Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Gao, Jianping; Xu, Zhiwei; Gao, Xiaojie

doi:10.1155/2019/1496202

Cited by 5 publications

(3 citation statements)

References 12 publications

(12 reference statements)

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“…For both cases, the driving behavior identification is modeled using Learning Vector Quantization (LVQ) NN on speed, idle time, acceleration and deceleration. The adaptive rule-based control strategy from [112] outperforms the classical original rule-based control strategies (that exclude driving behavior) by energy consumption reduction of 4.94%. The results are motivated by the exploitation of driving behavior information beside the effect of operation in different conditions.…”

Section: Urban Planningmentioning

confidence: 99%

“…The 96.08% classification performance provided 11.04% energy reduction under the urban driving behavior. Additionally, two more applications of fuel consumption efficiency for a plug-in and a series-parallel hybrid electric buses are proposed in [112] and [113], respectively. For both cases, the driving behavior identification is modeled using Learning Vector Quantization (LVQ) NN on speed, idle time, acceleration and deceleration.…”

Section: Urban Planningmentioning

confidence: 99%

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Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society;

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Section: Urban Planningmentioning

confidence: 99%

Section: Urban Planningmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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“…The 13 factors developed above were used to classify the drivers by the Calinski-Harabasz (CH) index. The optimal number of clusters occurs when the CH index is 3, which is true for the largest cluster, as shown in Figure (Gao, 2019) [35]. According to the rule of five-point Likert scales, the questionnaire scores of the three types of drivers were counted, and the result is shown in Table 4.…”

Section: Factor Analysis and Cluster Analysis Of Driver Classificationmentioning

confidence: 99%

The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

Zheng

Xiang

et al. 2020

IJERPH

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Urban expressway weaving sections suffer from a high crash risk in urban transportation systems. Studying driving behavior is an important approach to solve safety and efficiency issues at expressway weaving sections. This study aimed to investigate the influence of drivers’ individual differences on diverging behavior at expressway weaving sections. First, a k-means cluster analysis of 650 questionnaires was performed, to classify drivers into three categories: aggressive, conservative and normal. Then, the driving behavior of 45 drivers from the three categories was recorded in a driving simulator and analyzed by an analysis of variance. The results show that different types of drivers have different driving behaviors at weaving sections. Aggressive drivers have a higher mean speed and mean longitudinal deceleration, followed by normal and conservative drivers. Significant differences in the range of lane-change positions were found between 100, 150 and 200 m of weaving length for the same type of drivers, and the duration of weaving for aggressive drivers was significantly smaller than for normal and conservative drivers. A significant correlation was found between lane-change position and weaving duration. These results can help traffic engineers to propose effective control strategies for different types of drivers, to improve the safety of weaving sections.

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Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus

Liu

Chen

et al. 2021

Energy

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Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

Cited by 5 publications

References 12 publications

Context Aware Control Systems: An Engineering Applications Perspective

Context Aware Control Systems: An Engineering Applications Perspective

The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus

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