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Abstract

Battery management system (BMS) is an important part of electric vehicle that can improve the efficiency of battery and ensure the safety during operating process. State of charge (SOC) estimation is one of kernel technology of BMS. High precision of SOC estimation can not only increase service life of battery, but also enhance energy utilization efficiency and cut down the operational cost. Based on fully understand working principle of battery, three-order RC equivalent circuit is chose to simulate the external characteristics. Introduce the working theory of that combination of extended Kalman filter (EKF) and open circuit voltage (OCV), and the whole recursive algorithm of estimation SOC, using the OCV-SOC look-up table to solve the problem of initial value of SOC to a certain degree. The hardware circuit and software of BMS are designed. Hardware circuit consists of current measurement circuit, voltage measurement circuit, temperature measurement circuit and equalization circuit. The software is designed by C as the development language, the Microsoft Visual Studio 2008 as development environment and SQL Server 2008 as database management system. The experiments are carried out after the experimental platform of system hardware and software is constructed. The system realizes the real-time online monitoring of BMS, SOC estimation and etc. The result verifies the reliability and feasibility. The precision requirement of SOC estimation by using EKF algorithm and OCV-SOC look-up table meets the national standard for electric vehicle.

Keywords

Electric vehicle battery management system smart EFK algorithm extended kalman filter state of charge

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References

Plett

G.L.

, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs part 1: Background, Journal of Power Sources 134(2) (2004), 252–261.

W.X.

, Hardware design of distributed energy management system of electric vehicle, Journal of Benbu College 3(5) (2014), 1–4.

Plett

G.L.

, Sigma-point Kalman filtering for battery management system of LiPB-based HEV battery pack: Part 1: Introduction and state estimation, Journal of Power Sources 161 (2006), 1356–1368.

Plett

G.L.

, Sigma-point Kalman filtering for battery management system of LiPB-based HEV battery pack: Part 2: Simultaneous state and parameter estimation, Journal of Power Sources 161 (2006), 1369–1384.

Lin

J.C.M.

, Wu

C.Y.

and Dai

Y.L.

, Development of a battery management system for electric scooters, Journal of Technology 30(2) (2015), 83–91.

Xia

Y.Y.

, Zhang

R.Q.

, Wu

J.D.

and He

X.N.

, A newly battery management system based on a power/signal time division multiplexing bus, Power Electronics 50(11) (2016), 22–16.

Xiao

L.J.

, Chang

, Zhang

R.X.

and Gong

D.L.

, A voltage detection and equalizer for serial lithium batteries based on LTC6803, Measurement and Control Technology 34(4) (2015), 43–46.

Sethi

Dr.N.

and Panda

, Multi-Way encryption using elliptic curve cryptography, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(12) (2017).

Chaoui

and Ibe-Ekeocha

, State of charge and state of health estimation for lithium batteries using recurrent neural networks, IEEE Transactions on Vehicular Technology 66(10) (2017), 8773–8783.

10.

Dey

, Biron

Z.A.

, Tatipamula

, Das

and Mohon

, Model-based real-time thermal fault diagnosis of lithium-ion batteries, Control Engineering Practice 56 (2016), 37–48.

11.

M., Hannan

, Lipu

M.S.H.

, Hussain

and Mohamed

, A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations, Renewable & Sustainable Energy Reviews 78 (2017), 834–854.

12.

Gnana Guru

, Jothiraj

, Yogarajan

and Kandukuri

, Automated detection of cardiac arrest using heart beat sensor and provision of therapy through wearable defibrillator, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 6(1) (2018).

13.

Joaquín

K.B.

, Li

J.H.

, Günther

and Danzer

M.A.

, A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems, Applied Energy 155 (2015), 455–462.

14.

Stellarani

and Bhuwaneswari

, Cellular automata for mobile adhoc using EARP, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(11) (2017).

15.

Dey

, Ayalew

and Pisu

, Nonlinear adaptive observer for a lithium-ion, battery cell based on coupled, electrochemical-thermal model, Journal of Dynamic Systems Measurement and Control-Transactions of The Asme 137(11) (2015), 111005.

16.

Kim

, Wang

Y.B.

and Fang

H.Z.

, Model-based condition monitoring for lithium-ion batteries, Journal of Power Sources 295 (2015), 16–27.

17.

Wang

, Zhang

C.B.

and Chen

Z.H.

, A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy, Energy Procedia 75 (2015), 2635–2640.

18.

A Paul, Real-time power management for embedded M2M using intelligent learning methods, ACM Transactions on Embedded Computing Systems (TECS) 13(5s) (2014), 148.

19.

Tian

, Chen

, Xia

, Sun

and Xu

, An adaptive gain nonlinear observer for state of charge estimation of lithium-ion batteries in electric vehicles, Energies 7(9) (2014), 5995–6012.

Research on smart EFK algorithm for electric vehicle battery packs management system

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