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Therefore , incorporating artificial intelligence ( AI ) into the public transport industry is paramount in addressing these concerns . AI can facilitate obtaining realtime , actionable data insights on electric vehicle fleets . This allows operators to plan for a seamless , efficient and costeffective service that achieves the lowest possible cost-per-mile and accelerates the achievement of cost neutrality for EV buses .
A new approach to vehicle maintenance
By utilizing vehicle data to accurately predict the remaining useful life ( RUL ) of components , fleet operators can safely extend the longevity of parts and systems , making vehicle servicing more predictable and less costly . Employing AI to offer realtime , actionable insights into an electric bus ’ internal health enables maintenance managers to remotely diagnose malfunctions without taking the vehicle off the road for a physical examination .
Many of the largest transport operators are already equipping their vehicles with this technology . The vehicle sensor data is collected and sent back to the servers , where advanced AI and machine learning algorithms analyze it and transform it into actionable insights . This significantly reduces expenses , as dispatched engineers can assess faults beforehand and determine the most appropriate course of action . Vehicle servicing can be optimally scheduled , maximizing road time , preventing unforeseen breakdowns , and extending the operational life of components . As a result , electric buses remain on the road longer , breakdowns occur less frequently , and servicing schedules are optimized to minimize costly downtime and service disruption .
Fleets that are running fully electric operations are using a pattern-recognition and modelling analysis of the main breakdowns such as HV battery cell imbalance or diesel heater faults , leading to averted breakdowns , minimized unplanned downtime , and a better allocation of resources .
When it comes to EV batteries , public transport managers must consider battery pack degradation over time and its impact on range . Environmental factors like weather , and unpredictable variables like traffic that influence the distance a bus can travel on a single charge , are making it difficult for transport providers to efficiently plan their EV operations . For instance , a new electric bus that can run 300 km on a single charge , due to progressive battery degradation , may in a few years need an extra charging session to cover the same distance . Using advanced data analytics and machine learning to combine battery data with other range-affecting factors allows fleet managers to predict a vehicle ’ s remaining range accurately . Predictive battery analytics can also offer insights into the expected battery capacity loss for the coming years .
Operators cannot deliver a seamless , efficient , and cost-effective service without this understanding . Predictive battery analytics can provide a precise , comprehensive view of an EV bus ’ battery health evolution , allowing for effective route planning , charging requirements , and usage optimization metrics to extend the vehicle ’ s lifespan . By leveraging State of Charge ( SoC ) and Depth of Discharge ( DoD ) data , fleet managers can determine if the operation profile can be adjusted to maximize battery life , thus reducing electric buses ’ total cost of ownership . This analysis is crucial for a successful and profitable EV fleet deployment .
Looking into the future
The substantial initial investment required for bus operators to implement a new EV
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