Publications

Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this paper: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as ‘active’ actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Environmental knowledge is considered an important pre-cursor to pro-environmental behaviour. Though several tools have been designed to measure environmental knowledge, there remains no concise, psychometrically grounded measure. We validated an existing measure in a British sample, confirming that it had good one- and three-factor structures in line with previous literature. For the first time in this field, we built upon previous Classical Test Theory approaches and used discrimination values derived from Item Response Theory to select the best items, resulting in the 19-Item Environmental Knowledge Test (EKT-19). This measure retained a clear factor structure and had moderate-to-good internal reliability, indicating that it is a parsimonious and psychometrically robust measure for the assessment of overall and specific types of environmental knowledge. The theoretical implications and real-world applications of this measure are discussed.

The vehicle control systems domain encounters an increasing number of parameters and calibration targets considering the emerging technologies such as connected vehicles and automated driving. Accordingly, the calibration processes for such systems have become more complex and thus error prone and tedious. Moreover, the derived control policies are not easily transferable between different vehicle configurations, hence, the calibration effort is increasing dramatically with each configuration change. Therefore, the reduction of such efforts needed to setup the control policy is inevitable to further reduce cost of drivetrain development. The proposed methodology therefore is an important means to make BEVs (Battery Electric Vehicles) more attractive for car buyers.

The fast development of the Artificial Intelligence (AI) domain is opening the door to numerous opportunities and applications in the automotive industry. We utilize Reinforcement Learning (RL) techniques to design appropriate control strategies for different vehicle systems, thus improving the conventional approaches and reducing the development effort. Combining the expertise in simulation and big data, we propose a cloud-based solution that runs a high-fidelity simulation to train, test and deploy the thermal management control strategy for a fleet of BEVs. The benefits, among others, are that the RL-based control policies can be designed more rapidly and run more efficient than the traditional rule-based approaches. After deploying the initial model trained against the simulation, we have the capability of collecting data from a fleet of vehicles operating with the latest control strategy. Using collected data, we iteratively train and customize the strategy throughout the operation time.

We have tested the above-mentioned framework on the use case of cabin heating mode selection for BEVs. Our RL agents are trained and evaluated in a model-in-the-loop simulation environment. The policy evaluation is based on the agents’ performance on representative vehicle test measurements (drive cycles). The metrics are selected to quantify the energy efficiency and comfort individually, as well as aggregated to enable a fair comparison. Notably the trained agents achieved better results than the original control policy on most of the individual metrics and significantly better results on the aggregated metric.

At this moment, our framework is tested against simulated vehicle fleet. The first reasonable research question is if the trained control system can be directly transferred to the real vehicle, or whether additional adjustments must be performed to achieve the needed flexibility. Another open question refers to the adequate combination of RL algorithms to achieve even better performance on telemetry data from a connected fleet. Time will tell if the idealized case with continuing on-policy training, or the more complex case using the policy-agnostic offline algorithms, will provide the stronger solution. The main technical contribution of our work is the use case agnostic framework which iteratively improves a conventional rule-based control strategy. Following the automotive V-model, the design-, implementation-, and testing -phase is strictly separated from the in-use phase of a vehicle function. To leverage historic data from the in-use phase, our framework disrupts this classical model and embeds the DevOps and ML-Ops practices into the automotive engineering process.

In this article we suggest a framework which automates the complex and time-consuming creation process of control strategies. The trained control policies provide better results and allow for a continuous improvement after they are finally deployed on a fleet of vehicles.

As the adoption and development of battery electric vehicles (BEV) increases globally with the aim to eliminate tail-pipe emissions of the incoming generation of vehicles, the automotive industry faces new challenges to maintain cost and performance parity of the new battery vehicles with their internal combustion engine (ICE) counterparts. These challenges include the temperature sensitivity of powertrain components, user range anxiety, bottlenecks in charging infrastructure and crucially, the additional burden of auxiliary consumers such as climate control.

Yet, these challenges coincide with a new era of high-speed communication and connectivity which pose many opportunities for connected vehicles to solve and optimize complex control problems. Intelligent control solutions based on predicted knowledge of future driving conditions can be used to reduce the overall energy consumption of a vehicle journey, increasing potential range, and allowing the use of smaller, cheaper powertrain components. Furthermore, such intelligent control solutions when applied to the thermal management system could help reduce stress on thermally sensitive components, increasing their efficiency and lifespan.

We capitalized on a proposed holistic BEV thermal management system model developed at AVL to study the potential gains in energy consumption if using connected control optimization. The system model utilizes the idea that three key domains can be interchangeably defined as ‘heat sinks’ or ‘heat rejectors’, thus allowing for the transfer of excess heat energy from where it is not required to where it is. The three key domains include the battery, electric machine, and cabin air temperature. Depending on the state of each domain and the ambient conditions, heat energy can be transferred via heat pumps, allowing for holistic control of these three thermal states. This thermal management system operates categorically and is made of up of eight pre-engineering control modes which determine how heat energy is transferred around the vehicle. For heating purposes, if sufficient heat energy is not present in the system or atmospheric conditions, the electric heater is used, drawing energy from the battery.

