Energy management in electric vehicles: Development and validation of an optimal driving strategy

Electric vehicles (EVs) are a promising alternative energy mode of transportation for the future. However, due to the limited range and relatively long charging time, it is important to use the stored battery energy in the most optimal manner possible. Existing research has focused on improvements to the hardware or improvements to the energy management strategy (EMS). However, EV drivers may adopt a driving strategy that causes the EMS to operate the EV hardware in inefficient regimes just to fulfil the driver demand. The present study develops an optimal driving strategy to help an EV driver choose a driving strategy that uses the stored battery energy in the most optimal manner. First, a strategy to inform the driver about his/her current driving situation is developed. Then, two separate multi-objective strategies, one to choose an optimal trip speed and another to choose an optimal acceleration strategy, are presented. Finally, validation of the optimal driving strategy is presented for a fleet-style electric bus. The results indicated that adopting the proposed approach could reduce the electric bus’ energy consumption from about 1 kWh/mile to 0.6-0.7 kWh/mile. Optimization results for a fixed route around the Missouri S&T campus indicated that the energy consumption of the electric bus could be reduced by about 5.6% for a 13.9% increase in the trip time. The main advantage of the proposed strategy is that it reduces the energy consumption while minimally increasing the trip time. Other advantages are that it allows the driver flexibility in choosing trip parameters and it is fairly easy to implement without significant changes to existing EV designs. --Abstract, page iii

Stochastic fuzzy controller

A standard approach to building a fuzzy controller based on stochastic logic uses binary random signals with an average (expected value of a random variable) in the range [0, Introduction: As we know, an implementation of fuzzy inference can be done on general purpos

Robust Stability Analysis for Class of Takagi-Sugeno (T-S) Fuzzy With Stochastic Process for Sustainable Hypersonic Vehicles

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor’s expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics