Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization

Abstract In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin’s minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

[1]  P. Pei,et al.  A quick evaluating method for automotive fuel cell lifetime , 2008 .

[2]  Hongwen He,et al.  Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming , 2017 .

[3]  Ahmed Cheriti,et al.  Combination of Markov chain and optimal control solved by Pontryagin’s Minimum Principle for a fuel cell/supercapacitor vehicle , 2015 .

[4]  Shuo Zhang,et al.  Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming , 2015 .

[5]  Datong Qin,et al.  Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles , 2019, Journal of Power Sources.

[6]  Lin Liu,et al.  Optimal power source sizing of fuel cell hybrid vehicles based on Pontryagin's minimum principle , 2015 .

[7]  Pedro M. Munoz,et al.  Energy management control design for fuel cell hybrid electric vehicles using neural networks , 2017 .

[8]  Suk Won Cha,et al.  Optimal control in the power management of fuel cell hybrid vehicles , 2012 .

[9]  Shahrokh Asadi,et al.  Evolutionary fuzzification of RIPPER for regression: Case study of stock prediction , 2019, Neurocomputing.

[10]  Qi Li,et al.  An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive , 2018 .

[11]  Jingni Yuan,et al.  Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles , 2018 .

[12]  Xiaolin Tang,et al.  Adaptive Hierarchical Energy Management Design for a Plug-In Hybrid Electric Vehicle , 2019, IEEE Transactions on Vehicular Technology.

[13]  Feng Ding,et al.  Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control , 2017 .

[14]  Hassan Fathabadi,et al.  Combining a proton exchange membrane fuel cell (PEMFC) stack with a Li-ion battery to supply the power needs of a hybrid electric vehicle , 2019, Renewable Energy.

[15]  Joao P. Trovao,et al.  A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach , 2013 .

[16]  Yuan Zou,et al.  Reinforcement learning-based real-time energy management for a hybrid tracked vehicle , 2016 .

[17]  Liangfei Xu,et al.  Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles , 2016 .

[18]  S. H. Lui,et al.  Superlinear convergence of nonlinear conjugate gradient method and scaled memoryless BFGS method based on assumptions about the initial point , 2020, Appl. Math. Comput..

[19]  Angelika Heinzel,et al.  Power management optimization of fuel cell/battery hybrid vehicles with experimental validation , 2014 .

[20]  Mohammad J. Mahjoob,et al.  Optimal energy management in a dual-storage fuel-cell hybrid vehicle using multi-dimensional dynamic programming , 2014 .

[21]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[22]  Liangfei Xu,et al.  Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles , 2015 .

[23]  Xiaosong Hu,et al.  Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus , 2019, Applied Energy.

[24]  Hui Liu,et al.  Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles , 2018, Energy.

[25]  Huei Peng,et al.  Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle , 2011, IEEE Transactions on Control Systems Technology.

[26]  Mahmoud Moghavvemi,et al.  Energy management strategies in hybrid renewable energy systems: A review , 2016 .

[27]  Jianqiu Li,et al.  Application of Pontryagin's Minimal Principle to the energy management strategy of plugin fuel cell electric vehicles , 2013 .

[28]  Francisco Herrera,et al.  A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning , 2013, IEEE Transactions on Knowledge and Data Engineering.

[29]  Zoran Filipi,et al.  Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle , 2020 .

[30]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[31]  Shyi-Ming Chen,et al.  Heuristic target class selection for advancing performance of coverage-based rule learning , 2019, Inf. Sci..

[32]  Ahmed Cheriti,et al.  A real time fuzzy logic power management strategy for a fuel cell vehicle , 2014 .

[33]  Yang Zhou,et al.  Multi-objective energy management for fuel cell electric vehicles using online-learning enhanced Markov speed predictor , 2020, Energy Conversion and Management.

[34]  Omer Turksoy,et al.  Minimizing Capacitance Value of Interleaved Power Factor Corrected Boost Converter for Battery Charger in Electric Vehicles , 2019 .

[35]  Dongpu Cao,et al.  Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle , 2017, IEEE/ASME Transactions on Mechatronics.

[36]  Omer Turksoy,et al.  Review of fuel cells to grid interface: Configurations, technical challenges and trends , 2019, Journal of Cleaner Production.

[37]  Dongsuk Kum,et al.  Synthesis of Predictive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles Based on Closed-Form Solution of Optimal Equivalence Factor , 2017, IEEE Transactions on Vehicular Technology.

[38]  Suk Won Cha,et al.  Prolonging fuel cell stack lifetime based on Pontryagin's Minimum Principle in fuel cell hybrid vehicles and its economic influence evaluation , 2014 .

[39]  Wei-Song Lin,et al.  Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-contro , 2011 .

[40]  Jiayi Cao,et al.  Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle , 2018 .

[41]  Lin Yang,et al.  Dynamic programming for new energy vehicles based on their work modes Part II: Fuel cell electric vehicles , 2018, Journal of Power Sources.

[42]  Xiaosong Hu,et al.  An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles , 2018, Energy.

[43]  Bernd Marschner,et al.  Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils , 2019, Geoderma.

[44]  Jamal Shahrabi,et al.  RipMC: RIPPER for Multiclass Classification , 2016, Neurocomputing.

[45]  Ahmed Al-Durra,et al.  A comparative study of extremum seeking methods applied to online energy management strategy of fuel cell hybrid electric vehicles , 2017 .

[46]  Yuan Zou,et al.  Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle , 2015, IEEE Transactions on Industrial Electronics.

[47]  Kodjo Agbossou,et al.  Design of an adaptive EMS for fuel cell vehicles , 2017 .

[48]  Hong Chen,et al.  Optimal Energy Management for HEVs in Eco-Driving Applications Using Bi-Level MPC , 2017, IEEE Transactions on Intelligent Transportation Systems.