The global transition to electric vehicles (EVs) has seen remarkable growth in recent years, with governments around the world encouraging the purchase of electric vehicles and charging infrastructure. However, this rapid expansion has been marred by a lack of charging infrastructure and unreliable charging networks. Ford Motor Company CEO Jim Farley captured this sentiment well when he noted, “We’re looking at mass consumers who have a lot of charging anxiety. They don’t have range anxiety; they have load anxiety.”
Cost of unreliability
The frustration of encountering faulty or out-of-service chargers is a common experience for many electric vehicle owners. Surprisingly, more than one in five charging attempts fails, with 72% of these failures attributed to charger issues. This not only causes inconvenience for users, but also represents a significant financial burden. It is estimated that 20 billion dollars of A global investment of 100 billion dollars Electric vehicle charging infrastructure is currently wasted due to non-working chargers.
To solve this problem, significant investments are being made to repair or replace existing electric vehicle charging ports. Early January, U.S. Departments of Transportation and Energy receive nearly $150 million for projects to repair or replace nearly 4,500 existing electric vehicle charging ports, highlighting the urgency of the issue. With each repair/replacement costing an average of $33,000 per EV charger, the financial impact extends beyond repair costs alone, encompassing lost revenue from unused charging stations and dissatisfied customers.
Navigating the complexities
The reliability of electric vehicle chargers is influenced by a myriad of factors, from faulty installations to substandard maintenance practices. A major challenge is ensuring seamless communication between vehicles, smartphones, chargers and cloud-based management platforms. Any disruption to this complex network can lead to charging outages, frustrate users and undermine confidence in electric vehicle technology.
For example, connectivity issues, often attributed to multiple stakeholders including charging network operators, equipment vendors, cellular network providers and utilities, pose a significant challenge. Addressing these challenges requires a multi-faceted approach, involving rapid diagnosis, proactive problem prevention and resilient solutions.
Solutions through data analytics and AI
Data analytics and artificial intelligence (AI) have become essential tools to ensure the reliability of electric vehicle chargers. Proactive maintenance strategies, powered by data analytics and AI, enable operators to predict and prevent charger failures before they occur. By analyzing historical data and real-time sensor readings, machine learning algorithms can identify patterns and anomalies, facilitating targeted maintenance interventions.
Some of the applications of data analytics and AI in charger reliability are:
Establish causality: Unlike correlation-based techniques, causal AI techniques help identify system-level failures based on component-level issues. By modeling complex relationships between various factors, neural networks offer a pivotal approach that can elude human observers, facilitating automatic anomaly detection and root cause analysis of the underlying cause of an event and its relationship precise with the result.
Tailor-made predictive maintenance: Not all chargers are the same, and neither are their maintenance needs. As more drivers use a set of stations and their frequency of use increases, so does the risk of potential damage. AI algorithms analyze usage patterns, environmental conditions and historical performance data to create personalized maintenance programs for each charger. This approach minimizes unnecessary expenses by scheduling maintenance only when needed and ensures that shippers receive timely attention.
Detection of an anomaly: The condition of chargers is significantly affected by their location and prevailing weather conditions. Humidity, rain, intense cold and high temperatures can be particularly damaging. Inaccuracies in the data set or unforeseen issues can compromise predictive maintenance approaches, leading to incorrect recommendations and predictions. By leveraging historical data, AI models can automatically identify abnormal meter readings or records and alert technicians in real-time for verification and corrective action.
Inventory management: Effective inventory management is one of the most crucial elements of any maintenance strategy. AI-powered inventory management optimizes the supply chain, predicting when replacement parts will be needed and managing inventory accordingly. This minimizes transportation costs while ensuring that the necessary parts are always available.
Conclusion
As transportation electrification gains momentum, addressing the challenge of unreliable EV chargers is imperative to drive widespread EV adoption. By harnessing the power of data analytics and AI, stakeholders can proactively address charger reliability issues, improve user experience, and accelerate the transition to a sustainable mobility ecosystem. Through investments in predictive maintenance and infrastructure optimization, we can ensure the long-term viability and success of electric mobility.
(Mohanakrishnan P is Chief Growth Officer and Akshay Sasikumar is CEO of 82Volt Technologies.)
Disclaimer: The opinions expressed above are those of the author. They do not necessarily reflect the views of DH.
