Power Management of a Plug-in Hybrid Electric Vehicle Based on Cycle Energy Estimation

2012 Workshop on Engine and Powertrain Control,Simulation and ModelingThe International Federation of Automatic ControlRueil-Malmaison, France, October 23-25, 2012Plug-in Hybrid Electric Vehicles (PHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. There are many potential advantages of hybridization such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. In order to solve the power management problem, the controller proposed in this work is divided into two levels: the upper one calculates the power that must be supplied by the engine at each moment taking into account the estimation of the energy that must be supplied by the powertrain until the end of the journey. The lower one manages the torque/speed set points for all the devices. Besides, the operation modes are changed according to some heuristic rules. Several simulation results are presented, showing that the proposed control strategy can provide good performance with low computational load

Optimization of the powertrain of electric vehicles for a given route

The global challenge of reducing pollutant and greenhouse gas emissions has forced the development of alternatives to traditional internal combustion engine vehicles, such as electric or hybrid vehicles. Electric engines are the most efficient for delivery trucks or city buses. Their acceleration and deceleration patterns make them inefficient for the use of internal combustion engines. However, their range and purchase cost are the main factors limiting their use in these applications. The range and acquisition cost of an electric vehicle are mainly related to the energy storage system. Therefore, the optimal size of the battery pack should be considered as a design objective when its application is known. This paper presents a methodology to optimize the battery pack of an electric vehicle based on a given travel distance in a target time. Therefore, it would be applicable to delivery vehicles, buses and any vehicle whose route and travel time are known in advance. The proposed methodology allows minimizing energy consumption by determining the optimal gear ratio for a given route, setting the travel time as a target. A complete vehicle model and a multi-objective genetic algorithm are used for this purpose

Powertrain Systems for Net-Zero Transport

The transport sector continues to shift towards alternative powertrains, particularly with the UK Government’s announcement to end the sale of petrol and diesel passenger cars by 2030 and increasing support for alternatives. Despite this announcement, the internal combustion continues to play a significant role both in the passenger car market through the use of hybrids and sustainable low carbon fuels, as well as a key role in other sectors such as heavy-duty vehicles and off-highway applications across the globe. Building on the industry-leading IC Engines conference, the 2021 Powertrain Systems for Net-Zero Transport conference (7-8 December 2021, London, UK) focussed on the internal combustion engine’s role in Net-Zero transport as well as covered developments in the wide range of propulsion systems available (electric, fuel cell, sustainable fuels etc) and their associated powertrains. To achieve the net-zero transport across the globe, the life-cycle analysis of future powertrain and energy was also discussed. Powertrain Systems for Net-Zero Transport provided a forum for engine, fuels, e-machine, fuel cell and powertrain experts to look closely at developments in powertrain technology required, to meet the demands of the net-zero future and global competition in all sectors of the road transportation, off-highway and stationary power industries

MODEL-BASED CONTROL OF HYBRID ELECTRIC POWERTRAINS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Powertrain electrification including hybridizing advanced combustion engines is a viable cost-effective solution to improve fuel economy of vehicles. This will provide opportunity for narrow-range high-efficiency combustion regimes to be able to operate and consequently improve vehicle’s fuel conversion efficiency, compared to conventional hybrid electric vehicles (HEV)s. Low temperature combustion (LTC) engines offer the highest peak brake thermal efficiency reported in literature, but these engines have narrow operating range. In addition, LTC engines have ultra-low soot and nitrogen oxides (NOx) emissions, compared to conventional compression ignition and spark ignition (SI) engines. This dissertation concentrates on integrating the LTC engines (i) in series HEV and extended range electric vehicle (E-REV) architectures which decouple the engine from the drivetrain and allow the ICE to operate fully in a dedicated LTC mode, and (ii) a parallel HEV architecture to investigate optimum performance for fuel saving by utilizing electric torque assist level offered by e-motor. An electrified LTC-SI powertrain test setup is built at Michigan Technological University to develop the powertrain efficiency maps to be used in energy management control (EMC) framework. Three different types of Energy Management Control (EMC) strategies are developed. The EMC strategies encompass thermostatic rule-based control (RBC), offline (i.e., dynamic programing (DP) and pontryagin’s minimum principal (PMP)), and online optimization (i.e., model predictive control (MPC)). The developed EMC strategies are then implemented on experimentally validated HEV powertrain model to investigate the powertrain fuel economy. A dedicated single-mode homogeneous charge compression ignition (HCCI) and reactivity controlled compression ignition (RCCI) engines are integrated with series HEV powertrain. The results show up to 17.7% and 14.2% fuel economy saving of using HCCI and RCCI, respectively in series HEV compared to modern SI engine in the similar architecture. In addition, the MPC results show that sub-optimal fuel economy is achieved by predicting the vehicle speed profile for a time horizon of 70 sec. Furthermore, a multi-mode LTC-SI engine is integrated in both series and parallel HEVs. The developed multi-mode LTC-SI engine enables flexibility in combustion mode-switching over the driving cycle, which helps to improve the overall fuel economy. The engine operation modes include HCCI, RCCI, and SI modes. The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations. In the parallel HEV architecture, the results for the UDDS driving cycle show the maximum benefit of the multi-mode LTCSI engine is realized in the mild electrification level, where the LTC mode operating time increases dramatically from 5.0% in Plug-in Hybrid Electric Vehicle (PHEV) to 20.5% in mild HEV

