The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review
Abstract
1. Introduction
Research Questions, Aims, and Objectives
2. Methodology
3. Internet of Things (IoT) and Industrial Internet of Things (IIoT)
3.1. IIoT Architecture, Connectivity, and Standardization
- (1)
- Adoption and harmonization of existing standards (e.g., ISO 15118 for V2G communications and OCPP for charging station control) as baseline protocols.
- (2)
- Industry consortia and open reference implementations that demonstrate interoperability (manufacturers, utilities, and software vendors).
- (3)
- Middleware/gateway layers that translate between vendor-specific APIs and common schemas, enabling backward compatibility for legacy assets.
- (4)
- Regulatory and procurement incentives that require or reward adherence to open standards.
- (5)
- Phased certification programs (testbeds and interoperability plugfests) to reduce risk for OEMs and operators.
3.2. IoT Transition to IIoT
4. Applications of IoT and IIoT
5. IIoT Integration in Electric Vehicles (EVs)
5.1. Predictive Maintenance (PdM)
5.2. Vehicle Connectivity and Personalized User Management
- (1)
- Privacy-by-design minimizes data collection, keeps personal data on-device where possible, and apply anonymization.
- (2)
- Federated learning and differential privacy enable model training without centralizing raw user data.
- (3)
- User control and consent with clear opt-in/opt-out controls and granular consent for data uses.
- (4)
- Transparency and explainability: Provide interpretable explanations for personalization decisions and allow for users to review and correct preference models.
- (5)
- Fairness audits and bias testing: Regularly test personalization outcomes across demographic groups.
- (6)
- Regulatory compliance and governance that align design with GDPR/CCPA and maintain logging/audit trails for automated decisions. These practices reduce surveillance risk and preserve user autonomy while allowing for beneficial personalization.
5.3. Energy and Fleet Management
5.4. EV Charging and Battery Management
5.5. Autonomous EVs, Cyber Security, and Advanced Charging Systems
5.6. Advanced Driver-Assistance Systems (ADASs)
- Real-time sensor data for detecting obstacles, pedestrians, or other vehicles, enhancing the vehicle’s decision-making capabilities.
- Integration with outside data sources to enhance the vehicle’s awareness and reaction to its surroundings, such as weather and traffic data.
- Constant system tuning using machine learning methods to enhance the ADAS’ performance and adjust to various driving situations.
6. Case Studies
6.1. Case Study 1: Electric Bus Fleets in Shenzhen, China
6.2. Case Study 2: The Netherlands’ EV Charging Network
6.3. Case Study 3: Tesla Personalized User Management
6.4. Emerging Markets
7. Discussion and Analysis
7.1. Challenges and Solutions
7.2. Future Trends
7.2.1. Use of AI and ML
7.2.2. Technological Advancements
7.2.3. Introducing New Models
7.2.4. Unified Evaluation Framework
7.2.5. IoT and IIoT Expansion
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Acronyms
5G | Fifth Generation |
ADAS | Advanced Driver-Assistance Systems |
AI | Artificial Intelligence |
BMS | Battery Management Systems |
CCPA | California Consumer Privacy Act |
EMS | Electric Vehicles |
EVs | Energy Management Systems |
GDPR | General Data Protection Regulation |
HMI | Human–Machine Interface |
IDS | Intrusion Detection Systems |
IIoT | Industrial Internet of Things |
IoT | Internet of Things |
ISO | International Organization for Standardization |
IT | Information Technology |
LoRaWAN | Long Range Wide Area Network |
ML | Machine Learning |
MQTT | Message Queuing Telemetry Transport |
NLP | Natural Language Processing |
OT | Operational Technology |
SoC | State of Charge |
SoH | State of Health |
TLS | Transport Layer Security |
V2C | Vehicle-to-Cloud |
V2G | Vehicle-to-Grid |
V2I | Vehicle-to-Infrastructure |
V2V | Vehicle-to-Vehicle |
V2X | Vehicle-to-Everything |
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Application Area | IoT in EVs | IIoT in EVs | Key Outcomes | Ref. |
---|---|---|---|---|
