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

Hybrid electric vehicle fuel minimization by DC-DC converter dual-phase-shift control

The paper introduces an advanced DC-link variable voltage control methodology that improves significantly the fuel economy of series Hybrid Electric Vehicles (HEVs). The DC-link connects a rectifier, a Dual Active Bridge (DAB) DC-DC converter and an inverter, interfacing respectively the two sources and the load in a series HEV powertrain. The introduced Dual Phase Shift (DPS) proportional voltage conversion ratio control scheme is realized by manipulating the phase shifts of the gating signals in the DAB converter, to regulate the amount of DAB converter power flow in and out of the DC-link. Dynamic converter efficiency models are utilized to account for switching, conduction, copper and core losses. The control methodology is proposed on the basis of improving the individual efficiency of the DAB converter but with its parameters tuned to minimize the powertrain fuel consumption. Since DPS control has one additional degree of freedom as compared to Single Phase Shift (SPS) voltage control schemes, a Lagrange Multiplier optimization method is applied to minimize the leakage inductance peak current, the main cause for switching and conduction losses. The DPS control scheme is tested in simulations with a full HEV model and two associated conventional supervisory control algorithms, together with a tuned SPS proportional voltage conversion ratio control scheme, against a conventional PI control in which the DC-link voltage follows a constant reference. Nonlinear coupling difficulties associated with the integration of varying DC-link voltage in the powertrain are also exposed and addressed

Dynamic modeling platform for series hybrid electric vehicles

This paper introduces a simulation model that can be used to develop and test designs and control systems for hybrid electric vehicles (HEVs). The work involves a novel simulating platform, developed in Simulink, where each component of a series HEV is developed using a first-principles approach in a modular fashion, validated by available experimental data and then integrated to form a coupled nonlinear dynamic model. The vehicle model is capable to act as a platform for the design of supervisory control systems (SCSs) that optimize the energy flow in the powertrain. Simulations with two distinct SCSs and two driving cycles are used to analyze the vehicle performance under varying driving and operating conditions. The results demonstrate the applicability of the model for realistic prediction of both vehicle behavior and component energy losses, design optimization and control system design

Fuel cell-hybrid electric vehicle power train system design and control

Recently, due to elevated oil prices and the need for low emissions, the automotive industry has been clamoring for cleaner, more energy-efficient vehicles. Fuel cell-hybrid electric vehicles (FC-HEV) are considered to be one of the most promising alternatives, because of their evident advantages of much higher fuel efficiency and lower (or zero) emissions, without any significant restriction on driving range and vehicle performance. However, a number of severe obstacles need to be overcome to attain widespread commercialization of FC-HEVs. The most critical aspects of fuel cell vehicle research include the development of optimal power management strategies and design of efficient power train architectures. Firstly, this thesis attempts to solve the critical power management problem through the optimal design, modeling, and testing of innovative power control strategies. Thereafter, the advantages and limitations of the proposed strategies are compared and analyzed in depth. Secondly, the thesis also discusses the selection of suitable power train configurations, followed by the power electronic system design, based on hybridization degree and component characteristics. The circuit-level simulation results indicate that the power electronic control system can precisely implement the overall power control strategy, starting from the high-level supervisory control system. Finally, an attractive short-term future option, in the form of a plug-in fuel cell hybrid vehicle (FC-PHEV), is introduced. A suitable power management strategy is designed for the proposed FC-PHEV, with detailed discussions on critical performance as well as practical issues

