P. A. Jennings

Tools and techniques for understanding the fundamentals of automotive sound quality

Abstract Understanding the sounds that create the most positive subjective and emotional responses for customers and, importantly, linking this knowledge to objective engineering specifications is a major challenge. This paper presents research to create the tools and techniques to achieve this. It starts with work carried out to understand the language of sound perception and a means to capture it succinctly. Two perceptual dimensions were established that enabled the positioning of current, competitor, and target vehicles in a subjective space to be determined. Early work involved the study of fixed sounds, both real and simulated, in a listening room. However, this approach has been improved by enhancing the level of context and interactivity. Perception of the sounds of on-road cars is affected by stimuli for other senses (e.g. visual and vibrational), and the fact that an assessor is also concentrating on driving. Furthermore, drivers experience a full range of vehicle sounds, rather than fixed stimuli. The development and use of interactive simulation tools are therefore introduced. They provide a more confident assessment of preferences but also allow a greater understanding of the contributing factors to these preferences, e.g. those aspects of real-world driving that are most important in forming overall impressions of vehicle sound.

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.

A Novel Structure for Comprehensive HEV Powertrain Modelling

Current software packages for hybrid electric vehicle (HEV) modelling do not offer the comprehensive functionality to perform the wide variety of analyses required by both manufacturers and customers. The functionality is determined by the modelling structure, and often several structures must be employed to cater for all types of analysis. A new modelling structure for HEVs is proposed, employing an approach which overcomes the causes of limited functionality. It makes possible a wide range of investigations, the results of which offer quantification of the benefits that can be achieved in terms of, for example, tank-to-wheel analyses, emissions, and performance. The accuracy of calculations made with a software employing the new modelling structure is shown to be comparable to the accuracy of data supplied, satisfying the intended purpose

Early results from a systems approach to improving the performance and lifetime of lead acid batteries

Lead acid batteries offer important advantages in respect of unit cost and ease of recycling. They also have good power and low temperature performance. However, for hybrid electric vehicle (HEV) duty with their extreme rates and continuous PSoC operation, improvements are required to significantly extend service life. The Reliable Highly Optimised Lead Acid Battery (RHOLAB) project is taking a radical approach to the design of a lead acid HEV battery pack to address this issue, taking a systems approach to produce a complete pack that is attractive to vehicle manufacturers. This paper describes the project at an intermediate stage where some testing has been completed and the construction of the complete pack system is well under way.

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.

The influence of company identity on the perception of vehicle sounds

In order to determine how the interior of a car should sound, automotive manufacturers often rely on obtaining data from individual evaluations of vehicle sounds. Company identity could play a role in these appraisals, particularly when individuals are comparing cars from opposite ends of the performance spectrum. This research addressed the question: does company identity influence the evaluation of automotive sounds belonging to cars of a similar performance level and from the same market segment? Participants listened to car sounds from two competing manufacturers, together with control sounds. Before listening to each sound, participants were presented with the correct company identity for that sound, the incorrect identity or were given no information about the identity of the sound. The results showed that company identity did not influence appraisals of high performance cars belonging to different manufacturers. These results have positive implications for methodologies employed to capture the perceptions of individuals. Statement of Relevance: A challenge in automotive design is to set appropriate targets for vehicle sounds, relying on understanding subjective reactions of individuals to such sounds. This paper assesses the role of company identity in influencing these subjective reactions and will guide sound evaluation studies, in which the manufacturer is often apparent.

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.

Improving the hospital 'soundscape': a framework to measure individual perceptual response to hospital sounds.

Work on the perception of urban soundscapes has generated a number of perceptual models which are proposed as tools to test and evaluate soundscape interventions. However, despite the excessive sound levels and noise within hospital environments, perceptual models have not been developed for these spaces. To address this, a two-stage approach was developed by the authors to create such a model. First, semantics were obtained from listening evaluations which captured the feelings of individuals from hearing hospital sounds. Then, 30 participants rated a range of sound clips representative of a ward soundscape based on these semantics. Principal component analysis extracted a two-dimensional space representing an emotional–cognitive response. The framework enables soundscape interventions to be tested which may improve the perception of these hospital environments. Practitioner Summary: Hospital sound is commonly measured in terms of objective sound level. This does not consider the positive or negative subjective reactions to these sounds. This paper understands these reactions and produces a perceptual framework which can be used to measure the subjective response to a hospital soundscape.

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.