Predictive Maintenance | Basic predictive maintenance systems for early fault detection. Relies on cloud-based solutions but faces latency issues. | Integration of sensors for real-time vehicle health monitoring. Advanced connectivity with real-time data exchange and edge analysis. AI and machine learning for battery health diagnostics and motor efficiency. Uses robust industrial-grade IoT sensors for continuous monitoring. | IoT in EVs: Reduces maintenance costs by 10–20%. IIoT in EVs: Achieves 20–30% cost reduction with enhanced uptime. IIoT offers an edge in minimizing downtime for commercial fleets. | [31,33] |
Energy Management | Monitors energy usage via sensors. Optimizes charging cycles. Analyzes consumption trends. | Real-time optimization with edge computing. AI-based intelligent charging strategies. Prevents energy loss and extends battery life. | IoT: Efficient energy monitoring. IIoT: 15–25% cost reduction and improved battery life. | [34,35] |
Vehicle Connectivity | Real-time data exchange via IoV. Support V2V, V2I, and V2X for safety. | Advanced sensors with edge computing for real-time decisions. Predictive traffic management and dynamic routing. | IoT: Improved traffic flow, reduced accidents. IIoT: 20–30% congestion reduction, better autonomy. | [36,37] |
Domain | Application of IoT | Application of IIoT | Ref. |
---|---|---|---|
Healthcare | Remote patient monitoring through wearable IoT devices. Telemedicine platforms enabling real-time doctor-patient consultations. Tracking medication adherence using IoT-enabled reminders. | Smart hospital management with interconnected equipment and predictive maintenance. Real-time analytics for critical equipment in operating rooms and ICUs. Supply chain optimization for pharmaceutical manufacturing and distribution. | [39,40] |
Agriculture | Precision farming uses IoT sensors for soil moisture and weather monitoring. Livestock health monitoring through IoT-enabled collars and tags. | Smart irrigation systems are integrated with large-scale farming operations. Automation of food processing and storage systems to reduce waste. | [41,42] |
Manufacturing | Asset tracking and monitoring for production line equipment. IoT-enabled employee safety monitoring in hazardous environments. | Predictive maintenance for machinery to reduce downtime and increase efficiency. -Industrial automation using robotics and IIoT-integrated control systems. | [43,44] |
Energy Management | Smart meters for tracking energy consumption in households. IoT-based solar panel performance monitoring. | Real-time optimization of power grids to balance load and manage outages. -Integration of renewable energy sources with IIoT-enabled grid management systems. | [35,45,46] |
Retail | Personalized shopping experiences through IoT-based recommendation systems. Smart shelves that track product availability and expiration dates. | Automated warehousing and logistics using IIoT-powered robotics and analytics. Large-scale building management systems with IIoT integration for HVAC, security, and energy control. | [47,48] |
Transportation and EVs | IoT-enabled vehicle tracking and route optimization for public and private transportation and EVs. Real-time passenger information systems for smart transit networks. | Fleet management systems with predictive analytics to improve fuel efficiency and reduce operational costs. Integration of IIoT in autonomous vehicles for real-time decision-making. | [5,49,50] |
Logistics | IoT-based package tracking for last-mile delivery. Monitoring warehouse inventory with IoT sensors. | IIoT-powered supply chain visibility with real-time analytics. Automation of freight loading and unloading with robotics and IIoT-integrated systems. | [51,52] |
Environmental Monitoring | IoT sensors for detecting water quality and pollution levels in rivers and oceans. Air quality monitoring in urban areas. | Large-scale IIoT systems for climate monitoring and forecasting. Disaster response systems powered by IIoT analytics for real-time decision-making. | [53,54] |
Education | Smart classrooms with IoT-enabled devices for interactive learning. IoT-based attendance and resource management systems. | IIoT-powered infrastructure for smart campus management, including HVAC, lighting, and security systems. Predictive maintenance of educational facilities. | [55,56] |
