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This paper presents an integrated optimization framework of sizing and energy management for four-wheel-independently-actuated electric vehicles. The optimization framework consists of an inner and an outer layer that are responsible for energy management, i.e., torque allocation, and powertrain parameter optimizations. The optimal torque allocation in the inner layer is achieved via the dynamic programming (DP) method while the desirable powertrain parameters in the outer layer are pursued based on the exhaustive method. In order to verify the proposed optimization framework, two driving cycles are constructed to represent the comprehensive and realistic driving conditions. One cycle is built by combining six typical driving cycles, which cover urban, high-way and rural driving styles to enhance representativeness. The other one is synthesized using the Markov chain method based on a vast quantity of real-time operating data of electric vehicles in Beijing. Simulation results demonstrate that the proposed strategy decreases the power consumption by 15.1% and 13.3%, respectively, in the two driving cycles, compared to the non-optimal, even-torque-allocation strategy.

Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles