A Computer Science Approach to Hardware-Software Integration in Electric Vehicle Technologies

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A Computer Science Approach to Hardware-Software Integration in Electric Vehicle Technologies K. Venkatesh1, Dr. Addagatla Nagaraju2, *, Akkela Krishnaveni3, B. Prakash4, Ayesha1, G. Snehalatha5 1Lecturer in CSE, Government Polytechnic for Women, Medek 2Lecturer in EEE, Government Polytechnic for Women, Siddipet 3Lecturer in EEE, SRRS Government polytechnic, Sircilla

4Lecturer in CSE, Government Polytechnic for Women, Siddipet 5Head of EEE Department, Government Polytechnic, Cheriyal

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - The convergence of hardware and software in Electric Vehicles (EVs) has created a transformative platform for innovation, performance, and sustainability in the automotive industry. This paper explores a computer sciencedriven approach to hardware-software integration in EV systems, emphasizing embedded computing, real-time operating systems (RTOS), virtualized control, data communication protocols, cybersecurity, AI-based optimization, and software-defined vehicle (SDV) architectures. Drawing on contemporary developments and research, we propose a modular integration framework that enhances scalability, reliability, and adaptability. This interdisciplinary focus contributes to the evolving landscape of smart, secure, and autonomous EV technologies.

Figure 1: Similarity between the Modern Cloud Platform and Electronic Vehicle Infrastructure Moreover, heightened awareness of energy conservation and environmental sustainability has led to increased global acceptance of EVs. This includes battery-powered electric vehicles (BEVs), plug-in hybrids (PHEVs), and fuel cell vehicles. In the first half of 2022 alone, global sales of BEVs and PHEVs reached 4.3 million units—representing yearover-year growth of 37% and 75%, respectively. Projections estimate that EV sales will rise by another 57%, totaling 10.6 million units by the end of 2022 [3].

Key Words: Electric Vehicle, Hardware, Software, System 1.INTRODUCTION Over the past two decades, cloud computing has emerged as the third major wave of digital transformation, following the rise of personal computers and the Internet. It has revolutionized how computational resources are accessed and utilized. Recently, the rapid development of technologies like the Internet of Everything and 5G—offering high bandwidth and ultra-low latency—has accelerated the digitalization and electrification of automobiles. The demand for computing power and the complexity of automotive software have surged dramatically. For instance, in 1970, electronics comprised just 5% of a vehicle’s total cost; projections now indicate this could exceed 50% by 2030 [1].

This paper focuses on critical computer science elements that drive this integration, providing a comprehensive view of the computational and architectural challenges and opportunities. Electric vehicles have emerged as a key solution to global environmental and energy challenges. As EV adoption accelerates, the need for intelligent control, robust safety, and real-time optimization becomes paramount. Unlike internal combustion engine vehicles, EVs are primarily controlled by embedded computing systems and require intricate coordination between software logic and physical components.

As illustrated in Figure 1, the architecture of cloud computing platforms and intelligent electric vehicle (EV) systems share considerable similarities. Increasingly, EVs are being viewed as the next major computing platform and a new frontier for innovation. Government initiatives and regulatory frameworks are also contributing significantly to this shift. Notably, the U.S. government announced in 2022 its commitment to invest several hundred billion dollars to strengthen EV infrastructure and domestic manufacturing [5].

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The paper outlines the role of software engineering practices in developing dependable EV subsystems. From autonomous driving to thermal control, computer science facilitates reliable interaction between sensors, actuators, and datacentric cloud platforms. A modular design pattern is used to manage hardware variability and enable scalable upgrades.

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Motivation. However, despite being a rapidly developing computing platform, research and analysis related to EVs remain insufficient, particularly within academia. Firstly, with the exponential growth of computing power in hardware chips and software complexity within the automotive industry, investigating EV hardware and software has become crucial to keeping pace with technological advancements and innovations. Secondly, as EVs gain popularity, there is a pressing need to explore the hardware and software of EV platforms to support their development and integration into the next-generation transportation ecosystem. Thirdly, EVs confront numerous challenges, such as limited cruising range, extended charging times, inadequate supporting infrastructure, network security risks, etc. Understanding these challenges can motivate the exploration of solutions and innovations in EV technology. In this article, we analyze the current state of EV technology development, focusing on the global EV market and its trends. We delve into the hardware systems of EVs, encompassing various energy subsystems, charging infrastructure, high-efficiency control subsystems, powertrain subsystems, power electronics, and chip technology. Additionally, we examine numerous mainstream EV software architectures, technologies, developments, and applications. Furthermore, we address the limitations and challenges of current EV platforms that necessitate improvement in future EV technologies. To summarize, this paper has made the following contributions:

constructed what is recognized as the world’s first operational electric automobile. From the late 1800s through the 1920s, EVs experienced their first significant era of growth. At that time, internal combustion engines were still in their infancy and faced numerous performance limitations [4]. However, after the 1920s, advancements in gasolinepowered vehicles and a lack of adequate EV technology caused electric cars to decline in popularity.

This trend reversed in the 21st century, driven by substantial technological progress in battery systems, electric motors, and vehicle control mechanisms. These innovations have revitalized the EV market. As conventional fuel-powered vehicles contribute to energy depletion and environmental degradation, electric vehicles have emerged as a cleaner alternative. Increasing global emphasis on sustainability, including carbon neutrality and peak carbon policies, has accelerated EV adoption rates. For example, global EV sales exceeded 6 million units in 2021—up from just 2% of market share in 2019 to approximately 10% [7].

In the U.S. state of Georgia, the EV sector has also seen considerable growth. Following the creation of the Georgia Electric Mobility and Innovation Alliance (EMIA) in July 2021 [6], major manufacturers like Blue Bird [2], Rivian [8], and Hyundai Motor Group [4] have expanded their investments in electric vehicle production and supporting supply chains across various counties in the state. According to the Southern Alliance for Clean Energy, Georgia leads the nation with 27,817 newly announced EV-related manufacturing jobs, encompassing vehicle and battery production as well as charging infrastructure deployment.

• We systematically examined EV systems, encompassing the historical evolution of EVs, primary architectures, modules, technologies, developments, and applications in both hardware and software. Additionally, we scrutinized the most advanced technologies currently in use.

Electric vehicles offer several distinct advantages over traditional fossil fuel-powered automobiles. They are significantly more environmentally friendly, producing no tailpipe emissions such as carbon dioxide or nitrogen oxides.

They also operate much more quietly, reducing noise pollution. Furthermore, EVs have a simpler mechanical design, which results in fewer moving parts and therefore lower maintenance costs. Despite these benefits, EVs still face several challenges. Limitations such as shorter driving ranges, extended charging times, and the lack of widespread charging infrastructure remain barriers to mass adoption.

•We analyze the constraints and obstacles encountered by existing EV platforms and explore avenues for enhancing future EV technology development.

• We aim to draw attention from scholars in this domain toward the advancement of vehicle computing. Meanwhile, we hope this work can better shed light on further research on electric vehicle platforms and related technologies.

The rest of the paper is organized as follows. Section 2 briefly describes the electric vehicle’s history and its current development status. Section 3 studies the existing key components and techniques in hardware, such as the energy subsystem, control subsystem, sensors, etc. The EV software technology is covered in Section 4, including virtualization, hypervisor, OS, auto applications, etc. Section 5 summarizes this paper with a conclusion.

3 EV HARDWARE Unlike the rapidly evolving information technology (IT) sector, the automotive industry traditionally progresses at a much slower pace. Legacy vehicle systems rely on a distributed electrical and electronic architecture composed of numerous electronic control units (ECUs), which are often constrained by limited processing capabilities, low data transmission efficiency, and complicated wiring harness systems.

2. HISTORY OF EV AND CURRENT STATUS Electric vehicles (EVs) boast a legacy spanning over a century. In 1873, British chemist Robert Davidson

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However, the rise of next-generation communication technologies such as the Internet of Everything and 5G has significantly propelled the electrification and digital transformation of automobiles. These advancements have driven a surge in hardware computing power and led to exponential increases in software size and complexity. In response, the automotive industry is shifting from the conventional distributed ECU architecture to a more centralized and domain-oriented computing model.

