Sage Journals: Discover world-class research

Abstract

The power battery is a key component of the electric vehicle, and its State of health (SOH) parameters directly affect the safety and reliability of the electric vehicle. Considering the problem of the reduced SOH estimation accuracy of Li-ion battery, this paper proposes a joint algorithm of the firefly algorithm-back propagation neural network K-means (FA-BPNN-K-means) for SOH estimation to alleviate the wide voltage platform and severe polarization. In particular, the BPNN model of the battery is first established. The ohmic resistance, polarization resistance, and polarization capacitance of the battery are used as the input parameters of the model, and SOH was used as the output parameters. Secondly, the firefly algorithm (FA) is used to optimize BPNN for SOH estimation of Li-ion battery, solving the problem that BPNN is easy to fall into the local minimum and the convergence rate is slow. Finally, the predicted output of the FA-BPNN model is substituted into the K-means algorithm for clustering, and the data points for evaluation are obtained to reduce the cumulative error caused by the battery model. Compared with the BPNN algorithm, FA-BPNN-K-means joint optimization algorithm, obtaining lower error in SOH estimation, and it has good convergence. Besides, it is accompanied by higher prediction accuracy, which can guarantee the stable operation of the battery management system.

Keywords

Firefly algorithm (FA)Li-ion battery back propagation neural network (BPNN)K-means algorithm State of health (SOH)

Get full access to this article

View all access options for this article.

References

Shen

Ouyang

Feng

. The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles. IEEE Trans Veh Technol. 2018; 67(1): 92-103.

. State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model. IEEE Trans Instrum Meas. 2015; 64(11): 2937-2949.

Galeotti

Cinà

Giammanco

Stefano

Aldo

. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy. Energy. 2015; 89: 678-686.

Adewuyi

Lotfi

Landers

Park

. A single particle model with chemical/mechanical degradation physics for lithium-ion battery state of health (SOH) estimation. Appl Energy. 2018; 212: 1178-1190.

Wang

Miao

Pecht

. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. J Power Sources. 2013; 239: 253-264.

Salkind

Fennie

Singh

Atwater

Reisner

. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. J Power Sources. 1999; 80(1-2): 293-300.

Richardson

Osborne

Howey

. Battery health prediction under generalized conditions using a Gaussian process transition model. J Energy Storage. 2019; 23: 320-328.

Chaoui

Ibe-Ekeocha

. State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Trans Veh Technol. 2017; 66(10): 8773-8783.

Xie

Liu

Wei

. Improvement of the fast clustering algorithm improved by K-Means in the big data. Appl Math Nonlinear Sci. 2020; 5(1): 1-10.

10.

Yang

Wang

Pan

Chen

. A neural network-based state-of-health estimation of lithium-ion battery in electric vehicles. Energy Procedia. 2017; 105: 2059-2064.

11.

Dai

Zhao

Lin

Zheng

. A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov Chain. IEEE Trans Ind Electron. 2019; 66(10): 7706-7716.

12.

Talha

Asghar

Kim

. A neural network-based robust online SOC and SOH estimation for sealed lead-acid batteries in renewable systems. Arabian J Sci Eng. 2019; 44: 1869-1881.

13.

Sun

Wang

. Research of the relationship between li-ion battery charge performance and SOH based on MIGA-Gpr method. Energy Procedia. 2016; 88: 608-613.

14.

Feng

Weng

Han

Ren

Ouyang

. Online state-of-health estimation for li-ion battery using partial charging segment based on support vector machine. IEEE Trans Veh Technol. 2019; 68(9): 8583-8592.

15.

Nath

Chakravarty

Ghosh

Sarkar

. FPGA placement optimization using firefly algorithm. Adv Ind Eng Manage. 2017; 6(2): 97-102.

16.

Pan

Lü

Wang

Wei

Chen

. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine. Energy. 2018; 160: 466-477.

17.

Aravindan

Chuiling

Madhavi

. Electrochemical performance of NASICON type carbon coated LiTi2(PO4)3 with a spinel LiMn2O4 cathode. RSC Adv. 2013; 44(2): 7534-7539.

18.

Gan

Qiu

Duan

Wang

. An efficient hybrid approach based on PSO, ABC and K-means for cluster analysis. Multimedia Tools Appl. 2021; (2021): 1-19.

19.

Ezeigwe

Dong

Manjunatha

Tan

Yan

Zhang

. A review of self-healing electrode and electrolyte materials and their mitigating degradation of Lithium batteries. Nano Energy. 2021; 84: 105907.

20.

Fister

Yang

Brest

. A comprehensive review of firefly algorithms. Swarm Evol Comput. 2013; 13(1): 3446.

21.

Nayak

Naik

Kanungo

Behera

. An improved swarm-based hybrid K-means clustering for optimal cluster centers. Adv Intell Syst Comput. 2015; 339: 545-553.

22.

Sherly

Jaya

. Improved firefly algorithm-based optimized convolution neural network for scene character recognition. Signal, Image Video Process. 2021; 15(5): 885-893.

SOH estimation of Li-ion battery based on FA-BPNN-K-means optimization algorithm

Abstract

Keywords

Get full access to this article

References