EVs can be classified as Battery-powered Electric Vehicles (BEV), Hybrid Electric Vehicles (HEV), Plug-in Hybrid Electric Vehicles (PHEV), and Fuel Cell Electric vehicles (FCEV). They are collectively termed as New Energy Vehicles (NEV) as these are zero or low-emission vehicles that are either partially or fully powered by electricity. Rising oil costs and increasing environmental pollution are the two major reasons that countries around the world are quickly adopting NEVs. NEVs use energy conversion and storage devices like batteries and fuel cells to generate and store electricity, which in turn power electric motors. A summary table providing details about the different types of NEVs is shown in Table 1 below.

In 2023, global sales of BEVs and PHEVs increased by 31 %. 9.5 million of the 13.6 million EVs that were sold worldwide in 2023 were BEVs; the other vehicles were PHEVs. PHEVs are more efficient than HEVs but less efficient than BEVs; HEVs are the least efficient among the various EV types. BEVs are best suited as short range and small vehicles, and FCEVs as medium-large and long range vehicles [12]. Challenges involved in production, transportation and hydrogen storage, along with the high infrastructure cost of hydrogen supply stations are the cause of declining interest in the use of FCEVs for common passenger vehicle applications [13]. One major advantage of battery operated EVs over FCEVs is that they have higher energy efficiency and hence, lower fuel cost [14]. Also, in 2023, FCEV sales decreased by about 30 %, worldwide. EIS technique is useful in early detection of degradation and enhancement of efficiency, management and safety features of batteries and fuel cells in battery-operated EVs and FCEVs, respectively.

Batteries are the heart of EVs. A battery is an electrochemical system used to convert chemical energy into electrical energy, consisting of three major components, anode, cathode and an electrolyte that separates the two. Oxidation is the process of electron loss that transpires at the anode, and reduction is the process of electron gain that occurs at the cathode. An external circuitry provides the path for electrons from the oxidized species to travel to the reduced species, so that the resulting current can do useful work. To maintain electroneutrality, the two electrodes also exchange ions. Electrolyte, being a good ionic conductor and an electrical insulator, acts as a medium only for the exchange of ions. An illustrative schematic for a battery system is shown in Fig. 1. Ions transverse from cathode to anode during the charging cycle of battery, and in the opposite direction during the discharging cycle, with electrons forced through the load as shown in Fig. 1 [15]. In Fig. 1, M⁺ denotes ions and e^- denotes electrons. The kinetics of electrochemical reactions occurring inside a battery depend on ion transport through the negative and positive active materials, electron injection/ extraction at current collector/active material interfaces, and ion insertion/extraction at electrolyte/active material interfaces [16].

Since the time research towards vehicle electrification intensified, many different battery technologies like nickel–zinc (NiZn), lead–acid, and Lithium-ion batteries (LiB) have been explored. Lead-acid battery technology’s adoption in the EV industry has been limited due to its shortcomings like low specific energy, short lifecycle, poor cold-temperature performance, gas development during charging, sulfation and stratification effects, yet it has advantages of being inexpensive, safe, reliable, and recyclable [17].

In HEVs, the primary focus lies in utilizing the battery for a regenerative braking system for improved fuel economy. So, the emphasis is on power over energy to be able to withstand high current pulses during both charge and discharge; Ni-based battery chemistries more than meet the high specific power requirements. NiZn battery technology, in spite of having advantages like higher energy and lower costs compared to its other Ni-based counterparts, its commercialization did not go well due to issues with short circuits, cycle life, and gas recombination in a sealed cell. NiMH batteries are widely used in HEVs due to their high power, wide operating temperature range capabilities, cost-effective life-of-vehicle performance, and abuse tolerance. The main drawbacks of this technology are their high cost, the need to control hydrogen loss, the high self-discharge rate, and heat generation at high temperatures above 30°C [18]. In EV/PHEV applications, energy is emphasized over power to maximize vehicle range and minimize the weight penalty introduced by batteries. LiB technology has the upper hand over Ni-based batteries in terms of weight and size and also performance characteristics like higher efficiencies, lower self-discharge rate, longer cycle life, higher energy and power densities [19].

