access icon free State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network

© The Institution of Engineering and Technology
Received 27/04/2017, Accepted 17/01/2018, Revised 03/01/2018, Published 22/01/2018

Supercapacitors are increasingly applied to the field of electric vehicles. Although the state of charge (SOC) directly shows the remaining capacity of supercapacitors in an energy management system, vehicle drivers also require the changes of supercapacitors in the future for driving reference. How to predict SOC of supercapacitors has become a pressing problem. In order to solve the above problem, a method combining backpropagation (BP) neural network with the Kalman filtering algorithm is proposed to predict SOC in the future. The BP neural network inputs SOC estimated by the Kalman filtering algorithm as the training data to train network, and thereby being able to forecast the SOC in the future period. The algorithm is verified in simulations and experiments under the two conditions: NewYorkBus and NYCC, with the consideration of the influence of the length of training data and temperature. The results show the max absolute error during prediction at different lengths is under 6% in simulations and experiments. In addition, temperature has almost no effect on the prediction accuracy. The current research implies that this method can be applied to predict supercapacitors’ SOC in the future.

Inspec keywords: supercapacitors; electric vehicles; power engineering computing; neural nets; Kalman filters; backpropagation

Other keywords: supercapacitors; BP neural network; driving reference; NYCC; backpropagation neural network; energy management system; Kalman filtering algorithm; state-of-charge prediction; NewYorkBus; vehicle drivers; SOC prediction; training data

Subjects: Other power apparatus and electric machines; Digital signal processing; Transportation; Filtering methods in signal processing; Neural computing techniques; Other energy storage; Power engineering computing

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