In traditional architectures, each ECU is typically sourced from different suppliers and often lacks interoperability or standardization across systems. But as vehicles become smarter, the proliferation of ECUs has rendered this fragmented model inefficient. Centralized computing architectures are now essential to address this challenge and to support the emerging concept of software-defined vehicles (SDVs), where software updates can redefine vehicle functionality throughout its lifecycle [11]. Figure 2 illustrates this evolving hardware paradigm.

Figure 3: Hardware Cost Composition of Fuel Vehicles and Electric Vehicles 3.1 Energy Subsystem Charging System Electric vehicles (EVs) utilize several types of battery charging systems, including AC charging, DC charging, hybrid AC-DC systems, and wireless charging technologies [9-12].

EV charging infrastructure is commonly categorized into three levels:

Figure 2: Comparison of Distributed Electrical/Electronic Architecture in Traditional Vehicles and Centralized Computing Architecture for Electric Vehicles The hardware platform in electric vehicles forms the core infrastructure of the entire system. Taking the Tesla Model 3 as a reference, crucial components include the battery pack, centralized control unit, thermal management system, power electronics, intelligent driving processors, a suite of sensors, vehicle body structure, and tires. The cost structure of electric vehicles diverges significantly from that of conventional internal combustion engine (ICE) vehicles. As seen in Figure 3, traditional ICE vehicles allocate the bulk of their costs to mechanical elements such as the engine, chassis, and drivetrain. In contrast, smart EVs primarily invest in batteries, motors, controllers, and various highperformance electronic systems [8].



Level 1 charging uses a standard 120V AC household outlet and typically delivers a range of 3 to 5 miles per hour of charging.



Level 2 operates between 208–240V, offering significantly faster charging speeds—ranging from 28 to 80 miles per hour. These chargers are often installed in homes, workplaces, and public locations.



Level 3, also known as DC fast charging or supercharging, supplies between 400 and 900V and can provide 3 to 20 miles of range per minute.

Tesla’s V3 Superchargers and ABB’s Terra 360— capable of adding up to 100 km in under three minutes—are examples of this fast-charging tier [14].

The onboard charger in an EV is tailored to the specifications of each vehicle and typically supports input voltages ranging from 110V to 260V in single-phase setups and 360V to 440V in three-phase systems. The output voltage of EV battery packs generally falls within the range of 450V to 850V. Wireless charging is a newer alternative, less widespread, which uses a magnetic field generated between floor and car mats to transfer energy. Plugless Power pioneered this with a system delivering 20 to 25 miles per hour of wireless charging.

Battery Pack The battery pack serves as the core energy reservoir in electric vehicles. It stores DC power, which is then converted into AC by the inverter to drive the electric traction motor

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[16, 27]. Several battery chemistries are available in the EV market, each suited to different use cases: 1.

Lead-acid batteries: These are cost-effective and perform well in cold climates, but their low energy density, short service life, bulkiness, and safety issues limit their use to low-speed or utility vehicles [21, 25].

2.

Nickel-Metal Hydride (Ni-MH): Known for durability and established technology, Ni-MH batteries offer better performance than lead-acid types. However, their large size, lower voltage output, and modest energy density confine them to compact or hybrid EVs.

3.

Lithium Iron Phosphate (LiFePO₄): Widely adopted in current EV models, LiFePO₄ batteries provide strong thermal stability, high safety, moderate cost, and a long operational lifespan. Their drawbacks include limited driving range, reduced cold-weather efficiency, and slower charging speeds [15].

4.

5.

controller, which in turn adjusts the inverter's switching frequency to regulate motor torque and speed by converting direct current (DC) from the battery into alternating current (AC) for the traction motor.

In recent developments, multi-level inverter architectures have been implemented to enhance efficiency, minimize harmonic distortion, and increase power density. For instance, the Tesla Model S employs a silicon carbide (SiC) multi-level inverter, which offers improved thermal performance and switching capabilities. However, these advancements also introduce increased system complexity, higher manufacturing costs, and more demanding computational requirements in the control algorithms, presenting challenges in design and integration [25].

DC/DC Converter: The DC/DC converter plays a crucial role in managing voltage levels within the EV’s electrical system. It transforms high-voltage DC power from the main battery pack into lower voltage DC suitable for auxiliary systems such as lighting, infotainment, air conditioning, windshield wipers, and navigation units. These systems typically operate on voltages ranging between 12V and 48V.

