4A: Minimising Energy Through Optimised Logistics


This project will develop methods for reducing energy consumption of urban freight systems by combined optimization of vehicle technologies and logistical operating principles. 

Objectives

(i) Develop and validate a general-purpose mathematical model of fuel consumption in logistic operations, that can be applied to a variety of different operational scenarios in both urban and long-haul freight transport

(ii) Use the model to establish recommendations about the best ways to optimize urban freight systems in the light of newly available vehicle technologies, logistics infrastructure and operational practices in order to achieve the 2020 CO2 emissions reductions targets

(iii) Apply the model to other freight tasks such as long haul and refuse collection so as to explore ways to optimize logistics systems and minimize fuel consumption.

Programme and Deliverables

Project Tasks

Task 4A0: Vehicle model development and validation: Develop and validate components relevant to a fuel consumption model, including fuel/engine pairs, driving cycles and vehicle physical characteristics.  Creating urban vehicle powertrain models from the bottom-up increases the likelihood that technology switching and changes to driver behaviour may be accounted for accurately.  The model validation will exploit results obtained in 4D (alternative fuels) and 4E (Android application).

Task 4A1 Integrated model development: Integrate vehicle model (4A0) with logistics system to calculate fuel consumption for generalised freight operations, including the effects of traffic congestion, using data from projects 2A2C.  The model will exploit operational data available in the integrated logistics dataset (Project 2A).  By changing parameter sets, it will be possible to adapt the model to predict fuel consumption for a wide variety of geographies, freight tasks, vehicle types and traffic levels.

Task 4A2 Integrated model validation: Validate the model (4A1) by instrumenting in-service vehicles to record position, speed, routes, fuel consumption and all available powertrain operating parameters The in-service instrumenting will also allow collection of data needed to develop detailed engine and emissions maps for use in vehicle models (4A0).  Long-term model validation will be conducted through comparison of in-service routes and reported fuel consumption with model outputs.  Traffic patterns or conditions not well accounted for in the original model will be updated with further in-use monitoring as needed. The data gathered as a part of Task 4A2 will be used as inputs to other tasks throughout the project, including Task 4D.

Task 4A3 Case studies: Apply validated model from Tasks 4A1 and 4A2 to quantify potential fuel savings and greenhouse gas reduction. The work will seek to reduce energy use and greenhouse gas emissions through four freight task scenarios: deliveries to shops; deliveries to end-users; refuse-collection;  and long haul freight operation (cross-over with project 3D). We will investigate the importance of vehicle technologies, including hybrids, regenerative braking, use of electric vehicle technologies; vehicle size and weight limits; spatial factors in delivery – e.g. locations of distribution centres, consolidation centres and delivery locations; temporal factors in delivery, e.g. time of day, impact of traffic congestion, delivery curfews. Recommendations will be made regarding the best ways to optimize the overall logistics system to minimize fuel consumption in each of the four scenarios.

Task 4A4: Roadmap development: Develop a ‘roadmap’ for short, medium and long term interventions. Discuss with electric vehicle manufacturers, power utilities, vehicle operators and local authorities the options for and economics of creating recharging facilities for vans for shop and home delivery and the implications for transport costs and CO2 emissions of recharging urban freight vehicles at different times of day.  Additional drivetrain technologies such as hydraulic hybrids and dual-fuel systems will be investigated in coordination with retrofit companies.

Deliverables

The work will result in: (i) detailed, validated models of vehicle powertrain components yielding a toolbox of technology options; (ii) integration of vehicle technology and logistics for fuel consumption for four freight task scenarios, (iii) validation of the integrated model using data from vehicles in service; and (iv) an optimized logistics system which will considers advances in vehicle, policy and transport infrastructure.

Impact

Academic Impact:  This project will deliver a new general-purpose freight fuel consumption model, validated through field tests of different freight transport scenarios.  This model will be useful for government agencies and academics interested in determining the relative role of heavy goods vehicle energy and emissions reductions relative to other transportation mitigation strategies. The work on van battery recharging infrastructures, cost profiles and CO2 impacts will inform wider debate in the fields of energy economics, town planning and environmental management. The results will be disseminated through the normal academic channels as well as in professional journals.  It is anticipated that policy options will also arise from this project.  These will be communicated to the DfT and/or local authorities.

Commercial and Social Impacts: Reducing the fuel consumption of urban freight would improve the productivity of freight operations (commercial impact), reduce the cost of goods sold to consumers (social impact), and enhance the profitability of UK PLC, while lessening dependence on foreign oil and contributing to CO2 emissions targets (National importance).