The adoption of electric vehicles (EVs) has accelerated rapidly, driven by the need for sustainable mobility and

advancements in battery technologies. However, the large-scale deployment of EVs faces two key challenges:

efficient monitoring of battery health and effective management of charging infrastructure. Batteries degrade

reservation algorithms, and communication technologies for EV applications. It also highlights prototype

implementations, identifies gaps in existing approaches, and outlines future directions including AI-driven

Keywords: Battery, IoT, SOC and SOH, LoRa, Slot Booking

Electric vehicles (EVs) have become central to sustainable mobility due to their potential to reduce greenhouse

gas emissions and dependence on fossil fuels [1]. Governments and industries are promoting EV adoption

through incentives and the expansion of charging networks. However, this rapid growth has created new

challenges, particularly in charging infrastructure. Unlike traditional refueling, EV charging requires longer

durations and is constrained by the number of available stations. High adoption rates can cause overcrowding,

Monitoring parameters such as the State of Charge (SOC) and State of Health (SOH) ensures operational

efficiency while minimizing risks like overheating or deep discharge [3]. Nevertheless, charging infrastructure

availability equally affects user experience. Even if an EV can accurately assess its battery health, the lack of

accessible charging slots reduces its utility. Integrating battery monitoring with intelligent slot below a threshold,

reducing waiting times and preventing service interruptions. In fleet applications, predictive battery monitoring

The Internet of Things (IoT) and communication technologies play a vital role in enabling such smart EV

ecosystems. IoT platforms allow EVs to function as connected devices, transmitting SOC and SOH data to cloud

systems for analytics and decision-making [5]. Communication technologies such as Wi-Fi, Zigbee, LoRa WAN,

and 5G enable these interactions, each offering unique advantages. LoRa supports long-range, low-power

A Battery Management System (BMS) is the core unit responsible for monitoring and controlling the

performance of EV batteries. Its primary functions include measuring voltage, current, temperature, and SOC

of each cell, ensuring balanced charging and discharging, and providing safety mechanisms against

overcharging, deep discharge, and thermal runaway [7]. Architecturally, BMS consists of sensors, data

acquisition modules, microcontrollers, and communication interfaces that transmit battery parameters to

external systems or cloud platforms. In modern EVs, BMS not only protects the battery but also optimizes

energy usage and contributes to accurate range estimation. With the rise of connected mobility, BMS are

SOC & SOH Estimation Techniques

The State of Charge (SOC) and State of Health (SOH) are vital for efficient EV battery management. SOC

indicates the available capacity relative to the total and is crucial for range prediction, energy control, and

charging decisions [9]. Estimation methods include Coulomb counting, which is simple but prone to cumulative

errors, and model-based techniques like Kalman filters and Thevenin equivalent circuit models that offer better

accuracy but require complex computation. Recently, machine learning and hybrid model–AI methods have

improved adaptability to nonlinear behaviors [10]. SOH reflects the battery’s condition compared to its initial

capacity, helping predict its remaining useful life (RUL). Estimation techniques include direct measurement,

model-based, and data-driven methods. Hybrid approaches combining physics-based and AI models enhance

accuracy and interpretability, though challenges remain under diverse chemistries and conditions, demanding

while minimizing energy consumption. Cloud-based dashboards provide both users and operators with

actionable insights, such as early warnings of battery degradation or automated scheduling of charging slots

when the SOC falls below a threshold [12]. The use of edge computing further enhances these systems by

allowing real-time decision-making closer to the vehicle, reducing latency. IoT-enabled monitoring not only

improves safety and reliability but also enables integration with broader smart city infrastructures. However,

challenges such as data security, interoperability, and scalability remain significant barriers to widespread

Despite notable advancements, current battery monitoring approaches face several limitations. First, many SOC

and SOH estimation methods lack robustness under highly dynamic driving and charging conditions, reducing

Finally, battery monitoring solutions must also consider computational efficiency, as EVs have constraints on

onboard processing power. Addressing these limitations requires interdisciplinary research that combines

electrochemistry, data science, IoT, and communication technologies to design adaptive, scalable, and secure

Traditional EV charging models are based on a first-come, first-served (FCFS) approach, where vehicles arrive

at charging stations and wait for availability [15]. While simple to implement, this method often leads to

inefficiencies such as long queues, unpredictable waiting times, and user dissatisfaction, especially during peak

demand [15]. FCFS models also fail to account for the urgency of charging needs, as a vehicle with critically

Reservation-based charging systems aim to eliminate the uncertainty of traditional models by allowing users or

vehicles to book slots in advance . These systems leverage IoT platforms and communication protocols to enable

real-time coordination between EVs and charging stations. When the SOC of a vehicle drops below a certain

threshold, the system can automatically reserve a slot at the nearest available station, minimizing queuing time

[17]. Reservation frameworks also allow operators to optimize grid loads by scheduling charging during off-

peak periods. In practice, reservation systems have been implemented using mobile applications or vehicle-to-

infrastructure communication, giving drivers confidence that a charging point will be available upon arrival

[17]. However, challenges remain in ensuring fairness, preventing misuse of reservations, and handling no-

shows. Despite these limitations, reservation-based systems represent a significant step forward in user

Simple approaches include FCFS with priority queuing for low-SOC vehicles, while advanced methods employ

For fleet operators, scheduling algorithms can coordinate multiple EVs to avoid simultaneous charging,

optimizing fleet performance and minimizing downtime. Real-time scheduling supported by IoT systems

Despite progress, practical deployment faces challenges such as scalability, user compliance, and balancing

fairness with efficiency. Research continues to focus on adaptive scheduling frameworks that can respond to

Cellular networks provide wide coverage and reliable connectivity but come with higher operational costs and

AI for prediction, blockchain for secure transactions, and IoT protocols for seamless communication across