Abstract: The full-lifespan management concept provides a new pathway to seeking solutions from macro-application scenarios to micro-mechanism levels. This paper presents a cyber hierarchy multiscale optimal control method for multiple intelligent hybrid vehicles to fully release the potentials of vehicle components while guaranteeing driving safety and stability. It can be generally divided into the cyber intelligent driving system on the cyber-end and the intelligent vehicle system on the vehicle-end. On the cyber-end, the state information of the surrounding vehicles is transmitted via the Vehicle-to-Everything structure and further processed in the cloud platform to generate future driving behaviors based on a car-following theory. On the vehicle-end, an optimized control sequence for vehicle components at micro-levels is derived by incorporating a physics-informed neural network model for battery health prediction. The results show that global optimization needs high coupling between the macro- and micro-physical processes. By introducing the genetic algorithm for time smoothing, the improved driving strategy is capable of macro- and micro-coupling, and thus improves the controllable performance in time series. Moreover, this method spans the complexity of space, time, and chemistry, enhances the interpretation performance of machine learning, and slows down the battery aging in the process of multiscale optimization.