Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization

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1. Background

The present transportation sector has very high dependency on hydrocarbon fuels, inducing public concerns over energy sustainability/climate change. Plug-in hybrid electric vehicles (PHEVs) enable downsized internal combustion engines with high efficiency, while accommodating distributed electricity energy storage for renewable energy sources in the power sector. PHEVs are, therefore, crucial in a good interplay between both sectors for significantly reducing fossil fuel consumption and CO2 emissions. It is, however, challenging to unlock the full CO2 -saving potential of PHEVs, owing to intricate influencing factors, including charging strategy during parking, power management during on-road driving, power-source sizing, upstream grid generation mix, etc.

2. Methods

In this article, we present a synergistic examination of various influencing factors through integrating renewable energy and system-level hybrid powertrain optimization, with the goal to minimize the daily CO2 emissions of PHEVs. Two key contributions clearly differentiate our work from existing efforts. First, in contrast to diverse heuristic scenarios, CO2 improvements arising from renewable energy penetration and an integrated convex programming (CP) framework are explicitly quantified. We underscore the paramount importance of the renewables-optimization synergy. Second, our means is quantitatively compared with common heuristic scenarios, in terms of daily battery degradation, via a dynamic battery State-of-Health (SOH) model. Battery health directly dertermines the cost and carbon footprint of PHEVs from a cycle-life perspective. Specifically, the high-fidelity quasi-static modeling methodology is first leveraged to emulate the dynamics of a PHEV powertrain. Based on the model, a convex programming problem for seeking minimal CO2 emissions is formulated and rapidly solved by interior-point algorithms, with a guarantee of a global optimal solution. A vareity of comparative studies are carried out to sufficiently demonstrate the key role of synergizing renewables and rapid powertrain optimization.

3. Results

1.Gain of Integrating Renewables

Total CO2 emissions of five heuristic scenarios under varying levels of wind generation are illustrated in the figure below. The three PHEV scenarios noticeably benefit from wind power generation. As wind power increases, the CO2 emissions reduce. For example, the CO2 decrease in the PHEV-3 scenario with high wind power is up to 12.54% with respect to the case of no wind. A closer examination unveils that the PHEV scenarios outperform the HEV scenario in the presence of high wind.

2. Gain of Rapid Powertrain Optimization

The minimized daily CO2 emissions and corresponding battery sizes, along with optimization time, are listed in the Table below . The optimal battery size is overall augmented with increasing wind power. The size alteration is, nonetheless, insignificant. The CP framework is very rapid and efficient to solve the 24-hour optimization problem (only 10-20 seconds are needed). A comparison with the heuristic scenarios is indicated in the figure below. It is pronounced that the synergy between wind power and powertrain system optimization is necessary to maximize CO2 savings.

3. Battery Health Implication

Compared to heuristic scenarios, the SOH decay of the optimal scenario is smaller under low and average wind power.


4. Conclusions

This paper assesses the usefulness of renewable energy and powertrain system optimization to reduce the daily CO2 emissions of PHEVs. A plethora of influencing factors are taken into account, including the charging protocol, timing, on-road power management strategy, battery size, and grid CO2 intensity. The highly efficient CP framework is leveraged for optimizing the hybrid powertrain.


Five heuristic scenarios are introduced, and their disadvantages are exposed by comparisons with the cases with wind power penetration and rapid system optimization. The great significance of synergistic integration of wind power and CP framework is highlighted to maximize the CO2-saving potential of PHEVs. The optimal scenario with high wind power can reduce more than 21% CO2, compared to heuristic scenarios without wind power. Furthermore, the computational efficiency of the CP framework enables a prompt day-ahead adaptation of charging/power management control law, which significantly remedies the loss of CO2 savings evoked by wind variability.


In contrast to the heuristic PHEV scenarios, the battery aging of the CO2-optimal scenario is even smaller under low and average wind power.

5. Future ideas/collaborators needed to further research?

Future work could be twofold:



1.More sophisticated powertrain model and battery health model will be developed. Battery thermal aspects are expected to be considered in the near future. We may need researchers in the related area to collaborate.

2. More data on grid generation and renewables are useful to better assess the life-cycle CO2 emissions of PHEVs. Collaborations with researchers in the power sector are highly solicited.

6. Please share a link to your paper

https://doi.org/10.1016/j.energy.2016.06.037

https://www.sciencedirect.com/science/article/pii/S0360544216308118


Hu, Xiaosong, Y. Zou, and Y. Yang. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization." Energy 111(2016):971-980.

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Image1504778334?1504778334

Prof. Dr. Xiaosong Hu received the Ph.D. degree in Automotive Engineering from Beijing Institute of Technology, China, in 2012. He did scientific research and completed the Ph.D. dissertation ...

Round: Open Peer Vote
Category: Climate Impact Prize

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