25,380 research outputs found

    Energy management of hybrid and battery electric vehicles

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    This work focuses on improving the fuel economy of parallel Hybrid Electric Vehicles (HEVs) and dual-motor Electric Vehicles (EVs) through energy management strategies. Both vehicle models have two propulsion branches, each powering a separate axle: An engine and an electric motor in the HEV and two electric motors in the EV. This similarity in the vehicle models emphasises the need for similar energy management solutions. In Part Energy Management of HEVs of this thesis, a high-fidelity parallel Through-The-Road (TTR) HEV model is developed to study and test conventional control strategies. The traditional control strategies serve as a guide for developing novel heuristic control strategies. The Equivalent Consumption Minimisation Strategy (ECMS) is an optimisation-based control strategy used as the benchmark in this part of the work. A family of rule-based energy management strategies is proposed for parallel HEVs, including the Torque-levelling Threshold-changing Strategy (TTS) and its simplified version, the Simplified Torque-levelling Threshold-changing Strategy (STTS). The TTS applies a concept of torque-levelling, which ensures the engine works efficiently by operating with a constant torque as the load demand crosses a certain threshold, unlike the load-following approach commonly used. However, the TTS requires finely tuned constant torque and threshold parameters, making it unsuitable for real-time applications. To address this, two feedback-like updating laws are incorporated into the TTS to determine the constant torque and threshold online for real-time applications. Real-time versions of these strategies, Real-time Torque-levelling Threshold-changing Strategy (RTTS) and Real-time Simplified Torque-levelling Threshold-changing Strategy (RSTTS) are developed using a novel Driving Pattern Recognition (DPR) algorithm. The effectiveness of the RTTS is demonstrated by implementing it on a high-fidelity parallel hybrid passenger car and benchmarking it against ECMS. In Part Energy Management of EVs of the thesis, a low-fidelity model of a novel EV powertrain with two electric propulsion systems, one at each axle, has been developed to study and test its energy management with one of the main conventional optimal control methods, Dynamic Programming (DP). The EV model uses two differently sized traction motors at the front and rear axles. The thermal dynamics of the utilised Permanent Magnet Synchronous Motors (PMSMs) are studied. DP is first implemented onto the Baseline model that does not include any PMSM thermal dynamics, referred to as the Baseline DP, which acts as a benchmark since it is the conventional case. The thermal dynamics of the traction motors are then introduced in the second DP problem formulation, referred to as the Thermal DP, which is compared against the Baseline DP to evaluate the possible benefits of energy efficiency by the more informed energy management optimisation formulation. The best method is chosen to include these thermal dynamics in the overall energy management control strategy without significantly compromising computational time.Open Acces

    A novel topology of high-speed SRM for high-performance traction applications

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    A novel topology of high-speed Switched Reluctance Machine (SRM) for high-performance traction applications is presented in this article. The target application, a Hybrid Electric Vehicle (HEV) in the sport segment poses very demanding specifications on the power and torque density of the electric traction machine. After evaluating multiple alternatives, the topology proposed is a 2-phase axial flux machine featuring both segmented twin rotors and a segmented stator core. Electromagnetic, thermal and mechanical models of the proposed topology are developed and subsequently integrated in an overall optimisation algorithm in order to find the optimal geometry for the application. Special focus is laid on the thermal management of the machine, due to the tough thermal conditions resulting from the high frequency, high current and highly saturated operation. Some experimental results are also included in order to validate the modelling and simulation results

    Shape-Stabilization of PCM/HDPE Composites with 3D-Printing Applications for Battery Thermal Management

