**1. Introduction**

According to recent COP21, COP25, COP26 Conferences, and EU2030 targets, there is a need for significant reductions in CO2 and greenhouse gas emissions in a short span period, targeting the reduction of climate warming in 1.5–2.0°C up to 2030 [1]. With the worldwide acceptance of electric vehicles together with the new era of connected objects, ensuring battery reliability, lifetime, and sustainability is becoming a necessity [2]. In this way, batteries are currently seen as important technological enablers to drive the transition toward a decarbonized society. They have recently achieved considerable improvements in terms of technical performance and economic affordability [3]. However, for a successful mass introduction of electrified mobility,

renewable and clean energy systems with market competitive performances, fast charging capability, and substantial improvements in battery technologies (autonomy and safety) are required [4, 5].

Currently, to guarantee safe operation, a battery management system (BMS) only measures externally accessible parameters such as voltage, current, and temperature. The scarcity of information regarding the interior of the cell currently hinders the improvement of the accuracy and predicting capabilities of current BMS algorithms and models, while equally limiting attempts to refine the battery thermal design due to the absence of heat-transfer information. This has led to increasing interest in spatiotemporal imaging of the thermal flows within a cell using temperature sensors [6–12]. Typically, they are used in electronic sensing devices, such as thermocouples (TCs) [13, 14], thermistors [15], IR thermography [16], and resistance temperature detector (RTDs) [17]. However, in addition to short resolution and accuracy, huge measurement setup, or higher volume/size preventing them from being inserted in a cell, they cannot be appropriate to be embedded in batteries due to their electrochemical harsh environment.

Furthermore, batteries are breathing objects that expand and contract upon cycling, with volume changes that can reach up to 10%. These changes, together with the electrode volume expansion associated with the solid electrolyte interface (SEI) growth, lead to important mechanical stress inside the battery materials (like cracks) that are harmful to their performances. Methods, to sense intercalation strain and pressure, are equally critical to control the SEI dynamics affecting their states of charge (SoC) and health (SoH). The methods already used are not acceptable: straingauges fall short of providing spatial information and cannot also be embedded to internally sense battery cells [8, 18].

Alternative solutions, due to their full advantages, such as greater precision, multiplexing, immunity to electromagnetic interference, chemical inertness, small size/low invasiveness, and a possibility to be tailored regarding their dimensions and sensitivities, are sensors based on optical fiber technology [2, 19–59]. Since the first study developed by Pinto *et al*. in 2013 [19], OFS starts to be integrated into lithiumion batteries (LIBs) to monitor critical key parameters, such as temperature and/or thermal gradients, strain, gases, pressure, electrochemical events (chemical changes and lithiation), refractive index, and the states of charge, discharge, health, power, energy, and safety (SoX) battery indicators (**Figure 1**).

Performing a systematic review, we used two databases to retrieve scientific publications: Web of Science (www.webofknowledge.com, accessed on 16 November 2021) and Scopus (www.scopus.com, accessed on 16 November 2021). A comprehensive search on the use of OFS to monitor LIBs was performed based on a query by topic (title, abstract, and keywords) of the terms: ((optical AND fiber AND sensors\*) AND (lithium AND batteries\*)); spanning over the years 2013 to November 2021. The search query resulted in a total of 60 papers that were subsequently reviewed by the authors, of which 40 were considered eligible for the present work.

**Figure 2A** summarizes the number of studies published by year, since the first paper in 2013, regarding the use of OFSs to track LIBs parameters. From a critical analysis, an increase of publications can be observed from the beginning, however, with a lower number in 2020, probably due to the pandemic world situation. In **Figure 2B**, it is also presented an illustration of the critical parameters tracked in the LIBs. Temperature and strain were the parameters more studied followed by the correlation of the optical fiber signals with the electrochemical events and SoX battery *Tracking Li-Ion Batteries Using Fiber Optic Sensors DOI: http://dx.doi.org/10.5772/intechopen.105548*

**Figure 1.** *Critical key parameters identified to be tracked in LIBs.*

#### **Figure 2.**

*(A) Statistical summary of the number of papers by year, published since the first study, regarding the using of OFS in LIBs. (B) Percentual distribution of the critical parameters tracked by the OFS in the LIBs.*

indicators. The tracking of gasing, refractive index, and pressure variations are very recent topics of sensing inside the LIBs. However, due to the difficulty and complexity of sensing, the integration of the OFS inside the battery cells being necessary, they were not yet so explored. In this way, this chapter provides a complete overview of all studies published from 2013 to the present on the use of OFS to track critical key parameters in LIBs. Section 2 describes the theoretical approaches of the OFS used (fiber Bragg grating, interferometric, and evanescent wave sensors) to monitor the critical parameters. In section 3, all critical parameters (temperature, strain, SoX battery indicators, and electrochemical events) tracked so far using fiber optic sensing technology are presented and fully described.