Journey predictive energy management strategy for a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonisation of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialisation. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Further, recent studies suggest the use of \intelligent transport" infrastructure to include a predictive element to the energy management strategy to achieve reductions in emissions. The thesis addresses the problem of determining the links between component-sizing, real-world usage and energy management strategies for a PHEV. The objective is to develop an integrated framework in which the advantages of predictive energy management can be realised by component downsizing for a PHEV. The study is spilt into three sections. The first part presents the framework by which the predictive element can be included into the PHEV's energy management strategy. Second part describes the development of the PHEV component models and the various energy management strategies which control the split in energy used between the engine and the battery. In this section a new control strategy is presented which integrates the predictive element proposed in the first part. Finally, in the third section an optimisation framework is presented by which the size of the components within the PHEV are reduced due to the lower energy demands of the new proposed energy management strategy. The first part of the study presents a framework by which the energy consumption of a vehicle may be predicted over a route. The proposed energy prediction framework employs a neural network and was used o_-line for estimating the real-world energy consumption of the vehicle so that it can be later integrated within the vehicles energy management control system. Experimental results show an accuracy within 20%-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys … [cont.]

Model Based Automotive System Integration: Fuel Cell Vehicle Hardware-In-The-Loop

abstract: Over the past decade, proton exchange membrane fuel cells have gained much momentum due to their environmental advantages and commutability over internal combustion engines. To carefully study the dynamic behavior of the fuel cells, a dynamic test stand to validate their performance is necessary. Much attention has been given to HiL (Hardware-in-loop) testing of the fuel cells, where the simulated FC model is replaced by a real hardware. This thesis presents an economical approach for closed loop HiL testing of PEM fuel cell. After evaluating the performance of the standalone fuel cell system, a fuel cell hybrid electric vehicle model was developed by incorporating a battery system. The FCHEV was tested with two different control strategies, viz. load following and thermostatic. The study was done to determine the dynamic behavior of the FC when exposed to real-world drive cycles. Different parameters associated with the efficiency of the fuel cell were monitored. An electronic DC load was used to draw current from the FC. The DC load was controlled in real time with a NI PXIe-1071 controller chassis incorporated with NI PXI-6722 and NI PXIe-6341 controllers. The closed loop feedback was obtained with the temperatures from two surface mount thermocouples on the FC. The temperature of these thermocouples follows the curve of the FC core temperature, which is measured with a thermocouple located inside the fuel cell system. This indicates successful implementation of the closed loop feedback. The results show that the FC was able to satisfy the required power when continuous shifting load was present, but there was a discrepancy between the power requirements at times of peak acceleration and also at constant loads when ran for a longer time. It has also been found that further research is required to fully understand the transient behavior of the fuel cell temperature distribution in relation to their use in automotive industry. In the experimental runs involving the FCHEV model with different control strategies, it was noticed that the fuel cell response to transient loads improved and the hydrogen consumption of the fuel cell drastically decreased.Dissertation/ThesisMasters Thesis Engineering 201

Definition and verification of a set of reusable reference architectures for hybrid vehicle development

Current concerns regarding climate change and energy security have resulted in an increasing demand for low carbon vehicles, including: more efficient internal combustion engine vehicles, alternative fuel vehicles, electric vehicles and hybrid vehicles. Unlike traditional internal combustion engine vehicles and electric vehicles, hybrid vehicles contain a minimum of two energy storage systems. These are required to deliver power through a complex powertrain which must combine these power flows electrically or mechanically (or both), before torque can be delivered to the wheel. Three distinct types of hybrid vehicles exist, series hybrids, parallel hybrids and compound hybrids. Each type of hybrid presents a unique engineering challenge. Also, within each hybrid type there exists a wide range of configurations of components, in size and type. The emergence of this new family of hybrid vehicles has necessitated a new component to vehicle development, the Vehicle Supervisory Controller (VSC). The VSC must determine and deliver driver torque demand, dividing the delivery of that demand from the multiple energy storage systems as a function of efficiencies and capacities. This control component is not commonly a standalone entity in traditional internal combustion vehicles and therefore presents an opportunity to apply a systems engineering approach to hybrid vehicle systems and VSC control system development. A key non-­‐functional requirement in systems engineering is reusability. A common method for maximising system reusability is a Reference Architecture (RA). This is an abstraction of the minimum set of shared system features (structure, functions, interactions and behaviour) that can be applied to a number of similar but distinct system deployments. It is argued that the employment of RAs in hybrid vehicle development would reduce VSC development time and cost. This Thesis expands this research to determine if one RA is extendable to all hybrid vehicle types and combines the scientific method with the scenario testing method to verify the reusability of RAs by demonstration. A set of hypotheses are posed: Can one RA represent all hybrid types? If not, can a minimum number of RAs be defined which represents all hybrid types? These hypotheses are tested by a set of scenarios. The RA is used as a template for a vehicle deployment (a scenario), which is then tested numerically, thereby verifying that the RA is valid for this type of vehicle. This Thesis determines that two RAs are required to represent the three hybrid vehicle types. One RA is needed for series hybrids, and the second RA covers parallel and compound hybrids. This is done at a level of abstraction which is high enough to avoid system specific features but low enough to incorporate detailed control functionality. One series hybrid is deployed using the series RA into simulation, hardware and onto a vehicle for testing. This verifies that the series RA is valid for this type of vehicle. The parallel RA is used to develop two sub-­‐types of parallel hybrids and one compound hybrid. This research has been conducted with industrial partners who value, and are employing, the findings of this research in their hybrid vehicle development programs

Comparative Study of Hybrid Powertrain Architectures from a Fuel Economy Perspective

Depending on the structure of powertrain components, modern hybrid electric vehicles (HEVs) are usually categorized into different types, which influence the design and performance of energy management control strategies. This paper investigates the impact of non-plug-in HEV powertrain architectures on the fuel economy, where Dynamic Programming is used to find the optimal power split between the powertrain energy sources. The series and three parallel architectures that include through-the-road, pre- and posttransmission parallel, all with properly sized powertrain components, are compared. Three human-driver speed profiles collected respectively from urban, rural, and highway driving conditions are employed for the assessment. The comparative results demonstrate the energy saving potential of different types of HEVs and provide further insight into the practical choice of the hybrid powertrain architectures

Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency

The proposed supervisory control system (SCS) uses a control map to maximize the powertrain efficiency of a hybrid electric vehicle (HEV) in real-time. The paper presents the methodology and structure of the control, including a novel, comprehensive and unified expression for the overall powertrain efficiency that considers the engine-generator set and the battery in depth as well as the power electronics. A control map is then produced with instructions for the optimal power share between the engine branch and battery branch of the vehicle such that the powertrain efficiency is maximized. This map is computed off-line and can thereafter be operated in real-time at very low computational cost. A charge sustaining factor is also developed and introduced to ensure the SCS operates the vehicle within desired SOC bounds. This SCS is then tested and benchmarked against two conventional control strategies in a high-fidelity vehicle model, representing a series HEV. Extensive simulation results are presented for repeated cycles of a diverse range of standard driving cycles, showing significant improvements in fuel economy (up to 20%) and less aggressive use of the battery

Design, Modeling and Development of a Serial Hybrid Motorcycle with HCCI Engine

This paper discusses the design, modeling, and development of small motorcycle equipped with a HCCI engine in an series hybrid configuration. A mathematical model was developed using MATLAB/Simulink and used to size the powertrain components and to predict fuel economy. A conventional 125 cc spark ignition engine was modified to run in HCCI combustion mode and integrated into a prototype vehicle. Dual-fuel and external EGR strategies were used to upgrade the engine speed and torque capabilities of the engine to meet the requirements of the powertrain. An electrical generator, hub-motor, battery pack and other power electronics devices were used to form the electrical system for the vehicle. The advantages of the proposed design compared to the original motorcycle with SI engine and CVT transmission are: 1) a reduction in noxious emissions due to the HCCI combustion, and 2) higher fuel economy in city driving because of the HCCI engine and series hybrid powertrain. Fuel economy was measured by driving the motorcycle on a chassis dynamometer using a sequence of ECE-40 driving cycles. The overall fuel economy was measured to be 73.7km/L which represents a 139.3% increase in fuel economy over the baseline vehicle