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To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real-time applications. The simulation results show that Jaya-EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO-EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real-time applications. © 2020 John Wiley & Sons Ltd

Energy management strategies for an electric vehicle (EV) with multiple power sources have been widely described in literature. The investigated energy sources are batteries, ultracapacitors, fuel cells, flywheels and solar panels. The management strategy decides how to combine two or more sources in an optimal way. However, the behavior of these sources and also the behavior of the electric drives depend on their temperature. Moreover, the temperature can have extreme values in automotive applications and affect the energy management task. In this paper, to investigate the temperature effect on battery/ultracapacitor powered EV, temperature dependent models are presented for these storage components, as well as for the drive train components itself: power electronics and motor. The average motor iron loss and ultracapacitor loss tend to decrease with increasing temperature, while the average motor copper loss and power electronics loss tend to increase with increasing temperature. These two opposing trends cause the total loss of the drive train to have a rather small variation with temperature for the considered EV and in the considered temperature range. By consequence, the energy management strategy of the EV does not have to depend on the temperature in order to obtain maximal efficiency. (C) 2017 Elsevier Ltd. All rights reserved.

Conference Name: 2012 American Control Conference Date of Conference: 27–29 June 2012 In this paper, concurrent design of controllers for a vehicle equipped with a parallel hybrid powertrain is studied. Our work focuses on designing the two control algorithms, the energy management and the vehicle stability, concurrently which are traditionally considered separately. Dynamic Programming (DP) technique is used in order to obtain the optimal response trace for the controllers. In energy management strategy torque split ratio between engine and electric motor is used as a control signal. Additionally, in vehicle dynamics control strategy the torque split factor between front and rear axles is used as a control signal. Minimizing the fuel consumption and wheel slip is used as cost functions in energy management and vehicle dynamics control strategies respectively. Two dynamic problems are solved separately first and compared to the concurrent solution of the problems. Results show promising benefits can be obtained from the concurrent DP solution and rule extraction for designing better hybrid vehicle controllers. © 2012 AACC American Automatic Control Council).