Range predictions for electric trucks through data standardization
The key to reliable and cost-efficient electric freight lies in accurate range modeling, also known as energy consumption modeling. In this blog post, we outline Einride's approach to collecting and processing electric truck data to implement range models that consistently achieve over 90% accuracy.
To reach international targets of reduced global warming, the transport industry needs to accelerate its transition towards electric. In order to go electric at scale you need to allow for a mixed brand fleet setup. You also need accurate range predictions for these brands. Given that different truck manufacturers use different data models makes this challenging. What if we could define a standard data format that we could map all truck data to, regardless of brand? Read on to learn how Einride is working with standardizing data to enable accurate and unbiased range models.
The data used when training range predictive models
has a significant impact on transport planning
The importance of data standardization for range models
In order for machine learning models to excel, the data they are built on needs to accurately capture the reality. At Einride, we run electric fleets consisting of mixed truck brands. This is a necessity to get as many electric trucks on the roads as possible, with the target to reduce global emissions from road freight by 7%. This also means that we have to build predictive models that can accurately predict range, for all brands. If the model accuracy is worse for some brand, we will fail to assign the most suitable truck to a given transport flow.
However, given that all truck manufacturers have their own R&D departments with different developer’s perspectives, the telematics data they send is naturally not aligned. For diesel trucks there is a standard for how to communicate in-vehicle data, called SAE J1939. This standard is being adapted for electric, but since the electric truck is still a new product to the market with plenty of innovation we see little adoption of it.
Let us illustrate an example to better understand the problem of non-standardized data. One truck manufacturer might send information about energy consumption through an accumulated energy signal over time. If we want to know the energy consumed within a time period, we just take the difference between the start and the end time, see Fig 1. Another truck manufacturer might only send snapshots of battery power, in this case the signal needs to be either integrated over time or aggregated in some other way. The key here is that, in the end, we want to model energy consumption and then that feature needs to exist in a predefined way for all data sources.
Fig 1. Example of how data for energy consumption
can be mapped to a standardized format.
Standardizing the data should be done early. Since this data is used for many more applications than just range predictive models, we avoid costs and risks associated with repeated logic. We want all our stakeholders to reap the benefits of having well defined data with no need of manual pre-processing. We have learned that investing in standardizing data quickly pays off. When we do that for truck telematics data, we do not only unlock unbiased range models but we also unlock automated truck activity reporting, CO2 emission tracking and battery degradation analytics. All this combined enable us to find an optimal transport plan in a mixed fleet setup.
Now that we are all onboard with the concept of data standardization, how does one start when facing such a problem?
The process of standardizing data
Before getting started on building our data product, it is wise to take some steps back and make sure that we are building the right thing. At Einride we have had great outcome when taking the following approach
- Identify stakeholder and purpose of use
- Write basic requirements
- Identify a common data representation
- Write tests
- Start incremental development
- Monitor the quality
Let us scratch the surface and see what each step here entails.