In this section, we proceed by discussing the commonly used battery performance indicators available in the literature that are used in diagnosis and prognosis methods. The voltage and temperature values, cell balancing, coolant flow, etc. of the battery device is monitored to maintain the device within its Safe Operating Area (SOA) i.e., charge and discharge rates, voltage, current, and temperature windows defined by the cell manufacturer. If a battery leaves its SOA, its performance quickly degrades, leading to explosions or fires. The most critical indicators for any battery system are its SoC, SoH, SoF, and RUL.

A battery’s SoC value indicates how much energy it can deliver until it reaches its End of Discharge (EoD) time. This parameter depends on its discharge profile, and it changes non-linearly as the battery discharges. It affects battery cell aging and is necessary for increasing efficiency. A cell’s estimated SoC, which ranges from 0% to 100%, gives its current capacity as a function of its rated capacity. When a cell’s SoC is 100%, it is said to be completely charged; when it is 0%, it is said to be fully discharged. Also, as a cell ages, its maximum SoC value decreases. The fact that SoC estimation is highly reliant on the actual capacity of the battery cell, which is affected by temperature, age, and current rate, is a significant challenge. Apart from its inherent significance, the SOC serves as the cornerstone for other states [20].

A battery’s SoH value is the proportion of its maximum charge to its rated capacity. SoH is one of the factors that determine the correctness of the estimated SoC value. The value of SoH varies depending on the operating conditions faced by the battery over its lifespan and and their consequent influence on the battery’s aging mechanisms [21]. The value of this parameter is 100% for a new battery while it reaches about 80 % for a battery reaching its end of life, the value [20]. SoC estimation is useful in characterizing the short-term operation of batteries while estimation of SoH is associated with the long-term cycle life of batteries [22].

SoF gives continuous and instantaneous load capability of a battery. It shows whether the battery has sufficient power capability to carry out a specific function. It combines SoC and SoH information to give the remaining run time of the battery. By comparing estimated SoC value with the available capacity, this parameter observes battery readiness in terms of usable energy [23].

RUL is the amount of time giving the number of remaining charge/discharge cycles left in a battery before its end of life (EOL). Precise RUL prediction can help guarantee the safe operation of a battery, with its timely replacement and repair [24].

Internal impedance arises in a battery due to the inherent characteristics of the different materials used, the interaction between the materials, and the various chemical reactions. It provides insight into the state of operation and overall health of the battery. Internal resistance is a key indicator of a battery’s ability to carry current and hence, its capability to deliver power and should ideally be zero. Higher internal resistance causes higher energy loss in a battery, leading to increased heat dissipation and degradation of the battery. SoH and SoC parameters of a battery are related to its internal impedance; impedance increases as SoC decreases, the relation being more apparent at lower frequencies. Temperature also directly impacts battery impedance; impedance decreases as temperature increases, ignoring the extremes [25]. Moreover, changes in relaxation time also affect its impedance.

As a cell ages, its impedance increases and capacity reduces, due to degradation of its electrical contacts, electrode materials, and electrolyte. Hence, keeping track of variations in internal impedance with time will help in monitoring the deterioration of cells. The reduction in capacity mainly affects the amount of energy that the cell can deliver in each cycle. As a standard practice, a battery is considered degraded when it can deliver only 75% of its nominal capacity, and understanding how its internal impedance varies with time before reaching the degradation point helps in accurately determining the amount of energy that can be stored and delivered [22,26]. Cycle life for a rechargeable battery is the number of charge and discharge cycles that it can complete before its performance degrades significantly [23,27–29]. Calendar aging refers to the gradual degradation of batteries over time and without cycling.

