Design of Energy Management System of a PEMFC–Battery– Supercapacitor Hybrid Tramway

In this chapter, a hybrid power system that consists of multiple proton exchange membrane fuel cell (PEMFC) systems, batteries, and supercapacitors (SCs) is developed for a hybrid tramway. Three energy management strategies that included a fuzzy logic control (FLC), an equivalent consumption minimization strategy (ECMS), and a state machine strategy based on droop control (SMS-DC) are utilized to coordinate multiple power sources, avoid the transients and rapid changes of power demand, and achieve high efficiency without degrading the mechanism performance for an energy management system of hybrid tramway. A hybrid system model of tramway is established with commercially available devices, and then the different energy management strategies are evaluated with a real driving cycle of tramway from Turkey. The results compared with these strategies demonstrate that the higher average efficiencies of the tramway, the lower tramway-equivalent hydrogen consumptions, and more efficient use of the batteries and SCs energy are achieved by the SMS-DC. Therefore, the appropriate energy management system for high-power hybrid tramway will improve the hydrogen consumptions of overall hybrid system and the efficiencies of each power source

Design and experimental verification of a fuel cell/supercapacitor passive configuration for a light vehicle

The fuel cell/supercapacitor passive configuration without using any DC/DC converters is promising in auto-motive applications as it can downsize the fuel cell stack, maintain the peak power capability, improve the system efficiency, and remove the need of additional control. This paper presents the design and characterization of a fuel cell/supercapacitor passive hybrid system for a 60 V light vehicle. A detailed design procedure for the passive hybrid test platform is presented with each component modelled and experimentally verified. The voltage error of the fuel cell and the supercapacitor model in the steady state is within 2% and 3%, respectively. Experimental results also validate the function of the passive configuration under conditions of a step load and a drive cycle. The simulation model of the passive hybrid system matches the measurements when a step load current is applied. The supercapacitor provides the transient current due to its smaller resistance while the fuel cell handles the steady state current, which makes it possible to downsize the fuel cell stack. For the drive cycle examined in this paper, the fuel cell stack can be downsized to one third of the load peak power

An adaptive power split strategy with a load disturbance compensator for fuel cell/supercapacitor powertrains

Electric vehicles powered by fuel cell and supercapacitor hybrid power sources are of great interest. However, the power allocation between each power source is challenging and the DC bus voltage fluctuation is relatively significant in cascaded PI control schemes. This paper develops a power control strategy with an adjustable cut-off frequency, using an artificial potential field, to adaptively split the load current between the fuel cell and the supercapacitor under various load conditions. The adaptive cut-off frequency is calculated by cutting the load frequency spectrum with an allocation ratio that changes with the supercapacitor state of charge. Therefore, the relatively lower frequency portion of the load current is provided by the fuel cell and the supercapacitor handles the higher frequency portion. To enhance the control performance of the DC bus voltage regulation against the load disturbance, a load disturbance compensator is introduced to suppress the DC bus voltage fluctuation when the load variation occurs, which is implemented by a feed-forward controller that can compensate the load current variation in advance. The effectiveness of the proposed strategy is validated by extensive experiments

Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime

Hybrid fuel cell battery electric vehicles require complex energy management systems (EMS) in order to operate effectively. Poor EMS can result in a hybrid system that has low efficiency and a high rate of degradation of the fuel cell and battery pack. Many different types of EMS have been reported in the literature, such as equivalent consumption minimisation strategy and fuzzy logic controllers, which typically focus on a single objective optimisations, such as minimisation of H2 usage. Different vehicle and system specifications make the comparison of EMSs difficult and can often lead to misleading claims about system performance. This paper aims to compare different EMSs, against a range of performance metrics such as charge sustaining ability and fuel cell degradation, using a common modelling framework developed in MATLAB/Simulink - the Electric Vehicle Simulation tool-Kit (EV-SimKit). A novel fuzzy logic controller is also presented which mutates the output membership function depending on fuel cell degradation to prolong fuel cell lifetime – the Mutative Fuzzy Logic Controller (MFLC). It was found that while certain EMSs may perform well at reducing H2 consumption, this may have a significant impact on fuel cell degradation, dramatically reducing the fuel cell lifetime. How the behaviour of common EMS results in fuel cell degradation is also explored. Finally, by mutating the fuzzy logic membership functions, the MFLC was predicted to extend fuel cell lifetime by up to 32.8%

