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Title: Energy management of plug-in hybrid electric vehicles with unknown trip length

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Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of the Franklin Institute
Additional Journal Information:
Journal Volume: 352; Journal Issue: 2; Related Information: CHORUS Timestamp: 2017-07-05 09:08:41; Journal ID: ISSN 0016-0032
Country of Publication:
United States

Citation Formats

Hou, Cong, Xu, Liangfei, Wang, Hewu, Ouyang, Minggao, and Peng, Huei. Energy management of plug-in hybrid electric vehicles with unknown trip length. United States: N. p., 2015. Web. doi:10.1016/j.jfranklin.2014.07.009.
Hou, Cong, Xu, Liangfei, Wang, Hewu, Ouyang, Minggao, & Peng, Huei. Energy management of plug-in hybrid electric vehicles with unknown trip length. United States. doi:10.1016/j.jfranklin.2014.07.009.
Hou, Cong, Xu, Liangfei, Wang, Hewu, Ouyang, Minggao, and Peng, Huei. 2015. "Energy management of plug-in hybrid electric vehicles with unknown trip length". United States. doi:10.1016/j.jfranklin.2014.07.009.
title = {Energy management of plug-in hybrid electric vehicles with unknown trip length},
author = {Hou, Cong and Xu, Liangfei and Wang, Hewu and Ouyang, Minggao and Peng, Huei},
abstractNote = {},
doi = {10.1016/j.jfranklin.2014.07.009},
journal = {Journal of the Franklin Institute},
number = 2,
volume = 352,
place = {United States},
year = 2015,
month = 2

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.jfranklin.2014.07.009

Citation Metrics:
Cited by: 8works
Citation information provided by
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  • Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less
  • Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumentedmore » vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies over a set of real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which affect vehicle operation and efficiency. Even under these more aggressive operating conditions, PHEVs using a blended charge-depleting energy management strategy consume less than 50% of the petroleum used by similar conventional vehicles. Although true prediction of the widespread real-world use of these vehicles requires expansion of the vehicle sample size and a refined accounting for the possible interaction of several variables with the sampled driving profiles, this study demonstrates a cutting-edge use of available GPS travel survey data to analyze the (highly drive cycle-dependent) performance of advanced technology PHEVs. This demonstration highlights new opportunities for using innovative GPS travel survey techniques and sophisticated vehicle system simulation tools to guide vehicle design improvements and to maximize the benefits offered by energy efficiency technologies.« less
  • Accurate assessment of the impact of plug-in hybrid electric vehicles (PHEVs) on petroleum and electricity consumption is a necessary step toward effective policies. Variations in daily vehicle miles traveled (VMT) over time and among drivers affect PHEV energy impact, but the significance is not well understood. This paper uses a graphical illustration, a mathematical derivation, and an empirical study to examine the cause and significance of such an effect. The first two methods reveal that ignoring daily variation in VMT always causes underestimation of petroleum consumption and overestimation of electricity consumption by PHEVs; both biases increase as the assumed PHEVmore » charge-depleting (CD) range moves closer to the average daily VMT. The empirical analysis based on national travel survey data shows that the assumption of uniform daily VMT over time and among drivers causes nearly 68% underestimation of expected petroleum use and nearly 48% overestimation of expected electricity use by PHEVs with a 40-mi CD range (PHEV40s). Also for PHEV40s, consideration of daily variation in VMT over time but not among drivers similar to the way the utility factor curve is derived in SAE Standard SAE J2841 causes underestimation of expected petroleum use by more than 24% and overestimation of expected electricity use by about 17%. Underestimation of petroleum use and overestimation of electricity use increase with larger-battery PHEVs.« less
  • This paper studies the role of public charging infrastructure in increasing PHEV s share of driving on electricity and the resulting petroleum use reduction. Using vehicle activity data obtained from the GPS-tracking household travel survey in Austin, Texas, gasoline and electricity consumptions of PHEVs in real world driving context are estimated. Driver s within-day recharging behavior, constrained by travel activities and public charger network, is modeled as a boundedly rational decision and incorporated in the energy use estimation. The key findings from the Austin dataset include: (1) public charging infrastructure makes PHEV a competitive vehicle choice for consumers without amore » home charger; (2) providing sufficient public charging service is expected to significantly reduce petroleum consumption of PHEVs; and (3) public charging opportunities offer greater benefits for PHEVs with a smaller battery pack, as within-day recharges compensate battery capacity.« less