Stratégies de gestion d’énergie pour véhicules électriques et hybride avec systèmes hybride de stockage d’énergie

Les véhicules électriques et hybrides font partie des éléments clés pour résoudre les problèmes de réchauffement de la planète et d'épuisement des ressources en combustibles fossiles dans le domaine du transporte. En raison des limites des différents systèmes de stockage et de conversion d’énergie en termes de puissance et d'énergie, les hybridations sont intéressantes pour les véhicules électriques (VE). Dans cette thèse, deux hybridations typiques sont étudiées • un sous-système de stockage d'énergie hybride combinant des batteries et des supercondensateurs (SC) ; • et un sous-système de traction hybride parallèle combinant moteur à combustion interne et entraînement électrique. Ces sources d'énergie et ces conversions combinées doivent être gérées dans le cadre de stratégies de gestion de l'énergie (SGE). Parmi celles-ci, les méthodes basées sur l'optimisation présentent un intérêt en raison de leur approche systématique et de leurs performances élevées. Néanmoins, ces méthodes sont souvent compliquées et demandent beaucoup de temps de calcul, ce qui peut être difficile à réaliser dans des applications réelles. L'objectif de cette thèse est de développer des SGE simples mais efficaces basées sur l'optimisation en temps réel pour un VE et un camion à traction hybride parallèle alimentés par des batteries et des SC (système de stockage hybride). Les complexités du système étudié sont réduites en utilisant la représentation macroscopique énergétique (REM). La REM permet de réaliser des modèles réduits pour la gestion de l'énergie au niveau de la supervision. La théorie du contrôle optimal est ensuite appliquée à ces modèles réduits pour réaliser des SGE en temps réel. Ces stratégies sont basées sur des réductions de modèle appropriées, mais elles sont systématiques et performantes. Les performances des SGE proposées sont vérifiées en simulation par comparaison avec l’optimum théorique (programmation dynamique). De plus, les capacités en temps réel des SGE développées sont validées via des expériences en « hardware-in-the-loop » à puissances réduites. Les résultats confirment les avantages des stratégies proposées développées par l'approche unifiée de la thèse.Abstract: Electric and hybrid vehicles are among the keys to solve the problems of global warming and exhausted fossil fuel resources in transportation sector. Due to the limits of energy sources and energy converters in terms of power and energy, hybridizations are of interest for future electrified vehicles. Two typical hybridizations are studied in this thesis: • hybrid energy storage subsystem combining batteries and supercapacitors (SCs); and • hybrid traction subsystem combining internal combustion engine and electric drive. Such combined energy sources and converters must be handled by energy management strategies (EMSs). In which, optimization-based methods are of interest due to their high performance. Nonetheless, these methods are often complicated and computation consuming which can be difficult to be realized in real-world applications. The objective of this thesis is to develop simple but effective real-time optimization-based EMSs for an electric car and a parallel hybrid truck supplied by batteries and SCs. The complexities of the studied system are tackled by using Energetic Macroscopic Representation (EMR) which helps to conduct reduced models for energy management at the supervisory level. Optimal control theory is then applied to these reduced models to accomplish real-time EMSs. These strategies are simple due to the suitable model reductions but systematic and high-performance due to the optimization-based methods. The performances of the proposed strategies are verified via simulations by comparing with off-line optimal benchmark deduced by dynamic programming. Moreover, real-time capabilities of these novel EMSs are validated via experiments by using reduced-scale power hardware-in-the-loop simulation. The results confirm the advantages of the proposed strategies developed by the unified approach in the thesis

Real-time Energy Management System of Battery-Supercapacitor in Electric vehicles

This thesis presents the design, simulation and experimental validation of an Energy Management System (EMS) for a Hybrid Energy Storage System (HESS) composed of lithium ion batteries and Supercapacitors (SCs) in electric vehicles. The aim of the EMS is to split the power demand considering the weaknesses and strengths or the power sources. The HESS requires an EMS to determine power missions for the battery and SC in real time, where the SC is commanded to assist the battery during high power demand and recover the energy generated during braking. Frequency sharing techniques have been proposed by researchers to achieve this objective, including the Discrete Wavelet Transform (DWT) and conventional filtration methods (low and high pass filters). However, filtration approaches can introduce delay (milliseconds to tens of seconds) in the frequency components which undermines the hybridisation advantages. Hence, the selection of the filtration technique and filter design are crucial to the system's performance. Researchers have proposed power demand prediction methodologies to deal with time delay, however, the advantages and drawbacks of using such methods have not been investigated thoroughly, particularly whether time delay compensation and its inherent prediction error improves the system performance, efficiency, and timely SC contribution during the motoring and braking stages. This work presents a fresh perspective to this research field by introducing a novel approach that deals with delay without complicated prediction algorithms and improves the SC contribution during the motoring and braking stages while reducing energy losses in the system. The proposed EMS allows the SC to provide timely assistance during motoring and to recover the braking energy generated. A charging strategy controls energy circulation between the battery and SC to keep the SC charge availability during the whole battery discharge cycle. The performance and efficiency of the HESS is improved when compared to the traditional use of conventional filtration techniques and the DWT. Results show that the proposed EMS method improves the energy efficiency of the HESS. For the US06 driving cycle, the energy efficiency is 91.6%. This is superior to the efficiency obtained with an EMS based on a high pass filter (41.3%), an EMS based on DWT high frequency component (30.3%) and an EMS based on the predicted DWT high frequency component (41%)

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.</p

Performance analysis of batteries used in electric and hybrid electric vehicles

Hybrid electric vehicles (HEVs) and electric vehicles (EVs) are the most viable solutions to the undesirable high petroleum consumption by the present form of internal combustion engine driven vehicles. The varying requisites of HEVs and EVs have resulted in the advancement of battery technology in the area of chemical compositions such as electrode and electrolyte in addition to its electrical combination, control and protection schemes. The maximum utilization and protection of the battery is a challenge that needs to be tackled to improve its efficiency and reliability. A comprehensive study of the present battery technology has been performed in this thesis. The research is focused on battery modeling and its applications taking the complete electric drive train into consideration. Novel models and research perspectives have been proposed and analyzed. The scopes of increasing the accuracy of the present day battery management system have also been discussed

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

Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

Structured Analysis and Review of Filter-Based Control Strategies for Hybrid Energy Storage Systems

Hybrid energy storage systems (HESS), i.e., the combination of two different energy storage technologies, are widely discussed as a promising solution for energy storage problems. A common control scheme to allocate the power between these storages and the subject of this study is filter-based control, where a filter splits the input signal into a low-frequency and high-frequency part. It provides robust results and easy implementation, although more advanced strategies may perform better. Many publications use this controller for specific problems, but a structured analysis of this controller type that quantifies the advantages and disadvantages, traits, and setbacks is missing. This work fills this gap and structures, summarizes, and provides mathematical background and guidelines on filter-based control of hybrid energy storage systems. Numerical simulations are performed to quantify the impact of design variables, parameters, or the input signal by using a linear storage model with efficiency and self-discharge rate and a low-pass filter controller with constant energy feedback as a representative subtype of this control scheme. The present work proves the high cycle-reduction capabilities of filter-controlled HESS at the cost of overdimensioning compared to more advanced control strategies. It demonstrates that using a high-efficiency, high-power storage with a low self-discharge rate and high-energy storage leads to smaller overall dimensioning and losses than a single storage system. The study identifies the feedback factor of the controller as the most impacting design variable