Andrew McGordon

A Generalized Powertrain Design Optimization Methodology to Reduce Fuel Economy Variability in Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are considered to be one of the energy-efficient technologies for near-term sustainability of the transportation sector. Over the years, research has focused on improving fuel economy (FE) for a given drive cycle, but FE variability over a realistic range of real-world driving patterns has been generally overlooked, and this can lead to FE benefits not being fully realized in real-world usage. No systematic methodology exists to reduce FE variability by design optimization of powertrain components. This study proposes a methodology of powertrain component optimization to reduce the FE variability due to variations in driving patterns. In the proposed methodology, powertrain components are optimum over a range of driving patterns of different traffic conditions and driving styles simultaneously. The proposed methodology demonstrates the potential to reduce FE variability by up to 34% over six driving patterns of different traffic conditions and driving styles.

Defining the Accuracy of Real-world Range Estimations of an Electric Vehicle

Range anxiety is a major barrier for the mass adoption of electric vehicles (EVs), a contributing factor to this is the variability of the predicted range remaining presented to the driver in the vehicle. This study aims to better understand the causes of potential inaccuracies and how ITS can help resolve these issues. Eleven participants completed 141 logged journeys, with results showing that range (as predicted by the EV and presented to the driver) was overestimated by approximately 50% in comparison to journey distance. Driving style had the most significant impact on range prediction accuracy, where a more aggressive driving style led to greater inaccuracies. However, journey distance and type of road driven, which can be calculated from Satnav systems, were factors which were correlated with having a significant effect on range accuracy. Therefore incorporating these into future range prediction algorithms has the potential to increase the accuracy of information and subsequently increase driver trust.

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.

Development of a comprehensive and flexible forward dynamic powertrain simulation tool for various hybrid electric vehicle architectures

This paper presents a comprehensive and flexible forward dynamic powertrain simulation tool, WARwick Powertrain Simulation Tool for ARchitectures 2 (WARPSTAR2), for modelling of conventional internal combustion engine, hybrid, and pure electric vehicles. WARPSTAR2 includes physical powertrain component models and their controllers, a hybrid supervisory controller, the driver, and the environment model. The physical powertrain component models are developed in Dymola, while the component controllers, the hybrid supervisory controllers, and the driver model are realized in MATLAB/Simulink. Thus the power of these two software tools is combined. A generalized fuzzy-logic-based supervisory controller is proposed for all hybrid electric vehicle (HEV) architectures so that all HEVs with different architectures share the same structure of supervisory controller. The generalized formation can be used for the supervisory controllers of different HEV architectures with varied parameter settings, thus facilitating the controller design process. The rule-based supervisory controller is also developed in WARPSTAR2. Simulation is carried out for different HEVs with these two supervisory controllers in the driving cycles. The results of engine and battery power usages with these two supervisory controllers are similar and the differences of predicted engine fuel consumptions between the two supervisory controllers are within 5 per cent.

Comprehensive Forward Dynamic HEV Powertrain Modelling Using Dymola and MATLAB/Simulink

Abstract A modelling structure for different architectures of Hybrid Electric Vehicles (HEVs) is presented in this paper. This structure includes physical powertrain components and their controllers, hybrid supervisory controller, and the driver model. The physical powertrain component models are developed in Dymola, whilst the component controllers, hybrid supervisory controllers, and the driver model are developed in MATLAB/Simulink. The structure makes it possible to compare different hybrid vehicle architectures directly with the capability to study the influence of real-world driver behaviour on energy usage. Three types of HEV architectures, including Mild Parallel, Series, and Power Split, are illustrated to show the feasibility of this modelling technique.

Blended Rule-Based Energy Management for PHEV: System Structure and Strategy

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop-starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper.

A study of the effects of external pressure on the electrical performance of a lithium-ion pouch cell

The introduction of lithium-ion batteries for vehicle powertrain electrification has increased in recent years. They feature high energy density, high power density, long cycle life, and is also environmentally friendly compared with other types of batteries. A large number of Li-ion cells are usually required to meet the demand in capacity and power for automotive applications. Pouch cells have been favored by many manufacturers because of the high packaging efficiency and, therefore, a higher pack energy density. However, robust packaging is required for performance and safety criteria due to their low mechanical stability, which results in them being compressed in the module/pack. This paper describes research into the effects of external pressure on the electrical performance of lithium-ion pouch cells. The authors have adopted pulse power test, capacity test and electrical impedance spectroscopy test to characterize the effects and the test result indicates lithium-ion pouch cells performance changes under varying external pressures. Conclusions are drawn on how to make use of the results presented to influence and improve the design of automotive battery modules and packs to meet the challenges in the automotive industry.

A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells

The power capability of a lithium ion battery is governed by its resistance, which changes with battery state such as temperature, state of charge, and state of health. Characterizing resistance, therefore, is integral in defining battery operational boundaries, estimating its performance and tracking its state of health. There are many techniques that have been employed for estimating the resistance of a battery, these include: using DC pulse current signals such as pulse power tests or Hybrid Pulse Power Characterization (HPPC) tests; using AC current signals, i.e., electrochemical impedance spectroscopy (EIS) and using pulse-multisine measurements. From existing literature, these techniques are perceived to yield differing values of resistance. In this work, we apply these techniques to 20 Ah LiFePO4/C6 pouch cells and use the results to compare the techniques. The results indicate that the computed resistance is strongly dependent on the timescales of the technique employed and that when timescales match, the resistances derived via different techniques align. Furthermore, given that EIS is a perturbative characterisation technique, employing a spectrum of perturbation frequencies, we show that the resistance estimated from any technique can be identified – to a high level of confidence – from EIS by matching their timescales.

Transportation Safety of Lithium Iron Phosphate Batteries - A Feasibility Study of Storing at Very Low States of Charge

In freight classification, lithium-ion batteries are classed as dangerous goods and are therefore subject to stringent regulations and guidelines for certification for safe transport. One such guideline is the requirement for batteries to be at a state of charge of 30%. Under such conditions, a significant amount of the battery’s energy is stored; in the event of mismanagement, or indeed an airside incident, this energy can lead to ignition and a fire. In this work, we investigate the effect on the battery of removing 99.1% of the total stored energy. The performance of 8Ah C6/LiFePO4 pouch cells were measured following periods of calendar ageing at low voltages, at and well below the manufacturer’s recommended value. Battery degradation was monitored using impedance spectroscopy and capacity tests; the results show that the cells stored at 2.3 V exhibited no change in cell capacity after 90 days; resistance rise was negligible. Energy-dispersive X-ray spectroscopy results indicate that there was no significant copper dissolution. To test the safety of the batteries at low voltages, external short-circuit tests were performed on the cells. While the cells discharged to 2.3 V only exhibited a surface temperature rise of 6 °C, cells at higher voltages exhibited sparks, fumes and fire.

Modelling the electric air conditioning system in a commercially available vehicle for energy management optimisation

Among the auxiliary systems on electric and hybrid electric vehicles the electric air conditioning (eAC) system causes the largest load on the high voltage battery and can significantly impact the energy efficiency and performance of the vehicle. New methods are being investigated for effective management of air conditioning loads through their integration into vehicle level energy management strategies. For this purpose, a fully integrated vehicle model is developed for a commercially available hybrid vehicle and used to develop energy management algorithms. In this paper, details of the eAC model of this vehicle are discussed, including steady state component validation against rig data. Also results of simulating the cabin pull-down are included.