Optimization of shift schedule for hybrid electric vehicle with automated manual transmission

Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Bus driver rostering by hybrid methods based on column generation

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2018Rostering problems arise in a diversity of areas where, according to the business and labor rules, distinct variants of the problem are obtained with different constraints and objectives considered. The diversity of existing rostering problems, allied with their complexity, justifies the activity of the research community addressing them. The current research on rostering problems is mainly devoted to achieving near-optimal solutions since, most of the times, the time needed to obtain optimal solutions is very high. In this thesis, a Bus Driver Rostering Problem is addressed, to which an integer programming model is adapted from the literature, and a new decomposition model with three distinct subproblems representations is proposed. The main objective of this research is to develop and evaluate a new approach to obtain solutions to the problem in study. The new approach follows the concept of search based on column generation, which consists in using the column generation method to solve problems represented by decomposition models and, after, applying metaheuristics to search for the best combination of subproblem solutions that, when combined, result in a feasible integer solution to the complete problem. Besides the new decomposition models proposed for the Bus Driver Rostering Problem, this thesis proposes the extension of the concept of search by column generation to allow using population-based metaheuristics and presents the implementation of the first metaheuristic using populations, based on the extension, which is an evolutionary algorithm. There are two additional contributions of this thesis. The first is an heuristic allowing to obtain solutions for the subproblems in an individual or aggregated way and the second is a repair operator which can be used by the metaheuristics to repair infeasible solutions and, eventually, generate missing subproblem solutions needed. The thesis includes the description and results from an extensive set of computational tests. Multiple configurations of the column generation with three decomposition models are tested to assess the best configuration to use in the generation of the search space for the metaheuristic. Additional tests compare distinct single-solution metaheuristics and our basic evolutionary algorithm in the search for integer solutions in the search space obtained by the column generation. A final set of tests compares the results of our final algorithm (with the best column generation configuration and the evolutionary algorithm using the repair operator) and the solutions obtained by solving the problem represented by the integer programming model with a commercial solver.Programa de Apoio à Formação Avançada de Docentes do Ensino Superior Politécnico (PROTEC), SFRH/PROTEC/67405/201

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles

Commercial vehicles fulfill the majority of inland freight transportation in the United States, and they are very large consumers of fuels. The increasingly stringent regulation on greenhouse-gas emission has driven manufacturers to adopt new fuel efficient technologies. Among others, advanced transmission control strategy can provide tangible improvement with low incremental cost. An adaptive shift strategy is proposed in this work to optimize the shift maps on-the-fly based on the road load and driver behavior while reducing the initial calibration efforts. In addition, the adaptive shift strategy provides the fleet owner a mean to select a tradeoff between fuel economy and drivability, since the drivers are often not the owner of the vehicle. In an attempt to develop the adaptive shift strategy, the vehicle parameters and driver behavior need to be evaluated first. Therefore, three research questions are addressed in this dissertation: (i) vehicle parameters estimation; (ii) driver behavior classification; (iii) online shift strategy adaption. In vehicle parameters estimation, a model-based vehicle rolling resistance and aerodynamic drag coefficient online estimator is proposed. A new Weighted Recursive Least Square algorithm was developed. It uses a supervisor to extracts data during the constant-speed event and saves the average road load at each speed segment. The algorithm was tested in the simulation with real-world driving data. The results have shown a more robust performance compared with the original Recursive Least Square algorithm, and high accuracy of aerodynamic drag estimation. To classify the driver behavior, a driver score algorithm was proposed. A new method is developed to represent the time-series driving data into events represented by symbolic data. The algorithm is tested with real-world driving data and shows a high classification accuracy across different vehicles and driving cycles. Finally, a new adaptive shift scheme was developed, which synthesizes the information about vehicle parameters and driver score developed in the previous steps. The driver score is used as a proxy to match the driving characteristics in real time. Drivability objective is included in the optimization through a torque reserve and it is subsequently evaluated via a newly developed metric. The impact of the shift maps on the objective drivability and fuel economy metrics is evaluated quantitatively in the vehicle simulation. The algorithms proposed in this dissertation are developed with practical implementation in mind. The methods can reduce the initial calibration effort and provide the fleet owner a mean to select an appropriate tradeoff between fuel economy and drivability depending on the vocation

Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study

Local public transport companies, especially in large cities, are facing every day the problem of managing delays and small disruptions. Disruption management is a well-established practice in airlines and railways. However, in local public transport the approaches to these problems have followed a different path, mainly focusing on holding and short-turning strategies not directly associated with the driver scheduling. In this paper we consider the case of the management of urban surface lines of Azienda Trasporti Milanese (ATM) of Milan. The main issues are the service regularity as a measure of the quality of service, and the minimization of the operational costs due to changes in the planned driver scheduling. We propose a simulation-based optimization system to cope with delays and small disruptions that can be effectively used in a real-time environment and takes into account both vehicle and driver scheduling. The proposed approach is tested on real data to prove its actual applicability