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Knowledge of the upcoming traffic velocity along a route can help in many respects, among them optimizing energy management for hybrid vehicles, which, for instance, could reduce instantaneous battery usage if a traffic jam is upcoming in the next future. While such kind of knowledge can hardly be precise on a single-vehicle level, we show in this paper that a prediction method which combines present and past Vehicle-to-Everything (V2X) information can strongly improve the energy efficiency. Our approach is first compared with other prevailing prediction methods and its advantages in terms of stability and accuracy are shown. Then the prediction results are applied in a hybrid powertrain control example, in which its potential in fuel savings are illustrated.

A priori information about the future traffic conditions along the planned route can be essential for optimal hybrid powertrain energy management. However, due to the limited sensor range of a single vehicle, it cannot be acquired locally. In recent time, V2X (decentralized wireless vehicle to everything) has been receiving much attention as a way to obtain and share information from different distributed sources. V2X data can provide updated information on traffic at different locations. Still, this information will be obsolete when the corresponding positions are reached due to changing traffic, and an optimal strategy based on outdated information may not bring the full benefit. Against this background, we propose a method based on a velocity prediction approach which utilizes V2X data currently available in the market in combination with historical data, to obtain a prediction of the expected traffic conditions at in the close future. Actual measurements on a city highway in Linz, Austria, are used to estimate the potential of the approach. Even for rather mild changes in traffic conditions, a reduction of up to 4% in terms fuel consumption over this track was found, confirming the potential benefit of this method.

Using knowledge of the future route and its topology is known to offer substantial fuel savings, and this is even more true for hybrid electric vehicles, as the battery use can be planned in advance, for instance to take into account coming slopes. However, traffic or other environmental conditions can force to deviate from the initial planning making it no longer optimal.In this paper, we propose a flexible double layer approach for energy management of hybrid vehicles able to cope with traffic changes. First, before departure, an expected optimal speed and powertrain state reference is computed on a cloud and sent to an on-board controller. Simple, route-specific engine on/off rules are extracted by the controller and used for an on-board fast convex optimization, which can be conducted frequently along the drive, adapting the references to take into account changes of traffic conditions over longer sections of the route as communicated by V2X. Abrupt disturbances are handled by a lower level Model Predictive Control (MPC). If the condition changes are very substantial, so that the empirical on/off rule seems questionable, the cloud can be asked to perform a full optimization again.

The task of energy management for hybrid electric vehicles (HEV) consists in providing the requested traction power by splitting the demand between electrical and mechanical energy sources. The best performance can be achieved if the power split can be computed over the whole trip at once, e.g. using Dynamic Programming (DP). However, actual travel conditions are prone to change with respect to the assumed ones, so that updates are needed. Recomputing the optimal power split for the whole remaining route via DP is hardly feasible due to the actual travel conditions, so approximate solutions are needed. In this paper, we propose an online-capable strategy for a parallel HEV based on the minimization of total losses and heuristics for engine on/off and gear actions. It yields a very good approximation of the DP solution with little computational burden.

Entrances into highway represent critical sections, as entering vehicles are bound to merge with the existing traffic at a fixed junction point almost independently from the traffic conditions-in some sense a “constrained” cut-in manoeuver. Advanced driver-assistance systems (ADAS) need to perform correctly under these conditions as well, but could also be used to facilitate merging and reducing risks. In particular, vehicles on the main road could adapt their speeds-or even change lane-provided an estimate of the entering vehicle's time to merge is available, exactly as a human driver would do. This paper is concerned with providing such an estimate. To this end, we observe that the speed profile on the entering ramps is rather well predictable if the acceleration of the entering vehicles is described as a function of their actual distance from the junction point. Still, uncertainties remain and to cope with them, we use a stochastic prediction model based on Dynamic Bayesian Network. The result are probability distribution functions of the time to merge based on the observation of speed and distance of the entering vehicle once they become visible to the traffic on the main road. Experimental data are used to illustrate the model structure and the parameter determination. The model quality is then assessed by comparing statistics from simulations with the one recorded on road. The possible use of the model as a tool for traffic prediction algorithm embedded in ADAS and an extension to existing highway stochastic traffic models are shortly discussed at the end of the paper.

DC distribution network topology includes: chain structure, two-terminal structure and ring structure. This paper firstly introduces a device of Flexible Alternative Current Transmission System: static synchronous series compensator with fault current limiting. DC distribution network with SSSC-FCL can develop the power quality of distribution network and improve the stability. When the fault occurs, the SSSC-FCL can effectively limit the short circuit current to ensure the safety of system. And then this paper also introduces the main advantages of the DC distribution network and the topology, basic control methods of SSSC-FCL. Finally put forward a DC distribution network topology structure with SSSC-FCL. With the use of PSCAD/EMTDC to model and simulate the DC distribution network, the result of the simulation verified the feasibility and effectiveness of the DC network topology structure with SSSC-FCL.

In the new economic situation, more and more researchers began to pay attention to the electricity market. Government departments attach great importance to the growth of electricity consumption. This paper tries to investigate the relationship between electricity consumption and economic growth in Fujian Province China, using monthly data during January 2014 (2014m01) to May 2017 (2017m05). Investment, consumption and export data instead of only Gross Domestic Product (GDP) data is used to represent the economic growth, which indicates their individual effect on electricity consumption. Dickey-Fuller (DF) tests reveal that all the time series are non-stationary and individually integrated of order one. Long-run cointegration relationships between investment, consumption, export and electricity consumption individually are revealed by Engel-Granger (EG) tests. Furthermore, this paper shows that there exist unidirectional Granger causalities running from investment, consumption and export to electricity consumption based on Vector Auto-Regression (VAR) models, which are verified robust and can be used for policy analysis.

Compared with the AC distribution network, the DC distribution network has the advantages of lower line losses, better power quality, higher reliability and being suitable for all kinds distributed generation and DC load access, etc. This paper firstly studies the models of PV and energy storage connected to the DC distribution network. Then, a new type of DC distribution network topology with hybrid DC circuit breaker is proposed. A coordinated control strategy is also proposed, which can control energy storage to improve the stability of the system. Finally, using PSCAD/EMTDC to model and simulate. The simulation results show that the proposed topology of DC distribution network and control strategy can quickly remove the fault, shorten the fault power failure time and ensure the safe and stable operation of the system, which greatly improves the operation efficiency and safety level of the power grid.