State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PD

Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis

This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated

Investigation of different methods of online impedance spectroscopy of batteries

A key challenge in a battery energy storage system is understanding the availability and reliability of the system from the perspective of the end customer. A key task in this process is recognising when a battery or a module within a system starts to degrade and then mitigating against this using the control system or battery management system. Battery characterisation parameters such as internal impedance and state of health and state of charge of the battery are a useful representation of the battery conditions. This thesis investigates the feasibility of undertaking Electrochemical Impedance Spectroscopy (EIS) methods online to generate an understanding of battery impedance. In order to perform an EIS measurement, an excitation signal of fixed frequency must be generated and the voltage and current measured and used to calculate the impedance. This thesis proposed different methods of generating a low-frequency excitation signal using hardware found in most battery systems to extract the harmonic impedance of a battery cell to aim towards a low cost on-line impedance estimation. This work focuses on producing impedance spectroscopy measurements through the power electronics system, a battery balancing system and the earth leakage monitoring system to attempt to get comparable results to off-line EIS measurements under similar conditions. To generate an excitation signal through the power electronic circuit, different control methods were used including varying; the duty cycle, the switching frequency and the starting position of the switched wave and the addition of an impulse type function. Although utilising a variable duty cycle to generate a harmonic impedance has been previously published in literature, the other techniques analysed within this these have not previously been considered. The thesis looks at the theoretical analysis of the circuits and control techniques and then follows this up with simulation and experimental studies. The results showed that all the methods investigated have the capability to generate a low frequency perturbation signal to undertake online EIS measurement. However, there are potential trade-offs, for example increased inductor ripple current. Not all of the methods produce sufficiently accurate results experimentally .However, five of the methods were used to generate EIS plots similar to those undertaken offline