In order to improve the fuel economy of extended range electric motorcycles and address the issue of poor adaptability to working conditions in the traditional equivalent consumption minimization strategy, an adaptive equivalent consumption minimization strategy (A-ECMS) was proposed, and a method involving introducing a proportional-integral controller to dynamically and adaptively adjust the equivalent factor based on battery state of charge (SOC) feedback, while using a genetic algorithm to optimize controller parameters and comparing the control effects of the proposed strategy with other strategies under different standard working conditions was used to investigate the performance of the strategy. The results show that under three typical working conditions, the proposed energy management strategy has an average comprehensive fuel consumption of 1.42 L/(100 km), which is 26.04% lower than the commonly used rule-based strategy in engineering. The average final SOC deviation of A-ECMS is only 0.17%, with the maximum SOC fluctuation range controlled within 0.69%. Meanwhile, the average single-step calculation time of A-ECMS is 25 ms. It is concluded that the proposed A-ECMS can not only effectively improve fuel economy but also maintain the balance of battery SOC and has the potential for online application, providing a reference for the design and optimization of energy management strategies for extended range electric motorcycles.