Ternary Lithium (NMC/NCA): These batteries excel in energy density, lifespan, and low-temperature performance, making them ideal for premium EVs. However, their high cost is a major barrier to broader adoption.

This converter is typically bidirectional, allowing it to also direct power from low-voltage sources back to the highvoltage bus, especially during regenerative braking. As EV technology evolves, demands for higher efficiency, thermal stability, and compact power density in DC/DC converters are escalating. Advanced designs now focus on incorporating wide-bandgap semiconductors such as Gallium Nitride (GaN) and Silicon Carbide (SiC) to boost efficiency, reduce switching losses, and enable faster voltage regulation. Future innovations aim to deliver converters capable of precisely regulating voltage levels across diverse operating conditions, which is key to improving system reliability and extending component lifespan [18].

Sodium-ion batteries (emerging): Still in research and early development stages, sodium-ion batteries promise improved safety, affordability, and high retention capacity. Their limitations include greater weight and reduced energy density compared to lithium-ion alternatives [9].

Onboard Charger The onboard charger enables AC power from an external source to be converted into DC power suitable for battery charging. Its output power capacity is vehicle-dependent and typically falls between 3.7 kW and 22 kW. For instance, Tesla’s integrated onboard charger is specifically designed to align with its high-performance battery specifications, allowing faster and more efficient charging within the AC infrastructure.

Electric Traction Motor: At the heart of an EV’s propulsion system lies the electric traction motor, responsible for converting electrical energy into mechanical motion. Several motor technologies are used in EVs, each offering distinct benefits. Common types include:

3.2 Control Subsystem Power Electronics Controller: The power electronics controller is a pivotal element in the electric vehicle (EV) architecture. It acts as the interface between the energy storage system (battery) and the electric motor, ensuring efficient and controlled energy transfer.

Typically, this unit combines both inverter and converter functionalities. During regenerative braking, the controller captures the kinetic energy and converts it into electrical energy to recharge the battery pack. When the vehicle accelerates, signals from the throttle are sent to the

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DC Motors

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Brushless DC (BLDC) Motors

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Induction Motors

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Permanent Magnet Synchronous Motors (PMSM)

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Switched Reluctance Motors (SRM)

The Tesla Model S, for example, utilizes a three-phase AC induction motor, while the Model 3 incorporates an internal

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permanent magnet synchronous reluctance motor. Each type has unique advantages: 

BLDC motors are compact and efficient but more expensive.

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Induction motors are robust, require less maintenance, and are more cost-effective, making them a popular choice for high-performance vehicles.



PMSMs offer superior torque density and energy efficiency, though they involve higher costs due to the use of rare-earth magnets.



Switched Reluctance Motors provide a low-cost solution but are plagued by issues like high acoustic noise and torque ripple, limiting their adoption [35].

integrates components like image signal processors (ISP), general-purpose GPUs, and custom AI accelerators. This chip is capable of processing over 2,300 frames per second, delivering performance that is roughly 21 times more powerful than its predecessor, thereby substantially boosting autonomous driving capability and safety.

Integration of Chips and Sensors in EVs: Modern EVs, such as those from Tesla, incorporate a robust sensor suite with eight external cameras and twelve ultrasonic sensors distributed across the vehicle body. Data from these units is fed into the onboard SoC for real-time analysis and decision-making. This fusion of sensor input and processing power underpins the vehicle’s capabilities in autonomous navigation, safety monitoring, and driver assistance systems.

Although this paper does not delve into all hardware subsystems, such as the chassis, tires, or body components, it's important to acknowledge that these also play supporting roles in sensor deployment, thermal management, and safety system integration.

Among these, three-phase AC induction motors have become a leading option due to their balanced mix of performance, efficiency, and low operational costs. These motors eliminate the need for brushes and commutators, resulting in less maintenance and enhanced reliability.

4. ELECTRIC VEHICLE SOFTWARE

3.3 Automotive Semiconductors and Sensors

While hardware remains fundamental to EV performance, the importance of software in the automotive domain is rapidly accelerating, especially in areas like vehicle connectivity, automation, and intelligent functionalities. Over recent years, the complexity of both automotive components and their embedded software has increased considerably [13].

