Large-Scale Battery System Development and User-Specific
													Driving Behavior Analysis for Emerging Electric-Drive Vehicles

Wu, Jie; Li, Kun; Jiang, Yifei; Lv, Qin; Shang, Li; Sun, Yihe

doi:10.3390/en4050758

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…Uncertainty quantification (UQ) is a method to show the variation in the system inputs and outputs, yet it does not describe the relations between these The quantity of interest during the charging events was on the internal resistance of the battery pack. The internal resistance was dependent on aging, current, SOC, depth of discharge (DOD), and temperature of the battery [31][32][33]. The impact of temperature on the internal resistance of lithium-ion batteries was roughly an order of magnitude greater than the impact of degradation [34].…”

Section: Introductionmentioning

confidence: 99%

Energy Uncertainty Analysis of Electric Buses

et al. 2018

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Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

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Section: Introductionmentioning

confidence: 99%

Energy Uncertainty Analysis of Electric Buses

et al. 2018

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“…Driving Monitoring: Cameras have been used to monitor the driver's head motions, enabling drowsy driving and visual distraction detection [27,36,59,66,71,73]. There are a few advantages of camera systems over MagTrack: they do not require wearables on the users, and the eyelid movements can be recognized.…”

Section: State Of the Artmentioning

confidence: 99%

MagTrack

Huang

Chen

Lin

2019

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

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Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver's bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver's postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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“…Each cell has a nominal voltage of 3.6 V and a capacity of 20 Ah. Such a Li-ion cell [7] can be overcharged at 1.5 C and discharge at 5 C. The storage system voltage and temperature is controlled by a Battery Management System (BMS) [8] [9].…”

Section: Hybrid Powertrain Concept and Configurationmentioning

confidence: 99%

Hybrid Power Pack: Hybrid Powertrain for City Cars

Cignini¹,

Domenico²,

Martellucci³

et al. 2014

JTTs

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This paper presents a novel concept, the Hybrid Power Pack (HPP), which consists of a hybridization\ud kit for transforming small city cars, powered by an original diesel engine, into a parallel hybrid\ud vehicle. The study was jointly conducted by the University of Rome “Sapienza” and the Enea\ud Casaccia research center. The idea is to design a hybrid powertrain that can be installed in a typical\ud microcar, which means that all systems and components will be influenced by the limited space\ud available in the motor compartment of the vehicle. In this paper the details of the mechanical and\ud electrical realization of the powertrain will be discussed and the simulation of a small city car\ud equipped with HPP will be presented and the results discussed and analyzed. The hybrid system\ud also includes the battery pack which is composed of twenty-four Li-ion cells made by EIG, connected\ud in series. The storage system is controlled as regards the voltage and temperature by a\ud Battery Management System (BMS). All the above components are connected and managed by a\ud control unit. The HPP presented in this paper obtains a reduction in fuel consumption higher than\ud 20%. The solution presented with the HPP with its management strategy and the addition of the\ud “plug-in function” makes the hybrid vehicle suitable in terms of performance and consumption in\ud every driving conditions. The ideal strategy behind the “plug-in function” could represent a guideline\ud for further achievements and experimentations, because it offers a simple hardware layout\ud and a real reduction in fuel consumption

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Large-Scale Battery System Development and User-Specific Driving Behavior Analysis for Emerging Electric-Drive Vehicles

Cited by 19 publications

References 20 publications

Energy Uncertainty Analysis of Electric Buses

Energy Uncertainty Analysis of Electric Buses

MagTrack

Hybrid Power Pack: Hybrid Powertrain for City Cars

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