Adaptive Real-Time Energy Management for a Hybrid Energy Storage System Integrated with Gear Shift Control

To address the problem of real-time coordinated control in gear selection and power distribution for hybrid energy storage systems, an adaptive real-time optimal control strategy is proposed in this study. Firstly, a vehicle dynamics model with a hybrid energy storage system and a two-speed mechanical automatic transmission (2AMT) is constructed. Next, a nonlinear optimization problem aiming to minimize battery energy consumption is established. To address the challenge of high computational complexity, polynomial fitting and variable substitution are employed to transform the original nonlinear problem into a convex optimization framework. This transformation enables the control variables to be directly obtained through efficient matrix operations with a global optimal analytical solution, thereby significantly improving computational efficiency. The real-time adaptive control strategy achieves forward-looking coordinated optimization of power distribution and gear selection. The simulation results show that the proposed method can achieve an effect similar to that of dynamic programming (DP) in terms of energy consumption but gains much higher computational efficiency. Compared with the rule-based strategy, the battery energy consumption is reduced by approximately 10%. The method demonstrates advantages both in terms of economy and real-time performance.