Online Energy-Efficient Operation of Maglev Trains Considering Regenerative Energy Utilization

Maglev trains have the advantages of low noise, good climbing ability, and low environmental impact. Several maglev lines have been put into commercial operation in China in recent years, and the issue of energy saving in maglev trains has attracted many researchers' attention. Existing research on this topic mainly focuses on offline calculation of the optimal train control strategy and rarely considers the use of regenerative braking energy between trains. This paper proposes an online rolling optimization algorithm to obtain the energy-efficient control trajectory of maglev trains according to the actual running condition. In addition, the regenerative braking energy of nearby trains is also considered to adjust the maglev train's control stage to further reduce the net traction energy consumption. The pseudospectral method is used in each prediction horizon of the rolling optimization algorithm to solve the multi-phase optimal control problem. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.