An investigation on the effect of driver style and driving events on energy demand of a PHEV

Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied. Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand

Improving accessible capacity tracking at low ambient temperatures for range estimation of battery electric vehicles

Today’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the accuracy of the battery’s underlying state estimators, e.g., state-of-charge and state-of-energy. This is affected by accuracy of the models embedded in the battery management system. The performance of these models fundamentally depends on experimentally obtained parameterisation and validation data. These experiments are mostly performed within thermal chambers, which maintain pre-set temperatures using forced air convection. Although these setups claim to maintain isothermal test conditions, they rarely do so. In this paper, we show that this is potentially the root-cause for deterioration of range estimation at low temperatures. This is because, while such setups produce results comparable to isothermal conditions at higher temperatures (25 °C), they fail to achieve isothermal conditions at sub-zero temperatures. Employing an immersed oil-cooled experimental setup, which can create close-to isothermal conditions, we show battery state estimation can be improved by reducing error from 49.3% to 11.7% at −15 °C. These findings provide a way forward towards improving range estimation in cold weather conditions

Developing and testing of control software framework for autonomous ground vehicle

Automation in ground vehicles has been gaining momentum in recent years highlighted by the significant number of public demonstrations in the last two decades. This momentum has created an urgency within research organizations, vehicle manufacturers and academia to solve existing problems with autonomous vehicle technology to make it usable in the real world. As autonomous ground vehicles operate in close proximity to one another, the margin of error for navigation is smaller than in other domains such as aerospace and marine application. The real-world driving scenarios for the autonomous ground vehicle can sometimes be predictable and unpredictable at other times, demanding different behaviours from the autonomous vehicle for successful navigation. To satisfy such as requirement, the autonomous vehicle should exhibit the capability to adapt to through deliberative planning in predictable environments and reactive planning in unpredictable environments. In this paper, we describe a hybrid control software framework designed to incorporate behaviour planning algorithms that are capable of both deliberative and reactive planning. The paper describes the development of this novel adaptive autonomous control software framework and validates it through both virtual testing and real world testing environments

Prediction of cyclic ageing and storage ageing in a lithium ion battery using an electrochemical model

Prediction of ageing for lithium-ion cell is essential. However this is a complicated area with few modelling techniques available.The influence of cycling and storage on capacity fading side reaction is investigated for the first time using an electrochemical model. Thus this paper is a unique attempt toward developing a model which can predict combined cycling and storage. Also this work establishes guideline for calculating the SEI properties based on storage ageing experimentation. Very few works correlated the experimentally observed degradation characteristics with properties of SEI layer or chemical characteristics of a battery. The conventional cyclic ageing correlation cannot be used for storage ageing due to the weak relation of degradation with SoC. In this case, the cycling correlation predicts almost the same degradation at lower SoC and at higher SoC, which is counter intuitive to experimental observations.This limits the applicability of an electrochemical model for HEV storage-cycling drive cycle since the ageing characteristics predicted during the storage time will be erroneous. In this work, the Pseudo Two Dimensional Model (P2D) equations are modified to include a continuous solvent reduction reaction responsible for capacity fade which is well established and widely applied in previous literatures [1,2.3.4]. The capability of this model to predict the SEI layer growth and internal resistance increase under different operating conditions is carefully used to analyse the storage and cycling reaction contributions. The critical parameter controlling the rate of SEI layer growth is the side reaction coefficient. Another important parameter is the temperature of the battery which is found to accelerate cell ageing. However, in this work, the analysis is limited to isothermal condition since the dependency of temperature on cell operating parameters is complex