Impedance values are depicted in the form of Nyquist and Bode plots, which help in the analysis of coupled electrochemical reactions occurring at different rates in the system under study. In a Nyquist plot, real impedance, Z_r is plotted on the x-axis while –Z_i, negative imaginary impedance is plotted on the y-axis since most electrochemical systems show a more capacitive behavior. In a Bode plot, either the magnitude or the phase of complex impedance is plotted against frequency. Logarithmic scale is used for representation of frequency and impedance magnitude while linear scale is used for phase angle representation. Bode plot representation has two major advantages over Nyquist plot representation, which are frequency explicit data representation and ease in resolving small impedances in the presence of large impedances; in Nyquist plots, small impedances are swamped by large impedances. Nyquist plots are more complex to understand but are used more in electrochemistry due to two major reasons, their high sensitivity to changes and being able to directly read some parameters from the plots.

By analyzing impedance plots, equivalent electrical circuits can be derived for electrochemical systems. Apart from the conventional electric circuit elements, resistance, capacitance, and inductance, two other circuit elements are also defined for modeling of electrochemical systems i.e., Warburg impedance and Constant Phase Element (CPE). CPEs are used to represent non-ideal or imperfect capacitance effects inherently present in the system under study and they exhibit 80–90° phase shift. Warburg impedance represents diffusion control i.e., resistance offered to mass transfer and it exhibits a 45° phase shift. The important parameters that can be estimated from EIS data are electrolyte resistance, R_S, double layer capacitance, C_dl or CPE_dl, and charge transfer resistance, R_ct. R_S gives resistance of any ionic solution and depends on ionic concentration, type of ions, temperature as well as the geometry of the area in which the current is carried. Consider a bounded area with area, A, and length, l, carrying a uniform current, then R_S is defined as equal to ƿl/A. On the interface between an electrode and its surrounding electrolyte, an electrical double layer forms due to the adsorption of ions onto the surface of the electrode. An insulating space, in the order of Angstrom, separates the charged electrode from the charged ions, forming a parallel plate capacitor, represented by C_dl or CPE_dl. R_ct is resistance formed by a single kinetically controlled electrochemical reaction [30,31].

Every electrochemical interface, be it a solid/liquid interface or a solid/solid interface can be modeled as a Randles circuit, one of the simplest and most used equivalent circuit models. It consists of R_S in series with a parallel combination of C_dl or CPE_dl and Faradaic impedance. The simplest case of Randles circuit consists of charge transfer resistance, R_ct, solely as Faradaic impedance. Randles circuit appears as a depressed semicircle in the Nyquist plot, each semicircle is characterized by an RC time constant, . ‘’ gives the time taken by a capacitor to charge to about 63.2 % of its maximum value, or the time it takes to discharge to about 36.8 % of its maximum value.

EIS technique is a superior electroanalytical tool used to study the harmonic response of an electrochemical system and characterize its behavior. It can identify and distinguish between the various electrochemical processes taking place inside a system, which establishes its uniqueness over other analytical techniques. A small sinusoidal perturbation potential (or current), over a broad range of frequencies from microhertz to megahertz, is applied to the system to be studied and the resulting current (or potential) is recorded. The kinetic and mass transport properties, along with capacitive properties of the device under study can be deduced from real and imaginary components of its impedance calculated over the entire frequency range. The real component of impedance corresponds to a resistance in-phase with the applied voltage and the imaginary component, with a reactance out-of-phase with the applied voltage.

EIS methodology can be potentiostatic or galvanostatic depending on whether the applied signal is potential or current. The potentiostatic mode of operation is not well suited for battery studies since batteries have low impedance. Even a slight inaccuracy in the applied AC voltage could result in an unexpectedly large current that could change the battery’s SoC. However, in galvanostatic mode, the battery voltage and SoC are typically unaffected since the galvanostat can easily adjust the applied current to an accuracy of a few milliamperes [32]. EIS methodology can be linear wherein a small perturbation potential is employed or nonlinear, employing a perturbation potential of large amplitude and recording the resulting current response at higher harmonics along with the fundamental frequency. The advantage of EIS methodology over other electrochemical techniques is its unique ability to isolate and distinguish the various electrochemical processes happening at the same time in a system. If the processes have sufficiently different time constants, then EIS can easily differentiate between them.

