Battery Management, Key Technologies, Methods, Issues, and Future Trends of Electric Vehicles: A Pathway toward Achieving Sustainable Development Goals

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology.

Electric vehicle technology has recently drawn a lot of interest on a global scale due to improved performance in its efficiency and the capability to solve the problems of carbon emission. As such, electric vehicles are the key to achieving sustainable development goals. This review article analyzes deeply the previous technical developments of electric vehicles, focusing on important topics like battery management systems, technologies of power electronics, techniques of charging, and the relevant algorithms and improvements. In addition, several critical problems, and difficulties are presented in order to pinpoint the gaps in the literature. To address the analysis of battery behavior, battery condition monitoring, real-time control design, temperature control, fault diagnostics, and efficiency of battery model are considered. This study highlighted the estimation techniques that predict the internal battery conditions such as internal temperature, state of health, and state of charge, which are difficult to be directly monitored and determined. A lithium-ion battery, a super-capacitor, and related bidirectional DC/DC converters constitutes the infrastructure of a hybrid power system. This review offers useful and practical recommendations for the future development of electric vehicle technology which in turn help electric vehicle engineers to be acquainted with effective techniques of battery storage, battery charging strategies, converters, controllers, and optimization methods to satisfy the requirements of sustainable development goals. Accordingly, this review article will be a platform and future guide for those who are interesting in the field of energy management and its development.

The future of electric vehicles (EVs) is shaped by rapid technological innovations and evolving market trends that promise to revolutionize the automotive industry and accelerate the transition towards sustainable transportation. This paper explores key advancements in EV technology and emerging market trends that are driving the future of electric mobility. Technological innovations are central to the advancement of EVs, with significant improvements in battery technology being a primary driver. Recent developments in solid-state batteries offer enhanced energy density, faster charging times, and improved safety compared to traditional lithium-ion batteries. Advances in battery management systems and thermal management are further enhancing the performance and longevity of EV batteries. Additionally, innovations in electric drivetrains, such as more efficient motors and power electronics, are contributing to better performance and efficiency. Charging infrastructure is another critical area of innovation. The expansion of fast-charging networks and the development of ultra-fast chargers are addressing range anxiety and reducing charging times. Wireless and inductive charging technologies are also emerging, offering the potential for more convenient and seamless charging experiences. Vehicle-to-grid (V2G) technologies are enabling EVs to not only draw power from the grid but also supply energy back, contributing to grid stability and promoting renewable energy integration. Market trends indicate a growing adoption of EVs driven by supportive policies, incentives, and increasing consumer awareness of environmental issues. Governments worldwide are implementing stricter emission regulations and offering subsidies for EV purchases, which are accelerating market growth. Major automakers are investing heavily in EV development and expanding their electric vehicle portfolios, signaling a shift towards electrified transportation. Furthermore, the rise of shared mobility services and urbanization is influencing EV adoption, with electric ride-sharing and delivery vehicles becoming more prevalent. Advances in autonomous driving technology are also expected to complement the growth of EVs, providing enhanced safety and convenience. As technology continues to evolve and market dynamics shift, the future of electric vehicles promises to be characterized by greater efficiency, wider adoption, and a more sustainable transportation ecosystem. These advancements are set to redefine the automotive industry and contribute significantly to reducing carbon emissions and promoting environmental sustainability.

Electromobility is expected to successfully compete with conventional propulsion technology in the near future and thus creates new challenges for the automotive industry. Major issues with state of the art electric vehicles (EVs) include limited range due to insufficient energy density and heavy weight of existing battery storage systems, unsatisfactory charging duration as well as comparatively high costs. A leap in power electronics technology might meet these challenges, as the aforementioned characteristics are predominantly determined by existing EV system architectures and power converters. This paper presents a novel modular multilevel parallel converter-based split battery system for electric vehicles, enabling dynamic switching of battery cells in parallel and in series. Each individual battery cell may be interconnected to its neighbours according to operational needs, e.g. to provide optimum source resistance, lowest state-of-charge (SOC) cycling, and balanced aging, rendering separate battery management systems (BMS) unnecessary. Applying the proposed technology in EVs may fundamentally change existing powertrain architectures and charger topologies, as it merges the battery storage system and the power converter. This forms the basis for a highly integrated power electronics unit that includes traction converter, battery charger, and BMS.

Electric Vehicle (EV) technology has groomed the automotive industry and is becoming a more reliable source of mass transit. These battery-based vehicles are performing extraordinarily in developing pollution-free environment and better ecological surroundings. Battery Management System (BMS) is the backbone of the EVs that can undoubtedly increase the performance, operation, safety and lifetime of a vehicle’s battery or storage system. Li-ion batteries are performing outclass to provide a more sophisticated storage system for EVs. They have better characteristics than lead-acid or other types of batteries, hence being highly utilized in EV applications. This paper presents the comprehensive review of the BMS for the EV’s. It gives a detailed overview of the Battery Management System (BMS) for the EVs and detailed study of important aspects which comes under consideration during management of batteries. It further provides key factors and extrinsic issues e.g., battery modeling, state estimations and validation profiles etc. which are troubling in the proper and safe operation of the batteries in EV’s, and the algorithms and schemes designed and proposed for their betterment are presented.

