Abstract
The literature indicates catastrophic potential for electric vehicles around the year 2019. Amidst the predicted time, this research revisits the prequel analysis with state-of-the-art deterministic artificial intelligence methodologies to posit the potential for catastrophe in the upcoming year. The methodology has proven effective with motion mechanics, electrodynamics, and even financial analysis of sales date in the prequels, since the model commences with simple regression for mathematical model formulation asserting the certainty equivalence principle, followed by derivative modeling and eventually catastrophe analysis of the derivative models. The prequel analysis paradigms are retained in this sequel utilizing both monthly and cumulative sales data in simple least squares algorithms for predictive curve fitting to establish context and help correctly model the mathematical degree of the data. Extrapolation by forward time-propagation established predictions for models of various mathematical degrees (again merely for context). Next, catastrophe analysis (of the derivative form) revealed stable and unstable equilibrium points and then parametric variation was induced to evaluate the resulting behavior of the derivative models, highlighting the importance of the coefficient of the second order term (the acceleration or change of rate of sales as a forcing function). While the forcing function typically embodies both gasoline prices and vehicle charging proliferation, the relative stability of gas prices together with factors such as vehicle-to-grid elevate charging-station proliferation as the primary forcing function of slow-dynamics in catastrophe analysis. This brief manuscript revisits the prequel research to test the validity of those conclusions and with the benefit of the passage of time, reveal how well the mathematical modeling predicted real behavior. The main finding is the predicted potential catastrophe is less likely to occur and recommendations are made to insure catastrophe is averted.
Highlights
How does the study relate to previous work in the area? Subsequent to that prequel research, the rapid rise of non-stochastic artificial intelligence methodologies (Baker, 2018), (Sands, InTech, 2019), (Lobo, 2018) stemming from combinations of physics-based controls (Sands, Lorenz, 2009), (Sands, 2012), (Sands, 2015) and mathematical system identification from data (Sands, Comp., 2017), (Sands, J.Space Exp., 2017), (Sands, Kenny, 2017), (Sands, Phys J., 2017), (Sands, J.Space Exp., 2017), (Sands, Armani, 2018) together with adaptive systems methods (Nakatani, 2014), (Nakatani, 2016), (Sands, Aero, 2019), (Cooper, 2017), (Smeresky, 2018), (Sands, Algor., 2019), (Sands, Bollino, 2018) has been adopted and incorporated into new educational schemes driven by military operational imperatives (Kuklinski, et al, 2019), (Sands, Mihalik, 2016), (Bittick, et al, 2019), (Sands, “satellite”, 2009)
The following results follow the general process-flow of deterministic artificial intelligence: 1) perform optimal system identification, and 2) reparametrize the optimal system dynamics to bestow predictive decision making
3.4 Fast and Slow Dynamics of Catastrophe Theory Starts with Finding Equilibrium Points Continuing with our analysis, we investigate whether these system equations are susceptible to “jumps” associated with catastrophe theory
Summary
1.1 Introduce the ProblemRecent literature (Sands, 2017) revealed the distinct possibility of an unexpected catastrophic crash in sales of electric vehicles in 2019.1.1.1 Why is This Problem Important?How does the study relate to previous work in the area? Subsequent to that prequel research, the rapid rise of non-stochastic artificial intelligence methodologies (Baker, 2018), (Sands, InTech, 2019), (Lobo, 2018) stemming from combinations of physics-based controls (Sands, Lorenz, 2009), (Sands, 2012), (Sands, 2015) and mathematical system identification from data (Sands, Comp., 2017), (Sands, J.Space Exp., 2017), (Sands, Kenny, 2017), (Sands, Phys J., 2017), (Sands, J.Space Exp., 2017), (Sands, Armani, 2018) together with adaptive systems methods (Nakatani, 2014), (Nakatani, 2016), (Sands, Aero, 2019), (Cooper, 2017), (Smeresky, 2018), (Sands, Algor., 2019), (Sands, Bollino, 2018) has been adopted and incorporated into new educational schemes driven by military operational imperatives (Kuklinski, et al, 2019), (Sands, Mihalik, 2016), (Bittick, et al, 2019), (Sands, “satellite”, 2009), J. Electro., 2018) with accompanying educational imperatives (Mihalik, et al, 2017), (Camacho, et al, 2017). Electro., 2018) with accompanying educational imperatives (Mihalik, et al, 2017), (Camacho, et al, 2017) How does this manuscript differ from, and build on, the earlier report? These methods have been successfully applied to quite disparate disciplines piecemeal as the techniques have been developed, bestowing the ability for data-informed decision-making, e.g. should a military plan to invest heavily in electric vehicles with a realistic anticipation of a robust commercial industrial base. Shock events inherent in some classes of differential equations embody rapid, unexpected dramatic changes in data, and they are referred to as “jump discontinuities”
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
