使用学习曲线指导能源转型,以重型电动卡车为例
Auke Hoekstra1,2, Floor Alkemade3
1Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
概括
学习曲线可以通过识别政策转折点来加速电动卡车 (eTruck) 的采用. 基于证据的政策奖励范围,充电速度和网络互操作性,可以推动成本效益高的过渡到清洁的移动.
科学领域:
- 可持续的能源和运输系统
- 技术进步建模技术进步建模
- 基于证据的政策制定.
背景情况:
- 学习曲线准确地预测清洁能源和移动技术的进步.
- 学习曲线在电动卡车 (eTrucks) 政策中缺乏系统的整合.
- 对于去碳化运输部门而言,eTruck的采用至关重要.
研究的目的:
- 为欧洲的eTrucks开发一个系统级的学习模型.
- 为了说明学习曲线如何为eTrucks提供基于证据的政策.
- 确定快速采用eTruck的临界点.
主要方法:
- 用于eTruck驱动系统和电池组设计的使用学习曲线.
- 模拟的电池成本,耐用性和组合趋势.
- 集成的能源效率,二氧化碳排放,组件重量和电力/柴油成本.
- 分析了各种充电成本场景和eTruck车队范围.
主要成果:
- 确定了可以加速eTruck采用的多个转折点.
- 证明了电池开发和充电基础设施对采用率的影响.
- 突出了车辆续航里程和运营成本的影响.
结论:
- 学习曲线为基于证据的eTruck政策提供了坚实的框架.
- 政策应激励更长的续航里程,更快的充电,以及车辆到电网的能力.
- 开放和互操作的快速充电网络是快速和经济有效的eTruck转型的关键.
关键词:
电气和电子工程 电气和电子工程能量和行为.能源效率 能源效率是指能源的使用效率.能源网和网络 能源网和网络能源政策 能源政策能源供应和需求的能源供应和需求.环境影响环境影响环境影响机械工程 机械工程 机械工程更多相关视频
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