#software defined vehicles

EV Electronics Market is evolving rapidly as modern electric vehicles increasingly rely on the integration of software and electronic systems to deliver enhanced performance, safety, and user experience. Today’s EVs are no longer just mechanical machines but highly intelligent systems powered by advanced electronics, sensors, and software platforms working together in real time.

The integration of vehicle control units (VCUs), battery management systems (BMS), and infotainment systems enables seamless coordination across all vehicle functions. Software-defined vehicle architecture allows manufacturers to update features remotely through over-the-air updates, improving functionality without requiring physical modifications. This shift is significantly enhancing vehicle lifecycle value and customer satisfaction.

Artificial intelligence and machine learning are also playing a crucial role in optimizing EV performance. These technologies help manage energy consumption, predict maintenance requirements, and improve autonomous driving capabilities. Sensors continuously collect data, which is processed by onboard computers to make real-time driving decisions. This level of integration is improving safety, efficiency, and overall driving experience.

The increasing complexity of EV systems is driving demand for high-performance processors, domain controllers, and communication networks. These components ensure smooth data flow between various electronic modules within the vehicle. As a result, the role of software is becoming just as important as hardware in shaping EV performance.

According to the U.S. EV Electronics Market report, increasing software integration is a key factor driving market growth. The U.S. EV Electronics Market for power semiconductors achieved a sales volume of 80 million units in 2024 and is projected to surpass 200 million units by 2033, growing at a CAGR of 15.1% over the forecast period.

Automakers are investing heavily in digital platforms and connected vehicle ecosystems to enhance user engagement. These systems provide real-time navigation, remote diagnostics, and personalized driving experiences. The integration of cloud computing with vehicle systems is further expanding possibilities for innovation.

The automotive industry is experiencing a convergence of technological, regulatory, and competitive forces that are accelerating artificial intelligence adoption at unprecedented rates. Traditional OEMs that once measured development cycles in half-decade increments now compete against EV manufacturers deploying software updates monthly. NHTSA's evolving stance on autonomous vehicle testing, combined with the European Union's General Safety Regulation mandating advanced safety features, creates both opportunity and urgency for organizations capable of deploying AI-driven systems that meet stringent functional safety requirements while delivering measurable improvements in vehicle performance and safety outcomes.

Understanding current trajectories in Automotive AI Integration requires examining multiple concurrent trends reshaping how vehicles are designed, validated, and manufactured. The shift from distributed ECU architectures to centralized computing platforms, the emergence of V2X communication standards that enable cooperative perception, and the increasing sophistication of sensor fusion algorithms collectively represent a fundamental reimagining of vehicle systems engineering. General Motors' acquisition of Cruise and Ford's partnerships with Argo AI illustrate how traditional manufacturers are investing billions to acquire AI capabilities that complement their manufacturing and systems integration expertise.

Centralized Computing and Software-Defined Vehicles

The migration from dozens of specialized ECUs to centralized high-performance computing platforms represents one of the most significant architectural shifts in automotive history. This consolidation enables the software-defined vehicle concept where capabilities can be added, modified, or enhanced post-production through over-the-air updates. Tesla's early adoption of this architecture provided a multi-year competitive advantage in deploying AI-driven features like Autopilot and Full Self-Driving capabilities. Traditional OEMs are now racing to catch up, with platforms like BMW's iDrive 8 and Mercedes' MBUX demonstrating how centralized computing enables sophisticated AI applications spanning autonomous driving assistance, predictive maintenance, and personalized HMI experiences. This architectural evolution demands new approaches to component integration testing and system-level validation, as the interactions between software components become more complex and dynamic than traditional hardware-defined systems.

Regulatory Frameworks Accelerating AI Deployment

Regulatory mandates are proving to be significant AI adoption drivers, not barriers as initially feared. The European Union's requirement for advanced emergency braking and lane-keeping assistance across all new vehicles creates guaranteed market demand for ADAS technologies that rely on AI perception and decision-making systems. NHTSA's updated guidelines for autonomous vehicle testing provide clearer pathways for organizations developing higher levels of automation, reducing regulatory uncertainty that previously slowed investment decisions. ISO 26262 extensions addressing AI-specific functional safety considerations give systems engineers concrete frameworks for validation and verification, replacing earlier ambiguity about how to certify non-deterministic systems. Organizations investing in custom AI solutions aligned with these evolving standards position themselves to scale deployment rapidly as regulatory approval processes mature and standardize across jurisdictions.

Supply Chain Transformation and Vertical Integration

Supply chain disruptions affecting semiconductor availability have prompted strategic reassessments of vertical integration across the automotive value chain. Traditional Tier 1 supplier relationships assumed clear boundaries between component manufacturing and vehicle integration. AI deployment blurs these boundaries, as algorithm performance depends on intimate knowledge of sensor characteristics, processing hardware capabilities, and vehicle dynamics control systems. Toyota's investments in semiconductor manufacturing partnerships and Ford's creation of in-house software development teams reflect a broader industry trend toward vertical integration for AI-critical capabilities. The CAN bus protocols and telematics platforms that enable vehicle connectivity now represent strategic assets requiring direct control rather than outsourced commodities. This vertical integration extends to data pipelines and annotation workflows, as organizations recognize that proprietary training datasets derived from their specific vehicle platforms and customer populations provide competitive advantages that cannot be purchased from generic AI vendors.

