An Intelligent Controlling Method for Battery Lifetime Increment Using State of Charge Estimation in PV-Battery Hybrid System

Abstract

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.

A Review of Battery Charging - Discharging Management Controller: A Proposed Conceptual Battery Storage Charging – Discharging Centralized Controller

“…Therefore, both the available recourses are disconnected from the connected load. In another similar recent research [9], the intelligent power control shown in Figure 3 was developed to improve battery reliability and operational life. The power control operates in two control modes: 1) MPPT mode and 2) Battery Management System (BMS) mode.…”

“…Renewable energy systems are known as highly dependent systems to battery storages, which allows them to store the produced energy into the connected batteries [1][2][3] and optimize the battery charging-discharging operation [4,5]. Looking at the gaining popularity of the renewable energy systems, especially the microgrid renewable energy systems, battery storage charging and discharging [1,[6][7][8][9][10][11] are equally crucial for the renewable energy system in both islanded and non-islanded modes. Both modes need to ensure the reliability of the output energy with little to no disruption to the connected load during the operation hours [12].…”

“…Both modes need to ensure the reliability of the output energy with little to no disruption to the connected load during the operation hours [12]. Furthermore, the modes also need to ensure the battery storages can perform better and improve the utilization of renewable energy systems [9,[13][14][15]. Looking at the importance of battery storage charging -discharging, methods or mechanisms such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) [6,16,17], backtracking search algorithm [10,18,19], non-simultaneous charging and discharging [20], genetic algorithm [4,21], particle swarm optimized fuzzy controller [22][23][24][25][26][27], model predictive control [28][29][30][31][32], dynamic optimal power flow [33][34][35][36], and grey model and genetic algorithms [37] are commonly used to perform the battery storage charging and discharging.…”

“…Therefore, it is necessary to have a system that can assist, manage, and control the battery storage charging-discharging. Hence, selected papers such as [1,6,9] were extensively studied to understand the battery storage charging-discharging management systems. The studied papers were used to develop the conceptual methodology battery storage chargingdischarging strategy proposed in this review paper.…”

Cost-Effective Design of IoT-Based Smart Household Distribution System

“…The proposed method can also be used in renewable energy dependent devices as well as energy conversion systems [42][43][44][45][46][47][48][49][50]. It can also be adapted to monitor the condition of critical high voltage assets, power converters, and various flexible ac transmission system devices [51][52][53][54].…”