scholarly journals The efficient operating parameters estimation for a simulated plug-in hybrid electric vehicle

Author(s):  
Krishna veer singh ◽  
Rajat Khandelwal ◽  
Hari Om Bansal ◽  
Dheerendra Singh

Abstract Battery replacement and fuel costs are the major recurring costs over a lifetime for HEVs. Here, the authors attempt to identify the optimal operating points for the minimum and maximum state of charge (SoC) along with power ratings of the motor and fuel converter to increase the battery life and fuel economy without any detrimental effect on vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the Future Automotive Systems Technology Simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). The optimal determined values of the parameters led to a 3.8% reduction in the present value of the lifetime cost while improving the battery lifetime by over 18%. At the same time, a 4.3% improvement in the driving range have also been observed. This study will help in achieving optimal cost reduction in these vehicles.

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.


Author(s):  
Rahul Baghel ◽  
Harish Kumar Ghritlahre ◽  
Ram Narayan Patel ◽  
Ankit Sahu ◽  
Ashish Patel ◽  
...  

Abstract Hybrid energy in electric vehicles has been globally accepted as the fossil fuels will be exhausted by the middle or end of 21st century. The hybrid vehicles have emerged as a major source of clean and renewable energy to support the never-ending demand of vehicles. The main idea of the paper is the use of hybrid energy in working of e-rickshaw, which has become the most favorite area of research in automobile industry, recently. The proposed model is powered by lead-acid battery and this battery is charged by either using electric plug or solar panel. In the present study, hardware model has been analyzed e-rickshaw in terms of different parameters like mileage, charging and discharging time of battery, charging current, discharging current from the performance as well as security point of view. The hardware model has been developed with Photovoltaic (PV) module, capacitor bank, and Arduino circuit. Here, a modified e-rickshaw has been developed which has improved vehicle performance as compared to existing conventional e-rickshaw. The mileage has improved from 80 to 120.61 km and starting current has been reduced from 57 to 41 A on full load conditions. This has enhanced the battery life and Arduino circuit was implemented to enhance security features. The proposed model of e-rickshaw has improved vehicle performance as compared to existing conventional e-rickshaw in terms of mileage, starting current, battery life, and security feature of the vehicle.


Author(s):  
Jie Yang ◽  
Guoming G. Zhu

Hybrid Electric Vehicle (HEV) is capable of improving fuel economy with reduced emissions over traditional vehicles powered by the internal combustion engine alone. However the HEV durability is significantly limited by the battery useful life; and the battery life could be significantly reduced if it was operated over its allowed charging or discharging limits, which could occur especially at extremely low battery temperatures, leading to permanent battery damage and reduced battery life. In order to extend the battery life, this paper proposed a battery boundary management control strategy based upon the predicted desired torque to proactively make the engine power available to reduce future battery over-discharging. The proposed control strategy was validated in simulations and its performance was compared with the baseline control strategy under US06, and other four typical city and highway driving cycles. The simulation results show that the proposed control strategy is very effective when the battery temperature is under zero Celsius degree, and the over-discharged power is reduced more than 65% under aggressive US06 and ARB02 driving cycles, 45% under highway and city FTP and city NYCC driving cycles, and 30% under highway IM240 driving cycle, respectively.


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.


2012 ◽  
Author(s):  
Kandler Smith ◽  
Matthew Earleywine ◽  
Eric Wood ◽  
Jeremy Neubauer ◽  
Ahmad Pesaran

Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.


2019 ◽  
Vol 10 (4) ◽  
pp. 60
Author(s):  
Svenja Kalt ◽  
Lucas Brenner ◽  
Markus Lienkamp

Increasing environmental awareness leads to the necessity for more efficient powertrains in the future. However, the development of new vehicle concepts generates a trend towards ever shorter development cycles. Therefore, new concepts must be tested and validated at an early stage in order to meet the increasing time pressure. This requires the determination of real driving data in fleet tests in order to generate realistic driving cycles, which correspond as closely as possible to the actual driving behavior of the applications use case. Within the scope of this paper, real driving data are analyzed and used to create a representative driving cycle. The resulting driving cycle based on real driving characteristics is then used to investigate the impact of application-based design for powertrains on the design of electric machines, by illustrating the difference between synthetic operating points and real driving data.


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