Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented.

Optimization in microgrids with hybrid energy systems – A review

One of the major challenges in a battery/ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This paper presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multiobjective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc–dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle data sets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NNs). The proposed online intelligent energy management controller is applied to a midsize electric vehicles (EV). A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy.

A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System

The integration of renewable energy source (RES) and energy storage systems (ESS) in microgrids has provided potential benefit to end users and system operators. However, intermittent issues of RES and high cost of ESS need to be placed under scrutiny for economic operation of microgrids. This paper presents a two-layer predictive energy management system (EMS) for microgrids with hybrid ESS consisting of batteries and supercapacitors. Incorporating degradation costs of the hybrid ESS with respect to the depth of charge and lifetime, long-term costs of batteries and supercapacitors are modeled and transformed to short-term costs related to real-time operation. In order to maintain high system robustness at minimum operational cost, a hierarchical dispatch model is proposed to determine the scheduling of utilities in microgrids within a finite time horizon, in which the upper layer EMS minimizes the total operational cost and the lower layer EMS eliminates fluctuations induced by forecast errors. Simulation studies demonstrate that different types of energy storages can be utilized at two control layers for multiple decision-making objectives. Scenarios incorporating different pricing schemes, prediction horizon lengths, and forecast accuracies also verify the effectiveness of the proposed EMS structure.

A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs

The idea of Hybrid Energy Storage System (HESS) lies on the fact that heterogeneous Energy Storage System (ESS) technologies have complementary characteristics in terms of power and energy density, life cycle, response rate, and so on. In other words, high power ESS devices possess fast response rate while in the contrary, high energy ESS devices possess slow response rate. Therefore, it may be beneficial to hybridize ESS technologies in the way that synergize functional advantages of two heterogeneous existing ESS technologies As a consequence, this hybridization provides excellent characteristics not offered by a single ESS unit. This new technology has been proposed and investigated by several researchers in the literature particularly in the fields of renewable energy and electrified transport sector. In this context and according to an extensive literature survey, this paper is to review the concept of the HESS, hybridization principles and proposed topologies, power electronics interface architectures, control and energy management strategies, and application arenas.

Emergence of hybrid energy storage systems in renewable energy and transport applications – A review

Complementary features of batteries and supercapacitors can be effectively used in a hybrid energy storage system (HESS). The utilization of the HESS in electric vehicles (EVs) offers many advantages, such as efficient regenerative braking, battery safety, and improved vehicle acceleration. In this paper, a new regenerative braking system (RBS) is proposed for EVs with HESS and driven by brushless DC (BLDC) motor. During regenerative braking, the BLDC acts as a generator. Hence, by using an appropriate switching algorithm, the dc-link voltage is boosted and the energy is transferred to the supercapacitor or the battery through the inverter. The harvested energy can be utilized to improve the vehicle acceleration and/or keep the battery pack from deep discharging while driving uphill. To provide a reliable and smooth brake, braking force distribution is realized through an artificial neural network. Simultaneously, the braking current is adjusted by a PI controller for constant torque braking. To evaluate the performance of the proposed RBS, different simulations and experiments are carried out. The results confirm high capability of the proposed RBS.

An Efficient Regenerative Braking System Based on Battery/Supercapacitor for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles With BLDC Motor

Batteries and supercapacitors (SC) complement one another; a battery has a relatively high energy density but a low power density, whereas an SC has a relatively high power density but a low energy density. In order to offset their opposing limitations, an active battery/SC hybrid energy storage system (HESS) using a dc/dc converter has been proposed. The major problem concerning an active HESS is in how to control the current flow in order to achieve two objectives: the minimization of the magnitude/fluctuation of the current flowing in and out of the battery and the energy loss seen by the SCs. This problem has not been analytically investigated for an optimal solution regarding these two goals. In this paper, we present an optimal energy management scheme for active HESS. In order to obtain the optimal solution, we formulate the problem as an optimization problem concerning these two objectives. Observing that the feasibility and optimality of the solution critically depends on the boundary parameters of the problem, we present an algorithm that effectively adjusts the parameter values. The proposed algorithm is based on the multiplicative-increase- additive-decrease principle, which guarantees a feasible optimal solution. Through MATLAB simulations, we demonstrate that the proposed scheme can optimally minimize the magnitude/fluctuation of the battery current and the SC energy loss.

