Evaluation of Model Predictive and Conventional Method Based Hybrid Electric Vehicle Supervisory Controllers

Sengupta, Somnath; Gururaja, Chethan; Hingane, Sushant; Prajwal, A K; Maniar, Malay; Mikuláš, Ondřej; Pekař, Jaroslav

doi:10.4271/2017-01-1253

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…HEVs are complex hardware systems often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of different subsystems of HEV is an attractive proposition [7].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

et al. 2018

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The necessity of hybrid vehicles and electric vehicles is widely known for reasons such as fossil fuel depletion, climate change, emission norms mandated by regulations, and so on. Expansion of the hybrid vehicle market is a realistic way to respond to fuel efficiency regulations. Hybrid electric vehicles are continuously challenged to meet cross-attribute performance while minimizing energy usage and component cost in a highly competitive automotive market. Current optimization strategy for a parallel hybrid requires much computational time and relies heavily on the drive cycle to accurately represent driving conditions in the future. With increasing application of the lithium-ion battery technology in the automotive industry, development processes and validation methods for the battery management system (BMS) have attracted attention. The purpose of this study is to propose an algorithm to analyze charging characteristics and improve accuracy for determining state of charge (SOC), the equivalent of a fuel gauge for the battery pack, during the regenerative braking period of a TMED type parallel hybrid electric vehicle.

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Section: Introductionmentioning

confidence: 99%