How AI & Data Technologies Are Helping Fleets & EV Drivers Navigate Around Higher Charge Costs During Winter

Sumit Chauhan

By Sumit Chauhan

As more drivers, fleet operators and businesses continue their inexorable shift toward sustainable transportation, the electric vehicle (EV) market has continued to experience unprecedented growth ever since the pandemic. However, as the adoption of EVs accelerates, a new set of challenges emerges, particularly when it comes to charging in colder weather. Today’s drivers and fleet operators must navigate new operational strategies that not only include the complexities surrounding EV charging in colder temperatures, but they must also identify how to navigate around the potential for variable charge rates in these temperatures as well.

The Cold Conundrum

Cold weather presents a unique set of challenges for electric vehicles, impacting both their performance and the efficiency of charging infrastructure. One of the primary obstacles is the effect on battery performance. Batteries are less efficient in colder temperatures, leading to reduced driving range and slower charging times. This phenomenon, known as “cold soak,” hampers the overall effectiveness of EVs in regions where winter temperatures are more severe.

Charging Infrastructure Struggles

Beyond the impact on the vehicle itself, charging infrastructure faces its own set of challenges in cold weather. Charging stations, particularly those with exposed components, may experience decreased efficiency and, in extreme cases, operational issues. The need for temperature-controlled charging stations becomes crucial, adding an additional layer of complexity and cost to the establishment and maintenance of charging networks.

The Price of Cold Convenience

As the challenges of cold weather EV charging increase, the economics of charging come into sharper focus. The need for specialized infrastructure, increased energy consumption due to lower efficiency, and the potential for reduced charging speeds all contribute to higher operational costs. These costs, inevitably, are transferred to the fleet operators, businesses users and end consumers, impacting the final price of charging services.

Moreover, the demand for fast-charging options becomes more pronounced in colder climates as users seek to minimize the time spent in the cold while replenishing their vehicle’s battery. The race to provide faster and more efficient charging solutions in cold weather conditions adds another layer of competition among charging network providers, potentially driving up prices to recover investments in advanced technologies and infrastructure.

Understanding how AI and data can help control costs

Despite all these new challenges, leading AI and data technology are offering intelligent solutions that can reduce the headaches and costs associated with driving and charging an EV, or a fleet of EVs for a business – especially in cold weather climates and temperatures.

Today’s leading EV charge data solutions for fleets and vehicles benefit from an Augmented Deep Learning Platform (ADLP) that leverages machine learning and data science with unique indicators that allow predictive real-time data insights to OEMs that enhance their vehicle’s performance and quality as well as the customer experience related to vehicle usage.

This data connects and analyzes everything in real time from charging stations to optimized energy outputs at locations and time of day, cost savings, congestion reduction rates, and it can even predict failure cycles that holistically feeds data into smart city data infrastructure platforms. Based on advanced software, this data ensures EV users are charging their vehicles only during the most optimum time for charge rates. As an example, even if a typical EV driver returns their fleet vehicle (or at home) at 6 p.m. in the evening and immediately plugs in, the AI data and software will only allow for the commencement of charging when the electricity rates are at their lowest during the course of the evening or early the next morning.

This AI-driven connected vehicle data helps fleet customers and EV users make a more seamless, successful transition to a greener, cleaner, and sustainable future, while keeping the costs low Users can charge EVs with accurate energy cost and rate plan selections. Intelligent energy consumption means they will lower the impact on their energy bill and get the most out of their solar panels by charging EVs at the most optimal time. Lastly, they can leverage the power of smart cities by receiving in-car notifications for the nearest charging station and reserving charging slots in the near future.

With these AI and data strategies available, fleets and drivers of EVs will have a better experience in adopting a greener solution for transportation while better controlling the cost of charging – especially in colder weather climates.

About The Author: Sumit Chauhan is co-founder and chief operating officer of Cerebrum X, with more than 24 years of experience in automotive, IoT, telecoms and healthcare. Sumit has always played the leadership role that allowed him to manage a P&L of close to US $0.5B across various organizations, such as Aricent, Nokia and Harman, enriching their domestic as well as international business verticals. As co-founder of CerebrumX, he has applied his experience in the connected vehicle data domain to deliver the automotive industry with an AI-powered augmented deep learning platform (ADLP). Sumit is also passionate about mentoring and guiding the next generation of entrepreneurs.