Smart Cities | IoT-enabled smart street lighting for energy efficiency. Monitoring air quality and environmental conditions. | Large-scale traffic management systems using IIoT analytics and edge computing. Centralized control systems for utilities, waste management, and public safety. | [53,57,58] |
Feature | IoT in EVs | IIoT in EVs | Ref. |
---|---|---|---|
Focus | User-centric applications | Industrial-scale infrastructure | [36] |
Data Processing | Cloud-based | Edge computing with local processing | [62] |
Scalability | Limited to individual vehicles | Designed for large-scale operations | [62] |
Advantages | Improved user experience, route planning | Grid optimization, manufacturing efficiency | [36] |
Disadvantages | Cybersecurity risks, high costs | Privacy concerns, over-reliance on automation | [62] |
Communication | Vehicle-to-Cloud (V2C) interactions | Vehicle-to-Everything (V2X) integration | [63] |
Application | Navigation, smart charging | Fleet management, predictive maintenance | [63] |
Ecosystem | Isolated device interaction | Integrated supply chain and operations | [36] |
Latency | Higher latency due to cloud reliance | Low latency for critical decisions | [61] |
Reliability | Depending on network availability | Designed for high uptime and redundancy | [61] |
Energy efficiency | Focus on vehicle-level optimization | System-wide energy management | [61] |
Cost | Lower initial deployment cost | Higher investment but long-term efficiency | [63] |
Security protocols | Standard encryption methods | Advanced, industrial-grade security measures | [63] |
Maintenance | Reactive, user-initiated | Predictive, automated through sensors | [64] |
Integration | Focused on individual systems | Seamless integration across industries | [64] |
Data sharing | Limited to vehicle-owner interactions | Extensive sharing with industrial networks | [64] |
Decision-making | User or cloud-driven | Automated and real-time | [60] |
Standards | Consumer-grade protocols | Industrial compliance standards | [60] |
Testing | Functional and usability testing | Stress-tested for harsh industrial conditions | [62] |
Use case example | Personalized driving analytics | Real-time EV production monitoring | [62] |
Wireless Option | Bluetooth | ZigBee | LTE-M (LTE Cat-M1) | Passive RFID | UWB | 60 GHz mm Wave | LoRa/LoRaWAN |
---|---|---|---|---|---|---|---|
Frequency band | 2.4 GHz | 868 MHz, 915 MHz, 2.4 GHz | Licensed LTE bands | 915 MHz | 3.1–10.6 GHz | 57–64 GHz | Sub-GHz ISM |
Data rate | 1, 2, 3 Mb/s | 20–250 kb/s | Up to 1 Mbps | <4 Mb/s | 53.5–480 Mb/s | >1 Gb/s | 0.3 kbps to 50 kbps |
TX power | 1, 2.5, 100 mW | <1 mW | ~23 dBm (200 mW) | 0 | 1 mW/Mb/s | 10 mW | 14 dBm (25 mW), up to 20 dBm (100 mW) |
MAC Protocol | TDMA | CSMA/CA | LTE-based | EPC global | CSMA/CA and TDMA | CSMA/CA and TDMA | ALOHA-based |
Modulation | GFSK (1 Mb/s) π/4-DQPSK (2 Mb/s) 8DPSK | BPSK (868 MHz) BPSK (915 MHz) O-QPSK (2.4 GHz) | QPSK, 16QAM | BPSK | MB-OFDM | Single carrier, OFDM | Chirp Spread Spectrum (CSS) |
Application | Multimedia | Monitoring/ Control | Mobile IoT | Monitoring/Control | Multimedia | Multimedia | Long-range, Wireless |
Vehicle Communication System | Effect of IIoT Integration | Ref. |
---|---|---|
V2X | It dynamically balancing grid demand and charging rates, ensuring grid stability and efficiency It reduced traffic congestion by 40% and improve road safety by 30% reported by IoT analytics firms | [73,75,79] |
V2G | IIoT-based energy management systems in V2G networks are the reason for the decreased grid dependency during peak hours by 15–20% | [76,80] |
V2I | It builds the ability to enhance intelligent infrastructure from traffic flow optimization to the efficient management of charging stations | [72,78,81] |
V2V | It focuses on real-time data sharing to improve road safety, traffic efficiency, and hazard management | [77,81,82] |
IIoT Integration | Energy Management | Fleet Management | Ref. |
---|---|---|---|
Primary Focus | Optimization of charging cycles, energy consumption, and cost reduction. | Monitoring and optimizing vehicle operations, routes, and maintenance. | [35,45,90] |
Role of IIoT | Tracks real-time energy consumption. Optimizes charging schedules. Reduces energy waste. | Collects and analyzes real-time data on fleet performance. Enables predictive maintenance. | [35,45,90] |
