Acceleration Slip Regulation Control Method for Distributed Electric Drive Vehicles under Icy and Snowy Road Conditions  
[PERSON]  
1 College of Mechanical Engineering, Beihua University, Jilin 132021, China; [EMAIL_ADDRESS]  
2 School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China; [EMAIL_ADDRESS]  
2 School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China; [EMAIL_ADDRESS]. three types of road conditions and did not account for sudden changes in the friction coefficient [12]. A road identification method proposed by [PERSON] is based on the standard road u-S curve, which can accurately obtain the optimal slip ratio. Although the method accounts for variable friction coefficients, it overlooks changes in the longitudinal force of the wheels, resulting in an excessive slip ratio overshoot of nearly 250% [13]. The current road conditions were identified in real time by [PERSON] using slip ratio and tire-road friction coefficient, and the optimal slip ratio for the current conditions was calculated. However, the friction coefficient is significantly influenced by the vehicle's longitudinal force state, leading to inaccurate calculations [14]. As road estimation algorithms have evolved, the design of estimators now combines both vehicle dynamic responses and external sensor signals [15; 16; 17; 18]. For example, a normalized Duggoff tire model was developed by [PERSON], and a road friction coefficient observer was designed based on the unscented Kalman filter (UKF) algorithm and a 7-degrees-of-freedom vehicle model [19]. A dynamic joint estimation method for road friction coefficients based on inertial measurement units and built-in sensors was proposed by [PERSON]. This method employs machine learning and dual radial basis function neural networks, utilizing the extended Kalman filter to establish the road friction coefficient estimator. However, simulation results indicate significant fluctuations in the estimated road friction coefficient [20]. Overall, current methods for estimating road friction coefficients still face challenges such as low estimation accuracy, significant impact from road surface random noise on the estimation results, and limited research on conditions with sudden changes in the friction coefficient. The existing ASR control strategies can be categorized into three types based on their control objectives: slip ratio-based control, torque-based control, and a combination of both [21]. The corresponding control algorithms mainly include PID control, model predictive control (MPC), fuzzy control, adaptive control, and sliding mode control [22; 23; 24; 25; 26; 27]. For example, a variable universe fuzzy PID (VFPID) controller for ASR control was proposed by [PERSON] based on an improved fuzzy PID (FPID) algorithm [28]. However, PID control exhibits poor robustness when dealing with complex and variable road conditions, making it difficult to handle snow-covered surfaces. In contrast, sliding mode controllers and their improved versions are widely used for controlling low friction coefficient roads due to their excellent robustness. A conditional integrator sliding mode control strategy, combining anti-slip torque with feedforward control torque based on driver commands to achieve coordinated control of drive torque was adopted by [PERSON] [22]. Building on this, a saturation-resistant sliding mode control strategy based on conditional integration was proposed by [PERSON], and the control variable was redesigned as wheel angular velocity to achieve faster vehicle dynamic response [23]. An ASR control strategy based on saturation-resistant sliding mode control law, better addressing wheel model uncertainties, slip ratio estimation errors, and disturbances, was designed by [PERSON] [24]. To optimize control parameters, a fuzzy algorithm for adaptive adjustment of the sliding mode controller's switching function was introduced by [PERSON] [25].