Recommended System for Optimizing Battery Energy Management with Floating Car Data

• Autor(a) principal: Leonel Rocha Araujo
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/88253
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

Nowadays, heavy vehicles that transport temperature-sensitive goods, generally use a fuel-needy dedicated diesel engine. Towards solving this problem, an energy management system (EMS) capable of producing energy on-board of the vehicle is being developed. This recovery is possible due to the regenerative braking (RB) functionality, which consists in converting kinetic energy to electrical energy during a slowdown. The recovered energy is then stored in a set of batteries that supplies the refrigeration system when needed, allowing it to run in electrical mode. Using data retrieved from the vehicle's operation and this management system, an opportunity towards intelligently using the regenerative braking functionality emerges. By introducing an intelligence layer on the energy management system, a decision on applying the RB functionality could be made based on the trip's energetic potential. This decision will optimize the battery usage and reduce the load and wear on the EMS components. In order to calculate the energetic potential of a certain route, an estimation of the road is needed. This document presents context information and different approaches towards this end. In the modeling approach recommended and implemented, a route is divided in several spatial segments and each segment is categorized among three pre-defined classes. A classification model is used to predict traffic historical data as input. By using this modeling approach based on travel times, information on traffic flow and intersection queues are incorporated and by calculating the most likely sequence of states, a estimation of the road ahead is made. Using the information of the modeled path, when the RB systems detects a situation where the functionality can be applied, a decision will be made by weighting the energetic potential of the path ahead and the energy need. When the algorithm sees fit, a higher torque may be applied to the generator, which will result in a larger quantity of energy recovered. Since this causes stress to the system, this functionality needs a robust intelligence layer.