EV Fleet Management : Optimise Efficiencies with Data Analytics
WORDS BY JOHAN FORSEKE , GREATER THAN
The transition to electric vehicles ( EV ) is well underway for many organisations , resulting in companies looking ahead to a more modern fleet , reduced emissions , and lower operating costs .
But what many companies are failing to recognise is that the adoption of an EV fleet is not where efforts should end . In fact , there are many factors that influence EV efficiencies and that ’ s where data-driven insights can make a real difference in EV fleet management .
Battery range anxiety
One of the biggest worries for organisations is EV battery range . Many people driving for work purposes travel long distances and need to achieve as many miles as possible from each charge .
Technological developments mean that battery range is improving all the time . But the impact of driver behaviour on range should not be underestimated . In fact , driving in an eco-friendly manner can make a significant difference .
Utilising AI data analytics to measure a driver ’ s climate impact is an effective way to pinpoint the driver influence on EV energy consumption and identify any behaviours that are resulting in a higher climate impact . These insights can be used to target eco-training to the drivers who need it most .
EV charging infrastructure
The public EV charging network continues to improve . But concerns remain about charging infrastructure and how organisations can plan driver schedules around charging needs .
With the use of AI data analytics to measure their fleet ’ s climate impact , organisations have greater opportunities to optimise energy consumption and therefore reduce charging frequency . By minimising the frequency of charging stops , organisations reduce the amount of electricity being used to charge their vehicles , helping to further lower their impact on the environment . And , in cutting charging frequency they also help to maximise productivity .
Specialist EV maintenance
EVs are heavily computerised and require different skills to those of regular mechanics . That ’ s why , generally , there ’ s a skills gap in EV maintenance and why EV maintenance and repairs can be more costly . As the EV transition progresses further , it ’ s likely that this will be less of a problem . But , with AI data analytics , organisations can take steps to minimise EV maintenance requirements now .
By identifying the drivers with highest climate impact as a result of their attitude , organisations can act to improve eco-friendly driving and reduce vehicle wear and tear . Driving in a safe , eco-friendly manner also helps to reduce incidents and therefore damage and associated repairs . Not only that , but eco-friendly driving helps to reduce tyre wear , leading to fewer replacements and lower costs .
Putting the EV driver in control
Evaluating some of the biggest concerns related to EV adoption , it ’ s clear that the control is in the hands of the driver . That ’ s why , for companies looking to transition to EVs , AI data analytics is invaluable . AI can convert existing driving data into climate impact insights that measure the driver ’ s influence on EV battery consumption , regardless of vehicle type or location .
Even better , with these insights , organisations can identify the reasons behind a driver having a high climate impact , such as lack of focus , anticipation , or vehicle control . Just making drivers aware of this can result in positive change . And , where needed , training can be prioritised to optimise efficiencies .
How to access AI insights
Any telematics company or fleet management provider can adopt AI to provide an additional layer of insight to their existing solutions . Ask your solutions provider how they can help you to optimise efficiencies across your EV fleet .
32 ISSUE 46 APRIL 2024 / WWW . AFMA . ORG . AU