Matthew Barth.
Matthew Barth.

PHEVs Need Predictive Energy Management Strategies

Leveraging this outside information via predictive energy management strategies has been shown to boost the energy efficiency of PHEVs on the order of about 20 to 30%.

Your plug-in hybrid electric vehicle probably has a range of onboard sensors that are collecting a variety of information about what is happening across and within it. The PHEV leverages that intelligence, then, in decision making about when to draw energy from its internal-combustion engine or the battery-powered electric motor.

But what if your PHEV were better informed?

In addition to the data from all of those sensors monitoring the current state of your PHEV, there is a tremendous and growing range of information to be gathered from outside the vehicle in the quickly maturing world of connected and electrified transportation – data on factors such as the traffic around you, where you are and where you are going, your intended route, road grade, elevation change, etc.

How could that external information also be leveraged in more informed decision making toward an even more efficient PHEV?

This is the premise behind the predictive energy management strategies that are being developed and, in more and more instances, piloted for PHEVs. The research is encouraging. Leveraging this outside information via predictive energy management strategies has been shown to boost the energy efficiency of PHEVs on the order of about 20% to 30%.

Informing the Power-Split Ratio

One of the primary variables to be controlled in a PHEV is whether it sources its power from the internal-combustion engine or some storage resource such as a battery. This power-split ratio is a variable up to 100% – the PHEV could use all internal-combustion engine, all battery or a mixture of the two.

How this percentage is managed is key to the overall observed efficiencies, and today’s PHEVs capture a plethora of onboard data in informing their algorithms.

But what is happening outside the vehicle also is crucial. Say your PHEV could know a turn is coming a half-mile up the road. Or, say it could understand it is approaching a hill to be climbed or the freeway onto which it will soon merge is heavily congested.

Such data types are becoming more and more available to vehicles with the growth of connected transportation.

Take the hill example. If a PHEV could know it has an upcoming hill, it could manage its power-split factor in such a way to leave plenty of capacity in the batteries in order to recapture regenerative power on its descent. If the battery already is at 100% once the PHEV crests, that leaves no space to store energy. Regenerative power is wasted, and efficiency is not optimized.

Leveraging such predictive energy management strategies can lead to greatly enhanced decision making within the vehicle’s powertrain on which energy source to be used at what fraction and when, depending on terrain, traffic speeds, stop-and-go patterns, historical driving tendencies, etc.

Characterizing the benefit for different specific traffic patterns is one of the key areas of today’s research and development around predictive energy management strategies.

And now the research community that has been involved in bringing about these capabilities is starting to turn its focus more toward intelligent transportation systems, autonomous and automated driving and machine learning: How could predictive energy management strategies interface with the innovations to bring more benefit to consumers, to business, to society? 

Deployment Frontiers

In the past two years, the major automotive original equipment manufacturers have started looking more seriously at the viability of predictive energy management strategies for PHEVs.

Several companies, in fact, are starting to pilot-test such algorithms. Commercial vehicles are likely to be the earlier adopters and greater initial benefactors of such innovations.

Because fleet operators who manage a large number of trucks sink so much of their total business investment into fuel and driver costs, the efficiencies available to them via predictive energy management strategies are especially compelling.

And with the ongoing rollout of connected transportation, more and more external data sources are becoming available for PHEVs to manage power-split factors for maximum efficiency. Predictive energy management strategies figure to play an increasingly prominent role in the future of transportation electrification.

Matthew Barth is Vice President-Financial Activities for the IEEE Intelligent Transportation Systems Society.

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