Electrical energy storage (EES) systems, specifically in the form of high power lithium-ion (Li-Ion) battery packs, are gaining more importance mainly due to the increased penetration of renewable energy sources and the rapid proliferation of electric vehicle (EV) technologies. Battery management is a critical functionality in an EES, which maintains safe operation and also improves the efficiency of the system. With the increasing number of applications using battery packs and the demand for shorter time to market, the system integration aspects are gaining more attention. This has resulted in a natural trend towards decentralization of the battery management system (BMS) where the sensing, computation, and control units are distributed close to each cell, providing a high degree of scalability and customization-free plug and play integration. However, there exist several open challenges in the development of such distributed systems that requires special set of design automation tools to be developed. One of the major requirements for accelerating the design of such decentralized BMSs is prototyping where custom hardware and software implementations are required to be developed increasing the overall design time and cost. To overcome this, in this chapter, we present a modular hardware/software development platform for decentralized BMSs that can be used for evaluating the functionality of different BMS circuit architectures, active cell balancing topologies, and decentralized battery management algorithms. All design files of our proposed development platform are made publicly accessible and can be easily reproduced thereby saving the effort and time required for developing custom prototyping platforms.

Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements in the State of Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, in extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result in overheating, fires, and even explosions. Addressing these risks requires advanced diagnostic and management strategies, and machine learning presents a powerful solution due to its ability to adapt across multiple facets of battery management. The versatility of ML enables its application to material discovery, model development, quality control, real-time monitoring, charge optimization, and fault detection, positioning it as an essential technology for modern battery management systems. Specifically, ANN models excel at detecting subtle, complex patterns that reflect battery health and performance, crucial for accurate SOC estimation. The effectiveness of ML applications in this domain, however, is highly dependent on the selection of quality datasets, relevant features, and suitable algorithms. Advanced techniques such as active learning are being explored to enhance ANN model performance by improving the models’ responsiveness to diverse and nuanced battery behavior. This compact survey consolidates recent advances in machine learning for SOC estimation, analyzing the current state of the field and highlighting the challenges and opportunities that remain. By structuring insights from the extensive literature, this paper aims to establish ANNs as a foundational tool in next-generation battery management systems, ultimately supporting safer and more efficient EVs through real-time fault detection, accurate SOC estimation, and robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, and enhancing diagnostic precision, thereby broadening ANNs’ role in ensuring reliable battery management in electric vehicles.

The rapid adoption of electric vehicles (EVs) has driven the need for efficient and practical charging solutions to enhance reliability and reduce range anxiety. While advancements in lithium-ion battery technology have improved energy storage capacity, charging time and battery degradation remain critical challenges. Fast charging significantly reduces charging duration, making EVs more practical for daily use, but it accelerates battery aging, reducing long-term performance and sustainability. Addressing this issue requires innovative charging strategies that optimize battery performance without compromising lifespan. This mini-review explores recent advancements in smart charging technologies for EVs, with a focus on predictive algorithms and adaptive control strategies that enhance both charging efficiency and battery longevity. The integration of machine learning-based models, real-time monitoring, and thermal management systems has demonstrated the potential to mitigate battery degradation while maintaining high charging speeds. Additionally, the role of simulation tools such as Simulink in evaluating various charging methodologies is discussed, highlighting their significance in optimizing charging protocols. The review also examines developments in direct current (DC) fast charging infrastructure, including technologies such as Tesla’s Superchargers and AVL’s advanced charging solutions, which aim to provide rapid and efficient charging while addressing thermal and electrochemical challenges. Furthermore, strategies for reducing energy losses and improving battery management through state-of-charge (SOC) and state-of-health (SOH) monitoring are analyzed. By reviewing recent literature and advancements in EV charging, this study provides insights into the challenges and future directions of fast-charging technologies. Ultimately, the findings emphasize the importance of balancing fast charging with long-term battery health to support the widespread adoption of EVs. This mini-review serves as a foundation for future research and development efforts in the field of sustainable and intelligent EV charging systems, offering valuable insights for researchers, industry professionals, and policymakers.