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    Phase-change materials (PCMs) are a useful alternative to more traditional methods of thermal management of Li-ion batteries in electric or hybrid-electric vehicles. PCMs are materials which absorb large amounts of latent heat and undergo solid-to-liquid phase change at near-constant temperature. The goal of this thesis is to experimentally investigate the thermal properties of a novel shape-stabilized PCM/HDPE composite extruded filament. The extruded filament can then be used in a 3D printer for custom PCM/HDPE shapes. These custom PCM/HDPE shapes can be used to help reduce weight, energy consumption, and complexity of the thermal management of the ESS. The PCM used in the study is PureTemp PCM42. PCM42 is an organic-based material that melts around 42_C. Three PCM/HDPE mixtures were investigated (all percentages by mass): 80% HDPE/20% PCM (80/20), 70% HDPE/30% PCM (70/30), and 60% HDPE/40% PCM (60/40). A Filabot extruder was used to melt, mix, and extrude the PCM/HDPE composite filament. A differential scanning calorimeter (DSC) was used to measure the effective heat storage capacity at the operating conditions of the ESS. A scanning electron microscope was used to visually validate the mixing and bonding of the PCM and HDPE

    Multi-physics Model Of Key Components In High Efficiency Vehicle Drive

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    Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are crucial technologies for the automotive industry to meet society’s demands for cleaner, more energy efficient transportation. Meeting the need to provide power which sustains HEVs and EVs is an immediate area of concern that research and development within the automotive community must address. Electric batteries and electrical motors are the key components in HEV and EV power generation and transmission, and their performance plays very important role in the overall performance of the modern high efficiency vehicles. Therefore, in this dissertation, we are motivated to study the electric batteries, interior permanent motor (IPM), in the context of modern hybrid electric/electric drive systems, from both multi-physics and system level perspectives. Electrical circuit theory, electromagnetic Finite Element Analysis (FEA), and Computational Fluid Dynamic (CFD) finite volume method will be used primarily in this work. The work has total of five parts, and they are introduced in the following. Firstly, Battery thermal management design is critical in HEV and EV development. Accurate temperature distribution of the battery cells during vehicle operation is required for achieving optimized design. We propose a novel electrical-thermal battery modeling technique that couples a temperature dependent battery circuit model and a physics-based CFD model to meet this need. The electrical circuit model serves as a heat generation mechanism for the CFD model, and the CFD model provides the temperature distribution of the battery cells, which can also impact the heat generation of the electrical battery model. In this part of work, simulation data has been derived from the model respective to electrical performance of the battery as well iv as the temperature distribution simultaneously in consideration of the physical dimensions, material properties, and cooling conditions. The proposed model is validated against a battery model that couples the same electrical model with a known equivalent thermal model. Secondly, we propose an accurate system level Foster network thermal model. The parameters of the model are extracted from step responses of the CFD battery thermal model. The Foster network model and the CFD model give the same results. The Foster network can couple with battery circuit model to form an electric-thermal battery model for system simulation. Thirdly, IPM electric machines are important in high performance drive systems. During normal operations, irreversible demagnetization can occur due to temperature rise and various loading conditions. We investigate the performance of an IPM using 3d time stepping electromagnetic FEA considering magnet’s temperature dependency. Torque, flux linkage, induced voltage, inductance and saliency of the IPM will be studied in details. Finally, we use CFD to predict the non-uniform temperature distribution of the IPM machine and the impact of this distribution on motor performance. Fourthly, we will switch gear to investigate the IPM motor on the system level. A reduced order IPM model is proposed to consider the effect of demagnetization of permanent magnet due to temperature effect. The proposed model is validated by comparing its results to the FEA results. Finally, a HEV is a vehicle that has both conventional mechanical (i.e. internal combustion engine) and electrical propulsion systems. The electrical powertrain is used to work with the conventional powertrain to achieve higher fuel economy and lower emissions. v Computer based modeling and simulation techniques are therefore essential to help reduce the design cost and optimize system performance. Due to the complexity of hybrid vehicles, multidomain modeling ability is preferred for both component modeling and system simulation. We present a HEV library developed using VHDL-AMS

    The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle

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    The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization 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 commercialization. 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. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable

    A Novel Hybrid Battery Thermal Management System for Prevention of Thermal Runaway Propagation

