Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Online power management with embedded optimization for a multi-source hybrid with real time applications

The focus of this thesis is to develop a suitable power management optimization strategy for a three-source hybrid vehicle powertrain. This strategy takes into account the integration of optimized parameters that limit the battery and fuel cell current by utilizing a third power source, namely supercapacitor. The goal is to develop a modular structure with decoupled online and offline parts such that implementation in case of real driving conditions is feasible. Based on the literature review it can be concluded that providing optimal solutions in terms of multiple objectives online is an issue. Adaption of optimized control strategy to real driving data is another concern. The developed strategy employs an online rule-based control with embedded offline-optimized parameters. The parameters are optimized with respect to multiple and conflicting objectives such as fuel consumption and state-of-charge deviation minimization. By a suitable selection of parameters, operation of all three sources within desired working ranges is possible, keeping in mind the load demand. By varying the weights between the objectives, one or more objectives can be given more priority than others. The application of this concept to fuel cell-battery-supercapacitor hybrid is discussed in this thesis. Detailed modeling of all components along with verification and plausibility assessment is done. For the purpose of experimental validation, the real powertrain components are replaced by controllable power sources and sinks that emulate the dynamics of real components. Finally, a brief concept is presented to integrate the developed power management optimization in real driving scenarios. For validation/verification purposes, a driving simulator environment is connected to the experimental hybrid electric vehicle set-up and with the help of an illustrative example, the desired predicted optimal values are calculated online and displayed to the human driver by a suitable interface. The absence of online tuning of controller parameters in this example is counteracted by developing a concept based on literature. With the help of this concept, the adaption of the power management control concept, developed in this thesis, can be realized

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

This paper studies the impact of two significant aspects, namely fuel cell (FC) degradation and thermal management, over the performance of an optimal and a rule-based energy management strategy (EMS) in a fuel cell hybrid electric vehicle (FCHEV). To do so, firstly, a vehicle's model is developed in simulation environment for a low-speed FCHEV composed of a FC stack and a battery pack. Subsequently, deterministic dynamic programming (DP), as an optimal strategy, and bounded load following strategy (BLFS), as a common rule-based strategy, are utilized to minimize the hydrogen consumption while respecting the operating constraints of the power sources. The performance of the EMSs is assessed at different scenarios. The first objective is to clarify the effect of FC stack degradation on the performance of the vehicle. In this regard, each EMS determines the required current from the FC stack for two FCs with different levels of degradation. The second objective is to evaluate the thermal management contribution to improving the performance of the new FC compared to the considered cases in scenario one. In this respect, each strategy deals with determining two control variables (FC current and cooling fan duty cycle). The results of this study indicate that negligence of adapting to the PEMFC health state, as the PEMFC gets aged, can increase the hydrogen consumption up to 24.8% in DP and 12.1% in BLFS. Moreover, the integration of temperature dimension into the EMS can diminish the hydrogen consumption by 4.1% and 5.3% in DP and BLFS respectively. © 2020 Elsevier Lt

Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains

Implementation and in-depth analyses of a battery-supercapacitor powered electric vehicle (E-Kancil)

This thesis contributes to the research issue pertaining to the management of multiple energy sources on-board a pure electric vehicle; particularly the energy dense traction battery and the power dense supercapacitor or ultracapacitor. This is achieved by analysing real world drive data on the interaction between lead acid battery pack and supercapacitor module connected in parallel while trying to fulfil the load demands of the vehicle. The initial findings and performance of a prototype electric vehicle conversion of a famous Malaysian city car; the perodual kancil, is presented in this thesis. The 660 cc compact city car engine was replaced with a brushless DC motor rated at 8KW continuous and 20KW peak. The battery pack consists of eight T105 Trojan 6V, 225 Ah deep cycle lead acid battery which builds up a voltage of 48V. In addition to this, a supercapacitor module (165F, 48V) is connected in parallel using high power contactors in order to investigate the increase in performance criteria such as acceleration, range, battery life etc. which have been proven in various literatures via simulation studies. A data acquisition system is setup in order to collect real world driving data from the electric vehicle on the fly along a fixed route. Analysis of collected driving data is done using MATLAB software and comparison of performance of the electric vehicle with and without supercapacitor module is made. Results show that with a parallel connection, battery life and health is enhanced by reduction in peak currents of up to 49%. Peak power capabilities of the entire hybrid source increased from 9.5KW to 12.5KW. A 41% increase in range per charge was recorded. The author of this work hopes that by capitalizing on the natural peak power buffering capabilities of the supercapacitor, a cost effective energy management system can be designed in order to utilize more than 23.6% of the supercapacitor energy

