Siemens improves the charging of electric buses with Depotfinity

The objective of any bus operator of an electric bus fleet is simple: ensure vehicles are sufficiently charged for their next planned route while minimising the electricity bill. The objective is simple, but the challenge is not.

Siemens improves the charging of electric buses with Depotfinity
The Siemens DepotFinity will allow bus operators to maximise charging and save on electricity bills

As the number of electric buses increases, overnight charging infrastructure for hundreds of vehicles will become a common feature of our cities.

Simply putting charging points in place, however, will not suffice. The situation is vastly more complex: many vehicles enter and leave a typical bus depot at different times and have varying energy requirements.

In addition, the electricity rates usually change according to the time of day, there might be power constraints due to an insufficient grid connection, and different types of chargers might have to be considered.

To master this level of complexity, depots need to harness the power of Artificial Intelligence (AI) and the Internet of Things (IoT).


Siemens is a technology company that has been delivering charging and grid infrastructure hardware and software solutions for electric bus depots for over 10 years.

One of the world’s largest industrial software companies, Siemens has extended its vast experience in developing AI and IoT platforms to the DepotFinity charging management software. DepotFinity provides insight and sophisticated control of charging management hardware, enabling real time monitoring, historical analysis, load management and integration with other IT platforms and energy markets. 

Grant Mascord, national contracts manager at Custom Bus Group, connecting an Element bus to a SICHARGE UC charger.


The first step is to connect the chargers to the DepotFinity cloud-based IoT system, which collects data – typically more than 20 parameters such as energy used, voltage or temperature – and enables the chargers to be monitored and controlled. Connectivity is ensured through the Open Charge Point Protocol (OCPP) – to operate different chargers within the depot if needed.

This first step is the basis for optimising the power and energy usage within the depot. For this purpose, all aspects relevant to efficient charging need to be considered, e.g., vehicle parameters (battery type, maximum charging rate), charging station details (the charger’s maximum power, number of connectors, etc.), the specifics of the electricity bill (demand tariffs, time-of-use rates) and, lastly, the vehicle schedule (entry and exit times of each vehicle as well as the energy required for the planned route).

Once this information originating from different sources – e.g., the depot management system, the depot’s electricity provider, etc. – has been gathered and prepared, optimisation
can begin.

The aim of optimisation is to manage the combined charging schedules for each vehicle in order to deliver the objective – i.e., the lowest possible electricity bill for the depot. With one vehicle this is simple: electricity is typically cheaper at night during off-peak hours, so the solution is to charge the vehicle during this time. With five vehicles it may still be possible to find a feasible combination of charging times, but with several hundred vehicles optimisation will quickly spiral out of (manual) control.

And this is where some clever mathematics and computing power come into play. Called AI-driven smart charging, it searches the multitude of possible charging schedules for each charger and vehicle in order to find the solutions that deliver the lowest cost charging schedule while ensuring vehicles are sufficiently charged for their next planned route.

This is achieved by reducing the overall energy or power usage in the depot during expensive time periods. The optimal individual charging schedules for each charging station are then downloaded from DepotFinity using the charging stations’ inbuilt IoT capabilities.

Olivia Laskowski, promoter of electric vehicle charging infrastructure at Siemens.


Here’s an example that demonstrates the power of AI and shows how it can help to reduce the operating costs for a large depot of electric vehicles.

Imagine a depot that hosts 150 buses, each requiring around 160kWh of electricity every day. The depot is modelled with three waves of buses returning to the depot at the end of the day. With uncontrolled charging, each charger starts as soon as it is plugged in and this produces a power profile with two main peaks of 4.6MW and 3.9MW at 9pm and 2am, respectively, and a further smaller peak at around 9am.

To see what this means for the operational costs of the depot, consider that most commercial customers have two charges on their bill:

1. Usage tariffs: This is the energy charge for the electricity used (kWh) multiplied by the electricity price varied during the day ($/kWh).

2. Network charges or demand tariffs: This is the energy charge for the amount of electrical capacity needed. It’s the maximum amount of power (kW) used during the highest usage 30-minute interval, multiplied by the local retailer’s network charge ($/kW).

