保护隐私的大型电动汽车的协作电池故障警告通过充电站的异质数据来保护隐私
Haosen Yang1, Jinpeng Tian2,3, Weijie Mai4
1Research Centre for Grid Modernisation, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
本研究介绍了针对电动汽车电池故障检测的个性化联合学习框架. 它可以在没有数据共享的情况下开发合作模式,提高安全性并支持脱碳努力.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 电动汽车 (EV) 电池安全对于脱碳至关重要,但由于数据异质性和隐私问题,故障检测具有挑战性.
- 检测故障的机器学习 (ML) 需要大量的数据,通常由多方拥有,阻碍实际应用.
- 现有的联合学习 (FL) 方法与多样化的数据分布和定制需求作斗争.
研究的目的:
- 开发一个保护隐私的联合学习框架,用于协作电动汽车电池故障检测.
- 为了使数据所有者能够定制他们的故障检测模型,以提高异质数据的性能.
- 解决现有ML和FL方法在现实世界电动汽车电池管理中的局限性.
主要方法:
- 提出了一个个性化联合学习 (PFL) 框架,允许在没有直接数据共享的情况下进行合作模式培训.
- 实施了一个定制机制,允许数据所有者根据其特定的数据分布量身定制检测模型.
- 使用30个充电站的10,000多辆电动汽车的大规模现实充电数据集验证了框架.
主要成果:
- 在电池故障检测准确度方面,PFL框架显著超过了最先进的FL方法.
- 证明了强大的泛化能力,允许快速适应新参与者和各种充电条件.
- 证实了对数据异质性和输入数据窗口长度变化的弹性.
结论:
- 个性化联合学习为EV电池安全管理中的保护隐私的合作提供了可行的解决方案.
- 拟议的框架提高了检测性能和适应性,促进了在关键基础设施中更广泛地采用ML.
- 这种方法有望通过提高电动汽车可靠性和支持可持续能源转型,带来显著的经济和社会效益.
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