FPGA-based design of advanced BMS implementing SoC/SoH estimators

Kumar, Bijender; Khare, Neeta; Chaturvedi, Pradyumn

doi:10.1016/j.microrel.2018.03.015

Cited by 32 publications

(25 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A well identified DP model can be used to simulate the operation of the battery during any work cycle, particularly in conjunction with a BMS system, e.g., for estimating SOC, remaining travel range, and SOH status [132]. The DP model presented in this work will be expanded in the future with the following points considered:…”

Section: Discussionmentioning

confidence: 99%

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

et al. 2018

View full text Add to dashboard Cite

The following work presents the model-assisted research on Valve-Regulated Lead-Acid (VRLA) Absorbent Glass Mat (AGM) battery in pulse operation cycle. The experimental research was conducted for a constant value of State of Charge (SOC) of the battery, for values ranging from 0.2 to 0.8. Based on the conducted test stand research, the parameters of the battery were identified, which were later used to model the battery using the equivalent circuit based on dual polarization (DP) model with double Resistive-Capacitive (RC) loop. Simulations were performed for the identified parameters of the battery which are described by the general form of the polynomial. The second part contains the research on utilization of Nonlinear AutoRegressive eXogenous (NARX) recurrent neural network to predict SOC and a terminal voltage of the battery. Obtained validation results with the use of the identified parameters of the double RC loop and NARX model were discussed in the following work. The article also features the advantages and disadvantages of NARX model and DP model utilization for the use of in Battery Managements Systems (BMS) and micro-installations based on renewable energy sources. Furthermore, the advantages of the addition of more RC loops to describe the dynamic states of batteries in pulse states were discussed in the article.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

et al. 2018

View full text Add to dashboard Cite

show abstract

“…This allows for the implementation of the algorithm in a soft‐core processor and achieves communication between the microprocessor and the logic devices. Kumar et al presented a neuro fuzzy method to complete SOC estimation of BMS in FPGA. To solve the problem of energy management system in the micro‐grid, Liang et al chose a neural network to complete SOC estimation in FPGA.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

et al. 2019

Asian Journal of Control

View full text Add to dashboard Cite

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.

show abstract

“…The main parameters being monitored are the battery's state of charge (SOC) and depth of discharge (DOD). This is accompanied by analysis of the state of the battery following operation compared with the state prior to operation (state of health -SOH) [43,44].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of the Operating Parameters a Low-Temperature Fuel-Cell Stack for Applications in Unmanned Aerial Vehicles: Part I

et al. 2019

View full text Add to dashboard Cite

The paper presents a brief analysis of the potential use of hydrogen-oxygen low-temperature fuel cells with proton-exchange membrane fuel-cell stacks as power sources for the construction of drive units powering unmanned aerial vehicles. Hydrogen storage methods are discussed. Characteristics of selected aspects of monitoring the electrical and non-electrical operating parameters of a drive unit with fuel cells were investigated, with respect to the fuel cell, fuel-cell cooling system, and gas reagent supplying the system with hydrogen and air. Hydrogen fuel consumption for the production of electricity and purification, or so-called purge, was analysed. Based on the results of laboratory tests of the parameters of a generator with fuel cells, an exemplary method of monitoring operating parameters and controlling power sources involving fuel cells is proposed.

show abstract

FPGA-based design of advanced BMS implementing SoC/SoH estimators

Cited by 32 publications

References 8 publications

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle

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

Monitoring of the Operating Parameters a Low-Temperature Fuel-Cell Stack for Applications in Unmanned Aerial Vehicles: Part I

Contact Info

Product

Resources

About