Eco-Driving for Metro Trains: A Computationally Efficient Approach Using Convex Programming
Zhuang Xiao, Nikolce Murgovski, Mo Chen, Xiaoyun Feng, Qingyuan Wang, Pengfei Sun
Abstract
Eco-driving for trains has traditionally focused on minimizing mechanical energy consumption at wheels, while completely ignoring traction chain losses that are rather significant. This article presents a computationally efficient approach to minimize the total electrical energy consumption from traction substations (TS). After a nonlinear and non-convex program is formulated in time domain, a nonlinear and non-convex program is formulated in space domain to overcome the drawbacks of the model in time domain. By convex modeling steps, the non-convex program in space domain is reformulated as a convex program that can be efficiently solved. To further reduce computational effort, a real-time iteration sequential quadratic programming (SQP) algorithm is proposed to solve the convex program in a model predictive control framework. Numerical results indicate that the proposed SQP method yields a near-optimal solution with high computational efficiency. Compared to a traditional mechanical energy consumption model, a TS-to-traction energy efficiency can be improved.