Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV); hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.