Key Benefits | 20–30% reduction in energy consumption for fleets. 15–20% reduction in charging times. Reduced energy costs by 10–25%. | Early detection of inefficiencies. Better coordination across vehicles. Reduced operational costs. | [35,45,90] |
Technologies Used | Intelligent Energy Management Systems (EMS). Smart grids. Real-time IoT sensors. | The management of information and communication technology assets. Advanced analytics. Management and planning tools—predictive maintenance tools. | [47,91,92] |
Cost Saving | Performed using energy efficiency and demand management techniques. | By reducing time when the platform is inoperable and simultaneously enhancing a range of performance indicators. | [92,93] |
Operational Improvement | Reduces the load on the grid during some of the most critical hours. It also manages to extend periods of charging the batteries to improve their long periods of charges. | Reduces cases of accidental occurrences and energy loss. Improves assets productivity and protection. | [92,93] |
Sustainability Impact | Lowered emission of green-house gases due to efficient energy utilization. | Enables the improvement of the sustainable practices of the fleets by reducing Emissions and fuel waste. | [92,93] |
Scalability | Designed for individual and grid-wide applications. | Suitable for managing small to large-scale fleet operations. | [84,85,94] |
Data Management | Rely on real-time energy data aggregation and predictive models. | Utilizes historical and live fleet data to optimize decision-making. | [84,85,94] |
Collaboration | Integrates with utility companies and smart grid systems. | Connects with logistics, supply chains, and dispatching platforms. | [36,45] |
Challenges | Investment in smart grid related Infrastructure. Uncertainty of energy projection models. | High initial setup costs. Security in computer and communication networks for protection of data. | [36,45] |
Energy Sources | These integrate batteries storage systems for peak shaving and load balancing. | Controls battery conditions, and sets reminders for battery replacement, to minimize interruptions. | [84,85,93] |
Predictive Analytics | Predicts the customer’s energy requirement and adjusts the charging schedule for lower expenses. | Identifies when a vehicle component is faulty so that more | [93,94,95] |
Real-time Insights | Provides live updates on energy grid performance and usage trends. | Tracks vehicle locations, fuel consumption, and driver behaviors. | [93,94,95] |
Maintenance | Facilitating the same by assuring timely maintenance, mostly lightening infrastructural facilities to avoid time wastage. | It helps in fixing timely maintenance measures depending on its usage by the vehicles. | [93,94,95] |
Revenue Generation | In particular, it engages in energy trading markets in order to purchase excess energy it will retail in the markets. | Improves the revenues by optimizing fleets and spending less time associated with fleets that are not generating any income. | [93,94,95] |
Integration with AI | It actively participates in energy trading markets for purposes of purchasing excess energy that it can retail to the energy trading markets. | Uses Advanced Intelligent Automation for better routing, shorter time taken, and conservation of fuel. | [95,96] |
Grid Resilience | Enhances depot security from malicious attacks and prevents energy loss by planning EV recharging during late hours. | Improves resource utility by allowing fleet adaptability during crisis situations. | [95,96] |
User Experience | Offers end-users benefits in making informed decisions on charging stations that are cheap to manage. | Improves fleet management decision-making process by providing clear and easy to understand visualizations. | [96,97,98] |
Compliance | Comply with energy norms and supports the consumption of renewable energy sources. | It complies with the company policy on emissions on their fleets as well as the operational safety standards. | [99,100] |
Resource Allocation | Optimizes energy distribution among multiple charging stations. | Allocates vehicles dynamically based on demand and availability. | [99,100] |
Performance metrics | The tracking of energy consumption, charging time effectiveness, and the stability of the grid. | Monitors fleets productivity, maintenance milestones as well as operational down time. | [99,100] |
