A new method for dynamic energy management of energy-saving elevators based on super capacitors

This paper proposes a new method to dynamically manage the energy stored in super capacitors (SUPCAPs) which are equipped in an energy-saving elevator system. The back propagation neural network (BPNN) method is employed to evaluate the balance voltage (BV) of the SUPCAPs in every operation period of the elevator. At the beginning of the elevator works, the main controller calls the BPNN to predict the energy required by the elevator according to the present and target floor which is about to arrive at. Regulated by the BPNN, more energy can be saved in terms of different operation periods. Moreover, the energy storage capacity of the SUPCAPs is optimized to provide as much energy as possible in every operation period by an optimization method proposed in the paper. Not only is the peak power that emerges when the elevator works in heavy or full load eliminated, but also the capacity of the rectifier in the front end can be partly reduced. Simulation and experimental results show the effectiveness and feasibility of our method.