This paper deals with on-board integration of lithium-ion capacitors into plug-in hybrid electric vehicle applications. Power exchanges with the vehicle battery pack are controlled through a DC/DC bidirectional power converter, which is interposed between the DC-Link and a 125 V 73 F lithium-ion capacitor module. Specific energy management strategies have been evaluated and compared in this work with the aim of optimizing battery cycling life on different operative conditions (driving cycles and real driving mission profiles). Those evaluations are carried out in simulation environment, based on the use of detailed simulation models of a C-segment plug-in hybrid vehicle and lithium-ion capacitors. Models have been preliminary parametrized and validated by means of laboratory experimental activities. The obtained results highlighted the benefits of using hybrid energy storage systems, based on lithium-ion capacitors, in reducing power peaks for the battery pack and related impact on battery cycling life.
<div class="section abstract"><div class="htmlview paragraph">Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO<sub>2</sub> emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demand for testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign that provided the necessary input data. The controller is implemented in a vehicle simulation model that is parametrized to replicate the real operation of the vehicle. Using such a model it is possible to carry out virtual tests, aiming towards energy management optimization and efficiency improvement. The main target of the experimental campaign and the data analysis was to define the operational and energy management strategy of the vehicle using a back engineering approach. Laboratory tests were performed under legislated cycles and real-world driving profiles. In addition to the standard fuel consumption and emissions measurements, a power analyzer was implemented for the measurement of the currents and voltages, which were then used for the electric power calculation of the main powertrain components (electric machine and high voltage battery). This calculation allowed the evaluation of the power flow within the powertrain and the individual components. In addition, on-board data, such as battery state of charge, engine torque and total fuel and energy consumption (provided by the on-board fuel consumption monitoring measurement -OBFCM- system) were recorded from the on-board diagnostic (OBD) port. All the recorded data and the observations made during the experimental campaign were used to define the appropriate rules for the developed controller.</div></div>
The European Union has intensified efforts to reduce CO 2 emissions from the transport sector, with the target of reducing tailpipe CO 2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.
Vehicle fleet electrification is the main target of the following decades, as a measure to decarbonize the transport sector. Electric urban busses consist of a solution for upgrading the bus fleet aiming to an improvement of urban areas and city centres air quality. To electrify the bus fleet at the city of Athens, Greece, the responsible organization, Athens Urban Transport Organization S.A. (OASA), organized a pilot program for using battery electric buses (BEB) in existing bus lines. The goal of the pilot program was to evaluate the energy consumption of the selected busses under real operation scenarios. The main target of this study is to describe the combined experimental and simulation methodology followed for the evaluation of the BEBs' . The experimental part of the study is based on the monitoring of the BEBs under real operation for a specific bus line. During the operation of the vehicles, energy consumption along with environmental conditions and A/C usage were monitored. The simulation models were used to predict the energy consumption under different driving conditions and quantify the impact of operating parameters on energy consumption. Experimental results showed that the average daily energy consumption ranged between 96 kWh/km and 220 kWh/km, values strongly related to ambient temperature. Simulations highlighted that A/C usage can lead to two times higher energy consumption for the same route and load. Finally, the expected electric range of buses considered in the study calculated between 130 km and 170 km for the selected line, load equivalent of 25 passengers and 7 kW of A/C consumption.
The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.
