An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Sun, Xiaodong; Cao, Yunfei; Jin, Zhijia; Tian, Xiang; Xue, Mingzhou

doi:10.1109/tie.2022.3210549

Cited by 28 publications

(10 citation statements)

References 33 publications

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“…In [118], an adaptive EMS based on ECMS was proposed, incorporating real-time traffic information such as average speed, average acceleration, and the standard deviations of speed for different road sections. Markov speed prediction models were then established to predict velocity and applied to the energy management of a PHEB.…”

Section: Equivalent Consumption Minimization Strategiesmentioning

confidence: 99%

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An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

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Section: Equivalent Consumption Minimization Strategiesmentioning

confidence: 99%

“…Table 3. Comparison of the fuel economy for three road segments (Data from the source [118]). [119].…”

Section: ( ) ( )mentioning

confidence: 99%

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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“…Zamani and Etedali 23 applied fuzzy PID controller for non-linear system and observed effective control operations. Sun et al 24,25 developed adaptive ECMS based on traffic information and along with gear shifting control. Especially, Zamani and Etedali 26,27 and employed hybrid learning control method of adaptive Neuro-fuzzy approach for a non-linear system.…”

Section: Introductionmentioning

confidence: 99%

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

Rajasekar,

Ashok

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

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“…Fundamentally, unsurprised OD algorithms can be categorized into several basic types, including Angle-Based Outlier Detection (ABOD) [41], Cluster-based Local Outlier Factor (CBLOF) [42], Histogram-based Outlier Detection (HBOS) [35], Isolation Forest [43], and K-Nearest Neighbors (KNN) [44] algorithms. These diverse OD algorithms employ distinct approaches to measure deviations, and the corresponding datasets vary in terms of dimensions and features, while user interests also differ.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs

Wu,

Tu,

Whalin

et al. 2023

Vehicles

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Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories have predominantly been conducted at an aggregate level, relying on data aggregated from multiple drivers or vehicles. However, to gain a more nuanced understanding of driving behavior at the individual level, a more detailed and granular approach is essential. To bridge this gap, we developed a Data Anomaly Detection (DAD) model designed to assess a driver’s cognitive abnormal driving status at the individual level, relying solely on Basic Safety Message (BSM) data. Our DAD model comprises both online and offline components, each of which analyzes historical and real-time Basic Safety Messages (BSMs) sourced from connected vehicles (CVs). The training data for the DAD model consist of historical BSMs collected from a specific CV over the course of a month, while the testing data comprise real-time BSMs collected at the scene. By shifting our focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can significantly contribute to a more comprehensive comprehension of driving behavior. Furthermore, when combined with a Conflict Identification (CIM) model, the DAD model has the potential to enhance the effectiveness of Advanced Driver Assistance Systems (ADAS), particularly in terms of crash avoidance capabilities. It is important to note that this paper is part of our broader research initiative titled “Automatic Safety Diagnosis in the Connected Vehicle Environment”, which has received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.

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An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Cited by 28 publications

References 33 publications

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs

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