Enhancement of reliability in condition monitoring techniques in wind turbines

The majority of electrical failures in wind turbines occur in the semiconductor components (IGBTs) of converters. To increase reliability and decrease the maintenance costs associated with this component, several health-monitoring methods have been proposed in the literature. Many laboratory-based tests have been conducted to detect the failure mechanisms of the IGBT in their early stages through monitoring the variations of thermo-sensitive electrical parameters. The methods are generally proposed and validated with a single-phase converter with an air-cored inductive or resistive load. However, limited work has been carried out considering limitations associated with measurement and processing of these parameters in a three-phase converter. Furthermore, looking at just variations of the module junction temperature will most likely lead to unreliable health monitoring as different failure mechanisms have their own individual effects on temperature variations of some, or all, of the electrical parameters. A reliable health monitoring system is necessary to determine whether the temperature variations are due to the presence of a premature failure or from normal converter operation. To address this issue, a temperature measurement approach should be independent from the failure mechanisms. In this paper, temperature is estimated by monitoring an electrical parameter particularly affected by different failure types. Early bond wire lift-off is detected by another electrical parameter that is sensitive to the progress of the failure. Considering two separate electrical parameters, one for estimation of temperature (switching off time) and another to detect the premature bond wire lift-off (collector emitter on-state voltage) enhance the reliability of an IGBT could increase the accuracy of the temperature estimation as well as premature failure detection

A new consideration for validating battery performance at low ambient temperatures

Existing validation methods for equivalent circuit models (ECMs) do not capture the effects of operating lithium-ion cells over legislative drive cycles at low ambient temperatures. Unrealistic validation of an ECM may often lead to reduced accuracy in electric vehicle range estimation. In this study, current and power are used to illustrate the different approaches for validating ECMs when operating at low ambient temperatures (−15 °C to 25 °C). It was found that employing a current-based approach leads to under-testing of the performance of lithium-ion cells for various legislative drive cycles (NEDC; FTP75; US06; WLTP-3) compared to the actual vehicle. In terms of energy demands, this can be as much as ~21% for more aggressive drive cycles but even ~15% for more conservative drive cycles. In terms of peak power demands, this can range from ~27% for more conservative drive cycles to ~35% for more aggressive drive cycles. The research findings reported in this paper suggest that it is better to use a power-based approach (with dynamic voltage) rather than a current-based approach (with fixed voltage) to characterise and model the performance of lithium-ion cells for automotive applications, especially at low ambient temperatures. This evidence should help rationalize the approaches in a model-based design process leading to potential improvements in real-world applications for lithium-ion cell

Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper

Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout

A mass transfer based variable porosity model with particle radius change for a Lithium-ion battery

Micro pore-clogging in the electrodes due to SEI growth and other side reactions can cause adverse effects on the performance of a Lithium-ion battery. The fundamental problem of volume fraction variation and particle radius change during the charge-discharge process in a lithium-ion battery is modelled in this paper with the help of mass transfer based formulation and demonstrated on a battery with LiCoO2 chemistry. The model can handle the volume fraction change due to intercalation reaction, solvent reduction side reaction and the electrolyte density change due to side reaction contamination in the battery. The entire calculation presented in this paper models particle radius and volume fraction together and therefore gives greater accuracy in calculating the volume-specific-area of the reacting particles which is an important parameter controlling the Butler-Volmer kinetics. The mass deposit on the electrode (or loss of lithium) gives an indication of the amount of pre-lithiation required to maintain cell performance while the amount of mass deposited on the SEI helps to decide the safe operating condition for which the clogging of pores and capacity fade will be minimal. Moreover the model presented in this paper has wide applicability in analysing the stress development inside the battery due to irreversible porous filling

Electrochemical modelling of Li-ion battery pack with constant voltage cycling

In a battery pack, cell-to-cell chemical variation, or the variation in operating conditions, can possibly lead to current imbalance which can accelerate pack ageing. In this paper, the Pseudo-TwoDimensional(P2D) porous electrode model is extended to a battery pack layout, to predict the overall behaviour and the cell-to-cell variation under constant voltage charging and discharging. The algorithm used in this model offers the flexibility in extending the layout to any number of cells in a pack, which can be of different capacities, chemical characteristics and physical dimensions. The coupled electrothermal effects such as differential cell ageing, temperature variation, porosity change and their effects on the performance of the pack, can be predicted using this modelling algorithm. The pack charging voltage is found to have an impact on the performance as well as the SEI layer growth. Numerical studies are conducted by keeping the cells at different thermal conditions and the results show the necessity to increase the heat transfer coefficient to cool the pack, compared to single cell. The results show that the thermal imbalance has more impact than the change in inter-connecting resistance on the split current distribution, which accelerates the irreversible porous filling and ageing