Online state-of-charge estimation of lithium-ion and lead-acid batteries in electric vehicles

In vehicle applications, due to energy, power and voltage commitment the battery systems are usually composed of hundreds of cells that are connected in series and/or parallel configuration. To improve performance conditions and increase the battery lifespan, the battery management system (BMS) should makes decision for battery healthy based on the state of charge (SOC) estimation. In this paper, two strategies for SOC estimation of lithium - ion and lead-acid batteries are presented. In the first strategy, according to the suitability fuzzy logic to uncertainty modelling, the attempting to implement a decision-making system to estimate the battery's SOC is done. This strategy is designed based on the current, voltage and SOC data of the battery. The advantage of the fuzzy identification designing is its independency on battery modeling. In the second strategy, the SOC of the battery is estimated based on an online identification algorithm designed for its open circuit voltage (OCV). Recursive Least Squares (RLS) methode is utilized for identification algorithm According to the inherent relationship between the SOC and OCV, the SOC can be computed by the estimated OCV. The SOC-OCV relationship is modeled by a Neural Network which is trained by the actual data. According to the trained Neural Network, the SOC values are obtained corresponding to the estimated OCV values. In the RLS based strategy, battery dynamical models presented in section 2 are used. In validation section, it is observed that the second strategy shows the better results compared with the first strategy.

Keywords:

Li-ion Battery , Lead-acid Battery , SOC , Fuzzy logic , RLS algorithm , Neural Network