Search

In this paper, a new idea for designing the control strategy for the energy management of hybrid powertrains based on the driving cycle type is presented. Here, every instance of an unknown driving cycle is considered to be similar to the reference driving cycles using similarity weights. To determine the control output in the unknown driving cycle, the weights are applied to a linear combination of the optimal control decisions generated in each of the reference driving cycles. The weights which are between zero and one are determined using a fuzzy driving cycle identification agent based on the comparison of preselected driving features. The simulation studies in seven different driving cycles show that, while all driving patterns in every driving cycle are considered for the generation of energy management by online implementation of the proposed intelligent control strategy, some driving patterns would be eliminated by using a non-fuzzy identification agent. This leads to a significant reduction in the fuel consumption of the hybrid powertrain utilized with the fuzzy identification agent in some driving cycles in comparison with those without the use of non-fuzzy driving cycle identification. In addition, in some driving cycles, the intelligent control strategy has a performance close to that for the offline optimized control strategy. [ABSTRACT FROM AUTHOR]

• Proposed a Fuzzy FMEA for risk assessment of an immersion-cooled battery pack in EV. • Provide a detailed assessment of the probable causes and effects of failure to the design engineers. • An analysis of battery pack functions, failure modes, causes, and effects concerning their severity, occurrences, and detection ranks. • The most important causes of failure are sealing, BMS, structure design and assembly of mechanical components. • Using fuzzy inference engine, the RPN values are modified to improve the FMEA. Vehicle electrification is one of the changes in the modern-day car enterprise trend. The battery pack is the most vital and precarious part of a battery-powered electric vehicle, which necessitates accurate and reliable designs to ensure acceptable safety. To this end, one of the tried and tested methods that help identify problems and make products more reliable is Failure Modes and Effect Analysis (FMEA). This paper presents a Fuzzy FMEA for risk assessment of an immersion-cooled battery pack (ICBP) in electric vehicles. As a new technology, immersion cooling can facilitate high-rate fast charging and a longer battery life cycle for lithium-ion batteries. Different failure modes and relevant causes and effects are investigated in this vein. Each failure's severity, occurrence, and detection rank are extracted and displayed as a standard FMEA table. The monitoring results of all stages of design, manufacture, and initial tests indicate that the most important causes of failure in an ICBP are the sealing method, BMS function, cell temperature control, structure, and assembly of mechanical components. To some extent, it was attempted to reduce the occurrence of failures by recommending actions to reduce the occurrence of those failures. Consequently, a significant improvement was achieved, and the production and installation of problematic parts were managed efficiently and effectively with the help of an FMEA control plan table. [ABSTRACT FROM AUTHOR]