Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

State of Charge (SoC) of Lithium-ion battery is a key parameter in battery management systems for electric vehicles. This paper uses the fundamental theory of the smooth variable structure filter (SVSF) and proposes a SoC estimation algorithm for a Manganese Cobalt (NMC) cell with a nominal capacity of 20 Ah. Several tests are conducted considering different types of noise and parameters variation. A nonrandom Gaussian noise is first added to the battery voltage. The maximum root mean square error (RMSE) of the estimated SoC is about $2.8\%$ for a standard deviation of the noise set to $2.6e^{-3}$ P.U. The same noise is applied to the battery current and the maximum RMSE of the SoC is obtained as 1.36%. Moreover, an EMI noise is added to the battery voltage and the obtained RMSE of the SoC is about 1.73% for a peak amplitude of the noise set to 0.07 P.U. The convergence of the algorithm is also confirmed under battery parameters variation due to the temperature change. However, its accuracy degrades considerably. Finally, a comparative study is carried out with the extended Kalman filter and shows the superiority of SVSF in terms of accuracy and robustness against measurement noise.

Considering the expanding useofembedded devices equipped with rechargeable batteries, especially Li-ionbatteries that have higher power and energy density, the battery management systemis becomingincreasingly important. Infact, theestimationaccuracy of the amount of the remaining charges is critical as it affects the device operational autonomy.Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available charge. In this paper, a solution isproposed for Li-ion battery SOC estimation based on an enhanced Coulomb-counting algorithm to be implemented formultimedia applications.However,the Coulomb-counting algorithm suffers from cumulative errors due to the initial SOC andtheerrors ofmeasurements uncertainties,thereforeto overcome these limitations,we use the Open-CircuitVoltage (OCV),thushavinga piecewise linear SOC-OCV relationship andperformingperiodic re-calibration of the battery capacity. Thissolutionis implementedand validated on a hardware platform based onthePIC18F MCU family. The measured resultsarecorrelated withthetheoretical ones; they have shown a reliable estimation since accuracy is less than 2%.