This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.

Optimal Power Flow Management for Grid Connected PV Systems With Batteries

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.

https://www.eee.hku.hk/doc/ccchan/Electric,%20Hybrid,%20and%20Fuel-Cell%20Vehicles-%20Architectures%20and%20Modeling.pdf

Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling

Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming

Hybrid electric vehicles (HEVs) are one of the most viable technologies to achieve the goals of energy saving and environmental protection before a breakthrough in battery technology and fuel cell technology. Energy management strategy as a key technology of HEVs is studied extensively and deeply to improve the performance of HEVs and speed up the industrialization of HEVs. This paper quantitatively analyzes and evaluates current research status of energy management strategies for HEVs based on bibliometrics for the first time, through content analysis involving analysis of author keywords and abstracts. Then qualitative analysis is performed for all kinds of energy management strategies that are used in HEVs in detail, essential characteristics involving pros and cons, interconnections and improvement potential among various energy management strategies are revealed from the view of control theory. Finally, latest developing trends in energy management strategies of HEVs are presented to improve the performance of HEVs based on above quantitative analysis and qualitative analysis, covering driving cycle recognition/prediction algorithms, integrated multi-objective, coordinated optimization energy management strategies, good balance between computation complexity and optimization performance of energy management strategies, fair and credible evaluation system of energy management strategies. This paper not only first provides a comprehensive analysis of energy management strategies for HEVs, but also puts forward the emphasis and orientation of future study, which will broaden relevant researchers׳ vision and promote the development of a simple and practical energy management controller with low cost and high performance for HEVs.

A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics

A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off.

Online Energy Management for Hybrid Electric Vehicles

The electric power demand in road vehicles increases rapidly. Energy management (EM) turns out to be a viable solution for supplying all electric loads efficiently. The EM strategies developed in this paper focus on vehicles with a conventional drivetrain. By exploiting the storage capacity of the battery, the production, and distribution of electric power is rescheduled to more economic moments. In addition, this paper explores the advantages of electric loads with a flexible power demand. Based on optimization techniques, an optimal offline strategy as well as a causal online strategy are presented. Simulations illustrate the benefits of the EM strategies in terms of fuel economy. The online strategy has also been implemented in a series-production vehicle. Real-world experiments on a roller dynamometer test-bench validate the strategy, but also reveal additional fuel benefits due to unexpected side-effects from the engine control unit and the driver. Measured profits in fuel economy are as large as 2.6%, with only minimal changes to the vehicle hardware

http://www.mate.tue.nl/mate/pdfs/7358.pdf

Energy Management for the Electric Powernet in Vehicles With a Conventional Drivetrain

In modern vehicles, a significant amount of electric power is consumed, and more is to be expected in the future. For that reason, energy management for vehicle power nets gets a lot of attention in the automotive world. This paper describes a complete simulation environment for developing and evaluating energy management strategies. Two different model classes are distinguished: a simulation model to analyze the energy management strategy and a control model as part of the strategy itself. Experimental data from vehicle tests on a roller-dynamometer as well as power net measurements are used for model validation.

D.B. Kok

Papers

Online Energy Management for Hybrid Electric Vehicles

Energy Management for the Electric Powernet in Vehicles With a Conventional Drivetrain

Vehicle modeling for energy management strategies

The development of an optimal energy management system for vehicle electric power nets : a feasibility study from a control perspective