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As technology advances and cities become more innovative, the need to harvest energy to power intelligent devices at remote locations, such as wireless sensors, is increasing. This paper focuses on studying and simulating an energy management system (EMS) for energy harvesting with a battery and a supercapacitor for low power applications. Lithium-ion batteries are the primary energy storage source for low power applications due to their high energy density and efficiency. On the other hand, the supercapacitors excel in fast charge and discharge. Furthermore, supercapacitors tolerate high currents due to their low equivalent series resistance (ESR). The supercapacitor in the system increases the time response of the power delivery to the load, and it also absorbs the high currents in the system. Moreover, the supercapacitor covers short-time load demand due to the fluctuation of the renewable source. The EMS monitors the proposed system to maintain power to the load either from the renewable source or the energy storage. The power flow of the energy storage is controlled via DC-DC bidirectional converters. The lithium-ion battery is charged via a constant current (CC) using a sliding mode controller (SMC) and a constant voltage (CV) via a typical PI controller. The response of the SMC current controller is compared with PI and Fuzzy current controller. Furthermore, the performance of a system having and not having a supercapacitor is compared. Finally, MATLAB modeling system simulation and experimental implementation results are analyzed and presented. 

The lithium iron phosphate (LFP) battery has more nonlinear characteristic than other battery type. For this reason, when we use electrical equivalent circuit model and the extended Kalman filter (EKF) for estimating the SOC, the estimation performance can be decreased in the nonlinear region. This paper proposes an advance estimation method of state of charge (SOC) for lithium iron phosphate (LFP) batteries. To improve the model accuracy, this paper utilizes the nonlinear observer for identifying the internal parameters of batteries. Furthermore, to reduce the nonlinear effect of the LFP batteries, this paper recast the Kalman process. Therefore, through the proposed method, the performance of SOC estimation can be more accurate and the computational burden is decreased when we apply the embedded system. 

In this research, a Kalman filter-based Z-source inverter is proposed with an enhanced control algorithm for Maximum Power Pointer Tracking(MPPT) and this capacitor voltage stabilization. By implementing Unified Linear Kalman Filter Algorithm with Capacitor Voltage Control (CVC) algorithm for the Z-source inverter, the Kalman Filter can track Maximum Power Point (MPP) faster than traditional algorithm such as Perturb and Observation (P&O) algorithm, that has a minimum impact on rapidly changing atmospheric conditions. Thus, by using the Integrated Kalman Filter and CVC algorithm we can achieve faster, effective and capacitor voltage regulation at the same time. The effectiveness of this proposed Kalman Filter with CVC Algorithm for Z-source inverter is validated in MATLAB/Simulink and a hardware prototype has been built to verify the simulation and theoretical results.