Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.Guardiola García, C.; Plá Moreno, B.; Blanco Rodriguez, D.; Reig Bernad, A. (2014). Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles. International Journal of Computer Mathematics. 91(1):147-156. doi:10.1080/00207160.2013.829567S147156911Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment, 6(5), 325-345. doi:10.1016/s1361-9209(01)00003-7Q. Gong, P. Tulpule, V. Marano, S. Midlam-Mohler, and G. Rizzoni,The role of ITS in PHEV performance improvement, 2011 American Control Conference, June–July, San Francisco, CA, 2011, pp. 2119–2124.C. Guardiola, B. Pla, S. Onori, and G. Rizzoni,A new approach to optimally tune the control strategy for hybrid vehicles applications, IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM’12, October, Rueil-Malmaison, France, 2012.Johannesson, L., Asbogard, M., & Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. IEEE Transactions on Intelligent Transportation Systems, 8(1), 71-83. doi:10.1109/tits.2006.884887Liu, S., & Yao, B. (2008). Coordinate Control of Energy Saving Programmable Valves. IEEE Transactions on Control Systems Technology, 16(1), 34-45. doi:10.1109/tcst.2007.903073Paganelli, G. (2001). General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Review, 22(4), 511-518. doi:10.1016/s0389-4304(01)00138-2Rizzoni, G., Guzzella, L., & Baumann, B. M. (1999). Unified modeling of hybrid electric vehicle drivetrains. IEEE/ASME Transactions on Mechatronics, 4(3), 246-257. doi:10.1109/3516.789683Control of hybrid electric vehicles. (2007). IEEE Control Systems, 27(2), 60-70. doi:10.1109/mcs.2007.338280L. Serrao, S. Onori, and G. Rizzoni,ECMS as realization of Pontryagin's minimum principle for HEV control, 2009 American Control Conference, June, Saint Louis, MO, 2009, pp. 3964–3969.Serrao, L., Onori, S., & Rizzoni, G. (2011). A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles. Journal of Dynamic Systems, Measurement, and Control, 133(3). doi:10.1115/1.4003267Stockar, S., Marano, V., Canova, M., Rizzoni, G., & Guzzella, L. (2011). Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles. IEEE Transactions on Vehicular Technology, 60(7), 2949-2962. doi:10.1109/tvt.2011.2158565Sundström, O., Ambühl, D., & Guzzella, L. (2009). On Implementation of Dynamic Programming for Optimal Control Problems with Final State Constraints. Oil & Gas Science and Technology – Revue de l’Institut Français du Pétrole, 65(1), 91-102. doi:10.2516/ogst/2009020O. Sundström and L. Guzzella,A generic dynamic programming Matlab function, 18th IEEE International Conference on Control Applications Part of 2009 IEEE Multi-conference on Systems and Control, July, Saint Petersburg, 2009, pp. 1625–1630.R. Wang and S.M. Lukic,Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles, Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, September 6–9, Raleigh, NC, 2011, pp. 1–7

A stochastic method for the energy management in hybrid electric vehicles

There are many approaches addressing the problem of optimal energy management in hybrid electric vehicles; however, most of them optimise the control strategy for particular driving cycles. This paper takes into account that the driving cycle is not a priori known to obtain a near-optimal solution. The proposed method is based on analysing the power demands in a given receding horizon to estimate future driving conditions and minimise the fuel consumption while cancelling the expected battery energy consumption after a defined time horizon. Simulations show that the proposed method allows charge sustainability providing near-optimal results. (C) 2014 Elsevier Ltd. All rights reserved.This research has been partially supported by Ministerio de Ciencia e Innovacion through Project TRA2010-16205 uDiesel and by the Conselleria de Educacio Cultura i Esports de la Generalitat Valenciana through Project GV/2103/044 AECOSPH.Payri González, F.; Guardiola, C.; Plá Moreno, B.; Blanco-Rodriguez, D. (2014). A stochastic method for the energy management in hybrid electric vehicles. Control Engineering Practice. 29:257-265. https://doi.org/10.1016/j.conengprac.2014.01.004S2572652

Linking NMDA Receptor Synaptic Retention to Synaptic Plasticity and Cognition

NMDA receptor (NMDAR) subunit composition plays a pivotal role in synaptic plasticity at excitatory synapses. Still, the mechanisms responsible for the synaptic retention of NMDARs following induction of plasticity need to be fully elucidated. Rabphilin3A (Rph3A) is involved in the stabilization of NMDARs at synapses through the formation of a complex with GluN2A and PSD-95. Here we used different protocols to induce synaptic plasticity in the presence or absence of agents modulating Rph3A function. The use of Forskolin/Rolipram/Picrotoxin cocktail to induce chemical LTP led to synaptic accumulation of Rph3A and formation of synaptic GluN2A/Rph3A complex. Notably, Rph3A silencing or use of peptides interfering with the GluN2A/Rph3A complex blocked LTP induction. Moreover, in vivo disruption of GluN2A/Rph3A complex led to a profound alteration of spatial memory. Overall, our results demonstrate a molecular mechanism needed for NMDAR stabilization at synapses after plasticity induction and to trigger downstream signaling events necessary for cognitive behavior

Trafficking in neurons: Searching for new targets for Alzheimer's disease future therapies

Alzheimer's disease (AD) is the most common cause of dementia and no cure is available at the moment. As the disease progresses, patients become increasingly dependent, needing constant supervision and care. Prevention or delay of AD onset is among the most urgent moral, social, economic and scientific imperatives in industrialized countries. A better understanding of the pathogenic mechanisms leading to the disease and the consequent identification of new pharmacological targets are now a need. One of the most prominent molecular events occurring in AD patients' brains is the deposition of a peptide named amyloid-\u3b2 (A\u3b2). A\u3b2 derives from the concerted action of \u3b2-secretase, which mediates the amyloid precursor protein (APP) shedding at A\u3b2 N-terminus, and \u3b3-secretase, responsible for APP C-terminal stub cleavage. The production of A\u3b2 can be prevented by the cleavage of ADAM10 on APP. In regard of AD pathogenesis, it is notable that neurons are the cell type affected in AD and that APP and the secretases are all integral transmembrane proteins, and so they are dynamically sorted in neurons. Therefore, neuronal sorting mechanisms responsible for APP and the secretases colocalization in the same membranous compartment play important roles in the regulation of A\u3b2 production. In light of these considerations, this review provides an overview on the actual knowledge of the trafficking mechanisms involved in the regulation of APP and secretases localization, paying particular attention to the specific neuronal setting