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Automated Vehicles (AVs), the self driving vehicles, are expected to outperform human drivers and improve road safety in the near future. However, to achieve these goals, they need to communicate with each other and the other road participants and coordinate their actions. The systems of connected cooperative AVs are called Cooperative Intelligent Transport Systems (C-ITS). Similar to AVs, C-ITS are vulnerable to failures and cyberattacks. In our previous work, we proposed a method US2 for AV risk analysis. This paper extends US2 and presents a method for integrated C-ITS safety and cybersecurity risk analysis. It takes into consideration automotive safety and cybersecurity standards ISO 26262 and SAE J3061, and utilizes the elements of the previously proposed risk analysis methods US2, EVITA, TVRA, and RACE.

This paper proposed a novel approach to state-ofcharge (SoC) estimation of the lithium-ion batteries (LiBs) used in electric vehicles (EVs) based on the extended Kalman filtering (EKF). An improved lumped parameter model was developed for describing the dynamic behavior of the LiBs with an optimized open circuit voltage. This improved approach can reduces model error effectively. Other model parameters were identified via the genetic algorithm (GA) to optimizes the polarization time constant. Experimental and simulation results with two kinds of dynamic cycles show that, compared to the commonly used coulomb counting method, the EFK based SoC estimation algorithm is more precise. The proposed methodology can resolve the deficiency of coulomb counting method. The coulomb counting method fails to correct the erroneous initial SoC and is prone to cause greater accumulated error. In contrast, the proposed novel SoC estimation approach can accurately project the SoC trajectory. It employs real-time measurements of battery current and voltage. This approach then can be applied conveniently to battery management system in commercial electric vehicles.