An online driver behavior adaptive shift strategy for two-speed AMT electric vehicle based on dynamic corrected factor

The performance of the shift strategy can reduce the energy consumption of two automatic manual transmissions (AMT) electric vehicles while meeting the needs of various drivers. Hence, the adjustment of the shift strategy is complicated due to the uncertain driver behavior. To address the above issue, an online driver behavior adaptive shift strategy based on dynamic corrected factors is proposed. Firstly, the simplified models of the power system and conventional shift strategies are constructed for electric vehicles. Secondly, principal component analysis and k-means algorithms are implemented to classify driver styles. Next, Learning Vector Quantization neural network and Fuzzy neural network are applied to identifying driving style and driving intention in real-time. Then, according to the driver behavior, a dynamic corrected factor is introduced. The dynamic corrected factors of different driver styles are modified to adjust the proportion of power and economy in the shifting process. As a result, the proposed shift strategy based on dynamic corrected factors achieves a compromise between power and economy for two-speed AMT electric vehicles. The numerical validation results demonstrate that the proposed shift strategy is energy-saving compared with the conventional shift strategy and can satisfy the requirements of various driver styles.