Dual-objective energy management strategy for HEV

Yen Hsiang Huang, Nan-Chyuan Tsai

Research output: Contribution to journalArticle

Abstract

Based on equivalent consumption minimization strategy (ECMS), the approaches by genetic algorithm (GA), learning vector quantization neural networks (LVQ NNs) and fuzzy logic algorithm (FLA) are integrated to adjust/tune the power split ratio between internal combustion engines (ICE) and belt-driven starter generators (BSG). The proposed bi-object equivalent consumption minimization strategy (BOECMS) possesses three key features: being realtime, causal and capable of fulfilling two objects, namely, (i) minimizing fuel consumption, and (ii) ensuring a stable battery state of charge (SOC) within a relatively narrow range. A hybrid electric vehicle (HEV) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (advanced vehicle simulator) and Simulink at the design stage. For practicality, the proposed control strategy, BOECMS, is converted into C code and then written into the embedded micro-processor to conduct the necessary hardware-inthe-loop (HIL) experiments at the verification stage. According to computer simulation results, fuel economy improved by 40.39 % over pure ICE vehicles for the "MANHATTAN" drive cycle. In addition, the SOC can be retained within a relatively narrow range: [0.4, 0.6]. Finally and significantly, the experimental results by HIL converge well with computer simulation results using Simulink, implying BOECMS can potentially be applied to the real -world driving in the future.

Original languageEnglish
Pages (from-to)111-118
Number of pages8
JournalInternational Journal of Automation and Smart Technology
Volume7
Issue number3
DOIs
Publication statusPublished - 2017 Jan 1

Fingerprint

Energy management
Hybrid vehicles
Internal combustion engines
Simulators
Starters
Vector quantization
Computer simulation
Fuel economy
Fuel consumption
Fuzzy logic
Genetic algorithms
Neural networks
Hardware
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

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abstract = "Based on equivalent consumption minimization strategy (ECMS), the approaches by genetic algorithm (GA), learning vector quantization neural networks (LVQ NNs) and fuzzy logic algorithm (FLA) are integrated to adjust/tune the power split ratio between internal combustion engines (ICE) and belt-driven starter generators (BSG). The proposed bi-object equivalent consumption minimization strategy (BOECMS) possesses three key features: being realtime, causal and capable of fulfilling two objects, namely, (i) minimizing fuel consumption, and (ii) ensuring a stable battery state of charge (SOC) within a relatively narrow range. A hybrid electric vehicle (HEV) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (advanced vehicle simulator) and Simulink at the design stage. For practicality, the proposed control strategy, BOECMS, is converted into C code and then written into the embedded micro-processor to conduct the necessary hardware-inthe-loop (HIL) experiments at the verification stage. According to computer simulation results, fuel economy improved by 40.39 {\%} over pure ICE vehicles for the {"}MANHATTAN{"} drive cycle. In addition, the SOC can be retained within a relatively narrow range: [0.4, 0.6]. Finally and significantly, the experimental results by HIL converge well with computer simulation results using Simulink, implying BOECMS can potentially be applied to the real -world driving in the future.",
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Dual-objective energy management strategy for HEV. / Huang, Yen Hsiang; Tsai, Nan-Chyuan.

In: International Journal of Automation and Smart Technology, Vol. 7, No. 3, 01.01.2017, p. 111-118.

Research output: Contribution to journalArticle

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