Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data.

In order to be attractive in a very demanding market, hybrid electric buses and full electric buses need to improve the total cost of ownership compared to conventional buses. In this regard, the sizing of the onboard energy storage and the charging infrastructure becomes a key design stage. Sufficient onboard storage and charging facilities are necessary to offer an appropriate vehicle autonomy, but they involve high investment costs. Furthermore, the complex interrelations between these parameters make the best-performed system design a challenging process. To face this issue, the paper presents an optimization approach by means of the onboard storage capacity sizing, charging points rate and charging points location, aiming a total cost of ownership improvement for hybrid and full electric bus routes. A use case is selected and techno-economically evaluated regarding factors such as onboard storage cost, charging infrastructure cost, fuel cost, and electricity cost.

The aim of this paper is to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point, in order to fulfill the efficiency goals in a short term. However, with the aging, the initial conditions vary. The new trend of digitalization allows to monitor the operation, having the possibility to update the developed first strategy in a long term. Therefore a methodology for updating the energy management strategy throughout the bus lifetime is proposed, in order to improve the operation costs and extend the battery lifetime. This update is based on a dynamic programming optimization, tuning the fuzzy logic membership functions and output. Operation costs improvements up to 47 % are obtained beside a rule-based strategy and a BT lifetime extension of 2.94 % is reached.

This paper is focused on analysing the energetic key performance indicators of a hybrid electric bus fleet in order to improve its energy management (at local and fleet level) and profitability. The analysed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultra capacitors. The test routes have been selected from a data base of urban standardised cycles. In a first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g. traffic jams, auxiliary consumption and passenger variations) have been added to the routes to simulate "real" road and daily operation conditions. In this paper, a fleet learning methodology is proposed to analyse, process and decide based on the collected data from "real" conditions of the whole fleet. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behaviour. Finally, a decision making process is applied to improve the local energy management of the less-efficient bus.