The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.
BianXWeiZHeJ, et al. (2020) A novel model-based voltage construction method for robust state-of-health estimation of lithium-ion batteries. IEEE Transactions on Industrial Electronics: DOI: 10.1109/TIE.2020.3044779.
2.
BlekasKLikasA (2014) Sparse regression mixture modeling with the multi-kernel relevance vector machine. Knowledge and Information Systems39(2): 241–264.
3.
ChenHXuYWangM, et al. (2019) A balanced whale optimization algorithm for constrained engineering design problems. Applied Mathematical Modelling71(7): 45–59.
4.
ChenXYangHWunJ, et al. (2020) Life prediction of lithium-ion battery based on a hybrid model. Energy Exploration & Exploitation38(5): 1854–1878.
5.
DaiHZhaoGLinM, et al. (2018) A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Transactions on Industrial Electronics66(10): 7706–7716.
6.
FengFTengSLiuK, et al. (2020) Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model. Journal of Power Sources455: 227935.
7.
HanTLiuCWuL, et al. (2019) An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mechanical Systems and Signal Processing117(2): 170–187.
8.
HanTLiuCYangW, et al. (2020) Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA Transactions97(2): 269–281.
9.
HeJWeiZBianX, et al. (2020) State-of-Health estimation of lithium-ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Transactions on Transportation Electrification6(2): 417–426.
10.
HeZZhangXLiuC, et al. (2020) Fault prognostics for photovoltaic inverter based on fast clustering algorithm and Gaussian mixture model. Energies13(18): 4901. DOI: 10.3390/en13184901.
11.
KhaleghiSKarimiDBeheshtiS H, et al. (2021) Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network. Applied Energy282(1): 116159. DOI: 10.1016/j.apenergy.2020.116159
12.
LiPZhangZXiongQ, et al. (2020) State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. Journal of Power Sources459(5): 228069. DOI: 10.1016/j.jpowsour.2020.228069
13.
LiXYuanCWangZ (2020) State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression. Energy203(7): 117852. DOI: 10.1016/j.energy.2020.117852
14.
LiYStroeDIChengY, et al. (2021) On the feature selection for battery state of health estimation based on charging-discharging profiles. Journal of Energy Storage33: 102122.
15.
LiYYangYLiG, et al. (2017) A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection. Mechanical Systems and Signal Processing91(7): 295–312.
16.
LiuKLiYHuH, et al. (2020) Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Transactions on Industrial Informatics16(6): 3767–3777.
17.
MawonouKSREddahechADumurD, et al. (2021) State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking. Journal of Power Sources484(2): 229154.
18.
MohammadiBMehdizadehS (2020) Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm. Agricultural Water Management237(7): 106145.
19.
PhamQVMirjaliliSKumarN, et al. (2020) Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Transactions on Vehicular Technology69(4): 4285–4297.
20.
TangXLiuKWangX, et al. (2020) Model migration neural network for predicting battery aging trajectories. IEEE Transactions on Transportation Electrification6(2): 363–374.
21.
WangSFernandezCShangL, et al. (2018) An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs. Transactions of the Institute of Measurement and Control40(6): 1892–1910.
22.
WeiZZhaoJJiD, et al. (2017) A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model. Applied Energy204(10): 1264–1274.
23.
YanXJiaM (2019) Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection. Knowledge-based Systems163(1): 450–471.
24.
ZhangXHeZZhanZ, et al. (2021) Performance degradation analysis and fault prognostics of solid oxide fuel cells using the data-driven method. International Journal of Hydrogen Energy46(35): 18511–18523.
25.
ZhangYLiYTaoY, et al. (2020) Performance assessment of retired EV battery modules for echelon use. Energy193(2): 428–435.