Long-term Viability | Encourages investment in renewable energy integration. | Facilitates long-term sustainability of fleet operations. | [99,100] |
Cybersecurity | Implement robust measures to secure energy grid data and prevent breaches. | Employs advanced cybersecurity protocols to protect fleet data and communications. | [101,102] |
Lifecycle Management | Manages the lifecycles of the stations to understand when charging stations need upgrades or replacements. | Tracks the lifecycle of vehicles and parts to maximize ROI and minimize waste. | [101,102] |
Training and Support | Enables operators to examine energy systems and advance their utilization. | Offers fleet managers training on data interpretation and predictive tools. | [101,102] |
Case Study | Electric Bus Fleets Shenzhen, China | ||
---|---|---|---|
Proposed Solution | Obtained Results | Advantages | Limitations |
IIoT sensors monitored battery status, vehicle location, energy consumption, and charging cycles. Real-time data collected via a cloud-based platform for optimization. | Energy Optimization: Reduced charging costs by 20–30%. | Cost reduction. | Managing large fleet data, optimizing charging schedules. |
Cost Reduction Maintenance costs were reduced by 15%. | Scalability for large scale fleet operations. | Complex system High initial cost. | |
Predictive Maintenance: Reduced downtime and maintenance costs by 15%. | Reduced downtime and maintenance costs. | Continuous need for upgradation and monitoring. | |
Fleet Utilization: Improved efficiency by 35%. | Efficiency is improved by optimized fleet scheduling. |
Case Study | Netherlands EV Charging Network | ||
---|---|---|---|
Proposed Solution | Obtained Results | Advantages | Limitations |
IIoT sensors monitored State of Charge (SoC), peak demand times, and dynamically adjusted charging rates. Integrated Vehicle-to-Grid (V2G) technology for grid balancing. | Load Balancing: Reduced peak grid load by 15–20%. | During high demand hours, reduction in peak grid loads. | Grid capacity management and charging station distribution challenges. |
Cost Reduction Charging costs reduced by 25% during off-peak hours. | Reduction in charging cost. | Possible delay in communication. | |
Dynamic Pricing: Lowered consumer charging costs by 25% during off-peak hours. | V2G technology reduces strain of grid system. Improved energy efficiency and grid reliability. | Require high investments Challenges in the worldwide accessibility of V2G technology. | |
Energy Efficiency: Reduced grid strain by 18%. | Possible delay in communication. |
Case Study | Tesla Personalized User Management | ||
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Proposed Solution | Obtained Results | Advantages | Limitations |
IIoT collected data on driving behavior, vehicle usage, and battery health Personalized features like adaptive driving modes and predictive energy consumption | Driving Efficiency: Improved by 15–20% | Extended battery life | Continuous need of upgradation High initial investment |
Fleet efficiency Enhanced through adaptive driving modes | High user satisfaction | Risk of cyberbullying | |
Battery Life: Extended by 10–12% | Improved driving efficiency | Continuous need for upgradation Ensuring seamless adaptation to individual driving styles | |
User Satisfaction: 70% of users reported enhanced satisfaction with adaptive features | Better consumption based on driving tactics and road conditions |
Challenge | Description | Possible Solutions | Implementable Steps | Ref. |
---|---|---|---|---|
Data Security and Privacy | The vast amount of sensitive data exchanged between vehicles and external systems, such as user info, telemetry, and energy consumption, increases the risk of breaches | Implement robust encryption Secure communication protocols Regular security audits and updates | Use of encryption methods (e.g., AES) Employ secure communication protocols (e.g., MQTT) Performance vulnerability assessments and audits regularly | [14,63,145] |
Interoperability and Standardization | Lack of universal standards complicates seamless integration of various hardware and software platforms, causing compatibility issues | Establishing universal protocols and standards Foster industry collaboration to define common frameworks | Adopting standards like ISO 15118 (Vehicle-to-Grid communication) Engage in industry collaborations for developing protocols Standardize data formats | [145,146,147] |