Impact of power sharing method on battery life extension in HESS for grid ancillary services

Hybrid energy storage system (HESS) based on Li-ion and supercapacitor (SC) can play a potential role to stabilise the grid by providing the fast frequency ancillary services. The SC helps to reduce the battery charge/discharge stress and hence assists to extend the battery lifespan. The power sharing method (PSM) is the heart in control part to improve the HESS performance and reduce the battery stress. This paper proposes a hybrid PSM and investigates its impact on battery life extension along with its relation to the system design and regulation signal. The performance of hybrid PSM is compared with three other PSMs (low pass filter, first and second rule based) in a 10 MW / 10 MWh full-active parallel HESS for frequency regulation service in two networks: UK (national grid) and USA (PJM). Considering maximum possible battery lifetime up to 25 years, result shows that the hybrid PSM approach allows a degree of better performance for both grid while the sharing of SC is kept maximum 2.0% and 2.5% (for US and UK grid respectively) of the HESS capacity. This study also analyses the impact of PSM on shared capacity and design of HESS for different grid regulation signals

An adaptive power distribution scheme for hybrid energy storage system to reduce the battery energy throughput in electric vehicles

The battery/supercapacitor (SC) hybrid energy storage system (HESS) is widely applied in electric vehicles (EVs) in recent years due to the hybrid system which combines the benefits of both devices. This paper proposes an adaptive power distribution scheme for battery/SC HESS to maximise the usage of SC according to its stored energy and load current. In the approach, the low-pass filter is developed with adaptive algorithm to calculate the suitable cut-off frequency to allocate the power demand between the battery and SC. The approach can adjust the cut-off frequency but not change the structure of the control system, and thus its original property of simple implementation and stability is not affected. The comprehensive simulation study verifies the effectiveness of the proposed adaptive power distribution scheme in a battery/SC HESS and its stability is further validated using Lyapunov method. The result shows that the adaptive method performs better than a traditional control system with 20%–40% less battery energy throughput during operation and can adjust the dynamic response of the HESS according to the energy capacity of SC to further improve system efficiency. The proposed adaptive power distribution scheme is verified able to extend the service life of the HESS system in EV applications

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

An adaptive state machine based energy management strategy for a multi-stack fuel cell hybrid electric vehicle

This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks' performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs' performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique

Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends

Concerns over growing greenhouse gas (GHG) emissions and fuel prices have prompted researchers to look into alternative energy sources, notably in the transportation sector, accounting for more than 70% of carbon emissions. An increasing amount of research on electric vehicles (EVs) and their energy management schemes (EMSs) has been undertaken extensively in recent years to address these concerns. This article aims to offer a bibliometric analysis and investigation of optimized EMSs for EV applications. Hundreds (100) of the most relevant and highly influential manuscripts on EMSs for EV applications are explored and examined utilizing the Scopus database under predetermined parameters to identify the most impacting articles in this specific field of research. This bibliometric analysis provides a survey on EMSs related to EV applications focusing on the different battery storages, models, algorithms, frameworks, optimizations, converters, controllers, and power transmission systems. According to the findings, more articles were published in 2020, with a total of 22, as compared to other years. The authors with the highest number of manuscripts come from four nations, including China, the United States, France, and the United Kingdom, and five research institutions, with these nations and institutions accounting for the publication of 72 papers. According to the comprehensive review, the current technologies are more or less capable of performing effectively; nevertheless, dependability and intelligent systems are still lacking. Therefore, this study highlights the existing difficulties and challenges related to EMSs for EV applications and some brief ideas, discussions, and potential suggestions for future research. This bibliometric research could be helpful to EV engineers and to automobile industries in terms of the development of cost-effective, longer-lasting, hydrogen-compatible electrical interfaces and well-performing EMSs for sustainable EV operations

Comparison of decentralized ADMM optimization algorithms for power allocation in modular fuel cell vehicles

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems