title: Naturalistic drive cycle synthesis for pickup trucks.
abstract: OBJECTIVE
  Future pick-up trucks are meeting much stricter fuel economy and exhaust emission standards. Design tradeoffs will have to be carefully evaluated to satisfy consumer expectations within the regulatory and cost constraints. Boundary conditions will obviously be critical for decision making: thus, the understanding of how customers are driving in naturalistic settings is indispensable. Federal driving schedules, while critical for certification, do not capture the richness of naturalistic cycles, particularly the aggressive maneuvers that often shape consumer perception of performance. While there are databases with large number of drive cycles, applying all of them directly in the design process is impractical. Therefore, representative drive cycles that capture the essence of the naturalistic driving should be synthesized from naturalistic driving data.
  
  
  METHODS
  Naturalistic drive cycles are firstly categorized by investigating their micro-trip components, defined as driving activities between successive stops. Micro-trips are expected to characterize underlying local traffic conditions, and separate different driving patterns. Next, the transitions from one vehicle state to another vehicle state in each cycle category are captured with Transition Probability Matrix (TPM). Candidate drive cycles can subsequently be synthesized using Markov Chain based on TPMs for each category. Finally, representative synthetic drive cycles are selected through assessment of significant cycle metrics to identify the ones with smallest errors.
  
  
  CONCLUSIONS
  This paper provides a framework for synthesis of representative drive cycles from naturalistic driving data, which can subsequently be used for efficient optimization of design or control of pick-up truck powertrains.
  
  
  CONCLUSIONS
  Manufacturers will benefit from representative drive cycles in several aspects, including quick assessments of vehicle performance and energy consumption in simulations, component sizing and design, optimization of control strategies, and vehicle testing under real-world conditions. This is in contrast to using federal certification test cycles, which were never intended to capture pickup truck segment.
authors.affiliation: International Center for Automotive Research (ICAR), Clemson University, 4 Research Drive, Greenville, SC 29607, USA. Electronic address: zifanl@clemson.edu.
authors.forename: Zifan
authors.initials: Z
authors.lastname: Liu
authors.affiliation: International Center for Automotive Research (ICAR), Clemson University, 4 Research Drive, Greenville, SC 29607, USA. Electronic address: aivanco@clemson.edu.
authors.forename: Andrej
authors.initials: A
authors.lastname: Ivanco
authors.affiliation: International Center for Automotive Research (ICAR), Clemson University, 4 Research Drive, Greenville, SC 29607, USA. Electronic address: zfilipi@clemson.edu.
authors.forename: Zoran
authors.initials: Z
authors.lastname: Filipi
mesh_terms: D001334:Automobile Driving; D001519:Behavior; D003246:Conservation of Energy Resources; D004738:Engineering; D004777:Environment; D005544:Forecasting; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D018986:Motor Vehicles; D013647:Task Performance and Analysis; D001335:Vehicle Emissions
chemical_list: D001335:Vehicle Emissions
keywords: K-means clustering; Markov chain; Naturalistic drive cycle; Pickup trucks; Real-world driving; Representative drive cycle; Transition Probability Matrix (TPM)