Integration with Legacy Systems | Older EV models and manufacturing systems may not be designed for IIoT integration, leading to challenges in retrofitting and system compatibility | Use of IoT gateways for system integration Developing hybrid solutions for legacy and modern systems | Install IoT gateways to bridge legacy systems with IoT platforms Planning phased upgrades to minimize downtime and ensure compatibility with newer technology | [145,147] |
Scalability | Ensuring that IIoT systems can handle growing numbers of EVs and infrastructure without performance bottlenecks | Designing of modular systems Use of cloud-based architectures Optimize data storage | Adoption of scalable cloud platforms Implement modular architectures Regularly upgrade system capacity | [62,148,149] |
Real-Time Data Processing | Managing high volumes of data from numerous devices in real-time to support instant decision-making | Use of edge computing Implementing high-speed data processing algorithms | Deploying edge devices for localized computing Optimize data pipelines for real-time analytics | [64,149,150] |
Cost of Implementation | High initial investment in IIoT systems, including hardware, software, and integration processes, can deter adoption | Leverage government subsidies Optimizing resource allocation Use of open-source solutions | Applying for grants and incentives Allocating resources strategically Use of cost-effective open-source tools | [33,150] |
User Adoption | Resistance from stakeholders due to lack of awareness or training regarding IIoT benefits and usage | Conducting training programs Highlighting cost and efficiency benefits Providing user-friendly interfaces | Organizing stakeholder workshops Develop easy-to-use interfaces for IIoT systems | [33,150] |
Regulatory Compliance | Adhering to national and international regulations governing data usage, privacy, and energy consumption | Staying updated with legal requirements Implement compliance monitoring tools | Regularly reviewing relevant regulations Use of monitoring tools for continuous compliance | [150,151] |
Energy Demand Forecasting | Predicting energy requirements accurately to avoid grid overloads and optimize charging schedules | Implementing AI-based forecasting models Using historical and real-time data | Developing predictive models with AI Integrating weather and usage patterns for forecasting | [64,152,153] |
Maintenance and Support | Ensuring the reliability and longevity of IIoT devices and infrastructure through proper maintenance | Setting up predictive maintenance tools Schedule regular inspections | Using IIoT sensors for condition monitoring Automate scheduling for preventive maintenance | [64,152,153] |
Cybersecurity Threats | The risk of cyberattacks on IIoT networks and infrastructure, disrupting services and compromising data | Employing intrusion detection systems (IDS) Regularly updating security protocols | Installing IDS tools Regularly updating firewalls and encryption techniques | [154,155] |
Environmental Impact | Managing the ecological footprint of IIoT systems, including energy consumption and e-waste generation | Use of energy-efficient devices Development of recycling programs for outdated hardware | Choosing low-energy IoT devices Partner with e-waste recycling organizations | [156] |
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AlHousrya, O.; Bennagi, A.; Cotfas, P.A.; Cotfas, D.T. The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review. Appl. Sci. 2025, 15, 9290. https://doi.org/10.3390/app15179290
AlHousrya O, Bennagi A, Cotfas PA, Cotfas DT. The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review. Applied Sciences. 2025; 15(17):9290. https://doi.org/10.3390/app15179290
Chicago/Turabian StyleAlHousrya, Obaida, Aseel Bennagi, Petru A. Cotfas, and Daniel T. Cotfas. 2025. "The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review" Applied Sciences 15, no. 17: 9290. https://doi.org/10.3390/app15179290
APA StyleAlHousrya, O., Bennagi, A., Cotfas, P. A., & Cotfas, D. T. (2025). The Role of the Industrial IoT in Advancing Electric Vehicle Technology: A Review. Applied Sciences, 15(17), 9290. https://doi.org/10.3390/app15179290