Leveraging outside of vehicle information could boost the energy efficiency of PHEVs on the order of about 20 to 30 percent

Almost every modern plug-in hybrid comes with a magnitude of sensors installed. They keep track of a variety of information relevant to the state the vehicle is in. This helps the onboard management system to decide when to draw energy from the batteries and power the electric motor or when to use the internal-combustion engine. In turn, this allows each hybrid to better utilize its dual powertrain in various driving conditions, helping it to save fuel and improve performance. But, what if your PHEV could use other, outside information to achieve even better efficiency?

In today's highly connected world, there's a tremendous and growing volume of information that can be gathered from outside the vehicle. This data includes information such as traffic, your vehicle's trajectory, intended route, road grade, the status of traffic lights, upcoming elevation and other items. And we can utilize all that information, factoring in different data to allow your PHEV to be more efficient.

This is the core premise of predictive energy management strategies currently being developed and piloted in PHEVs. The findings from the research done until now are really interesting. If such outside information is leveraged, these predictive energy management strategies have shown that they could boost the energy efficiency of PHEVs in an order of about 20% to 30%.

The best laid out example is when a PHEV knows it will need to traverse a rather steep hill. If it could manage its power-split factor, it could factor in all its outside data and make its energy usage and fuel consumption a lot more optimized. The PHEV would utilize battery power to traverse the hill, then, knowing what the upwards and downwards gradients are, leave plenty of capacity in the batteries in order to recapture regenerative power on its descent. If the PHEV ascends over the crest of the hill with a 100% battery state, that leaves no space to store the energy it could recuperate with regenerative braking. In turn, regenerative power is wasted, and efficiency is not optimized.

This technology could even be better implemented in autonomous driving vehicles. With those, the human factor is taken out. In turn, this allows the vehicle to always use the most optimized route, road grade, and elevation, adjusting for current traffic and making for a much more calculated drive. With such implementation, predictive energy management strategies can lead to greatly enhanced decision making for the vehicle powertrain, allowing it to select the optimal energy source to combat the route & conditions ahead.

The premise is simple: utilize all the available data and make for a vehicle that uses predictive energy management strategies to save both money and emission rates. Commercial vehicles are most likely to present the first adopters and greater initial benefactors of such innovations. Fleet operators spend most of their funding on fuel and driver costs. The improvements in efficiency that would become available to them via predictive energy management strategies are especially compelling.

The addition of autonomous driving and an increased rollout of connected transportation makes for an even more compelling argument for this technology. With those circumstances in place, the predictive energy management strategies are set to play an increasingly prominent role in the future of transportation electrification. In the end, these implementations improve vehicle fuel consumption and power usage, but also, greatly help reduce Co2 emissions as well.

Source: Wards Auto

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