Selecting which mode to use and when poses a complex control problem, but if implemented well, can reduce battery energy consumption while ensuring passenger comfort. Future knowledge of the journey makes it possible to model the predicted state conditions and apply optimization techniques to select the most suitable sequence of control modes for a given journey. This paper evaluates the application of Dynamic Programming (DP) for use with this categorical type of control system. The DP algorithm views the problem as a whole, therefore providing a global optimum solution based on a full journey, as opposed to a rule-based system which makes decisions based only on current and past state conditions. DP is computationally intensive and is therefore typically applied as an “offline” optimizer from which to benchmark, thus, this research acts as a precursor to the development of a real-time optimized controller. As the DP results identify the best possible system gains, these will be used as a benchmark when implementing an online optimized controller, and to assess the level of investment appropriate considering the available gains.

This study has used a thermal management model and integrated this with the highly optimized Dynamic Programming MATLAB toolbox, “DPM”, developed at ETH Zurich by Sundstrom, O et al. Initial simulations over the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) resulted in a maximum improvement in cold ambient conditions where the powertrain components are at their least efficient. The potential gains diminish at higher ambient temperatures as the powertrain components are closer to their operating windows. The results of this study provide a valuable insight into the optimization of the thermal management of a BEV which is potentially critical for their success as a long-term viable alternative to ICE powered vehicles.

A pilot study run by the University of Bath in partnership with Bath & North East Somerset Council, Chapter2 Architects and the South West Net Zero Hub

We are grateful to the three commentators on our original article (Donegan et al, 2023), particularly for the diversity of perspectives. Consistent with the global nature of this journal, commentary was derived from: the UK (Professor Sarah Sharples, who wears two UK `hats’, as a Professor of Human Factors at the University of Nottingham and Chief Scientific Adviser for the UK Government’s Department of Transport); Israel (Professor Ben-Elia at Ben-Gurion University of the Negev); and Japan (Professor Fujii at Kyoto University). We are grateful for their engagement and can offer six points of reply to build on their contributions.

Engineering of today’s complex automotive systems relies on heterogeneous, model-based development toolchains to compute various performance measures to demonstrate product quality and support decision making. Despite advances in System Modelling and simulation, their integration often hits a bottleneck due to cumbersome, expensive and error-prone process of manual transformation of information regarding verification criteria from authoritative System Models into domain specific simulation toolchains. To overcome these obstacles, we introduce a novel methodology that facilitates integration of System Models, built using System Modelling Language (SysML), with simulation, to enable evaluation of quantitative Key Performance Indicators (KPIs) at every system life cycle stage. This is demonstrated in a five-step process using an automotive Adaptive Cruise Control (ACC) application. First, we develop a comprehensive SysML model to analyse the system in its intended context. Second, we extract the KPIs from system requirements written in natural language and formalize them in Signal Temporal Logic (STL) to define constraint parameters. Third, we capture the atomic propositions of STL KPIs to establish traceability between constraint parameters, system properties and interfaces. Fourth, we establish correspondence between the System Architecture and domain-specific models. Fifth, simulation capabilities are leveraged to evaluate temporal, quantitative KPIs, providing insight into the system performance in achieving the desired task. This novel integration eliminates the need for manual transformation of information from System Models to simulation toolchains, reduces opportunity for error, and enhances scalability to streamline the development process of automotive systems using Model Based Systems Engineering (MBSE).

This article considers how theories of social cooperation might be helpful in developing policy levers for changing travel behaviours towards environmentally beneficial outcomes, especially in reducing private car use. ‘Theories of cooperation’ can be described as a shift away from a ‘traditional’ economic focus on selfish individuals to one where individuals care what those around them are doing and even sometimes identify with, and think as, groups. We use a simplified ‘game’ to show how game theory offers a very constrained backdrop to thinking about cooperation in a transport setting: it neglects important social factors, both strategic ones and the general social interactions and ease that may be required as a backdrop to cooperation in real life. We then apply this to ‘use cases’ (lift sharing, on-site travel planning, safe cycle storage and peer-to-peer information sharing) that bridge the gap between the abstractions of theories of cooperation, on the one hand, and the practicalities of policymaking and lived reality, on the other. In doing this, we show how cooperation in travel behaviour can develop in two different ways: as emergent social phenomena (for example, the informal-economy approach to car or bicycle repair) and purposeful policy initiatives (for example, rail-fare discounts for two people travelling together, such as the UK’s ‘two together’ railcard). Somewhat reductively, these could be described as ‘bottom-up’ and ‘top-down’ elements within behaviour-change processes. The article shows that: (1) cooperation exists ‘naturally’ in the ‘travel-behaviour policy space’; (2) there is a wealth of opportunities for policy to help make cooperation happen more and/or work better; and (3) this includes opportunities to create the conditions required for cooperation to exist and flourish.

Mobility as a Service (MaaS) integrates multiple transport modes into a single mobility service accessible on-demand. This research addresses the lack of empirical evidence to substantiate the service’s impacts by conducting a systematic literature review on MaaS trials in urban areas. A total of nine trials were reviewed, with MaaS impacts in diverse areas, from economic to environmental effects. Further research calls for long-term trials focused on environmental and political MaaS impacts. Even so, this review provides a foundation on the mobility-related areas that are important to target in future MaaS trials as well as the challenges that need to be addressed when an implementation of the service is attempted.