks. IEEE Trans Autom Contr, 55(10): 2251-2258.
[6]Cheng DZ, Zhao Y, Xu TT, 2015. Receding horizon based feedback optimization for mix-valued logical networks. IEEE Trans Autom Contr, 60(12):3362-3366.
[7]Fornasini E, Valcher ME, 2014. Optimal control of Boolean control networks. IEEE Trans Autom Contr, 59(5): 1258-1270.
[8]Guo YQ, Zhou RP, Wu YH, et al., 2019. Stability and set stability in distribution of probabilistic Boolean networks. IEEE Trans Autom Contr, 64(2):736-742.
[9]Huang C, Lu JQ, Ho DWC, et al., 2020. Stabilization of probabilistic Boolean networks via pinning control strategy. Inform Sci, 510:205-217.
[10]Ivarsson M, 2009. Fuel Optimal Powertrain Control for Heavy Trucks Utilizing Look Ahead. PhD Thesis, Linköpings Universitet, Linköping, Sweden.
[11]Kamal MAS, Mukai M, Murata J, et al., 2009. Development of ecological driving system using model predictive control. Proc ICCAS-SICE, p.3549-3554.
[12]Kang MX, Shen TL, 2013. A torque demand strategy of IC engines for fuel consumption improvement using traffic information. IFAC Proc Vol, 46(21):700-705.
[13]Kang MX, Wu YH, Shen TL, 2017. Logical control approach to fuel efficiency optimization for commuting vehicles. Int J Autom Technol, 18(3):535-546.
[14]Le ST, Wu YH, Sun XM, 2019. Congestion games with player-specific utility functions and its application to NFV networks. IEEE Trans Autom Sci Eng, 16(4):1870-1881.
[15]Li FF, Sun JT, Wu QD, 2011. Observability of Boolean control networks with state time delays. IEEE Trans Neur Netw, 22(6):948-954.
[16]Li HT, Ding XY, 2019. A control Lyapunov function approach to feedback stabilization of logical control networks. SIAM J Contr Optim, 57(2):810-831.
[17]Li HT, Wang YZ, Liu ZB, 2014. Stability analysis for switched Boolean networks under arbitrary switching signals. IEEE Trans Autom Contr, 59(7):1978-1982.
[18]Li YF, Zhu JD, 2019. On disturbance decoupling problem of Boolean control networks. Asian J Contr, 21(6):2543-2550.
[19]Li YY, Liu RJ, Lou JG, et al., 2019. Output tracking of Boolean control networks driven by constant reference signal. IEEE Access, 7:112572-112577.
[20]Liu JY, Liu Y, Guo YQ, et al., 2019. Sampled-data state-feedback stabilization of probabilistic Boolean control networks: a control Lyapunov function approach. IEEE Trans Cybern, in press.
[21]Liu Y, Sun LJ, Lu JQ, et al., 2016. Feedback controller design for the synchronization of Boolean control networks. IEEE Trans Neur Netw Learn Syst, 27(9):1991-1996.
[22]Liu Y, Li BW, Chen HW, et al., 2017. Function perturbations on singular Boolean networks. Automatica, 84:36-42.
[23]Liu Y, Cao JD, Wang LQ, et al., 2020. On pinning reachability of probabilistic Boolean control networks. Sci China Ser F, 63:169201.
[24]Lu JQ, Zhong J, Ho DWC, et al., 2016. On controllability of delayed Boolean control networks. SIAM J Contr Optim, 54(2):475-494.
[25]Ngo DV, 2012. Gear Shift Strategies for Automotive Transmissions. PhD Thesis, Technische Universiteit Eindhoven, the Netherlands.
[26]Ngo V, Hofman T, Steinbuch M, et al., 2012. Optimal control of the gearshift command for hybrid electric vehicles. IEEE Trans Veh Technol, 61(8):3531-3543.
[27]Tong LY, Liang JL, Chen HW, 2019. State feedback controller design for anti-synchronization of Boolean control networks: an event-based idea. Asian J Contr, 21(6):2674-2684.
[28]Toyoda M, Wu YH, 2019. On optimal time-varying feedback controllability for probabilistic Boolean control networks. IEEE Trans Neur Netw Learn Syst, in press.
[29]Wang LQ, Liu Y, Wu ZG, et al., 2019. Stabilization and finite-time stabilization of probabilistic Boolean control networks. IEEE Trans Syst Man Cybern, in press.
[30]Wu YH, Shen TL, 2015. An algebraic expression of finite horizon optimal control algorithm for stochastic logical dynamical systems. Syst Contr Lett, 82:108-114.
[31]Wu YH, Shen TL, 2017. Policy iteration approach to control residual gas fraction in IC engines under the framework of stochastic logical dynamics. IEEE Trans Contr Syst Technol, 25(3):1100-1107.
[32]Wu YH, Kumar M, Shen TL, 2016. A stochastic logical system approach to model and optimal control of cyclic variation of residual gas fraction in combustion engines. Appl Therm Eng, 93:251-259.
[33]Wu YH, Sun XM, Zhao XD, et al., 2019. Optimal control of Boolean control networks with average cost: a policy iteration approach. Automatica, 100:378-387.
[34]Xu XY, Dong P, Liu YF, et al., 2018. Progress in automotive transmission technology. Autom Innov, 1(3):187-210.
[35]Yan YY, Yue JM, Chen ZQ, 2019. Algebraic method of simplifying Boolean networks using semi-tensor product of matrices. Asian J Contr, 21(6):2569-2577.
[36]Yin XF, Lu H, Zhao XJ, et al., 2016. Performance Evaluation Approach Improvement for Individualized Gearshift Schedule Optimization. SAE Technical Paper No. 2016-01-1147.
[37]Yu KJ, Yang JQ, Yamaguchi D, 2015. Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information. Contr Theory Technol, 13(1):17-28.
[38]Zhang JY, Wu YH, 2018. A stochastic logical model-based approximate solution for energy management problem of HEVs. Sci China Ser F, 61:70207.
[39]Zhang KZ, Zhang LJ, 2014. Observability of Boolean control networks: a unified approach based on the theories of finite automata and formal languages. Proc 33rd Chinese Control Conf, p.6854-6861.
[40]Zhang YJ, Chu L, Fu ZC, et al., 2017. Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction. Mechatronics, 46:177-192.
[41]Zhao Y, Li ZQ, Cheng DZ, 2011. Optimal control of logical control networks. IEEE Trans Autom Contr, 56(8): 1766-1776.
[42]Zhong J, Li BW, Liu Y, et al., 2020. Output feedback stabilizer design of Boolean networks based on network structure. Front Inform Technol Electron Eng, 21(2):247-259.
[43]Zhu QX, Liu Y, Lu JQ, et al., 2018. On the optimal control of Boolean control networks. SIAM J Contr Optim, 56(2):1321-1341.
[44]Zhu QX, Liu Y, Lu JQ, et al., 2019 Further results on the controllability of Boolean control networks. IEEE Trans Autom Contr, 64(1):440-442.
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