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Current research on energy related problems such as eco-routing, eco-driving and range prediction for electric vehicles (EVs) primarily considers the effect of longitudinal dynamics on EV energy consumption. However, real-world driving includes longitudinal as well as lateral motion. Therefore, it is important to understand the effects of lateral dynamics on battery energy consumption. This paper conducts an analysis of the stated effect and validates its significance through simulations. Specifically, this study demonstrates that inclusion of the effect of lateral dynamics can improve accuracy and reliability of solutions in eco-routing, eco-driving and range prediction applications.

In this paper, we simultaneously tackle the problem of energy optimal and safe navigation of electric vehicles in a data-driven robust optimization framework. We consider a dynamic model of the electric vehicle which includes kinematic variables in both inertial and body coordinate systems in order to capture both longitudinal and lateral motion as well as state-of-energy of the battery. We leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we present the finite horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption. Finally, we show the effectiveness of the proposed approach in reducing energy consumption and ensuring safe navigation via extensive simulations involving curved roads and multiple obstacles.

Developing statistical models for seismic noise is an exercise of high value in seismic data analysis since these models play a critical role in detecting the onset of seismic events. A majority of these models are usually built on certain critical assumptions, namely, stationarity, linearity, and Gaussianity. Despite their criticality, very little reported literature exists on validating these assumptions on real seismic data. The objectives of this work are (i) to critically study these long-held assumptions and (ii) to propose a systematic procedure for developing appropriate time-series models. A rigorous statistical analysis reveals that these standard assumptions do not hold for most of the data sets under study; rather they exhibit additional special features such as heteroskedasticity and integrating effects. Resting on these novel discoveries, ARIMA-GARCH models are developed for seismic noise. Studies are carried out on $185$ real-time data sets over different time intervals to study the daily and seasonal variations in noise characteristics and model structure. Nearly all datasets tested positive for first-order non-stationarity, heteroskedasticity, and Gaussianity, while $19\%$ tested negative for linearity. Analysis of the structural uniformity of the developed models with respect to daily and seasonal variations is also presented., Comment: 21 pages, 6 figures and 2 tables