Addressing human factors in electric vehicle system design: Building an integrated computational human–electric vehicle framework

Wu, Changxu; Wan, Jingyan; Zhao, Guozhen

doi:10.1016/j.jpowsour.2012.04.053

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…The integrated computational human-electric vehicle framework (ICHEV) is developed to take both human and engineering factors into account on the EV platform (Wu, Wan, & Zhao, 2012) (see Figure 1 and 2). An EV driving simulator which is able to simulate the states of battery with a man-machine interface and interactive driving scenarios as shown in Fig.…”

Section: A New Design Of Bmss Based On Prediction Of Electric Currentmentioning

confidence: 99%

The Effects of Driver Speed Prediction-Based Battery Management System on Li-ion Battery Performance for Electric Vehicles

Wan

2014

Proceedings of the Human Factors and Ergonomics Society Annual

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The electric vehicle (EV) technologies have received great attention due to the potential contributions to reducing emissions and energy consumption. The relatively short travel distance and battery lifetime of EV are major concerns for consumers. It is suggested that driver behavior plays important role in determining EV battery's electric current and heat generation, which have effects on travel distance of EV and its battery lifetime. To address this problem, the current work proposes a Driver Speed Prediction-Based Battery Management System (P-BMS). P-BMS is able to innovatively predict both electric current and temperature of EV battery based on driver behavior, proactively manage and control both electric current and temperature level of the EV battery in order to minimize energy consumption and maximize both travel distance of EV and EV battery lifetime. Results suggest that P-BMS can significantly reduce energy, extend travel distance in one charge and prolong EV battery lifetime.

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Section: A New Design Of Bmss Based On Prediction Of Electric Currentmentioning

confidence: 99%

The Effects of Driver Speed Prediction-Based Battery Management System on Li-ion Battery Performance for Electric Vehicles

Wan

2014

Proceedings of the Human Factors and Ergonomics Society Annual

Self Cite

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“…When undertaking the development of a new electric automotive product, a set of initial requirements is established in order to best meet the needs of the potential market space, covering aspects such as performance, cost of manufacture, cost of use, lifespan, price point and many other factors of its design [1]. This underpins further requirement specifications such as sizing, technologies to use and other more engineering aspects of the initial design [2].…”

Section: Introductionmentioning

confidence: 99%

Improved Sizing Calculator for Rapid Optimisation of Pack Configuration at Early-Stage Automotive Product Development

Grandjean

Taylor

McGordon

et al. 2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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The specifications that define a new automotive product development are established at an early stage in the product life cycle and define the direction of such a development, and changing these decisions becomes costlier the further the project evolves towards introduction into the market. Simulations and predictions underpin the crucial decisions made at the inception stage of the new product development life cycle, since these tools inform prototype development, production strategies, and improve profitability. The tool presented facilitates the decisions required when embarking on the new product development of a vehicle that incorporates electric-drive technologies and the vital choices made regarding the battery pack powering by such a vehicle. The tool functions can be split into two parts, firstly it incorporates a sizing model for determining the number of cells and the configuration required to meet a specified battery requirement. Secondly, a 1-D model is implemented to determine some of the basic thermal and power characteristics that can then be utilised to inform other parts of the design specification. Improvements are proposed that improve previous model accuracy from 8-9% to 6% for thermal predictions and down to 3% for electrical simulations. When integrated with a database containing cell characteristics, the tool can identify candidate cells that meet the proposed requirements. In addition, the tool's rapid execution time allows fact comparison between cell choices, at a level comprehensible by all project stakeholders in the decision making process.

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“…When undertaking the development of a new automotive product, a set of initial requirements is drawn in order to meet the intended market space, covering aspects such as performance, lifespan, cost of use, cost of manufacture, price point and many other factors of its design [1]. This leads to other requirement specifications such as sizing, technologies to use and other more engineering aspects of the initial design [2].…”

Section: Introductionmentioning

confidence: 99%

Sizing tool for rapid optimisation of pack configuration at early-stage automotive product development

Taylor

Ball

McGordon

et al. 2015

WEVJ

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The specifications that define an automotive development project are established at an early point in the process and define the direction of such a development, and changing these decisions becomes more costly the further the project progresses. Tools to enable better consideration of choice can help prevent this. The tool presented is designed to aid with the decisions needed when embarking on the development of a vehicle that incorporates electric-vehicle technologies and the important choices made regarding the battery pack required by such a vehicle. The tool incorporates a sizing model for determining the number of cells and the configuration required to meet a specified battery requirement. The tool then uses a 1-d model to determine some of the basic thermal and power characteristics that can then be used to inform other parts of the design specification. When attached to a database containing cell information, the tool can pre-select candidate cells to meet the requirement, and rapid execution time of the tool means that it can be used to quickly compare between cell choices, at a level understandable by all stakeholders in the decision making process.

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Addressing human factors in electric vehicle system design: Building an integrated computational human–electric vehicle framework

Cited by 8 publications

References 25 publications

The Effects of Driver Speed Prediction-Based Battery Management System on Li-ion Battery Performance for Electric Vehicles

The Effects of Driver Speed Prediction-Based Battery Management System on Li-ion Battery Performance for Electric Vehicles

Improved Sizing Calculator for Rapid Optimisation of Pack Configuration at Early-Stage Automotive Product Development

Sizing tool for rapid optimisation of pack configuration at early-stage automotive product development

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