Commercial Viability Assessment and Planning of Safety-Critical Embedded SW of Electrified Road Vehicles
- 提供方法
- 版元よりダウンロードリンクを連絡
- 形態
- 価格
- 一般価格(税込)：￥6,600 会員価格(税込)：￥5,280
- 文献・情報種別
- SAE Paper
No.2021-01-0132
- 掲載ページ
- 1-12(Total 12 p)
- 発行年月
- 2021年 4月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- SAE WCX Digital Summit 2021
書誌事項
| 著者（英） | 1) Abhishek Shah Alias Sangani, 2) Caner Harman, 3) Emrah Kinav, 4) Mehmet Göl, 5) Ahu Ece Hartavi |
|---|---|
| 勤務先（英） | 1) University of Surrey, 2) Ford Otomotiv Sanayi A.S., 3) Ford Otomotiv Sanayi A.S., 4) Istanbul Gelisim University, 5) University of Surrey |
| 抄録（英） | Recent extraordinary technological progress in the field of high-voltage-batteries has led an evolution in the automotive industry, resulting in vehicle manufacturers to shift from conventional powertrains towards electric ones. However, electrified road vehicles are amazingly complex. Today, the number of operations for a modern electric vehicle grew from millions to billions per second. This is mainly fueled by the electrification and safety requirements in addition to critical core functionalities. This emphasizes the importance of software development cost, effort, and production planning in the automotive industry. In this paper, in the framework of EU funded H2020 OBELICS project, a detailed-COCOMO approach is proposed for a manually coded safety-critical embedded SW for an electric vehicle not only to plan the project well in advance but also to assess its commercial viability using quantifiable cost metrics to make the process more objective and repeatable. In this context, a case study is given for a battery-management software using an algorithmic model. The case-study has demonstrated not only the planning requirements but also the impact of project type on effort and time estimation in each phase of the software lifecycle. The results have shown that inaccurate software project type estimation can lead an error of up to 16% in effort and 19% in development time according to the phase. It also shows that reliability, database size and complexity are the major contributors of the total time, effort and hence of the cost. The results have also demonstrated the impact of model-based design and automated code generation tools on planning for different phases of the V-cycle briefly. 翻訳 |
