How IIoT is transforming electric vehicle performance and connectivity

Electric vehicles (EVs) are moving from niche products to mainstream transportation, and a new academic review reveals how the Industrial Internet of Things (IIoT) will determine whether this shift succeeds on a large scale. 

In a comprehensive study titled “The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review”, researchers argue that IIoT technologies are central to unlocking the full potential of EVs, from predictive maintenance and connectivity to fleet optimization and smart charging systems.

The paper, published in Applied Sciences, analyses more than 150 sources to map out how industrial-grade IoT is reshaping the EV ecosystem. Their findings place IIoT at the center of both technological breakthroughs and critical bottlenecks facing the global EV market.

Building the technological foundation for smart EVs

The study distinguishes IIoT from consumer-grade IoT, underscoring its industrial robustness and scalability for complex systems like EV networks. IIoT integrates layers of connectivity, from CAN and Ethernet protocols inside vehicles to advanced V2X communication technologies like 5G, LTE, and IEEE 802.11p that enable real-time interaction between vehicles, infrastructure, and the grid.

According to the authors, a stable framework of standardized protocols, including ISO 15118 for smart charging and the Open Charge Point Protocol (OCPP), is key to achieving interoperability across devices and systems. The layered architecture enables seamless communication between infrastructure, platform, and application layers, allowing a range of functionalities from predictive maintenance to autonomous driving.

Importantly, the review highlights the scalability of IIoT systems, which can dynamically allocate resources and adjust workflows as demands shift. This adaptability is critical as EV adoption surges worldwide, demanding smarter grids and optimized fleet operations.

The paper also underscores the role of AI-driven optimization algorithms. These technologies process vast streams of operational and user data to improve decision-making, minimize latency, and enhance efficiency in critical areas like battery management and vehicle connectivity.

Optimizing Performance Through Predictive Intelligence and Connectivity

The research focuses on predictive maintenance (PdM), one of the standout applications of IIoT in EV technology. By harnessing continuous sensor data and machine learning models, including recurrent neural networks like LSTM and GRU, IIoT platforms can forecast component failures and estimate the remaining useful life of critical systems. The review points to real-world data showing repair cost reductions of up to 25% and notable efficiency gains in operational throughput when predictive analytics are applied in vehicle manufacturing and fleet operations.

Connectivity also emerges as a key driver of personalization and performance. Vehicle-to-everything (V2X) systems enhance driver experience and operational efficiency by enabling vehicles to communicate seamlessly with each other, the infrastructure, and even the grid. The research notes that such intelligent interaction can boost energy efficiency by as much as 20%, especially when combined with real-time traffic analysis and adaptive energy management.

However, this growing connectivity introduces significant challenges. Privacy risks, algorithmic biases, and potential misuse of personal data pose ethical and operational hurdles. The authors recommend implementing privacy-by-design principles, adopting federated learning techniques to protect sensitive data, and conducting regular audits to maintain trust and compliance with evolving regulations.

Fleet management is another area where IIoT’s potential is evident. The study outlines how IIoT-enabled energy management systems balance depot charging schedules with grid conditions, optimizing routes and charging patterns. This approach delivers energy savings of up to 30% for fleet operators and cuts downtime by streamlining the scheduling of maintenance and charging sessions.

Addressing challenges and charting future directions

IIoT into the EV ecosystem is not without hurdles. Cybersecurity threats loom large as attackers target vulnerabilities in communication protocols like MQTT and CoAP, as well as backend services that manage critical operational data. The authors recommend robust encryption, anomaly detection systems powered by machine learning, and selective deployment of blockchain to enhance security while managing cost and complexity.

Regulatory disparities further complicate adoption. While regions like the European Union have developed frameworks under GDPR to safeguard data, other markets remain fragmented, leading to challenges in implementing uniform security and privacy standards. The paper stresses the need for harmonization of global standards, improved certification processes, and coordinated industry efforts to create open, interoperable platforms that can evolve alongside technological advances.

Case studies highlighted in the review reinforce IIoT’s real-world potential. In Shenzhen, China, an electric bus fleet leveraged IIoT to integrate real-time telemetry with predictive analytics, achieving charging cost reductions of up to 30% and improving overall operational efficiency by more than a third. Similarly, in the Netherlands, IIoT-enabled smart charging networks demonstrated peak load reductions of 15–20% while lowering user costs by nearly a quarter. These examples underscore the tangible benefits of IIoT adoption when supported by strong infrastructure and effective management strategies.

The study envisions deeper integration of AI and machine learning into IIoT ecosystems to enable self-learning platforms capable of adapting to rapidly changing operational environments. Self-supervised and generative AI models could play a pivotal role in predictive forecasting, enhancing grid resilience, and managing fleet operations in real time. Moreover, the evolution of edge computing is expected to address latency issues and deliver real-time decision-making capabilities critical for applications like autonomous driving and advanced driver-assistance systems (ADAS).

Achieving the full potential of IIoT in the EV sector will require a multi-pronged approach that balances technological innovation, cybersecurity, and regulatory alignment. Strategic investments in infrastructure, standardization, and workforce training will be vital to ensure that these technologies can scale effectively and sustainably, the study asserts.