Electronic Chips: Semiconductors are central to the transition of modern vehicles into intelligent, autonomous platforms. On average, a contemporary vehicle is embedded with approximately 1,400 semiconductor components sourced from key suppliers like Infineon Technologies, NXP Semiconductors, and Texas Instruments [1]. These chips perform a wide variety of functions, ranging from power management to control systems and data processing.

Centralized Approaches

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Service-Oriented

4.1 Virtualization in EVs Virtualization, a key enabler in cloud computing, is now being adapted for in-vehicle systems to manage growing system complexity and computational demands [18]. Though originating in the cloud domain, vehicle virtualization differs in several critical aspects:

Modern system-on-chip (SoC) designs consolidate processing modules such as CPUs, GPUs, FPGAs, and neural network accelerators into a single chip. These SoCs act as the computational backbone of smart EVs. Tesla’s Full SelfDriving (FSD) chip, for instance, is a prime example. It

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and

In traditional systems, individual ECUs are responsible for specific functions such as lighting, sensors, seating, and networking. These functions interact through distinct signal pathways across buses like CAN (Controller Area Network).

The emerging model, however, emphasizes a serviceoriented architecture (SOA) where software services are decoupled from hardware modules and are deployed across a unified computational platform using high-speed protocols like Ethernet.

A critical class of automotive semiconductors includes power devices such as metal–oxide–semiconductor field-effect transistors (MOSFETs). These are predominantly used to regulate vehicle functions like acceleration, regenerative braking, and electronic steering. As vehicle electrification and connectivity have advanced, so has the demand for enhanced processing and storage. The storage requirements in smart EVs have expanded significantly—from gigabytes (GB) to terabytes (TB)—especially in the context of highresolution sensory data. For example, autonomous vehicles operating at Level 4 autonomy can generate and store approximately 2TB of visual and sensor data every two hours, due to increased sensor density and image fidelity.

Consequently, memory chips have become an essential part of the EV’s computational infrastructure.

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Architectures

1.

Performance Orientation: While cloud virtualization is optimized for scalable resource allocation, automotive virtualization must

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prioritize real-time responsiveness and functional safety.

2.

guarantees while allowing for system customization and innovation.

Resource Constraints:



Green Hills Software offers a hypervisor known as µvisor, which integrates with ARM Cortex-A9-based processors. This solution uses para-virtualization and incorporates a certified real-time microkernel, enabling the concurrent execution of multiple guest OSes, such as Android. Its scalable and efficient architecture ensures that these OSes run on shared hardware without mutual interference.

Furthermore, hardware-enforced partitioning ensures that each virtual machine can reach its optimal performance levels, even within complex automotive environments.



Blackberry QNX Hypervisor stands out as the most widely adopted open-source solution in the automotive domain. Built upon a microkernel architecture, it is engineered to meet the functional safety and real-time demands of key vehicle systems like instrument clusters, advanced driver-assistance systems (ADAS), and vehicle control modules. With a significant market share in the RTOS segment, QNX has become the industry benchmark for multi-OS vehicle environments. It is deployed by leading automakers such as BMW, Ford, GM, Toyota, Mercedes-Benz, Honda, Volkswagen, and Bosch, among others.

Unlike cloud servers, in-vehicle systems have constrained processing power, making efficient resource usage a critical requirement.

3.

Hardware-Software Coupling: Cloud architectures favor layered abstraction. In contrast, vehicle virtualization involves tightly integrated hardware-software interactions, requiring tailored solutions for each hardware configuration.

In contemporary EV electronic and electrical systems, multiple ECUs offer distinct functionalities, each with varying criticality and performance requirements. For example, the instrument cluster, which displays key vehicle parameters, must operate in close synchronization with the engine control system, necessitating low-latency, highreliability processing.

4.2 Hypervisor A hypervisor serves as a virtualization platform that enables multiple applications or entire operating systems (OSes) to operate concurrently on a single physical hardware unit, as though each were running independently [10, 17]. It acts as a mediating software layer between the hardware and virtual machines, allowing for efficient abstraction and partitioning of hardware resources. Through the hypervisor, systems with differing operational requirements ranging from mission-critical real-time processes to general-purpose applications can coexist on the same platform while maintaining robust security isolation and reliable system performance.

