# Design of Optimized Energy Management Strategy for All-Wheel-Drive Electric Vehicles
## 1 Introduction  
With the development of battery technology, pure electric vehicles occupy an increasingly large proportion of the way people travel nowadays. The electric configuration can briefly be divided into driving by front motor, rear motor, and driving by four in-wheel motor. The difference in each vehicle configuration is that the power and the control strategy used in each model are different. Electric vehicles (EVs), with the benefit of low cost and zero-emission [1; 2], are widely used. In addition, the EV has the advantage of high torque reserve at low speed in the driving system, which can realize the shift frequently switch in the start-stop mode and with the recovery of braking energy [3; 4; 5]. The all-wheel-drive electric control strategy (EMS) is central, and an excellent EMS is the foundation of the EV's comprehensive performance. The EMS ensures the optimal torque distribution in front and rear shifts, including driving and braking mode. Many methods have been studied in the EV control process. The dominant thoughts of energy management can be classified into rule-based EMS and optimization-based EMS [6; 7]. The rule-based control strategy is widely used in the vehicle control unit (VCU) for its simplified calculation in VCU. However, the rule-based EMS of the determined threshold has poor adaptability to dynamic conditions. In recent years, more attention has been paid to EVs' energy management control strategy. For example, some researchers proposed a genetic algorithm to optimize the torque distribution ratio considering constraints of motor characteristics [8]. Other researchers used the least-squares method to get the control logic of the in-wheel motor [9]. [PERSON] [10] coupled the following distance and power distribution of the electric truck in a model predictive controller and co-optimized the safety and energyconsumption. [PERSON] [11] proposed an intelligent energy control strategy that combines fuzzy controller and Savitzky-Golay selective filtering for hybrid storage system control. [PERSON] [12] presents a model predictive control (MPC) power management strategy for a novel anti-idling system, designed for service vehicles. The MPC requires the driving cycle or driver command as a priori information to be known. [PERSON] [13] introduces an online and intelligent energy management controller to improve the fuel economy of a power split for PHEV. The genetic algorithm is used to optimize the engine power threshold. Off-line global optimal torque distribution is frequently executed in plug-in hybrid electric vehicles (PHEVs). In terms of all-wheel-drive EVs' energy management strategy, [PERSON] [14] present an adaptive equivalent consumption minimization strategy (A-ECMS) for a four-wheel-drive hybrid powertrain system. The results show that A-ECMS achieves better fuel economy than regular ECMS. [PERSON] [15] proposed a charge depletion/charge sustaining strategy for an all-wheel-drive plug-in hybrid electric vehicle to realize the torque distribution mainly according to the engine optimal working region to achieve better fuel economy. The traction enhancement is beneficial to the improvement of fuel efficiency. [PERSON] [16] proposed a multi-objective optimal torque distribution strategy, taking into account the front and rear axle load transfer and the variations of adhesion characteristics. The advantages of the proposed strategy are verified through simulation studies in terms of vehicle energy consumption and wheel slip ratio. The attainment of the online torque distribution algorithm goal is to reflect vehicle dynamic driving cycle characters and energy efficiency. However, the traditional dynamic programming (DP) algorithm is mostly involved in known conditions, and the traffic information is measured previously. The off-line global optimal torque distribution is restricted in real-time application. In the actual driving cycle, the next-moment speed will correlate with the current status, and the status is not associated with the previous speed.