Abstract: Aiming at the problem that the endurance of the S project is not up to standard, the S energy flow test scheme is designed, and an intelligent diagnosis system for energy consumption area analysis is constructed based on the energy flow theory for the research of vehicle energy consumption control, which solves the problem that it is difficult to define the distribution of vehicle energy consumption in the early stage of energy consumption control research. Based on the CLTC working condition, a multi-objective collaborative simulation platform for vehicle energy consumption control is built so as to achieve multiple optimization objectives such as vehicle energy consumption reduction, cost control enhancement, and user experience improvement, etc. The evaluation model of electric vehicle energy consumption and theoretical recovery algorithm are established, and the concept of optimal recovery rate of energy recovery is proposed for the first time. Based on the optimization of vehicle wind resistance and weight, and the improvement of transmission efficiency, the recovery strategy and accelerator pedal map are optimized to obtain the optimal recovery rate of the vehicle. A toolkit for drag reduction and efficiency improvement of pure electric and hybrid vehicle is established; and a set of flow system for research and application of energy-saving and consumption reducing technologies with enterprise characteristics is also established. The test results show that the vehicle's endurance is improved by 40 km+, and the power consumption is reduced by 0.5 kWh/100 km. The toolkit is applied to commercial vehicle EV platforms, with an estimated cost savings of about 1 billion+/year.