Exploring Adversarial Threat Models in Cyber–Physical Battery Systems

Technological advancements such as the Internet of Things have facilitated data exchange across various platforms. This data exchange across various platforms has transformed the traditional battery system into a cyber–physical system. Such connectivity makes modern cyber–physical battery systems vulnerable to cyberthreats where a cyberattacker can manipulate sensing and actuation signals to bring the battery system into an unsafe operating condition. Hence, it is essential to build resilience in modern cyber–physical battery systems under cyberattacks. The first step of building such resilience is to analyze potential adversarial behavior, that is, how the adversaries can inject attacks into the battery systems. However, it has been found that in this underexplored area of battery cyber–physical security, such an adversarial threat model has not been studied in a systematic manner. In this study, we address this gap and explore adversarial attack generation policies based on optimal control framework. The framework is developed by performing theoretical analysis, which is subsequently supported by evaluation with experimental data generated from a commercial battery cell.