Throughout a battery’s lifetime, EIS can be used for prognosis and diagnosis by studying the impact of manufacturing factors, temperature, cell charge/discharge, state parameters, and other storage/operating conditions on its degradation mechanisms [33]. A typical Nyquist plot obtained for a lithium-ion cell along with an equivalent electrical circuit derived from the plot is shown in Fig. 2. A detailed analysis of the Nyquist plot allows us to evaluate various phenomena in different parts of the battery. Lithium-ion migration in the electrolyte is evident as impedance in the high-frequency region, near about 1 kHz. Lithium-ion diffusion occurs within the electrode in the low frequency region, at frequencies less than 1 Hz and Li-ion transfer reactions occur in the intermediate frequency region of 1 to several hundreds of Hz. Impedance measurements recorded at lower frequencies in a battery, give information about electrochemical reactions happening at its electrode/electrolyte interfaces. For batteries involving intercalation of species, impedance data at low frequencies (less than 1 Hz) give diffusion coefficient for the process. The ohmic resistance of electrolyte can be derived from impedance data recorded at high frequencies (above 1 kHz), while Solid Electrolyte Interphase layer (SEI) capacitance and electron transfer rate can be derived from impedance data recorded at intermediate frequencies (1 Hz–1 kHz). Meaningful interpretation of EIS data requires mapping of the various spectroscopic features to battery impedance components. Each section of the impedance spectrum is associated with a specific electrochemical phenomenon; the circuit element or combination of circuit elements corresponding to each section is shown directly below it in Fig. 2.

Ohmic resistance, Rs of the equivalent circuit represents the intersection of the Nyquist plot with the real axis at a non-zero value. This value equals the sum of current collectors, active material, electrolyte, and separator resistances. In the high-frequency region of the plot, inductor L represents the inductive effect in the battery caused by the geometry and porosity of its electrode plates. The first semicircle in the intermediate frequency region is generated by lithium-ion diffusion through the Solid-Electrolyte Interface (SEI) layer, given by CPE₁|R_SEI. The second semi-circle in the intermediate frequency region corresponds to double layer capacity and charge transfer resistance at the electrodes, given by CPE₂|RCT [34]. The diagonal line with a constant, positive slope shown at low frequencies corresponds to Warburg impedance, W in the equivalent circuit. This impedance is due to the diffusion of lithium ions in the electrodes and it depends on the frequency of the applied perturbation potential. At high frequencies, the Warburg impedance obtained is small because diffusing reactants do not cover large distances. At low presence of double-layer capacitance, CPE can be accounted for by non-ideal frequencies, the reactants diffuse farther, resulting in higher values of Warburg impedance [35]. The properties of battery materials like inhomogeneity, surface roughness, porosity, etc. but the exact reasons are unclear.

With variations in operating point and aging of a battery, the form of the Nyquist plot derived for the battery also varies. In applications that involve battery charging and discharging at temperatures as low as –30°C, the battery degrades with the slowing down of chemical processes, resulting in the widening of both semi-circles, corresponding to higher cell impedance. At high temperatures of about 50°C, both semi-circles tend to merge and cannot be distinguished anymore. This is because the time constants of the internal processes associated with the semi-circles are getting closer in value [36]. Also, the real axis crossing point increases with decrease in temperature. In the semi-circle corresponding to lower frequencies, the radius increases strongly with a reduction of SoC, especially for SoC values under 30%. In an aging battery, the impedance spectrum shifts towards the right, as a manifestation of the increase in series resistance. Some more research groups have also reported increments observed in a radius of semi-circles of the Nyquist plot [37–39].

Voltage measurement, internal resistance measurement and Ampere-hour counting are the most popular methods utilized for the assessment of a battery’s charge state. The Ampere-hour counting method is used when the initial value of SoC is known, whereas for the internal resistance measurement approach, the value of the initial SoC is not needed, however, due to minimal changes in internal resistance during discharging, high accuracy in not achievable. Monitoring of voltage of a battery is a quick way of determining its SoC, but this method is highly unreliable and inaccurate since open circuit voltage must be measured after relaxation time, and suitable temperature correction needs to be applied. EIS method of determining battery SoC is more accurate than the above-mentioned three methods and can be carried out, both ex situ i.e., current-off state as well as in situ i.e., during charging or discharging state [9].