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

Edge computing for vehicle battery management: Cloud-based online state estimation

Programmable logic controlled lithium-ion battery management system using passive balancing method

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include digital twins (DTs), cloud computing, and wireless BMSs. Nonetheless, there are still issues with cost optimization, cybersecurity, and computing efficiency. This study presents key advancements in battery modeling and BMS applications, including defect diagnostics, temperature management, and state-of-health (SOH) prediction. A comparison of machine learning (ML) methods for SOH prediction is given, emphasizing how well neural networks (NNs) and transfer learning function with real-world datasets. Additionally, future research objectives are described, with an emphasis on next-generation sensor technologies, cloud-based BMSs, and hybrid algorithms. Distinct from existing reviews, this paper integrates academic modeling with industrial benchmarking and highlights the convergence of hybrid physics-informed and data-driven techniques, multi-physics simulations, and intelligent architecture. For high-performance EV applications, this analysis offers insight into creating more intelligent, adaptable, and secure BMSs by addressing current constraints and utilizing state-of-the-art technologies.

Over the past decade, transportation electrification has emerged as a pivotal focus of the article. Electric vehicles (EVs) have progressively gained traction in the market, displacing conventional internal combustion engine vehicles. This surge in EV popularity has led to a corresponding increase in the number of charging stations, thereby significantly influencing the power grid (PG). Various charging strategies and grid integration approaches are being devised to mitigate the potential negative impacts of EV charging while optimizing the advantages of integrating EVs with the grid. This paper provides a comprehensive overview of the current state of the EV market, standards, charging infrastructure, and the PG’s response to the impact of EV charging. The article provides a comprehensive assessment of how forthcoming advancements in EV technology, including connected vehicles, autonomous driving, and shared mobility, will intricately influence the integration of EVs with the PG. Ultimately, the article concludes by meticulously analyzing and summarizing both the challenges and recommendations pertinent to the prospective expansion of EV charging infrastructure and grid integration. The proliferation of venture capital investments in nascent start-up ventures specializing in EV and battery technologies has experienced a pronounced surge, reaching an impressive sum of nearly USD 2.1 billion in 2022. This notable increase represents a substantial uptick of 30% compared to the figures recorded in 2021. Furthermore, these investments have been directed towards two key areas: advancements in battery technology and the acquisition of critical minerals. This discernible shift in investment trends underscores the growing recognition of the strategic importance and potential profitability associated with innovations in EV and battery technologies. In 2022, global expenditures on EVs surpassed USD 425 billion, marking a substantial 50% increase compared to the previous year, 2021. Remarkably, a mere 10% of these expenditures can be attributed to governmental support, with the bulk stemming from consumer investments.

With the rapid advancement of intelligent technologies in electric vehicles, various control technologies and algorithms are emerging. Most existing research, however, is limited to simulations of single modules such as suspension, braking, and battery management, lacking comprehensive modeling and simulation for the entire vehicle system, which impedes the integrated development and verification of advanced intelligent technologies. Therefore, this article focuses on the vehicle control system of electric vehicles. It first analyzes the overall scheme and clarifies the core functions of system operation control, fault detection, and storage. Subsequently, a data acquisition simulation platform for the vehicle control system is established based on MATLAB/Simulink, creating simulation modules for accelerator pedal, braking pedal, key position, and gear signal, forming a complete vehicle simulation platform. For the established simulation platform, specific electric vehicle model parameters are set, and under the QC/T759 urban driving conditions, simulations of the electric vehicle’s operation are conducted to obtain relevant signals such as vehicle speed, accelerator pedal, and braking pedal, verifying the feasibility of the vehicle control system. Finally, a hardware platform for the entire vehicle power system is built, and based on the PCAN-Explorer5 software, the connection and debugging of the vehicle controller, battery management system, and motor control unit are achieved to obtain the status parameters of each system and debug the vehicle control system, laying the foundation for the actual operation of the pure electric SUV. Through the simulation of the electric vehicle’s control system, the R&D cycle is greatly shortened, development costs are reduced, and a foundation is established for the actual vehicle debugging of electric vehicles.

Recently, the use and production of electric vehicles has been increasing. In parallel with this increase, electric vehicle technologies are constantly developing. With the increase in the use of electric vehicles, the importance of BYS (Battery Management System), which controls the charge and discharge cycles of batteries, is increasing and researches are being carried out to increase their efficiency. Batteries with high energy density used in electric vehicles need to be kept under constant control. A large number of cells are used to obtain the high voltage and capacity required by electric vehicles. Each cell needs a battery management system to keep it under control. CAN (Controller Area Network) is used in BYS communication with the vehicle. In this study, the data traffic on the CAN line of the BYS used in today's electric vehicles has been analyzed. In this analysis, instant data of the battery pack is sent over the CAN line. The system includes BYS, charger and VCU (Vehicle Control Unit). An experimental setup with BYS, VCU and charger nodes was prepared and line load calculation was made on this experimental setup. Three different methods were used in the line load calculation process and the relationship of these methods with each other was compared. Different CAN speeds were used in the test stages and line load analysis was performed by changing the message sending frequencies. According to the data obtained from the prepared experimental setup, it was seen that the load of the messages on the CAN line was between 1.97% and 19.58%.