In driving force distribution research, many distribution algorithms have been proposed for realizing the EV's turning stability, reducing the tire-road slip, or improving the performance in energy consumption [20; 21; 22]. Rule-based distribution and optimization-based distribution methods are mainly applied to allocate the torque of each wheel. Due to the lack of optimality and accuracy of the rule-based distribution algorithm [23], methods based on the optimization theory are widely used to obtain optimal control inputs. The optimization theory is the law that establishes an objective function and solves its minimum by taking into account system constraints [24]. [PERSON] et al. [25] investigated different objective functions and constraints on the basis of the motor performance, battery capacity, and tire-road adhesion for 4 WD EVs. [PERSON] et al. [26] proposed a nonlinear control distribution scheme for 4 WD EVs to improve vehicle stability with the constraints of the driven motors and tire-road adhesion. [PERSON] et al. [27] proposed a novel optimization allocation strategy for a vehicle's longitudinal and lateral control and verified its effectiveness by simulation and experiments. [PERSON] et al. [28; 29] investigated the directed yaw control method based on driver operation intention and designed allocation control algorithms with a dynamic efficiency matrix for the stability control of 4 WD EVs. Not only have distribution algorithms for 4 WD EVs been developed, but distribution algorithms for 6 WD and 8 WD EVs have also seen many research achievements. [PERSON] et al. [30] utilized the optimal driving force distribution for 6 WD/6 WS EVs, in which the objective function is based on the sum of each tire's workload rate while considering tire-road adhesion and friction circle. [PERSON] [31] investigated an objective function consisting of the sum of six tire utilization rates and minimized its value using the Lagrange multiplier method for tire force distribution. [PERSON] [23; 32] and [PERSON] [33] researched the driving force allocation schemes for 8 WD EVs to enhance stability and maneuverability. Nevertheless, the aforementioned literature is focused on passenger EVs, EVs with Ackerman steering gears, and few papers have concentrated on skid-steering EUGVs. Due to the difference in vehicle dynamics models between differential-steering vehicles and Ackerman-steering vehicles, there are some problems that need to be solved: (1) For passenger EVs and Ackerman-steering EVs, the vehicle dynamics models are established by considering the wheel steering angle. However, each wheel steering angle of the skid-steering EUGV is zero in its dynamics model. This increases the understeer behavior performance and steering difficulty factor. (2) By reason of the steering difficulty, it is a challenge to design a driving force control and distribution algorithm for tracking the desired lateral yaw rate. To this end, a novel driving force distribution and control strategy with a hierarchical controller structure is proposed in this paper and was implemented on a skid-steering EUGV. The major contributions lie in the following aspects. A nine-degrees-of-freedom (DOFs) dynamics model of a 6 WD skid-steering EUGV with a vehicle system dynamics model, wheel dynamics model, and tire model was established. A hierarchical controller that is composed of the upper layer controller and lower layer controller is proposed. The upper layer controller is devised to determine the resultant force and yaw moment to make sure the vehicle motion states track the desired values by using the integral sliding mode controller and PID controller. In the lower layer controller, a driving force distribution scheme is proposed based on the optimization allocation. The objective function is formulated based on the longitudinal tire workload rate and weighting factors. The weighting factors are established based on the ratio of the tire vertical force considering the vehicle motion conditions and vertical load change of each tire.