A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

Abstract

Lithium-ion (Li-ion) battery pack is vital for storage of energy produced from different sources and has been extensively used for various applications such as electric vehicles (EVs), watches, cookers, etc. For an efficient real-time monitoring and fault diagnosis of battery operated systems, it is important to have a quantified information on the state-of-health (SoH) of batteries. This paper conducts comprehensive literature studies on advancement, challenges, concerns, and futuristic aspects of models and methods for SoH estimation of batteries. Based on the studies, the methods and models for SoH estimation have been summarized systematically with their advantages and disadvantages in tabular format. The prime emphasis of this review was attributed toward the development of a hybridized method which computes SoH of batteries accurately in real-time and takes self-discharge into its account. At the end, the summary of research findings and the future directions of research such as nondestructive tests (NDT) for real-time estimation of battery SoH, finding residual SoH for the recycled batteries from battery packs, integration of mechanical aspects of battery with temperature, easy assembling–dissembling of battery packs, and hybridization of battery packs with photovoltaic and super capacitor are discussed.

Comparison of machine learning techniques for SoC and SoH evaluation from impedance data of an aged lithium ion battery

“…However, improper hypotheses in the Fuzzy rules [3] and reduced set of observations can lead to substantial errors. The Support Vector Machine is a regression algorithm which converts nonlinearities in a lower dimension space to a linear model developed in a higher-dimensional one [20]. Examples of application of this technique applied to SoH are reported in [21]- [25].…”

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

“…The problem of state measurement of LIBs has been widely studied, most notably for estimation of the battery state of charge (SoC), while literature pertaining to SoH estimation remains less prevalent. Focuses of recent works in the area of SoH estimation have included incremental capacity (IC)/differential voltage (DV) measurement, 19 Coulomb counting, 20 (dual) extended Kalman filters 21,22 or empirical health degradation models such as those developed by Perez et al 23 However, the bulk of literature pertaining to SoH estimation focuses on prognostics and health management of existing battery systems, 24 with less emphasis placed on end of life characterisation of batteries. Furthermore, a majority of SoH estimation works focus on single cells.…”

State-of-Charge Monitoring and Battery Diagnosis of NiCd Cells Using Impedance Spectroscopy

“…As an alternative to Figure 7, the relative loss of charge between new and old batteries can be drawn (i.e., ((Q(EoL) − Q(BoL))/Q(BoL)), which shows the same trend in the reverse direction. Useful frequencies for practical SOH determination lie below 1 Hz or below 100 Hz whether or not the internal resistance of the battery is considered as a proper aging criterion with calculating the pseudo-capacitance, C(ω) = Re C, according to Equation (5). Above 500 Hz, the spiral roll of the cell generates inductive impedance values, which are not useful for state-of-charge determination.…”

“…Based on preliminary work on SOC determination by impedance spectroscopy [1][2][3][4][5][6][7], we studied new and aged batteries. Since the frequency response depends on the cell chemistry, we focused on nickel-cadmium batteries [8][9][10] in this work.…”