A Case Study: How to Charge 100 Electric Buses
2021 is going to be a big year for the electrification of transportation.
Already, there has been plenty of positive news in the media.
For example, GTT, a transport operator in Turin, announced a deal with BYD, a Chinese bus manufacturer, to deliver 50 new electric buses to Italy.
This is just one small example of how cities and transport organizations are looking to electrify their fleet.
Another example is Amsterdam airport, where they have begun to operate 100 electric buses, offering clean rides for passengers from the terminal to their airplanes.
Of course, all this is great news for the EV industry (and the environment), but there are potential challenges that transport operators need to take into account.
The main issue is charging. How can the bus fleet operator run them efficiently and avoid delays due to the charging process?
This article will outline a best-practice approach for electric fleet charging for buses.
Top 3 Challenges for Electric Bus Fleets
1. Range — Ensuring enough charge for the planned trip
Vehicle breakdown has been identified as one of the top 10 risks for fleet operators. Traditionally, with combustion engine vehicles, bus fleet operators have mitigated this risk by scheduling regular maintenance (predictive maintenance), carrying out daily inspections, training drivers, and other methods.
With EV bus fleets, the risks are different. Mechanical failure is less likely, although inspections are still important. However, the biggest risk for fleet operators is that the bus runs out of energy before completing its journey. In this instance, the bus will need to make an unplanned stop at a fast charger, or they’ll be stranded and unable to finish the planned trip.
The fleet operator must avoid empty batteries during any trip. To achieve this, when a vehicle is plugged in at the end of the workday, the fleet operator needs to calculate how much energy this vehicle will need for the next day.
2. Leaving on-time
It is important that EV buses are fully charged in time to fit in with departure schedules. Unfortunately, fleet operators do not have the luxury of unlimited time to charge all the vehicles. They are on a tight schedule to get all the buses charged and ready for departure.
To do this, fleet operators often use workforce management systems, and can prioritize vehicle charging based on their departure time.
3. Keeping operation costs low
Electrification of vehicles has a high initial cost. Ordering new electric buses can cost companies several million dollars or more. Additionally, the depot needs to be equipped with sufficient charging stations.
Therefore, it’s crucial that fleet operators keep running costs low to make up for the initial outlay.
Charging stations are only one part of the cost to upgrade a depot for EVs. There are also costs associated with grid upgrades, project budgets, labor costs, etc.
Even after installing the charging infrastructure, the company has to factor in monthly energy bills to their utility company or energy supplier.
If not managed correctly, grid connection and energy costs can easily kill ambitious EV fleet projects. The operating costs can sometimes be too high for the company to survive. However, there are smart ways to avoid this (more in the next section).
The Case Study: 100 buses with overnight charging
Overview
This case study will examine the example of bus charging for a transit authority.
The company is operating 100 busses for medium to long distances. Consequently, they decided to use large batteries with 400 kWh. This gives them a range of around 300 miles (480 km).
The buses usually operate only during the day. The vehicles return to the depot between 9 and 11pm, where they are plugged in to their charge points.
Each charge point provides a maximum power output of 100 kW, but can be controlled and adjusted. The vehicles then remain at the depot for around 10 to 12 hours.
To summarize:
- 100 busses
- 100 x 100 kW charge points
- Arrival of busses: 9–11 PM
- Stay-time: 10–12 hours
- Required energy: 400 kWh for 300 miles (480 km)
Objectives
The transit authority recently transitioned from internal combustion engines (ICE) to electric vehicles (EV) and they are unsure how this will affect the efficient operation of their vehicles.
The fleet manager has 3 KPIs to monitor and manage:
- On-time departures from depots
- Total cost of ownership
- Number of vehicle breakdowns (out-of-service)
To achieve good KPI results in all three areas, the fleet manager must ensure that all vehicles are charged on-time with the required energy (kWh) for the planned trip while avoiding high energy costs.
The energy costs are mostly influenced by their peak demand (demand charges).
Applying AI-powered Software to Automate the Operation
In this case, the operator has equipped their vehicles with data loggers and telematics, connected to a fleet software system. Thus, all the latest information and updates on the vehicles’ battery system are monitored, sent and stored in the cloud.
To improve efficiency, schedules are calculated or defined on a daily or weekly basis. Each vehicle has an expected departure time, expected arrival time, and the planned distance of the next trip (in km).
When purchasing the 100 buses, the company decided to apply optimization software to take advantage of these data points and use them to automate decisions in their depot.
To demonstrate the positive effect of that optimization; we’ve prepared results for non-optimized charging and optimized charging.
Non-optimized charging of 100 electric buses:
This graphic shows 100 buses charging simultaneously. The vehicles arrive between 9 and 11 pm, and start charging immediately. As the charge points have a maximum power capacity of 100 kW, most vehicles are fully charged after 4 hours. Consequently, no vehicle is left charging after 3 am the next day.
This situation guarantees the on-time departure of the buses and that they have sufficient energy (kWh) for their next trips.
However, it leads to a total power demand of nearly 10 MW. 10 MW is a power demand that most depots don’t receive from their utility provider as standard, and would lead to very high monthly energy bills.
Based on the typical demand charges in the US, this can easily lead to astronomical monthly energy bills of USD 112,000 to 515,000.
In this case, the fleet operator would achieve only 2 out of 3 KPIs. It would result in the total cost of ownership exceeding their limit.
Optimized charging of 100 electric buses:
In the graph above, you can see how the fleet operator charges their fleet of 100 electric buses.
The vehicles become fully charged by 6 to 9 AM the next morning. Although they used the same amount of energy to charge the vehicles as they did in the example without optimization, the fleet operator only needs around 4 MW of total power.
Reducing the power demand saves 60% of their monthly budget for energy and operation and makes the electrification of the bus fleet possible and achievable.
The reason for their success is that instead of burning through 10 MW of power, the depot only requires 4 MW. This places much less demand on their energy provider and infrastructure investments will be much lower as a result.
Therefore, by using intelligent optimization software for electric fleets, the fleet operator achieves all three KPIs:
- On-time departure
- Sufficient energy per vehicle
- Lower total costs of ownership (TCO)
Conclusion
By comparing the power demands of non-optimized charging with optimized charging, it becomes clear that applying a smart charging solution for fleet depot charging can make the difference between successfully operating a viable electric fleet and failure.
To find out more about Ampcontrol’s AI-driven smart charging solution, get in touch today.
