State of charge and state of power estimation for power battery in HEV based on optimized particle filtering

2020 ◽  
pp. 1-20
Author(s):  
Xiaoyan Niu ◽  
Guosheng Feng
Keyword(s):  
Particle Filtering ◽  
Power Estimation ◽  
State Of Charge ◽  
Power Battery ◽  
State Of Power

A linear recursive state of power estimation method based on fusion model of voltage and state of charge limitations

2021 ◽  
Vol 40 ◽  
pp. 102583
Author(s):  
Bowen Li ◽  
Shunli Wang ◽  
Carlos Fernandez ◽  
Chunmei Yu ◽  
Lili Xia ◽  
...  
Keyword(s):  
Estimation Method ◽  
Power Estimation ◽  
State Of Charge ◽  
Fusion Model ◽  
State Of Power

A novel fusion model based online state of power estimation method for lithium-ion capacitor

2021 ◽  
Vol 36 ◽  
pp. 102387
Author(s):  
Wenxin Chen ◽  
Cheng Xu ◽  
Manlin Chen ◽  
Kai Jiang ◽  
Kangli Wang
Keyword(s):  
Estimation Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
Fusion Model ◽  
Model Based ◽  
Lithium Ion Capacitor ◽  
State Of Power

SOC Estimation of Power Battery in Electric Vehicle Based on MC9S12DG128

2013 ◽  
Vol 318 ◽  
pp. 167-170
Author(s):  
Wen Li Xu ◽  
Ji Ming Lyu ◽  
Hong Hui Zhao ◽  
Zhi Yu Lan
Keyword(s):  
Equivalent Circuit Model ◽  
State Of Charge ◽  
Hardware In The Loop ◽  
Test Results ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Hardware Platform ◽  
Soc Estimation ◽  
Power Battery ◽  
Error Accumulation

This paper explored how to make use of extended Kalman filter (EKF) technique to estimate the state of charge (SOC) of battery. Based on the equivalent circuit model of battery, a mathematical model of EKF was established, and an EKF algorithm of SOC estimation was presented. By single chip microcomputer (MCU) of MC9S12DG128, a hardware platform for experimenting with this algorithm was built up. Then this algorithm was verified by two kinds of discharge tests on the hardware in the loop platform. The test results show that this algorithm is able to satisfy the requirement of SOC estimation. It has a high accuracy, and a low error accumulation.

A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs

2019 ◽  
Vol 24 ◽  
pp. 100758 ◽  
Author(s):  
M.J. Esfandyari ◽  
M.R. Hairi Yazdi ◽  
V. Esfahanian ◽  
M. Masih-Tehrani ◽  
H. Nehzati ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Lithium Ion Batteries ◽  
Hybrid Model ◽  
Control Method ◽  
Lithium Ion ◽  
Power Estimation ◽  
State Of Power

scholarly journals Battery Capacity Estimation of Low-Earth Orbit Satellite Application

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Myungsoo Jun ◽  
Kandler Smith ◽  
Eric Wood ◽  
Marshall.C. Smart
Keyword(s):  
Particle Filtering ◽  
Lithium Ion ◽  
Difficult Problem ◽  
Low Earth Orbit ◽  
State Of Charge ◽  
Simultaneous Estimation ◽  
Earth Orbit ◽  
Orbit Satellite ◽  
Battery Capacity ◽  
Low Earth Orbit Satellite

Simultaneous estimation of the battery capacity and state- of-charge is a difficult problem because they are dependent on each other and neither is directly measurable. This paper proposes a particle filtering approach for the estimation of the battery state-of-charge and a statistical method to estimate the battery capacity. Two different methods and time scales have been used for this estimation in order to reduce the dependency on each other. The algorithms are validated using experimental data from A123 graphite/LiFePO4 lithium ion commercial-off-the-shelf cells, aged under partial depth-of- discharge cycling as encountered in low-earth-orbit satellite applications. The model-based method is extensible to bat- tery applications with arbitrary duty-cycles.

scholarly journals State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Shang-Rui Chen ◽  
Huai-Bin Gao ◽  
Ke-Jun Xu ◽  
Meng-Yue Yang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Training Data ◽  
Particle Swarm Algorithm ◽  
Power Battery

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

Power battery state of charge estimation based on extended Kalman filter

2019 ◽  
Vol 11 (1) ◽  
pp. 014302 ◽  
Author(s):  
Qi Wang ◽  
Xiaoyi Feng ◽  
Bo Zhang ◽  
Tian Gao ◽  
Yan Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Of Charge ◽  
Power Battery ◽  
Battery State Of Charge ◽  
State Of Charge Estimation
Sign in / Sign up

Export Citation Format

Share Document