Different approaches are followed to determine a battery’s SoC value from its impedance spectrum, by utilizing either the correlation between internal resistance and SoC, which is not well established for all systems, the relation between high-frequency resistance RHF and frequency at which RHF is measured with SoC or the relation between parameters of derived equivalent electrical circuit with SoC [40]. A battery’s internal resistance does not strictly correspond to its SoH. Further tests are needed to estimate SoH. The variation of EIS spectra with SoC and SoH is critical for effective battery management, with some example data shown in fig. 3. SoF of a battery is often defined on a digital scale, whether it can support a particular application in its current state or not. This battery parameter is affected by other parameters such as SoH, SoC, temperature and terminal voltage of the battery [41]. Prediction of RUL is very challenging since it depends on many factors such as the battery’s current health state, historical data, and failure mechanisms [42]. Table 2 lists out all the research works carried out in utilization of EIS for battery state estimation.

Temperature significantly affects performance of batteries, limiting their applications. To ensure safety, durability and efficient management of a battery, understanding the effect of temperature on battery performance is crucial. –20°C to 60°C is the acceptable operating region for LiBs. At both high and low temperatures outside of this region, their performance degrades and they lose a significant amount of their power and energy, along with irreversible damages, such as lithium plating and thermal runaway [64]. In LiBs working at low temperatures, Li plating causes permanent reduction in its performance while declined reactivity and diminished ionic conductivity cause temporary reduction in its performance, drastically affecting its usable capacity and impedance [65]. However, to track cell temperature, it is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Also, conventional temperature sensors such as thermocouple or thermistor is mounted on the cell surface and do not detect the core temperature, hence detecting an offset due to the temperature gradient.

EIS is a sensor less temperature estimation method for batteries that utilizes the strong dependency of battery temperature on its characteristic impedance response. At low temperatures, the impedance increases due to higher resistances and lower reaction rates, resulting in lower capacity and power output. At high temperatures, the impedance decreases due to lower resistances and faster reaction rates. Hence, this technique can help identify degradation mechanisms inside a battery that are accelerated by high or low temperatures. This technique has upper hand over other sensorless methods which either require great computational effort for solving partial differential equations or require error-prone parameterization.

Table 3 lists out all the research carried out in implementation of EIS for temperature studies in batteries.

EIS can be used for characterizing electrode materials, electrode kinetics electrode/electrolyte interfaces to obtain insights into their electrochemical behavior, performance, health and detection of internal faults and anomalies. Table 4 lists all the research carried out towards the application of EIS in battery characterization.

Ageing diagnosis is essential to maintain reliability and optimum performance of a battery system, over time. In the conventional BMS, battery ageing is monitored with the help of two metrics: capacity and power fade. However, these metrics are not sufficient to identify the root causes of battery ageing. EIS technique can be applied in study of degradation and ageing processes related to cycling as well as calendar ageing, in batteries. EIS technique helps in identifying and quantifying aging mechanisms over time, thus by integrating this methodology with BMS, battery lifetime control strategies can be implemented within the BMS. Cycling performance of a battery is defined by the number of times it can be charged and discharged before it reaches its end of life. Impedance data obtained for a battery can also be correlated with its cycling performance. Fig. 4 shows Nyquist plot data obtained for fresh and aged LiBs. Table 5 summarizes all the research carried out towards the same. Integration of EIS into BMS enhances battery monitoring, management, and optimization capabilities, enabling improved performance, reliability, and lifespan of battery.

