A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data

Hu, Lin; Tian, Qingtao; Zou, Changfu; Huang, Jing; Yao, Ye; Wu, Xianhui

doi:10.1016/j.rser.2022.112416

Cited by 66 publications

(17 citation statements)

References 46 publications

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“…This paper uses a new sparsity range determination method proposed by (45). The sparsity range was determined to be 10-38% and eight functional brain networks were created at each sparsity level, i.e., 10, 14,18,22,26,30,34, and 38%, when a growth step of 4% was chosen. In addition, in order to comprehensively compare the differences between functional brain networks of different sparsity, AUC was used as an overall evaluation index.…”

Section: Eeg Source Connectivity Resultsmentioning

confidence: 99%

“…They were all developed using subjective assessments as criteria for setting environmental parameter thresholds. However, most of the related research has been carried out in the laboratory or driving simulators, which could only simulate one or two environmental parameters (18)(19)(20)(21)(22). The complex environment in HSR and the cross-fertilization of all factors makes it challenging to analyse influencing factors of passengers overall comfort according to subjective indicators in the laboratory.…”

Section: Related Work Subjective Indicatorsmentioning

confidence: 99%

See 1 more Smart Citation

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report

Fan

Lin²,

Lin³

et al. 2022

Front. Public Health

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Overall comfort is the priority for the high-speed railway (HSR) passengers, while its influencing factors and mechanism are not yet apparent. According to the source functional brain network and subjective report, this study revealed the potential influencing factors and mechanisms of passengers overall comfort in high-speed railway environments. Here, an ergonomics field test with 20 subjects was conducted where subjective reports and electroencephalography (EEG) were collected. The electric-source imaging and functional connectivity were used to build the source functional brain network from EEG and network indices were extracted. Statistics analysis results showed that static comfort played the most critical role in the overall comfort, followed by emotional valence, emotional arousal, aural pressure comfort, vibration comfort, and noise comfort. Thermal and visual comfort were insignificant due to the well-designed heating, ventilation, and air conditioning (HVAC) and lighting system of HSR. In addition, the source functional brain network of passengers who felt uncomfortable had the higher clustering coefficient, assortativity coefficient and global efficiency, which meant greater activation of brain compared with passengers who were in a state of comfort. According to the local attributes indices analysis, most key brain regions were located in the frontal and hippocampus, which revealed emotion and spatial perception contribute to the whole comfort degradation process. This work proposed novel insights into HSR passengers overall comfort according to subjective and objective methods. Our findings demonstrate emotional regulation and seat improvements are key factors for future improvement of HSR passengers overall comfort.

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Section: Eeg Source Connectivity Resultsmentioning

confidence: 99%

Section: Related Work Subjective Indicatorsmentioning

confidence: 99%

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report

Fan

Lin²,

Lin³

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…For example, the question of whether a driver has mental fatigue should be identified. In addition, the impact of the same warning function and different warning methods on driving safety and economy of vehicle should also be focused on in the future, as in [ 54 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

The Real-World Effects of Route Familiarity on Drivers’ Eye Fixations at Urban Intersections in Changsha, China

Guo

Huang

et al. 2022

IJERPH

Self Cite

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A crucial factor, route familiarity, can affect traffic safety. Nevertheless, focus on the influence of route familiarity on drivers’ eye fixations at urban intersections has received less attention. Identifying the real-world effect of route familiarity on drivers’ eye fixations at urban intersections in Changsha, China, was the objective of this study. Their visual fixation indicators were recorded while unfamiliar drivers and familiar drivers drove a 9 km-long route with nine intersections in an urban environment, but their effectiveness was indicated by the data collected 150 m before the lane stop and 50 m after the lane stop at these intersections. From the analysis of the extracted data, the results indicated that route familiarity could influence drivers’ processing times in the left window (LW) and other areas (OT). Compared with familiar drivers, unfamiliar drivers had longer processing times and higher mental workloads for the right front (RF). For the vehicle’s front (RF, FL, FR), the sampling rates and mental workloads of unfamiliar drivers were higher than those of familiar drivers, but it was the opposite for the driver’s sides (LW, RW) and rear (LM, RM, ReM). It was also indicated that the phenomenon said to increase familiarity with the route and make drivers more likely to be distracted in urban intersections had not been found. From the present findings, the effect of route familiarity on drivers’ eye fixations at urban intersections was confirmed. The high accident risk of familiar drivers could be partly explained by the decrement in drivers’ eye fixation strategies. However, the strategies could not account for the phenomenon that more familiar drivers are involved in rear-end accidents. Therefore, the reason can be investigated based on drivers’ visual scanning strategies, their physiological signals and driving behavior in the future.

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“…HEVs employ batteries to store electrical energy that is used to power the motor and small engine. It may provide higher fuel economy while emitting less harmful pollutants [1]. The HEV's two energy sources, "the engine and battery," must be regulated in such a way that they operate within their efficient ranges while meeting the driver's power requirement [2].…”

Section: Introductionmentioning

confidence: 99%

Energy Management in Electric Vehicles Using Improved Swarm Optimized Deep Reinforcement Learning Algorithm

Jawale¹,

Pawar²,

Korde³

et al. 2023

J. Nano- Electron. Phys.

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The internal combustion engine-based transportation system is causing severe problems such as rising levels of pollution, rising petroleum prices, and the depletion of natural resources. To divide power between the engine and the battery in an effective manner, a sophisticated energy management system is required to be put into place. A power split strategy that is efficient may result in higher fuel economy and performance of Electric Vehicles (EVs). In this paper, we propose the reinforcement learning method using Deep Q learning (DQL), which is a novel Improved Swarm optimized Deep Reinforcement Learning Algorithm (IS-DRLA) designed for energy management control. To perform an update on the weights of the neural network, this method computes the use of a modified version of the swarm optimization technique. After that, the suggested IS-DRLA system goes through training and verification using high-precision realistic driving conditions, after which it is contrasted with the standard approach. The performance indices such as State of Charge (SOC) and fuel consumption and loss function are analyzed for the efficiency of the proposed method (IS-DRLA). According to the findings, the newly proposed IS-DRLA is capable of achieving a higher training pace with a lower overall fuel consumption than the conventional policy, and its fuel economy comes very close to matching that of the worldwide optimal. In addition to this, the adaptability of the suggested strategy is demonstrated by utilizing a different driving schedule.

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A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data

Cited by 66 publications

References 46 publications

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report

The Real-World Effects of Route Familiarity on Drivers’ Eye Fixations at Urban Intersections in Changsha, China

Energy Management in Electric Vehicles Using Improved Swarm Optimized Deep Reinforcement Learning Algorithm

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