1 |
LI B, MAO Z Y, SONG B W, et al. Thermal management performance improvement of phase change material for autonomous underwater vehicles' battery module by optimizing fin design based on quantitative evaluation method[J]. International Journal of Energy Research, 2022, 46(11): 15756-15772.
|
2 |
CHEN C, XIONG R, YANG R X, et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter[J]. Journal of Cleaner Production, 2019, 234: 1153-1164.
|
3 |
YE M, GUO H, XIONG R, et al. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries[J]. Energy, 2018, 144: 789-799.
|
4 |
XIONG R, YU Q Q, WANG L Y, et al. A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter[J]. Applied Energy, 2017, 207: 346-353.
|
5 |
MESBAHI T, KHENFRI F, RIZOUG N, et al. Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm[J]. Electric Power Systems Research, 2016, 131: 195-204.
|
6 |
MENG J H, RICCO M, ACHARYA A B, et al. Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles[J]. Journal of Power Sources, 2018, 395: 280-288.
|
7 |
MENG J H, RICCO M, LUO G Z, et al. An overview and comparison of online implementable SOC estimation methods for lithium-ion battery[J]. IEEE Transactions on Industry Applications, 2018, 54(2): 1583-1591.
|
8 |
JULIER S J, UHLMANN J K. New extension of the Kalman filter to nonlinear systems[C] //Proc.aerosense Int.symp.aerospace/defence Sensing Simulation, 1997: 182-193.
|
9 |
TONG S J, LACAP J H, PARK J W. Battery state of charge estimation using a load-classifying neural network[J]. Journal of Energy Storage, 2016, 7: 236-243.
|
10 |
HUANG C, WANG Z H, ZHAO Z H, et al. Robustness evaluation of extended and unscented Kalman filter for battery state of charge estimation[J]. IEEE Access, 2018, 6: 27617-27628.
|
11 |
SUN W, QIU Y, SUN L, et al. Neural network-based learning and estimation of battery state-of-charge: A comparison study between direct and indirect methodology[J]. International Journal of Energy Research, 2020, 44: 10307-10319.
|
12 |
CHARKHGARD M, FARROKHI M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE Transactions on Industrial Electronics, 2010, 57(12): 4178-4187.
|
13 |
KOLLMEYER P. Panasonic 18650PF Li-ion battery data, Mendeley Data[DB/OL]. [2023-09-02]. https://data.mendeley.com/datasets/wykht8y7tg/1.
|
14 |
CHEN P Y, LU C Y, MAO Z Y, et al. Evaluation of various offline and online ECM parameter identification methods of lithium-ion batteries in underwater vehicles[J]. ACS Omega, 2022, 7(34): 30504-30518.
|