In car-following scenarios, accurate previews of the preceding vehicle’s trajectory are essential for minimizing the energy consumption of a following automated vehicle. This work presents a novel design strategy for data-driven vehicle speed predictors to increase the energy efficiency of the following connected and automated vehicle. The loss function is formulated as a weighted-mean-squared error, where the weights are tuned based on the influence of uncertainty at an individual prediction step on the energy consumption of the automated following vehicle. The efficacy of the proposed loss function is validated by applying it to training representative predictors and testing the predictors in the energy-optimal control of the following vehicle. The energy saving of the optimal controller is evaluated by comparing the electricity consumption of a battery electric vehicle with the human driver following, emulated by an intelligent driver model. Simulation results show that the energy consumption of an electric vehicle is reduced by an average of 12% compared to a human driver following demonstrated by an intelligent driver model, whereas using a conventional loss function, a mean-squared error loss function, reduces by 10%.

Plug-in electric vehicles (PEVs) have the highest promise for dramatically reducing transportation emissions. No other option has comparable emission reduction potential or as a promising pathway. Still, PEVs can offer more than green transportation. In particular, their onboard storage can further serve the society by providing an energy buffer to increase the reliability, affordability, and sustainability of electric services. These benefits are only achievable by fully exploiting the multifaceted flexibility provided by PEVs' mobility, charging adaptability, and bidirectional flow of power, as well as adopting effective decision-making and control algorithms, while minding the likely unfavorable side effects, such as shortened battery life span. This work takes a closer look at different elements of this puzzle. The main subject of this survey is behind the meter energy management with vehicle to building (V2B). We focus on different V2B application ideas and review energy management methods in smart buildings with V2B integration. Recent findings on battery capacity fade resulting from the bidirectional flow of power and extra discharging cycles with V2B are reviewed, and the methods for integrating the battery degradation in energy management formulation are discussed. Finally, the main findings of this review and research gaps are summarized and clarified.

Advanced battery management system, which leverages an in-depth understanding of the battery state of health, can improve efficiently and safely. To this end, we introduce the electrode-level battery state of health (eSOH) estimation problem with open-circuit voltage (OCV) data. In real-world applications, collecting the full-range OCV data is difficult since the battery is not deeply discharged. When data is limited, the estimation accuracy deteriorates. In this article, we quantify the uncertainty of the electrode parameter estimation with partial data based on the Cramer-Rao bound and confidence interval. By introducing a voltage constraint in the estimation problem, the positive electrode parameters can be estimated with sufficient accuracy over a wide range of state of charge. However, the estimation accuracy of the negative electrode parameters is more sensitive to the depth of discharge. The proposed framework can be used as a guideline for selecting proper data windows and understanding the impact on parameter estimation.