# Energy Management of a Multi-Source Power System
## 2 Literature Review
### Scheduling Approaches  
Researchers have proposed different approaches to optimize the usage of multiple energy sources. One of the approaches is predictive modeling. [PERSON] et al. [12] used predictive control to manage the energy generated from a fuel cell, battery, and super capacitor operated tramway in Spain. The predictive control collected data to generate a sequence of operations of the three sources. [PERSON] et al. [13] compared a stochastic predictive control model of a hybrid electric bus to the traditional dynamic programming with no prediction. The electric bus has two sources of energy; the battery and an engine that uses petrol. The stochastic predictive control model uses Markov Chain Monte Carlo methods to predict the future velocities of the bus. The forecasted velocities are then input into a dynamic programming algorithm to provide the optimal control sequence to minimize the energy consumption of the bus while taking into consideration the state of charge of the battery. [PERSON] et al. [2] used convex programming on a hybrid bus, integrating a fuel cell and battery, to optimize the power management and sizing of the sources. [PERSON] et al. [14] also used convex programming for the energy management of an electric powertrain. The researchers used a convex model of the powertrain to minimize the fuel consumption of the vehicle. They compared the model results to traditional dynamic programming. Convex programming provided an optimal solution close to that of dynamic programming. However, convex programming provided one advantage of requiring a lower computation time compared to dynamic programming. Another method for scheduling sources in electric vehicles is real-time programming. [PERSON] et al. [15] developed an optimal real-time energy management architecture for electric vehicles using two different sources. The system restricts its search for the optimal solution to the high level categorization considering the capabilities of the available sources to preserve the battery's state of charge by trying to rely heavily on the super capacitors to meet the demands of the vehicle. At the middle level, the energy management system is used to ensure that the power supply is uninterrupted and minimizes the difference in the power demanded and power supplied. [PERSON] et al. [16] also studied another real-time energy management approach using a fuzzy logic approach on a three-wheeled vehicle. In this approach, a super capacitor was combined with a battery on an electric vehicle. The battery is responsible for supplying the average power demand of the vehicle, while the super capacitor provided the rest of the required energy. They found that there was a 3% reduction in energy consumption by the vehicle, and the battery current RMS value was reduced by 12%. [PERSON] et al. [17] proposed an online energy management strategy that combines both an online and offline approach test on hybrid vehicles consisting of a fuel cell and battery. The online energy management is based on the time series prediction model nonlinear autoregressive neural network. After the data is collected, offline optimization-based strategies are used to optimize the use of the sources in the next time window. Additionally, [PERSON] et al. [18] implemented online energy management on a hybrid electric vehicle to reduce energy consumption. The online energy management strategy is divided into two layers. The first layer is used to determine whether the battery alone or the battery and engine will supply the energy demand of the vehicle. The second layer of the strategy is used for the power allocation between the battery and engine when both sources are being used based on a sequence generated by dynamic programming. Under specific driving conditions, the researchers were able to reduce energy consumption by up to 5.77%. [PERSON] et al. [19] employed neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state-of-charge (SOC). [PERSON] et al.