Summary To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.

To further improve the energy economy of a four-wheel independent drive electric vehicle (FWIDEV) in the process of vehicle stability control, in this paper, the influence of different wheel torque distributions on vehicle stability and energy economy during vehicle steering is analyzed in depth. Then the wheel torque distribution scheme when the vehicle steering is established. Combined with the economic-based torque distribution strategy applied in the straight running condition, an optimal wheel torque distribution strategy is proposed for FWIDEV to adapt different driving conditions. And the controller designed in this paper adopts hierarchical control structure. The upper controller calculate the corrective yaw moment based on the sliding mode control. The lower controller implements wheel torque distribution according to the proposed strategy. Finally, the simulation results under different driving scenarios indicate that the proposed control strategy can achieve the same effect as the conventional control strategy in terms of vehicle stability, but the energy economy is improved by about 2.4%.INDEX TERMS Electric vehicles, stability control, optimal torque distribution, energy economy.

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To improve the fuel efficiency and battery lifespan of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery's life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery's life decay drop by 8.6% and 0.3%, respectively.