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.
Range anxiety is one of the problems that hinders the large-scale application of electric vehicles (EVs). We propose a driving-behavior-based State of Charge (SoC) prediction (DBSP) algorithm to overcome this problem. This algorithm can determine whether drivers can reach their destinations while also predicting the SoC if drivers were to return the trip. First, two supercapacitor equivalent circuit models are established, with one based on the historical average power and the other based on the equivalent current, which is proposed in this algorithm. Then, based on the equivalent transformation of the two models, an analytical expression relating the historical average power and the predicted SoC is derived by using the equivalent current as a 'bridge'. Therefore, the predicted SoC can be dynamically adjusted in response to recorded historical data, including the output power, speed and distance of EVs powered by supercapacitors. The simulation results demonstrate that the total prediction error is less than 0.5% of the real SoC at different initial SoC and temperature, which represents idealized behavior-based driving. In contrast, in actual driving experiments, the total prediction error is less than 3% of the real SoC at different initial SoC and temperature.
Hoisting equipment is core to many industrial systems and therefore their state of health significantly affects production lines and personnel safety; this is especially important in environments such as coal mines. The health of the hoisting system, can be estimated by deploying energy harvesting wireless sensor nodes that monitor the drum surface stress. In this network of sensor devices, it is very costly to send highly sampled data as it causes radio congestion and consumes energy. However, from our experience of sensing hoist systems, we note that the data observed at the upper surface of the hoist is significantly more indicative of the state of health of the whole system, compared with data sensed at the lower surface. Therefore, we need to take advantage of this to optimise the communications of sensor nodes. However, scarce energy can be collected for these devices from the hoist itself, along with the prioritised Quality of Service (QoS) requirements (throughput, delay) of monitoring signals, raises important challenges for energy management. In this paper, we use Lyapunov optimisation techniques and propose an Energy-neutral and QoS-aware Protocol (EQP), including duty cycling and network scheduling to solve it. Extensive simulations show that EQP helps sensor nodes realize consecutive monitoring, and achieve more than 38% utility gain compared with existing strategies.
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