On Chip Kalman Filtering Strategy-Based for VRLA Battery Control Unit

Abstract

Based on existing understanding of residual capacity, the paper adopts Kalman filtering method on SOC estimation. The battery control unit(BCU) includes voltage, current and temperature sampling module. Due to Lead-Acid battery charging and discharging are a complex electrochemical process, we suggest a non-linear least squares regression method (NLLSRM) on open circuit voltage (OCV) method is proposed to aim at Lead-Acid battery. At the same time, a detailed hardware circuit schemes transfer module and on chip embedded Kalman filter method are employed. Moreover, A Local Interconnect Network (LIN) Bus Communication Technology is applied to communicate to Electronic Control Unit (ECU). Validation experimental results show that the proposed Lead-Acid BCU is high credible and the state of charge (SOC) estimation average relative error is about 3%.