FPGA implementation of extended Kalman filter for SOC estimation of lithium‐ion battery in electric vehicle

Abstract

A FPGA implementation for a model-based state of charge (SOC) estimation is described in this paper. A Thevenin equivalent circuit model is designed for SOC estimation. The extended Kalman filter (EKF) is designed to complete the SOC estimation, and the error is within 1 % . The FPGA is chosen to achieve realtime SOC estimation. A fast matrix method is proposed to improve the calculation speed of the EKF in FPGA because the EKF algorithm requires many matrix operations. In addition, the embedded system based on the FPGA with a system on a programmable chip (SOPC) technique is built using the Qsys platform in Quartus II. Based on the embedded system, an online testing platform is established to monitor the terminal voltage and load current of the experimental battery in real time; experimental results show that the online SOC estimation is successful. The measurement results show that the FPGA embedded scheme of the EKF allows for successful implementation of the SOC estimation with accuracy and speed. The fast matrix method requires 0.00007 s to implement the SOC estimation and is four times faster than the conventional matrix method.

Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

“…e remaining capacity of the battery refers to the capacity released during the discharge period from the current state to the terminal voltage under certain discharge conditions. By definition, the most basic method for calculating SOC is the current integration method [4][5][6][7]. Assuming that the initial battery allowance is SOC 0 , the integral value of the current in charging and discharging directly determines the allowance SOC (t) at the next instant, namely,…”

Pure hardware design and implementation on FPGA of an EKF based accelerated SoC estimator for a lithium‐ion battery in electric vehicles

“…More complex models can also be used, however, they are not suitable for real-time implementation due to the high number of parameters and the synthesis complexity. In this work the sixth-order polynomial form described by Equation ( 2) is adopted since it provides a compromise between accuracy and implementation simplicity [6,7].…”

“…Extended Kalman Filter (EKF) [7] is an ECM based-method that enables a proper estimation of the SoC and other battery internal parameters such as the open-circuit voltage. This powerful tool has been widely used to estimate the SoC of Li-Ion batteries in EV, where various modified algorithms have been developed in the recent literature.…”

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

“…Considering a significant error in the initial state, the LLKF takes a longer time to minimize the error and reach the actual state estimate, which will be proved in the Results section. The EKF and unscented Kalman filter (UKF) are the developed versions of the Kalman filter, these can be used for nonlinear systems [22,23,39]. The UKF creates a new distribution for the nonlinear function based on weighted points called sigma points (ơ).…”

“…The method complexity and the difficulty of obtaining a realistic model for the battery are two vulnerabilities of this method. Hence, finding a precise and straightforward model is a vital issue that demands the model-based method be utilized for estimating the SOC broadly [19][20][21][22][23]. A recent trend to estimate the correct SOC that utilizes the hybrid combination of two or more of the previous estimation methods was proposed in [6,24].…”