Open AccessA Heuristic Rule-based Control Strategy Using for Series Hybrid Tracked Vehicles
Author(s) -
Ruiping Zhai,
Yecheng Zou,
X. D. Zhang,
Z. G. Zhang,
H. H. Lei
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012049
Subject(s) - heuristic , fuel efficiency , battery (electricity) , matlab , minification , computer science , series (stratigraphy) , control (management) , driving cycle , energy management , energy consumption , rule based system , electric vehicle , automotive engineering , mathematical optimization , control engineering , energy (signal processing) , engineering , artificial intelligence , mathematics , paleontology , power (physics) , statistics , physics , electrical engineering , quantum mechanics , biology , programming language , operating system
A heuristic rule-based energy management for a series hybrid electric tracked vehicle is proposed and verified in this paper. Based on the results of equivalent consumption minimization strategy (ECMS), the new control strategy extracts the optimized control rules and considers the fuel consumption and battery manners together by adjusting the control mechanism. The series hybrid electric tracked vehicle model and the driving cycle are built in MATLAB/Simulink. The simulation results show that the proposed strategy have a better fuel economy and battery manner than traditional rule-based strategies. Compared with the equivalent consumption minimization strategy, the new method also shows the easiness and convenience, but a nearly close fuel consumption and battery manner. This heuristic strategy can show us its advantages and serve some experiences when designing the control strategies in practical application.