Furthermore, for lithium-ion batteries parameters exist that are indeterminate using regression processes on full cell experiments. Shifting parameters against each other can lead to the same electrical behavior but completely changes the internal state of the battery.
Conventional methods for lithium-ion parameters are slow and ineffective. Study proposes efficient parameter optimization using the Shepherd model. Minimizing the RMSE between the battery's real data and its model. Reducing the overall voltage error to 4.2377 × 10 -3 and the RMSE to 8.64 × 10 -3.
An accurate lithium-ion battery model not only effectively improves the accuracy of state of charge (SOC) and state of health (SOH) estimation, but also enhances the simulation effectiveness when formulating the vehicle control strategy.
The estimation of each battery model parameter is made to lithium-ion battery with a capacity of 20 Ah, and the presented methodology can be easily adapted to any type of battery. The mean objective of the results is estimate the battery parameters to posteriorly use the battery model to estimate the SoC by adaptive method.
The lithium-ion battery modeling plays a crucial role in the analysis and control of electric vehicle power systems. To improve the accuracy, robustness and rapidity of lithium-ion battery models, many scholars have conducted relevant research and exploration.
To the knowledge of the authors no work modelling lithium-ion batteries exists where a simulation model was completely parameterized by parameters determined for the special material under consideration using samples taken from the test object.