urban-mobility

July 4, 2025August 3, 2025 byMWB Team

A Strategic Outlook: EV Charging Ecosystem in Middle East

As the world races toward cleaner and smarter mobility, the Middle East is stepping on the accelerator. Electric vehicles (EVs) are no longer just a trend – they’re fast becoming a pillar of the region’s sustainable future.

Governments are acting with intent. The UAE has already converted nearly 20% of federal government vehicles to electric and aims for 30% of public sector fleets and 10% of all vehicles to be electric or hybrid by 2030. Saudi Arabia has set an ambitious target of 30% EV adoption in Riyadh by the same year. Similar ambitions are emerging across Oman, Bahrain, and Kuwait, supported by renewable energy targets and smart city plans.

But here’s the big question:
Is the EV charging ecosystem scaling fast enough to match this momentum?

Behind every successful EV rollout lies a strong location strategy, policy support, and usage trend analysis. For investors and stakeholders, these aren’t just operational details – they’re the foundation of a profitable and sustainable EV future in the region.

The GCC’s EV charging market is projected to grow from $2.04 billion in 2024 to $5.58 billion, reflecting a CAGR of 18.3% (Research and Markets). In the UAE alone, EVs could make up 25% of new vehicle sales by 2035 (PwC).

While consumer interest grows, the gap in high-speed, reliable, and accessible charging stations threatens to hold back adoption. This gap is where the most lucrative and strategic investment opportunities lie.

Planning Smart: The Importance of Location Strategy

To meet growing EV demand and support net-zero goals, location is everything. Strategic site selection doesn’t just boost convenience – it drives ROI by maximizing charger utilization and reducing infrastructure redundancy.

And while urban hubs often take center stage, rural and less-populated areas can’t be overlooked. Expanding access across all regions helps combat range anxiety and accelerates adoption. Smart location planning requires:

Maximize Utilization: Place chargers where demand is consistent to ensure high turnover and faster ROI.

Destination Charging: Install at high-dwell locations such as shopping malls, cafes, supermarkets, office, parking lots, parks, and highways where EV drivers naturally spend time

Think Beyond Cities: Rural and suburban coverage is key to building confidence among drivers.

Plan Around Constraints: In urban centres with limited space for grid upgrades, strategic deployment is even more critical.

Policy and Regulatory Drivers

Governments across the globe are stepping up to fast-track the electric vehicle revolution. From policy incentives to infrastructure mandates, public sector backing is stronger than ever.

Incentives & Subsidies: Grants, low-interest loans, and installation cost offsets to encourage adoption across municipalities and private developers.

Infrastructure Integration: Mandate EV charging inclusion in new construction, commercial buildings, smart parking facilities, and highway rest stops.

Banking Support: Financial institutions can step in with discounted interest rates, reduced processing fees, and flexible financing for chargers and accessories.

Public-Private Partnerships: Fast-track deployment through collaborative investments between governments, utilities, and private companies.

OEM Engagement: Attract automakers to the region to lower vehicle and charging costs, making EV ownership more accessible.

Public vs. Private Charging: Usage Patterns and Priorities

If mass EV adoption is the goal, public charging infrastructure is the gateway. It fills the gaps where private charging isn’t practical. It also plays a critical role in building trust and convenience for everyday drivers.

Here’s what’s driving public charging behaviour and where the returns lie:

Urban Demand is Surging: City centres see the highest need for public chargers due to limited private parking and home charging options. Stations in dense areas often achieve top-tier utilization, making them a smart investment with high ROI.

Highway Fast Charging: Chargers along intercity routes serve long-haul drivers and EV fleets gives users range confidence. Backed by government funding and paired with retail or rest stops, these hubs offer strong and predictable usage and make EVs viable for long-distance travel.

Home Charging is the Backbone: Most EV drivers charge overnight at home. It’s convenient, cost-effective, and ideal for daily top-ups. While direct profit is limited, bundling with smart grid or solar services creates added value.

Fleet Charging is a Commercial Power Play: Fleets such as delivery vans to taxis are shifting fast to EVs. Centralized depot charging with consistent, high-volume use delivers rapid ROI and makes fleet electrification a no-brainer for operators.

Infrastructure Challenges and Opportunities

While the EV revolution is racing ahead, the road isn’t without its bumps. From overloaded grids to inconsistent charging speeds, the technology powering EV infrastructure faces real-world limitations. But with the right solutions, these challenges become opportunities to lead.

Grid capacity and load management: Even the best EV chargers fail if the grid can’t handle the load. Many areas aren’t built for multiple fast chargers, causing delays, blackouts, and costly upgrades. At MWB, we conduct grid impact assessments and design smart charging frameworks to reduce strain, cut costs, and speed up deployment.

Limited Charging speeds: Fast chargers can fall short due to battery limits and weak power supply, causing slowdowns and reduced charger turnover. At MWB, we use GIS mapping and data modelling to pinpoint high-demand locations optimizing performance, user experience, and ROI.

Hardware reliability and maintenance: Frequent breakdowns from cheap hardware or poor maintenance don’t just frustrate drivers- they lead to underused assets and sunk costs. At MWB, we ensure reliability with tailored O&M plans, smart vendor selection, and remote diagnostics that keep chargers up and running.