Energy Management Optimization in a Battery/Supercapacitor Hybrid Energy Storage System

Batteries mounted on electric vehicles (EVs) are often damaged by high peak power and rapid charging/discharging cycles, which are originated from repetitive acceleration/deceleration of vehicles particularly in urban situations. To reduce battery damage, the battery/supercapacitor (SC) hybrid energy storage system (HESS) has been considered as a solution because the SC can act as a buffer against large magnitudes and rapid fluctuations in power. While the traditional purpose of employing the HESS in EVs is to minimize the magnitude/variation of battery power or power loss, the previous approaches proposed for controlling the HESS have some drawbacks; they neither consider these objectives simultaneously nor reflect real-time load dynamics for computing the SC reference voltage. In this paper, we present a power control framework consisting of two stages: one for computing the SC reference voltage and another for optimizing the power flowing through the HESS. In the presented framework, we propose a methodology for calculating the SC reference voltage considering the real-time load dynamics without given future operation profiles. In addition, we formulate the HESS power control problem as a convex optimization problem that minimizes the magnitude/fluctuation of battery power and power loss at the same time. The optimization problem is formulated so that it can be repeatedly solved by general solvers in polynomial time. Simulation results carried out on MATLAB show that the magnitude/variation of battery power and power loss can be concurrently reduced in real time by the proposed framework.

Real-Time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles

Batteries are considered to be one of the key components in EV. Although the energy density in batteries has continuously been evolving, they can be easily damaged by the peak current or steep variation of current. In order to overcome this weakness, an active battery/supercapacitor (SC) Hybrid Energy Storage System (HESS) has been proposed. The major problem concerning an active HESS in EV is in how to control the current flow in order to minimize the magnitude/ fluctuation of the current flowing in and out of the battery of EV. It is well known that future load profiles can be used to obtain more optimal control of current flow in EV. However, it is difficult to acquire the accurate load profiles because the vehicle movement relies on many factors, e.g., the traffic on the road or the driving pattern of a driver. In this paper, we propose an optimization framework for computing the sub-optimal current flow of the HESS in EV although the future load profile is not precise. Simulation results show that the proposed scheme can efficiently minimize the magnitude/fluctuation of the battery current in EV.

Robust energy management of a battery/supercapacitor Hybrid Energy Storage System in an electric vehicle

The Gaussian mixture probability hypothesis density (GM-PHD) filter has been widely adopted to track multiple targets, because it can effectively handle target birth/death without the track-to-measurement data association process. However, the GM-PHD filter is known to have serious problems related to birth intensity generation and target tractability. In addition, weight underestimation/overestimation may occur if there are missing detections or measurement clutters. Since these problems may lead to severe estimation errors, many researchers have tried to find solutions. However, none of the researchers have been successful at solving these problems simultaneously. In this paper, we propose a robust multitarget tracking scheme based on the GM-PHD filter to improve estimation accuracy, even if there are many false detections. The proposed scheme includes the processing step of evaluating multiple states/measurements, which is designed to overcome the weight underestimation/overestimation problems. Furthermore, it includes generating the birth intensity for the next iteration using measurements not associated with any tracked states. We also show that the proposed method can be extended to nonlinear Gaussian models. The simulation results demonstrate that the proposed scheme can provide relatively accurate multitarget estimates compared with the previous approaches when the measurements include many false positives/negatives.

Robust Multitarget Tracking Scheme Based on Gaussian Mixture Probability Hypothesis Density Filter

The advancement of electronic technology has made significant contributions to the safety and convenience of modern vehicles. New intelligent functionalities of vehicles have been implemented in a number of electronic control units (ECUs) that are connected to each in vehicle control networks (VCNs). However, with the rapid increase in the number of ECUs, VCNs currently face several challenges, e.g., design complexity, space constraints, system reliability, and interdependency. Considering these factors, the complexity of the VCN design problem exponentially increases, which means that the problem cannot be solved within a reasonable time using conventional optimization techniques. In this paper, we report a new methodology for the optimal design of VCNs. An analytical model was derived to examine the fundamental characteristics of the VCN design problem. Compared with the case of a conventional data network, which typically considers temporal scheduling over a fixed physical topology, the VCN design problem should also consider spatial constraints, e.g., volume, position, and weight. Moreover, the spatial constraints change during the solving procedure. Such temporal and spatial joint optimization problems with varying constraints incur extremely high computational complexity. To tackle the high complexity, this paper proposes a fast solution based on a repeated-matching method, which reduces the problem complexity from O(NNN) to O(NN3). By applying our methodology to a number of different real-world VCN design scenarios, this proposal can produce a 1% near-optimal design within a significantly reduced time.

Mid-Eum Choi

Papers

Energy Management Optimization in a Battery/Supercapacitor Hybrid Energy Storage System

Real-Time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles

Robust energy management of a battery/supercapacitor Hybrid Energy Storage System in an electric vehicle

Robust Multitarget Tracking Scheme Based on Gaussian Mixture Probability Hypothesis Density Filter

Design Optimization of Vehicle Control Networks