<p>This paper proposes a drive slip control (ASR) strategy combining improved road identification and chattering suppression to address excessive wheel slip and instability in distributed drive electric vehicles (DDEVs) under complex low-adhesion conditions. First, we propose a road friction coefficient estimation algorithm based on a genetic algorithm-optimized backpropagation (BP) neural network (GA-BP) to overcome the slow convergence and susceptibility to local extrema associated with traditional BP networks. This algorithm uses a seven-dimensional vehicle dynamics feature vector and globally optimizes the network’s initial weights and thresholds via the Genetic Algorithm, achieving rapid millisecond-level convergence and high-precision identification of the peak friction coefficient. Second, we design an integral sliding mode controller (ISMC) to address the steady-state error and inherent chattering phenomena of traditional sliding mode control (SMC). By constructing a sliding surface with an integral error term and employing a continuous saturation function instead of a discontinuous sign function, the controller ensures robust performance against parameter perturbations while effectively suppressing high-frequency chattering in control inputs. This approach significantly improves the smoothness of motor torque output and mitigates impacts on the mechanical transmission system. CarSim/Simulink co-simulation results demonstrate that under conditions such as low adhesion, abrupt friction changes, and split-mu roads, the proposed strategy precisely tracks the optimal slip ratio, effectively suppresses drive wheel slip and system chattering, and significantly improves vehicle acceleration performance and driving stability.</p>