MWB Powers the Shift

MWB provides comprehensive support to investors, developers, and utilities through a full-stack advisory and engineering offering. Their services encompass site selection and GIS-based planning, electrical infrastructure and load assessments, demand forecasting and financial modelling, as well as deployment planning and vendor advisory. MWB also guides clients through policy, permitting, and compliance processes, while offering strategic support for operations, maintenance, and uptime optimization.

Special Mention: This article has been co-authored by Prohit Keshavlal.

Meet The Author

MWB Team

The MWB Editorial Team is the collective voice behind all content, news, and updates published on the MWB website. We are a dedicated group of writers, editors, and researchers committed to providing accurate, timely, and relevant information.

For any corrections, feedback, or inquiries, feel free to reach out to us at info@mwb-me.com.

June 13, 2025June 22, 2025 bySandeep Singh

V2X – Shaping Smart Cities

V2X technology uses sensors, cameras and wireless connectivity- like Wi-Fi, radio frequencies and 5G cellular technology for cars to connect and communicate with their drivers and surroundings. Cars have always communicated with drivers in elementary ways. For example, interior lights stay on when you accidentally leave a door open OR seatbelt reminders when occupants aren’t buckled in, etc. V2X technology promises that cars will be able to talk to pedestrians and bicyclists, traffic signals and road signs too. It creates a connection between cars and their surroundings that makes roads easier and safer to travel.

Read more “V2X – Shaping Smart Cities”

Mobility AI

March 7, 2023January 13, 2025 byMWB Team

Predictive Maintenance with AI For Urban Mobility Systems

Urban mobility systems are the lifelines of modern cities, encompassing infrastructure such as roads, bridges, tunnels, and transit networks. Maintaining these systems efficiently is paramount to ensuring uninterrupted operations, safety, and cost-effectiveness. Predictive maintenance, powered by Artificial Intelligence (AI), has emerged as a game-changer, enabling infrastructure managers to anticipate failures and optimize maintenance schedules.

Predictive maintenance involves the use of advanced analytics, machine learning (ML), and IoT sensors to monitor the condition of assets in real-time. By analyzing historical and real-time data, predictive models identify patterns and anomalies that signal potential failures. This approach contrasts with traditional reactive maintenance, which addresses issues after they occur, and preventive maintenance, which relies on fixed schedules irrespective of actual asset conditions.

Core Components of AI-Driven Predictive Maintenance

IoT Sensors and Data Collection:
Sensors capture diverse data streams such as temperature, vibration, pressure, and structural stress in real-time. For example, accelerometers and strain gauges embedded in bridges detect vibrations that could indicate structural weaknesses.
Machine Learning Algorithms: ML models process and analyze data to identify degradation patterns and predict time-to-failure. For example, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used for time-series analysis in dynamic systems like escalators or traffic signals.
Digital Twins: Digital twins—virtual replicas of physical assets—simulate operational conditions to forecast failures and test maintenance strategies. For example: A digital twin of an urban rail system can model wear and tear based on real usage patterns.
Centralized Monitoring Platforms: SCADA and cloud-based platforms aggregate and visualize data, allowing operators to monitor infrastructure health remotely. Integration with AI enhances decision-making by providing actionable insights.

Applications in Urban Mobility Systems

AI-powered sensors monitor structural integrity, detecting cracks or corrosion before they escalate in the wide spectrum of urban infrastructure, including tunnel and bridges, traffic signal systems, public transit and roadway infrastructure. Predictive models optimize inspection schedules, reducing costly manual checks, downtime. Anomaly detection algorithms predict hardware failures, enabling timely replacements. Pavement sensors and ML algorithms can also predict pothole formation based on traffic load and environmental conditions for proactive repair schedules.

A Technical Glance at AI in Predictive Maintenance

The anomaly detection is usually carried out by identifying the outliers in sensor data using techniques like k-means clustering or autoencoders. For predictive modelling, failure probabilities can be forecasted through regression models, decision trees, and neural networks. The maintenance schedules can be further optimized by reinforcement learning from simulated environments.

However, the high frequency sensor data requires scalable storage solutions, which are often cloud based. The data quality remains as one of the biggest challenges which needs to be addressed by preprocessing techniques such as imputation and smoothing. The learning from the predictive models must integrate seamlessly with legacy systems and SCADA platforms, requiring robust APIs and middleware, which must be planned in advance.

“AI in predictive maintenance isn’t just about anticipating failures – it’s about redefining the way we maintain, operation, and extend the life of critical infrastructure.”
Head of ITS & TECH, MWB

What we can expect in future

Based on the findings from the research and prototype of predictive maintenance project through a strategic collaboration of MWB, we can expect significant accuracy (>80%) in predicting motor failures to reduce downtime (<20%) in addition to a estimated lower operational costs by 15% within the first year itself.

With the future technologies around Edge Computing (real-time analytics at the edge itself to reduce latency and improve responsiveness), Hybrid AI Models (combined physics-based models with ML for greater predictive accuracy) and Sustainability practices (reduced resource waste and energy consumption), we can only expect that AI driven predictive maintenance will only get better, more accurate and the DNA of urban infrastructure technology.