Part of the book: Energy Management
Electrification of powertrain system is a great technical progress of traditional vehicle, leading to a significant reduction of fuel consumption and emission pollution. Energy storage system (ESS) normally consisting of batteries is a key component of an electric vehicle or hybrid electric vehicle. An ESS can recover braking energy during the regenerative braking process. Currently, lithium-ion batteries are the main energy storage device due to their high energy density. However, sometimes, a sudden large increase of operation current is required during acceleration or regenerative braking processes, which will jeopardize the operation life of batteries. A supercapacitor takes advantage of high power density and can tolerate large current in a short time. Application of supercapacitor in an ESS can reduce the peak current of batteries effectively, and the life time of batteries can be extended. Meanwhile, the braking energy can also be recovered sufficiently. Supercapacitors can be used solely in some hybrid electric vehicles. In this chapter, the application of supercapacitors in electric vehicles or hybrid electric vehicles is reviewed briefly. Then, the performance of a series hybrid transit bus, which uses a compressed natural gas engine and supercapacitors as power sources, is analyzed.
Part of the book: Science, Technology and Advanced Application of Supercapacitors
Electric vehicles powered by lithium-ion batteries take advantages for urban transportation. However, the safety of lithium-ion battery needs to be improved. Self-induced internal short circuit of lithium-ion batteries is a serious problem which may cause battery thermal runaway. Accurate and fast identification of internal short circuit is critical, while difficult for lithium-ion battery management system. In this study, the influences of the parameters of significance test on the performance of an algorithm for internal short circuit identification are evaluated experimentally. The designed identification is based on the mean-difference model and the recursive least square algorithm. First, the identification method is presented. Then, two characteristic parameters are determined. Subsequently, the parameters of the significance calculation are optimized based on the measured data. Finally, the effectiveness of the method for the early stage internal short circuit detection is studied by an equivalent experiment. The results indicate that the detection time can be shortened significantly via a proper configuration of the parameters for the significance test.
Part of the book: Applied Electromechanical Devices and Machines for Electric Mobility Solutions