Accessing a Data-Driven Approach for the Transition to Zero Emission Vehicles

By Giorgio Sarno, Data Scientist at Stratio

Bus operators face a myriad of challenges when considering the transition to zero emission and electric vehicles. The new technology creates new breakdown patterns and failure modes and requires a new knowledge-set in relation to life cycle costs, battery maintenance, route management and more.  However, the added operational complexity is not yet supported by current industry expertise. Moreover, the greater up front, maintenance and infrastructure costs of the transition to zero-emission vehicles mean that operators must have a detailed strategy in place to minimise the impact of the shift on their bottom line.

Adopting a data-driven approach can help address these challenges and create the deeper strategic understanding of the transition requirements that is needed to be successful. The growing value of data has resulted in a race to monetise or extract value from it. For this reason, it’s important for transport operators to take ownership of their data now – both to gain a competitive advantage, but also to address the challenges of transitioning to electric vehicles.

Electric buses offer more data points compared to internal combustion engine (ICE) buses, as they are equipped with more sensors that make these vehicles easier to digitise. This provides greater insights into the health of the vehicle and allows for more accurate predictions. From managing battery range, state of charge and state of health to understanding the greater operational complexity, new breakdown patterns and failure models of electric buses; operators need to harness the full breadth of the data at their disposal to establish a successful EV fleet deployment strategy.

Harnessing data collection and processing and autonomous AI to create a continuous feedback loop.

By combining large-scale data processing with autonomous AI systems and the granular collection of vehicle information, public transport operators can gain valuable insights from the data they have access to. With the right solution in place, they can create a continuous feedback loop that constantly increases the ways in which data can be leveraged. This will go a long way towards helping them achieve the strategic understanding required to make sense of new electric fleets. The performance, faults, and range of EVs can be analysed and used to inform the planning of smooth, efficient, and profitable operations.

In fact, electric buses need to operate for longer periods and more intensively compared to traditional ICE fleets in order to take advantage of the cheaper running costs and recoup the higher upfront investment. This means keeping vehicles out of the workshop and on the road, as well as extending the life of the more expensive EV components to save on maintenance costs. Vehicle maintenance therefore has to be transitioned from an inefficient preventive approach, which involves the replacement of parts before their expected end-of-life to a data-led predictive method.

Adopting a new predictive maintenance approach to extend component lifespan.

By collecting vehicle data to predict the true remaining useful life (RUL) of components, fleet operators are able to safely extend the longevity of parts and systems, making vehicle servicing more predictable and less expensive. By using AI to provide real-time, actionable insight into the internal health of electric buses, maintenance managers can diagnose malfunctions remotely, without having to recall a vehicle off the road to physically look into it. This significantly reduces costs, as engineers that are dispatched on site can assess the faults in advance and determine the best course of action ahead of time. Vehicle servicing can be optimally scheduled, maximising time on the road, avoiding unexpected breakdowns and extending the operational life of parts. Electric buses stay on the road for longer, breakdowns happen less frequently, and servicing schedules are optimised to curb costly downtime and service disruption.  

EV batteries represent the clearest example of the benefits of increasing the lifespan of components. Accounting for approximately 40% of the total vehicle cost, public transport managers must extend the battery’s life cycle if they hope to achieve a profitable shift to electric fleets. They must account for the degradation of the battery pack over time and the impact this has on range, as well as a range of uncontrollable environmental factors such as weather or traffic which can alter the distance a bus can travel on one charge.

An electric bus that has a maximum range of 300 km on one charge when new, for example, may require an additional charging session to complete the same distance after a few years, as the battery degrades. This can create route planning complications. Moreover, the range of one charge may be impacted by variables such as weather and route. Using advanced data analytics and machine learning to combine battery data with other factors affecting range allows fleet managers to accurately predict a vehicle’s remaining range. Predictive battery analytics can also provide insights into the expected battery capacity loss for the next few years to come.

Without this understanding, it is impossible for operators to deliver a smooth, efficient and cost-effective service. 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 usage optimisation metrics to extend the lifespan of the vehicles. 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, reducing the total cost of ownership of electric buses. This type of analysis is fundamental for an operationally successful and profitable EV fleet deployment.

Creating clear visibility for efficient operations and ROI

Visibility over the state of health of EV batteries is critical for operators planning or managing the transition to electric buses. Predictive maintenance and predictive battery analytics represent a crucial means of keeping EVs on the road for more time and for longer distances. This accelerates the achievement of cost neutrality, while extending the life of battery packs further reduces the whole life cost of vehicles.

With deadlines for the elimination of ICE vehicles on European roads fast approaching, the transition to EV vehicles is more a question of when than if for public transport providers. In order to make the change successfully, they must combine sustainability with reliability, accessibility and profitability – achieving the lowest cost per mile both financially and environmentally. Only by taking a holistic, data-driven approach to maintenance, operations, and sustainability will bus operators be able to offer a greener, more cost efficient, and reliable service that passengers can trust.

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