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    Lithium-ion batteries (LiBs) are extensively used in electric vehicles (EVs) because of their high energy density and long service life. Designing a battery thermal management system (BTMS) that prevents thermal runaway (TR) propagation in the event of abusive accidents is crucial. The goal of this study is to design a novel hybrid BTMS with both active liquid cooling (LC) and passive cooling for preventing TR propagation in the battery module. A numerical model for a battery module (16 cylindrical 18 650 cells) was developed in COMSOL multiphysics software to examine the TR propagation caused by a single cell. Copper foam and expanded graphite-paraffin (EG-PCM) composite material were used for passive cooling. In addition to the TR scenario, the thermal behaviors of the battery module with the hybrid BTMS were evaluated under the 3C discharging and driving cycle circumstances. A conventional BTMS with natural air cooling is chosen as the baseline. The findings reveal that the proposed hybrid BTMS, which uses EG-PCM with a melting temperature of 52 °C and thermal diffusivity of 9.68 mm2/s and copper foam with a porosity of 0.7–0.9, is capable of limiting the maximum cell temperature below the thermal safety threshold (80 °C) to prevent TR propagation. Under the New European Driving Cycle (NEDC) load cycle, the battery module can be maintained within an optimal working temperature range by passive cooling only. By applying active LC with a flow rate of 0.3 m/s for BTMS, the average temperature reduction of the battery module at a 3C discharging rate can be up to 72.5% and 52.7% compared to passive cooling with copper foam and EG-PCM, respectively. The study highlights that the combination of active LC and appropriate passive cooling is an efficient thermal management solution for Li-ion battery applications in EVs, notably in the consideration of thermal safety

    Improved thermal performance of a large laminated lithium-ion power battery by reciprocating air flow

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Thermal safety issues are increasingly critical for large-size laminated Lithium-Ion Batteries (LIBs). Despite a number of investigations conducted on the Battery Thermal Management System (BTMS) with reciprocating air-flow cooling, large laminated power LIBs are still not sufficiently investigated, particularly in the view of battery thermal characteristics. The present study investigates the thermal behaviors of an air-cooled NCM-type LIB (LiNi1−x−yCoxMnyO2 as cathode) from an experimental and systematic approach. The temperature distribution was acquired from different Depth of Discharge (DOD) by the infrared imaging (IR) technology. A reciprocating air-flow cooling method was proposed to restrict the temperature fluctuation and homogenize temperature distribution. Results showed that there was a remarkable temperature distribution phenomenon during the discharge process, the temperature distribution was affected by direction of air-flow. Forward air-flow (from current collector side to lower part of battery) was always recommended at the beginning of the discharge due to the thermal characteristics of the battery. After comprehensive consideration on battery temperature limit and cooling effect, the desired initial reversing timing was about 50% DOD at 3 C discharge rate. Different reversing strategies were investigated including isochronous cycles and aperiodic cycles. It was found that the temperature non-uniformity caused by heat accumulation and concentration was mitigated by reciprocating air-flow with optimized reversing strategy

    Thermodynamic investigation of a shared cogeneration system with electrical cars for northern Europe climate

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    Transition to alternative energy systems is indicated by EU Commission as a suitable path to energy efficiency and energy saving in the next years. The aims are to decrease greenhouses gases emissions, relevance of fossil fuels in energy production and energy dependence on extra-EU countries. These goals can be achieved increasing renewable energy sources and/or efficiency on energy production processes. In this paper an innovative micro-cogeneration system for household application is presented: it covers heating, domestic hot water and electricity demands for a residential user. Solid oxide fuel cells, heat pump and Stirling engine are utilised as a system to achieve high energy conversion efficiency. A transition from traditional petrol cars to electric mobility is also considered and simulated here. Different types of fuel are considered to demonstrate the high versatility of the simulated cogeneration system by changing the pre-reformer of the fuel cell. Thermodynamic analysis is performed to prove high efficiency with the different fuels

    Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

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    Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications
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