New ICE concept for hybrid application

Due to increasingly strict regulations on automobile CO2 emission around the world, this thesis focuses on the development of the control strategies of a plug-in series hybrid electric vehicle (HEV) with the goal of minimizing CO2 emission. The thesis consists of three parts. The first target is to set up an electric vehicle (EV) model, which is the base of a plug-in series hybrid electric vehicle. The electric machine and battery are sized, and range capability and energy consumption are evaluated for a vehicle running in EV mode. The second objective is the assessment of the reference performance of the Range Extender (R-EX) architecture through the dynamic programming (DP) function in MATLAB, in terms of minimizing CO2 emissions in the charge-sustaining condition. The third one is the development of the rule based control strategy through the analysis of the DP results by rules extraction. In this thesis, a B-segment hatchback passenger car is modelled. The simulations were carried out along seven standard driving cycles that were developed to model different road conditions. This thesis also evaluates the effect of different values of auxiliary power on the electric range, energy consumption and thresholds of the rule-based control strategy. A sensitivity analysis of the carbon intensity of electricity is performed from a worldwide perspective. Finally, the minimum values of CO2 emission and the optimal engine operating points over different driving cycles are obtained from the dynamic programming; two flow charts of the proposed rule-based control strategies are derived, which are implementable for an electrical control unit to determine the power split between different energy sources

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

Powertrain Architectures and Technologies for New Emission and Fuel Consumption Standards

New powertrain design is highly influenced by CO2 and pollutant limits defined by legislations, the demand of fuel economy in for real conditions, high performances and acceptable cost. To reach the requirements coming from both end-users and legislations, several powertrain architectures and engine technologies are possible (e.g. SI or CI engines), with many new technologies, new fuels, and different degree of electrification. The benefits and costs given by the possible architectures and technology mix must be accurately evaluated by means of objective procedures and tools in order to choose among the best alternatives. This work presents a basic design methodology and a comparison at concept level of the main powertrain architectures and technologies that are currently being developed, considering technical benefits and their cost effectiveness. The analysis is carried out on the basis of studies from the technical literature, integrating missing data with evaluations performed by means of powertrain-vehicle simplified models, considering the most important powertrain architectures. Technology pathways for passenger cars up to 2025 and beyond have been defined. After that, with support of more detailed models and experimentations, the investigation has been focused on the more promising technologies to improve internal combustion engine, such as: water injection, low temperature combustions and heat recovery systems

Powertrain Systems for Net-Zero Transport

The transport sector continues to shift towards alternative powertrains, particularly with the UK Government’s announcement to end the sale of petrol and diesel passenger cars by 2030 and increasing support for alternatives. Despite this announcement, the internal combustion continues to play a significant role both in the passenger car market through the use of hybrids and sustainable low carbon fuels, as well as a key role in other sectors such as heavy-duty vehicles and off-highway applications across the globe. Building on the industry-leading IC Engines conference, the 2021 Powertrain Systems for Net-Zero Transport conference (7-8 December 2021, London, UK) focussed on the internal combustion engine’s role in Net-Zero transport as well as covered developments in the wide range of propulsion systems available (electric, fuel cell, sustainable fuels etc) and their associated powertrains. To achieve the net-zero transport across the globe, the life-cycle analysis of future powertrain and energy was also discussed. Powertrain Systems for Net-Zero Transport provided a forum for engine, fuels, e-machine, fuel cell and powertrain experts to look closely at developments in powertrain technology required, to meet the demands of the net-zero future and global competition in all sectors of the road transportation, off-highway and stationary power industries

Challenges of micro/mild hybridisation for construction machinery and applicability in UK

In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology