Data-Driven Battery Attack Detection and Control Behavior Determination for Vehicle Driving Safety
Pure electric vehicles (EVs) have become popular in current transportation systems because of their zero air pollution emissions. The battery management system (BMS) in an EV monitors battery information (current, voltage and temperature) in real time to prevent batteries from overcharging or overheating and also shares these battery information to outside-vehicle environments (e.g., smartphone apps) to enrich vehicle usage experiences. Many researches have studied various aspects regarding vehicle driving safety, few works have comprehensively studied battery security and its effects on driving safety of an EV. Besides, autonomous vehicles (AVs) have been adopted to reduce traffic congestion and multiple AVs will drive on the same road with the AV population growth. Therefore, It is critical to make optimal control decisions of AVs in real time to ensure driving safety. The proposed research will improve vehicle driving safety by analyzing battery security and making control decisions. Specifically, we will focus on three areas: (1) battery authentication system for detecting battery attacks (i.e., malicious AC-turn-on requests or battery-charge-stop requests) in electric vehicles; (2) control policy based driving safety system for an individual AV; and (3) multi-AV decision making system for multiple AVs to ensure their driving safety. For each area of the proposed research, we aim to increase vehicle driving safety and will evaluate the proposed systems through comparing their performance with the state-of-art methods based on real vehicle driving data.
- Yangfeng Ji, Committee Chair (Department of Computer Science)
- Haiying Shen, Advisor (Department of Computer Science)
- Lu Feng (Department of Computer Science)
- Haifeng Xu (Department of Computer Science)
- Brian L. Smith (Department of Engineering Systems and Environment)