Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 1869–1881 | Cite as

A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems

  • Muhammad TalhaEmail author
  • Furqan Asghar
  • Sung Ho Kim
Research Article - Electrical Engineering


Sealed-type lead–acid batteries are most common energy storage devices used with renewable systems. Battery state of charge (SOC) and state of health (SOH) estimation is a crucial part which requires maximum possible accuracy to ensure a secure and long-lasting battery energy storage system by cutting off charging and discharging processes at right time (100–40%). In this research work, a neural network (NN)-based simplified SOC and SOH estimation technique is proposed. Proposed technique provides an accurate online SOC and SOH estimation without battery internal parameter information. This technique only requires real-time battery voltage and current information and very simple mathematical calculations to estimate SOC and SOH, which makes it easy to implement at any low-cost micro-controller unit (MCU). Training data for NN have been acquired by using Arduino mega MCU, voltage sensor circuit, and current sensor. The NN program is designed and trained by backpropagation technique in Arduino mega. The calculated weights are further used for estimation. Terminal voltage \((V_{\mathrm{t}})\) and open-circuit voltage \((V_{\mathrm{oc}})\) are measured for different charging currents \((I_{\mathrm{ch}})\) and discharging currents \((I_{\mathrm{dch}})\). Later on, these data are used for training the NN. Experimental results are provided to prove the preeminence of proposed SOC and SOH estimation technique.


Battery energy storage system (BESS) State of charge (SOC) estimation State of health (SOH) estimation Neural network (NN) Arduino mega 



State of charge


State of health

\(\mathrm{SOC}^{{\prime }}\)

SOC slope


Battery energy storage system


Battery terminal voltage.


Battery open-circuit voltage


Charging current


Discharging current


Micro-controller unit


Extended Kalman filter


Cubature Kalman filter


Adaptive cubature Kalman filter


Maximum battery capacity at time


Nominal battery capacity


Actual battery voltage

\(V_{\mathrm{Bn measured}}\)

Measured battery voltage

\(\hbox {weights}_{\mathrm{hidden}}\)

Hidden weights

\(\hbox {weights}_{\mathrm{output}}\)

Output weights

\(\hbox {weights}_{\mathrm{Bhidden}}\)

Bias hidden weights

\(\hbox {weights}_{\mathrm{Boutput}}\)

Bias output weights


Hidden activation value

\(\hbox {Output}_{\mathrm{act}}\)

Output activation value

\(\nabla _{\mathrm{output}}\)

Output delta

\(\nabla _{\mathrm{hidden}}\)

Hidden delta

\(\hbox {Change}_{\mathrm{Whidden}}\)

Change in hidden weights

\(\hbox {Change}_{\mathrm{Woutput}}\)

Change in output weights


Learning rate




Sum square error


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Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Kunsan National UniversityGunsanRepublic of Korea
  2. 2.School of electrical engineering, The University of FaisalabadFaisalabadPakistan

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