Both the usage and the demand tariffs are cheaper at certain times in the day – peak and off-peak. Evidently, in order to reduce operating costs, the depot should use most of the energy when it is cheapest.

Figure 1. Exemplary energy cost calculation for a large depot with 150 e-buses with uncontrolled charging, based on commercial and industrial default electricity rate from Origin Energy.1



To illustrate this, let’s apply the commercial and industrial default electricity rate from Australian energy provider Origin Energy, which is publicly available (see Figure 1).

In this example, approximately 13,000kWh of electricity are consumed at the peak rate and 12,000kWh at the off-peak rate. The daily electricity costs would be A$4,420. To slash costs, the electricity usage needs to be moved from peak to off-peak times.

To minimise power costs, the overall power level for the depot is also controlled. The AI algorithms are going to search for an optimum solution that both shifts the energy usage (load shifting) and also reduces the maximum power (load shaving) for the depot – effectively a multi-dimensional problem.

Figure 2. Exemplary electricity cost calculation for a large depot with 150 e-buses with controlled charging using Siemens’ DepotFinity digital solution, based on commercial and industrial default electricity rate from Origin Energy.1

The optimal power profile for the same depot, as determined by the AI algorithms, can be seen in Figure 2. It features 150 individual charging schedules (one for each of the 150 vehicles) which, when combined, shift the energy usage out of the expensive time period while also reducing the overall power level.

The result is a new electricity bill of $3,850 per day. This corresponds to savings of around 13 per cent and is achieved without any operative restrictions – the schedule is maintained, vehicles still enter and leave the depot at the required times and receive the required energy to fulfil their mission.


Demand tariffs are based on the maximum power the customer uses and usually also depend upon the time of day. To reduce these charges there are two levers.

Firstly, power peaks need to be reduced and, secondly, power needs to be shifted away (load shifting) from the primary demand region, or after 8pm in this example using the Ausgrid EA305 (low voltage) summer peak period of 2pm to 8pm.

Figure 3. Exemplary demand tariffs calculation for a large depot with 150 e-buses with uncontrolled charging, based on EA305 (low voltage) summer peak period demand tariffs of AusGrid.2

The peaks produced by uncontrolled charging, shown in Figure 3, have a strong financial impact, with a particularly decisive peak at around 7:30pm to 8pm. It triggers high charges for the peak demand tariff period. The monthly demand tariffs total almost A$49,800.

Figure 4. Exemplary demand tariffs calculation for a large depot with 150 e-buses with controlled charging through Siemens’ DepotFinity digital solution, based on demand tariff EA305 of AusGrid.2

The AI-optimised power profile for the depot (Figure 4) shows a significant reduction of the overall peaks from 4.6MW to around 2.5MW. Furthermore, the power usage has shifted away from the primary demand tariff region. Together, these two improvements can reduce the monthly demand tariffs entirely compared to uncontrolled charging processes. As in the first case this can be achieved without impacting the operations – the vehicles are charged and ready to be deployed when needed.


Managing depot charging is key for the economic and efficient operation of an electrified fleet and can significantly boost decarbonised mobility.

With smaller fleets, load management or smart charging is a key tool to lower both capital and operating expenditure. As fleets grow, cutting-edge technologies such as AI need to be leveraged to manage complexity.

It should be noted Origin and other energy retailers may negotiate more attractive rates for large scale energy consumers or fixed term contracts than outlined here. Similarly demand charges could also be reduced by moving a site from a low to high voltage customer.

Although exact figures vary from location to location, retailer and contract structure, charging management software like DepotFinity nevertheless is critical for dramatically reducing energy costs of electric bus depot. This scale of savings bolsters the business case and is essential for the long-term success of eMobility.


1 Origin Energy commercial and industrial energy pricing:

2 Ausgrid Network Price List 2021–22:

For further information on Siemens DepotFinity – digital solution for EV depots, please see

For further information on Siemens Smart Infrastructure, please see

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