Conclusion

Mechanical Domain Architecture

The significant reduction in the number of parts in an electric vehicle, has led to modular architecture, which gives multiple advantages to an OEM to design and manufacture various types of vehicles in different lengths, sizes, and shapes in a short duration.

Electrical and Electronic Domain Architecture

To achieve the simplicity of a smartphone in the automobile domain, zonal and distributed types of EE architecture have evolved related to domains like ADAS, Powertrain, and Passive Safety, which are centred around the domains. Depending on which type of architecture is used by an OEM, the usage of ECUs varies.

Electrical Vehicle Challenges

Even though the EV market is increasing for automobiles, the biggest challenge of high-voltage batteries (HVB) still exists. A simple case of HVB during high or low outside temperatures makes the battery inefficient due to a reduction in range or increased charging times. In short, the challenges of SDVs and their extensive use in EVs require very efficient thermal management, and virtual validation is a well-known domain that is significantly used by OEMs to keep automobiles cool and smart by making Software-Driven Vehicles work efficiently.

Importance Of CAE Simulations In Engineering

In the engineering world, it is a well-known fact and an established process to use Virtual Validation, also called Computer-Aided Engineering (CAE), in the development cycle of a new product. The product can range from a small plastic clip to an automobile, industrial heavy machinery, or an aeroplane, which needs to be developed from a concept phase to production. CAE is a discipline that guides a design team in a product’s journey, ensuring it is competitive in terms of weight and manufacturing cost by analyzing techniques in selecting the right material and manufacturability to meet its ultimate function.

With the advent of high-end computing, CAE has become a principal domain with multiple sub-domains evolving to cater to manufacturing simulations, Crash & Safety, Computational Fluid Dynamics (CFD), Noise Vibration & Harshness (NVH), Durability, and Multi-body dynamics (MBD). The various disciplines need various mathematical models leading to software like LS-DYNA, ABAQUS, NASTRAN, MSC ADAMS, ALTAIR’S OPTISTRUCT, and Star CCM+, to name a few. Virtual validation plays a significant role in left shifting the development cycle, saving millions of dollars by replacing physical prototypes during the initial stages of product development.

A decade ago, there used to be high-performance computers (HPCs) that would take a good amount of time to simulate a test case scenario. However, due to the advent of fast semiconductor chips, this situation has improved significantly. Earlier, engineering companies, whether OEMs or Engineering Service Providers (ESPs), were either purchasing or leasing HPCs and spending millions of dollars for years to provide speedy products at competitive pricing. The in-house on-premises HPC would incur IT costs to maintain the server running 24/7 and electricity costs to keep the HPC machine cool to avoid overheating, leading to downtime.

Cloud computing is the new trend where large software companies are ready to provide services to OEMs and ESPs alike. This changes the entire landscape of HPC and CAE offerings. Cloud services reduce significant investment and logistics costs, giving OEMs a competitive advantage.

Future Of Virtual Validation and Software Driven Vehicles

Both virtual validation and SDVs are going to take advantage of Artificial Intelligence and Machine Learning (AI/ML) tools, which will help OEMs left shift the development cycle. In the coming decade, the usage of these tools will be as high as 60%, making automobiles safer and smarter. Thus, most of the passive safety features today will be part of active safety services due to smarter automobiles, be they passenger vehicles or commercial vehicles.

OEMs are also finding multiverse computer platforms with many advantages, like digital twinning and Universal Scene Description (USD), which will allow IT, CAD, CAM, and CAE engineers to access various design models simultaneously, leading to fast solutions. This provides a huge advantage to be competitive as a product developer, as logistics are simplified. Exciting times are yet to come with SDVs involving SOTA and FOTA, making the automobile not just a means of transportation but a more adaptive entity that will evolve and adapt to the users’ needs, providing a delightful experience to the customer.

Original source: https://www.tatatechnologies.com/media-center/computer-aided-engineering-simulation-solutions-for-sdvs-on-cloud/

How will software-defined Vehicles (SDVs) shape the future of mobility?

URL: https://www.kpit.com/software-defined-vehicle/

Status: Yet to post

Description:

World over there are big megatrends that are impacting mobility:

➡️ Extended ownership for vehicles

➡️ Expectation that vehicles can be personalized to every user's needs

➡️ Climate consciousness and regulatory environment leading to cleaner vehicles

➡️ Safety emphasis leading to more driver assistance and autonomous driving

The net result is that consumers worldwide are expecting more from their vehicles. They want to be able to use features on demand, personalize, and control the entire experience.

Over the last 15 years, the role of software in vehicles has entered new paradigms and become the key enabler for transformation.

The mobility Industry is trying to harness the power of software, semiconductors, and cloud to create a new vehicle architecture that will power multiple use cases and unlock competitive advantage in meeting consumers' diverse needs.