Multi-objective optimization shift strategy for vehicle based on adaptive linear weighted sum method

Abstract

The shift strategy plays a pivotal role in influencing the power and economy performance of vehicles. In order to improve the adaptability of shift to dynamic driving conditions, this paper proposes a comprehensive shift strategy. This study employs the FMRLS algorithm to establish a model for identifying vehicle mass and road slope and presents a model for altitude identification based on SVM. The comprehensive shift strategy is formulated by using the NSGA-II algorithm to calculate the Pareto front of the shift speeds and determine the power and economy weights for different driving conditions based on the linear weighted sum method in an adaptive manner. The simulation results show that the energy consumption of the comprehensive shift strategy is close to the economy shift strategy under the CHTC-HT condition. Significantly, the maximum difference is 2.97% in acceleration time between the comprehensive and the power shift strategy under plateau condition. Additionally, the comprehensive shift strategy can eliminate cyclic shift on upslope and provide braking force in response to the driver intention to decelerate on downslope, improving vehicle safety, and dynamic adaptability.