With ambitious net-zero emission targets and the race towards cleaner mobility such as EVs, analyzing troves of data using AI, ML, etc is essential to balance profitability with larger sustainability goals.
The world is swiftly shifting from combustion engines to alternative means of transportation, fueled by environmental concerns and unstable oil prices. Greenhouse gas emissions from transportation account for about 29% of total U.S. greenhouse gas emissions, making it the largest contributor to GHG emissions according to the Environmental Protection Agency.
Meanwhile, the European Commission’s stated ambition to make all new city buses 100% zero emission from 2030, and to reduce bus and truck CO2 emissions by 90% by 2040, is moving sustainability rapidly up the priority agenda for manufacturers and operators alike.
Public transport companies are undoubtedly among the most responsible in helping to limit global climate change. However, public transport providers must still turn a profit to remain economically viable whilst pivoting their vehicle strategy to reach these goals. The upfront investment required to operate a lower-emission vehicle fleet can be very high.
With bus companies already facing financial challenges due to falling passenger numbers, skyrocketing costs, and the end of emergency government funding following Covid, this is a hard step to take.
Though multiple bus companies such as First Bus and National Express West Midlands, are already making considerable investments in electric buses, the changeover to electric vehicles will be costly and inefficient without a proven strategy for bus operators to follow.
The European Commission outlined that it will be up to manufacturers to decide which technologies they use to achieve future CO2 standards. However, choosing between battery and hydrogen fuel cell vehicles is only part of the challenge.
The operational landscape for lower emission vehicles such as EVs represent new territory for traditional fleet managers, requiring a totally different skill set relating to life cycle costs, battery maintenance, route management, and more.
Furthermore, with high upfront and repair costs for EVs, fleet reliability and vehicle availability will be critical to the successful rollout of electric buses. They need to be operating at all times by ensuring operations are planned efficiently and that there are no faults that require the vehicle to be taken off the road.
The life cycle of battery packs and other components, such as motors, inverters and chargers, must be extended as much as possible.
To effectively utilize the investment and successfully adjust their operations in a way that does not negatively impact the bottom line, public transport companies will need a deeper strategic understanding of the EV transition.
Thankfully, the availability of predictive maintenance technologies that leverage data, AI, and Machine Learning capabilities can help transportation companies bring about a smoother and more profitable EV transition.
Leveraging Data
EVs produce far more data than internal combustion engine ( ICE ) vehicles. Nearly all of the components have electronic control, which means there are a lot of sensors by default, making it easier to digitize the entire vehicle with more granularity. This offers an opportunity for fleet managers to put the right tools in place to leverage vehicle data to increase the efficiency and reliability of operations.
The best way to leverage the data collected from EVs is to turn it into intelligence that will maximize uptime and dependability of operations.
Fleet management is important, but without an advanced maintenance strategy in place, and without visibility over the state-of-health of key components, managers won’t be able to reap the opportunities for efficiency and reliability that a fully electric fleet represents.
By integrating predictive solutions that collect and analyze vehicle data during regular operation fleet managers are able to establish the true remaining useful life (RUL) of components and use the information to intelligently schedule vehicle servicing in order to extend operational life.
Moreover, by gaining real-time, actionable insight into the internal faults of electric buses, maintenance managers can diagnose malfunctions remotely.
Vehicles stay on the road for longer, maintenance becomes more predictable and less expensive and breakdowns happen less frequently, preventing costly downtime and service disruption.
EV batteries give us the best example of the benefits of increasing the lifespan of components. Accounting for around 40% of the total vehicle cost, it’s imperative to extend the battery’s life cycle in order to ensure a profitable electrification.
Calculations must be made to account for the degradation of the battery over time and the impact this has on range, as well as a range of out of control factors such as weather, traffic, route and load which can alter the distance a bus can travel on one charge.
Without this understanding and visibility, it will be impossible to plan for a smooth, efficient and cost-effective service. But predictive battery analytics can provide an accurate, comprehensive view of the battery health evolution of an EV bus, allowing for effective route planning and charging requirements, as well as operation optimization metrics to extend the lifespan of the batteries.
By leveraging State of Charge (SoC) and Depth of Discharge (DoD) data, fleet managers can understand if the operation profile can be changed to maximise battery life.
This means that the initial investment is spread over a longer period of time, enabling a more efficient planning of maintenance operations and charging sessions so as to minimize downtime and increase service reliability – as well as reducing the total cost of ownership of electric buses.
This type of analysis is fundamental for an operationally successful and profitable EV fleet deployment.
Efficiency Gains
There are significant efficiency gains and cost-saving opportunities for operators looking to transition to EVs which will help to bridge the crucial gap between sustainability and profitability.
However, a successful and cost-efficient electrification requires a plan to leverage the opportunity that comes with additional data and transform it into extended vehicle uptime, utilization, and useful life for EVs.
Expertise and visibility are also a factor, as EVs are still new tech and unfamiliar to workshops worldwide. Partnering with a predictive maintenance solution accelerates capability building, as platforms embed industry knowledge and best-practices that may take years to acquire successfully.
With the EU’s climate goals setting the bar high for the speed at which operators are expected to switch to zero-emission vehicles, it is essential for all operators to look into solutions that can make this transition successful by turning EV data into intelligence and accelerating the achievement of cost neutrality.
Any organization considering transitioning to EVs should prioritize implementing a suitable maintenance strategy to ensure a successful and cost-efficient electrification.