Nowadays, batteries have an operational lifetime of more than 10 years [8]. A battery needs a Battery Management System (BMS), an embedded system comprising hardware and software subsystems, for its safe, reliable, and efficient functioning. BMS monitors the voltage, temperature, cell balancing, coolant flow, etc. of the battery device it is connected to, to maintain the device within its Safe Operating Area (SOA) i.e., charge and discharge rates, voltage, current, and temperature windows defined by the cell manufacturer. If a battery leaves its SOA, its performance quickly degrades, leading to explosions or fires. The major functionalities handled by BMS are monitoring, control, and parameter estimation. The three most critical indicators estimated by the BMS to monitor battery performance are State of Charge (SoC), State of Health (SoH), and State of Function (SoF). These states cannot be measured and hence, are estimated from measured parameters, voltage, current, and temperature of the battery. The accuracy of the estimated battery states decides the quality of battery management achieved by BMS [21]. SoC value gives the amount of energy that a battery can deliver until it reaches its End of Discharge (EoD) time. SoH value is the ratio of maximum charge in a battery to its rated capacity. SoC of a battery depends on its discharge profile and it changes non-linearly as the battery discharges. SoH is one of the factors that determine the accuracy of the estimated SoC value. SoF gives continuous and instantaneous load capability of a battery. It combines SoC and SoH information to give the remaining run time of the battery.

Often, only surface temperature and cell voltage data are available to online algorithms running on BMS, to keep track of battery performance and health, neglecting the impact of changing environmental factors [89]. Nonlinear state estimators like extended Kalman filtering and sigma-point Kalman filtering have been implemented for battery systems, resulting in improved accuracy but increased computation [90]. Complex physics-based modeling approaches have also been explored, but they are not implementable in real-time applications due to their impractical demand for computational resources. Since onboard battery systems in EVs face harsh working conditions, they are highly susceptible to thermal runaway occurrences due to mechanical, electrical, or heat triggers. Thermal safety is currently the major bottleneck for mass adoption of EVs [91]. A battery pack in an EV consists of hundreds of thousands of cells connected in parallel and/or series. They need to be resistant to crash events since damaging effects can turn out severe due to large amounts of stored energy [92]. Also, in an EV, BMS must interface with several other onboard systems and work in real-time with rapid charging/discharging cycles of the battery as the vehicle is accelerating/ braking. With the rising market demand for fast–charging batteries, the batteries need to be protected from the formation of dendrites at anode material and temperature peaks. Hence, battery management is much more demanding in EVs than in portable batteries. The BMS, with its complex network of sensors and heat conditioning system, must ensure delivery of the required range of voltage and current for a duration of time, against expected load scenarios in the vehicle and work towards enhancing the vehicle’s battery life and driving cycle, all the while ensuring its safe operation [93].

Integration of EIS into BMS enhances battery monitoring, management, and optimization capabilities, enabling improved performance, reliability, and lifespan of battery systems across various applications. Table 6 summarizes the research carried out so far, in integrating EIS technique with BMS systems.

Nonlinear EIS (NLEIS), a moderate amplitude extension to linear EIS, when implemented as an add-on to traditional linear EIS, provides complementary information to EIS which helps gain new insights into charge transfer kinetics, thermodynamics, and mass transport processes happening inside a battery [24]. Table 7 summarizes research carried out in implementation of nonlinear EIS technique in study of batteries.

For automotive applications, it is not sufficient for batteries to just deliver moderate power for a long time, but also support pulse power applications i.e., deliver high power for short periods. Batteries need to support boost mode for hybrid vehicles, x-by-wire technologies, idle stop mode for mild hybrid vehicles, and cranking of a conventional motor vehicle. Hence, a reliable prediction of a vehicle’s power capability is expected from the BMS integrated into it. For safety-critical steer-by-wire and brake-by-wire applications, the user must get an early warning when the battery leaves its safe operating area due to aging or insufficient charging. An intelligent energy management system estimates the state variables of the vehicular battery system so that necessary measures can be taken [99].