In summary, hypervisors have become essential in modern electric vehicle software stacks, enabling secure, modular, and efficient multi-OS deployments on limited hardware platforms. Their role is pivotal in supporting the increasing demand for computational diversity, functional safety, and real-time responsiveness in today’s intelligent automotive systems.

4.3 Operating Systems in Electric Vehicles In electric vehicles (EVs), the operating system (OS) forms a foundational software layer that manages both hardware resources and application software, while also offering standardized interfaces for developers. As modern vehicles are equipped with numerous electronic control units (ECUs)—including systems for motors, battery management, infotainment, and driver assistance—the OS must efficiently coordinate real-time operations, task scheduling, and interprocess communication across heterogeneous hardware platforms.

The hypervisor facilitates flexible resource allocation, allowing various software environments to share the same hardware platform without compromising system integrity.

This is especially critical in electric vehicles (EVs), where safety-critical control systems must be securely isolated from non-trusted or entertainment-based software layers.

Several companies are at the forefront of developing hypervisor solutions tailored for the automotive industry.

These include Wind River Systems, Green Hills Software, Continental AG, Mentor Graphics, Blackberry, and Sasken Technologies. 

Given the diverse requirements of automotive subsystems, two primary types of operating systems are employed:

Wind River Hypervisor is a well-established embedded hypervisor recognized for its compact design, low latency, and optimized execution. It offers robust partitioning mechanisms, enabling strict isolation between software components with varying safety levels. Importantly, it supports real-time performance

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Real-Time Operating Systems (RTOS): These are used in safety-critical applications such as airbag deployment systems, braking control, and battery management systems. These components demand deterministic timing behavior, where even millisecond-level delays

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can lead to hazardous failures. RTOS ensures minimal latency and strict task prioritization, thus supporting life-critical functionality in vehicles. 

While various policy, regulatory, technical, and marketbased barriers still hinder the full-scale deployment of EVs, we hope this review encourages greater academic engagement and innovation in electric vehicle platform development. As a continuation of this work, our future research will focus on the co-design of software and hardware systems aimed at building intelligent, reliable, and energy-efficient EV infrastructures for the next generation of mobility.

Time-Sharing Operating Systems (TSOS): These are used in non-critical but resource-intensive applications, such as infotainment systems and navigation interfaces, where multitasking and user interactivity are essential.

For instance, a driver might simultaneously use GPS navigation and initiate a phone call—both functions managed seamlessly by a TSOS with multiplexing capabilities.

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Android Automotive OS (AAOS) AAOS has rapidly gained traction as an in-vehicle infotainment (IVI) platform. Unlike Android Auto, which mirrors a smartphone interface, AAOS is an embedded automotive variant of the Android operating system that runs natively within the vehicle’s head unit. It is employed in EVs like the 2021 Volvo XC40 Recharge, 2020 Polestar 2, and the 2022 GMC Hummer EV. One of the key advantages of AAOS is its intuitive user interface, optimized for in-vehicle screens, as well as its compatibility with downloadable applications directly from the car's interface—without requiring a tethered smartphone. Moreover, AAOS leverages Android’s widespread ecosystem, reusing familiar APIs, development tools, and programming languages (e.g., Kotlin and Java), thus facilitating the contribution of a large global developer base and expediting application development and deployment.

5 CONCLUSIONS With the rapid rise in popularity, electric vehicles (EVs) are now recognized as emerging computational platforms and central to the next wave of technological innovation. Unlike traditional internal combustion engine (ICE) vehicles, EVs offer notable benefits such as enhanced performance, reduced maintenance, eco-friendliness, and an improved driving experience. However, these advantages are accompanied by significant technological and infrastructural challenges.

This paper provided a comprehensive overview of the current state and future direction of EV technology. We examined the core hardware components—including energy systems, powertrains, electronic control units (ECUs), and chip-level technologies—and outlined prevalent software architectures and their role in the evolving EV ecosystem.

Furthermore, we discussed emerging trends, ongoing challenges, and potential technological breakthroughs in the domain.

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