Flexible Pricing Strategies in Electric Free-Floating Bicycle Sharing

Bike sharing is an important tool to reduce congestion and pollution in urban areas. Electrically Power Assisted Bicycles (EPAC's) make cycling possible also for sedentary people. Standard EPAC's are difficultly integrable into a free-floating sharing system because the battery pack requires frequent recharging. This paper studies the challenges, opportunities and solutions of implementing a free-floating bike sharing system based on electric bicycles. The analysis revolves around the charge sustaining paradigm. The idea of charge sustaining leverages the metabolic efficiency gaps to reduce the overall physical effort required without determining a net discharge of the battery. Already validated in private bicycles, the idea needs to be modified and adapted to the challenges of a shared fleet. The paper analyzes two approaches to the fleet level energy management and assistance control of a fleet of charge sustaining bicycles. Specifically, we compare a fixed price approach against a flexible pricing approach where the user can select the cost based on the pedaling effort they are willing to exercise. A simulation framework (calibrated on data collected during a large trial in Milan, Italy) assesses the operational costs and revenues of the two approaches quantifying how they depend on the design and environmental parameters. We provide and validate a lower bound in terms of usage rate that guarantees economic sustainability, additionally showing that a flexible pricing strategy can lower this bound and grant more degrees of freedom to the users

Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed

Integrated optimisation for dynamic modelling, path planning and energy management in hybrid race vehicles

Simulation software has for many years been developed to enhance the research and development phase of new vehicle introductions. With the introduction of the testing embargo in most forms of world championship motorsport, model validation is a necessity. To optimise the unknown vehicle and tyre parameters and to reduce the error between measured and simulated data in such a multi-input multi-output non-convex optimisation problem, a novel multi-objective particle swarm optimisation (PSO) technique is applied to ensure a fully validated vehicle model is developed and analysed for speed and performance. These optimisation algorithms are further developed to explore the trajectory planning problem to improve the lap time for the shortest path, minimum curvature and a combined approach, producing optimal racing line pathways and vehicle dynamic inputs and output responses by exploring trajectories and vehicle traction circle limits. Finally, a hybrid electric vehicle transient dynamics model for the control of energy management is presented. The hybrid powertrain contains an internal combustion engine, kinetic energy recovery system and heat energy recovery system with deployment and harvesting control parameters. The performance of single-objective and multi-objective particle swarm optimisation algorithms are compared and analysed. The proposed simulation model and optimisation techniques are applied to address an array of problems, including model validation, racing line trajectory design, fastest lap time problem, and energy management strategies. All results are validated and optimised with respect to the experimental data collected on the real track in Silverstone to ensure the results can be applied to physical real-world scenarios

Definition and verification of a set of reusable reference architectures for hybrid vehicle development

Current concerns regarding climate change and energy security have resulted in an increasing demand for low carbon vehicles, including: more efficient internal combustion engine vehicles, alternative fuel vehicles, electric vehicles and hybrid vehicles. Unlike traditional internal combustion engine vehicles and electric vehicles, hybrid vehicles contain a minimum of two energy storage systems. These are required to deliver power through a complex powertrain which must combine these power flows electrically or mechanically (or both), before torque can be delivered to the wheel. Three distinct types of hybrid vehicles exist, series hybrids, parallel hybrids and compound hybrids. Each type of hybrid presents a unique engineering challenge. Also, within each hybrid type there exists a wide range of configurations of components, in size and type. The emergence of this new family of hybrid vehicles has necessitated a new component to vehicle development, the Vehicle Supervisory Controller (VSC). The VSC must determine and deliver driver torque demand, dividing the delivery of that demand from the multiple energy storage systems as a function of efficiencies and capacities. This control component is not commonly a standalone entity in traditional internal combustion vehicles and therefore presents an opportunity to apply a systems engineering approach to hybrid vehicle systems and VSC control system development. A key non-­‐functional requirement in systems engineering is reusability. A common method for maximising system reusability is a Reference Architecture (RA). This is an abstraction of the minimum set of shared system features (structure, functions, interactions and behaviour) that can be applied to a number of similar but distinct system deployments. It is argued that the employment of RAs in hybrid vehicle development would reduce VSC development time and cost. This Thesis expands this research to determine if one RA is extendable to all hybrid vehicle types and combines the scientific method with the scenario testing method to verify the reusability of RAs by demonstration. A set of hypotheses are posed: Can one RA represent all hybrid types? If not, can a minimum number of RAs be defined which represents all hybrid types? These hypotheses are tested by a set of scenarios. The RA is used as a template for a vehicle deployment (a scenario), which is then tested numerically, thereby verifying that the RA is valid for this type of vehicle. This Thesis determines that two RAs are required to represent the three hybrid vehicle types. One RA is needed for series hybrids, and the second RA covers parallel and compound hybrids. This is done at a level of abstraction which is high enough to avoid system specific features but low enough to incorporate detailed control functionality. One series hybrid is deployed using the series RA into simulation, hardware and onto a vehicle for testing. This verifies that the series RA is valid for this type of vehicle. The parallel RA is used to develop two sub-­‐types of parallel hybrids and one compound hybrid. This research has been conducted with industrial partners who value, and are employing, the findings of this research in their hybrid vehicle development programs