Robinson et al. successfully demonstrated the utilization of system noise as an excitation signal to carry out EIS studies of batteries connected to operating equipment. However, they implemented the offline Fourier transform method to obtain the impedance spectrum of the battery system and did not interpret the recorded impedance spectra, in their work [100]. Bohlen and Gelbke presented a procedure to directly determine the pulse power capability of battery systems based on its impedance model, without the need for difficult-to-determine state variables like SoC and SoH. Parameters of the derived impedance model can be calculated during normal operation of the vehicle by evaluating the dynamic behavior of the battery. This methodology was lab-tested and a prototype was implemented towards the prognosis of cranking capability in a conventional motor vehicle [99]. Wang et al. designed a low cost, small sized on-board EIS measurement system for LiBs, suitable for vehicle applications, to investigate relation between SoC, SoH, internal temperature and electrochemical impedance spectroscopy [101].

Zhao et al. developed and experimentally evaluated a low-cost LabVIEW-based EIS measurement system, within the frequency range of interest for EV batteries [102]. Gong et al. developed a novel, optimized integrated circuit (IC) to carry out cell-level EIS measurements, with a peak perturbation current of 200 mA and also proposed a hybrid power architecture to improve the signal processing capabilities of the IC while increasing the perturbation range [103]. Decrease in performance of EV battery at low temperatures, greatly affects the vehicle’s driving range. Nemati et al. carried out EIS studies on vehicular LiBs and investigated their cycle life and calendar life for different operating temperatures varying from 55°C to –20°C [104]. Li et al. proposed a real-time method for monitoring of battery impedance with DC current using three-phase motor drive inverter [105].

Wang et al. proposed a new method to establish a nonlinear battery model from the EIS data and identify model parameters in the frequency domain, aimed at the BMS application of EVs [106]. Zhang et al. developed a series electrochemical impedance model for quantitative analysis of internal processes occurring inside LiBs, in EV applications [107]. Jiang et al. analyzed the impedance spectrum of large LiBs used in EVs and developed a charge polarization model for them, estimating parameters like ohmic and charge transfer resistances [108]. A major issue faced by LiBs is lithium plating i.e. deposition of lithium on the anode surface of battery under low-temperature and fast-charging conditions, drastically reducing their life and fast-charging capability and causing internal shorts [109]. Tsioumas et al. proposed and experimentally verified an impedance spectroscopy-based lithium plating diagnostic method for several battery charging and temperature operating conditions [110].

A lot of research has been going on towards improving the monitoring of SoC and SoH as these are pertinent aspects for EV usage and their battery management system (BMS) throughout the lifetime of operation. Remmlinger et al. monitored the SoH parameter for a LiB used in a hybrid vehicle with the help of a special-purpose model, obtained from an equivalent electric circuit derived from the battery’s impedance data [111]. Barre et al. carried out statistical analysis of recorded capacity fade and resistance increase data, for aging LiBs used in EVs [112]. Farooq et al. developed a low-cost, high voltage EIS instrument to measure the impedance of LiB module upto 100 V, for estimation of RUL of EV batteries [113]. Rastegarpanah et al. demonstrated a model-free neural network-based approach for SoH estimation based on a dataset of impedance spectroscopy measurements, for end-of-first life EV batteries [114]. EIS has to be performed at module level in EVs since most extracted modules from EVs have little to no access to individual cells. Savca et al. studied the reliability of EIS measurements done at the module level and the amount of information of each cell reflected on module measurement while determining elements sensitive to SoH [11].

Babaeiyazdi et al. implemented an ML approach for accurate estimation of SoC of LiBs for onboard BMS applications in EVs, by employing impedance spectroscopy measurements in linear regression and Gaussian process regression (GPR) models [115]. For accurate estimation of SoC in EVs and HEVs, Wang et al. integrated the EIS technique into BMS and developed a new model updating strategy based on EIS for SoC estimation, with a focus on aging batteries [116]. Resting time of a battery during its charging and discharging processes affects EIS measurements carried out on the battery. SoC and SoH values estimated for a battery vary significantly if EIS measurements are recorded without taking resting time into consideration [117]. Li et al. measured EIS spectra at a constant SoC for different resting times and studied the effect of resting time on the impedance behavior of LiBs used in EVs [118].