Going Beyond Average Range with Electric Fleets

Taking the guess work out of electric vehicle range with route energy modeling

At MGL we’ve recognized over the last few years that when it comes to fleet electrification there is a tendency to jump right into trial and error, which exacerbates the biggest challenges in planning, funding, and operating electric fleets. In this article we address an underpinning approach to take the guess work out of electric bus range with route energy modeling.

The Challenge

One of the main challenges with electrifying fleets is that operators are unsure if electric vehicles (EVs) will work effectively in their operational environment. Fleet vehicles travel in all directions, various distances and speeds, and across changing terrain. For vehicle operators the question is threefold – Will the vehicle operate for an entire driver shift without recharge? If so, which routes and schedule can be served by today’s battery electric vehicles? Will that plan standup to the test of very cold or very hot days?

It would seem the simple answer to this critical question is to use the low end of the range or distance as stated by the manufacturer of the specific vehicle. It turns out even that low end number is not reliable enough to operate service by. As result we see fleet operators contemplating whether a certain size battery and its estimated milage will work for their operations, or if they should consider bigger batteries and the resulting price premium which could add several hundred thousand dollars in the case of electric transit buses.

Suffice it to say, planning fleet operations for electric vehicles is bit of take-on-faith and lot of reliance on anecdotal experience. Charging must also be addressed, not just compatibility but power rating or speed of charge, which also brings with it the price premium consideration. Low power chargers are low cost and reliable, but may not deliver enough energy to meet the operating schedule. High power chargers are expensive and often cause delays in utility grid assessment and transformer upgrades. This conundrum puts fleet operators in the position to pilot electric vehicles (a three-year cycle) before they can confidently plan for facilities upgrades, or hire an engineering firm to figure this all out, which inevitably results in over design / over build to land outside the margin of design error.

The bottom-line problem with planning for fleet electrification is that average range as tested by OEMs, which are best case scenarios, simply do not bear out in more difficult environments. We’re left with the option to over design, significantly running up capital costs, or delay purchase decisions to replace old vehicles with new zero emission vehicles until the pilot testing phase is complete.

There is a better way than overdesigning for fleet electrification. Right-sizing vehicle batteries and charging system design is possible with accurate energy stacks.

Average vs Accurate

The error in using average efficiency to determine electric vehicle range to plan vehicle operations is that what matters is minimum range, not average. Minimum range is an important data point when reliable operations and delivering people on time to their destination is the mission. Planning service around minimum range and considering worst-case scenarios generates an actionable operational plan for fleet electrification. The question is: What is the worst case? And how to ensure it is properly accounted for?

Deriving worst case range starts with the duty cycle itself, how the vehicle is typically operated and the local driving environment. Add to that cabin heating and cooling loads, a critical consideration given its substantial impact in extreme temps when the drive battery is not in its optimal thermal range and the HVAC load is pulling considerable amounts of power. Add on battery degradation and state of charge limitations over the life of the vehicle (both reduce the usable portion of the battery) to clarify available capacity at year 10 or 15, which can be 25% less than original purchase. There are also auxiliary loads for systems and battery thermal management, not to mention driver behavior that of course affects power utilization (milage efficiency).

Worst case range in severe conditions can be as little as 50% of expected range, but that’s not a sufficient method for planning system operations and capital budgets.

Duty cycles are what’s needed to calculate range in the local operating environment across all seasons; energy stacks of each vehicle type that account for drive energy, speed/stop, load, terrain, auxiliary energy, and heating and cooling loads related to the ambient temperature. All routes are not the same of course and the variation in efficiency can be incredibly high.

Local operating environment matters, a lot, and route energy calculations that incorporate all the variances in energy consumption provides accuracy for system design.

Fleet Planning

Duty cycle modeling across all routes and vehicle types determines route energy consumed for the worst operational days and clarifies the battery size needed for each portion of the fleet (short runs, medium runs, impossible to electrify with today’s technology runs). It’s a common question when transitioning to electric vehicles is to ask whether, for example, a 440kWh battery will serve the shift or if stepping up to 550kWh is the only reliable answer. Duty cycle modeling removes the guess work, so a common battery size for all vehicles can be determined allowing for a standard vehicle configuration if so desired.

These energy consumption calculations are invaluable for sizing the charging system. In knowing the energy needed to be delivered under the worst-case energy consumption day, the optimal size of charger is also known. There are many charger sizes available and being clear about whether 20kW or 60kW or 120kW is needed has a huge impact on capital costs and the time for design and installation. The difference in peak load between 20 chargers at 20kW and 20 at 60kW is profound, resulting in the ability to install charging under the existing transformer capacity with no added costs (1 year), or requiring a million dollar substation upgrade (and 5 years to completion).

Right-sizing the charging system can reduce peak load and capital costs by 40%.

Considering fleet operations include different vehicles, a wide range of routes, and importantly the local operating environment and the extremes therein (hottest day and coldest day), it quickly becomes a complex calculation to optimally size the EV fleet and infrastructure. Well vetted duty cycles can determine the minimum vehicle range, which is also the worst-case energy consumption day, which can occur up to 20% of the time, and clarify the charger power levels that will deliver enough energy to meet the operating schedule. Designing around the worst-case (20%) creates confidence in the system plan and allows for optimizing around typical (80%) operational days. At MGL we think of this planning approach as fleet-as-system, minimizing the capital costs first and optimizing operational savings.

A prudent approach is to plan around routes that are easily electrifiable with today’s technology (no regrets),

Validating performance of vehicles and chargers and vetting against expectations has always been the deliberate approach to measure and verify new technologies. Performance informs and adapts the ongoing plan because planning is not a one and done exercise

At MGL we think in fleet-as-system and have built a fleet electrification planning, simulation, and optimization platform to aid fleet operators and their engineering firms in preliminary system design. Check out EVopt, it’s available to you, and we can help with phase-in and onsite energy where necessary.

Chuck Ray is a credible mobility and energy expert with fifteen years in energy management and alternative mobility, and serves as MGL’s business director.

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