Optimal cost minimization strategy for fuel cell hybrid electric vehicles based on decision making framework

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed

Implementation and in-depth analyses of a battery-supercapacitor powered electric vehicle (E-Kancil)

This thesis contributes to the research issue pertaining to the management of multiple energy sources on-board a pure electric vehicle; particularly the energy dense traction battery and the power dense supercapacitor or ultracapacitor. This is achieved by analysing real world drive data on the interaction between lead acid battery pack and supercapacitor module connected in parallel while trying to fulfil the load demands of the vehicle. The initial findings and performance of a prototype electric vehicle conversion of a famous Malaysian city car; the perodual kancil, is presented in this thesis. The 660 cc compact city car engine was replaced with a brushless DC motor rated at 8KW continuous and 20KW peak. The battery pack consists of eight T105 Trojan 6V, 225 Ah deep cycle lead acid battery which builds up a voltage of 48V. In addition to this, a supercapacitor module (165F, 48V) is connected in parallel using high power contactors in order to investigate the increase in performance criteria such as acceleration, range, battery life etc. which have been proven in various literatures via simulation studies. A data acquisition system is setup in order to collect real world driving data from the electric vehicle on the fly along a fixed route. Analysis of collected driving data is done using MATLAB software and comparison of performance of the electric vehicle with and without supercapacitor module is made. Results show that with a parallel connection, battery life and health is enhanced by reduction in peak currents of up to 49%. Peak power capabilities of the entire hybrid source increased from 9.5KW to 12.5KW. A 41% increase in range per charge was recorded. The author of this work hopes that by capitalizing on the natural peak power buffering capabilities of the supercapacitor, a cost effective energy management system can be designed in order to utilize more than 23.6% of the supercapacitor energy

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

Assessment of Supercapacitor performance in a hybrid energy storage system with an EMS based on the discrete wavelet transform

This is the author accepted manuscript.When battery and supercapacitor (SC) Energy Storage Systems (ESSs) coexist in electric vehicles, energy management is imperative to ensure efficient power distribution based on the strengths and weaknesses of each ESS. The decoupling of highly dynamic power demands into components that match the dynamic nature of each ESS is essential. The Discrete Wavelet Transform (DWT) has been widely recommended for this purpose as part of real time energy management systems. However, due to DWT signal processing, delays in the frequency components can undermine the benefits of hybridization. This paper analyses the contribution of the SC to alleviate the battery when the DWT is used with and without time delay compensation using future demand prediction. Four different implementation strategies for a DWT based EMS have been evaluated using different metrics to quantify energy circulation and SC assistance during acceleration and braking. Simulation results using urban and highway driving cycles, show that obtaining the SC current reference as the difference between the real time current demand and the DWT low frequency component enhances SC assistance during acceleration and braking at the expense of higher energy circulation. The complexity added by future demand prediction does not reap SC performance benefits

Design of Energy Management Strategies for a Battery-Ultracapacitor Electric Vehicle

The battery pack is the most expensive component in electric vehicles. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. One solution to this issue would be increasing the battery capacity to meet the high energy requests. However, increasing the battery size is not reasonable due to the high cost and volume. An alternative solution is integrating other energy storage systems into the vehicle powertrain. The additional energy storage system highlights an energy management strategy to distribute the power among onboard energy storage systems effectively. Energy management systems incorporate different strategies classified based on their computational time, implementability in real-time, and measurable performance to be optimized. This thesis considers the case study of Chevy Spark model year 2015 with a hybrid energy storage system including battery and ultracapacitor. First, an overview of diffrent energy storage systems is presented, followed by a review of different hybrid energy storage' configurations. Second, energy management strategies are categorized into three main classifications: rule-based, optimization-based, and data-based algorithms. Third, the selected vehicle model with an embedded rule-based energy management strategy is developed in MATLAB Simulink, and battery performance is validated against available real-world data. Optimal power distribution among battery and ultracapacitor is achieved through an offline global optimal algorithm in chapter 5 in a way to improve battery life. Finally, optimal results are used as a training dataset for an online data-based energy management strategy. Results prove the strategy's effectiveness by improving battery life by an average of 16% compared to the rule-based and 12% difference from the globally optimal strategy on various driving conditions. The proposed energy management strategy provides near-optimal performance while it is real-time implementable and does not need to have beforehand knowledge of driving cycles