scholarly journals 1 DESIGN OF MODULAR ARCHITECTURE FOR EV AND HEV FOR PASSENGER CARS

2021 ◽  
Author(s):  
Midhun Muraleedharan ◽  
◽  
Amitabh Das ◽  
Dr. Mohammad Rafiq Agrewale ◽  
Dr. K.C. Vora ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Modular Architecture ◽  
Passenger Cars ◽  
Passenger Car ◽  
Vehicle Performance ◽  
Matlab Simulink ◽  
System Designer ◽  
Battery Energy

Hybridization is important to obtain the advantages of both the engine and motor as the sources of propulsion. This paper discusses the effect of hybridization of powertrain on vehicle performance. The Hybrid architectures are differentiated on the basis percentage of power dependency on the engine and motor. Passenger car with hybridization ratios of 20%, 40%, 60%, 80% and 100% are modelled on MATLAB/Simulink using the backward facing approach with the engine and motor specifications remaining constant. The hybridizations ratios and the energy consumption in terms of fuel and battery energy are obtained from the model and compared. Neural network is implemented to determine the fuel consumption. The outputs can be used by a system designer to determine a desirable hybridization factor based on the requirements dictated by the specific application.

scholarly journals Comparative tests of a passenger car with compression ignition engine on chassis dynamometer during NEDC and WLTC tests and during RDE road test

2019 ◽  
Vol 178 (3) ◽  
pp. 228-234
Author(s):  
Wojciech GIS ◽  
Maciej GIS ◽  
Piotr WIŚNIOWSKI ◽  
Mateusz BEDNARSKI
Keyword(s):  
Fuel Consumption ◽  
Winter Period ◽  
Compression Ignition ◽  
Chassis Dynamometer ◽  
Vehicle Exhaust ◽  
Road Test ◽  
Passenger Cars ◽  
Compression Ignition Engine ◽  
Passenger Car ◽  
Comparative Tests

Air pollution is a challenge for municipal authorities. Increased emission of PM10 and PM 2.5 particles is particularly noticeable in Poland primarily the autumn and winter period. That is due to the start of the heating season. According to the above data, road transport accounted for approximately 5% of the creation of PM10 particles, ca. 7% of PM2.5 and approximately 32% for NOx. In Poland, suspended particles (PM10 and PM2.5) cause deaths of as many as 45,000 people a year. The issue of smog also affects other European cities. Therefore, it is necessary to undertake concrete efforts in order to reduce vehicle exhaust emissions as much as possible. It is therefore justifiable to reduce the emission of exhaust pollution, particularly NOx, PM, PN by conventional passenger cars powered by compression ignition engines. Emissions by these passenger cars have been reduced systematically. Comparative tests of the above emission of exhaust pollution were conducted on chassis dynamometer of such passenger car in NEDC cycle and in the new WLTC cycle in order to verify the level of emissions from this type of passenger car. Measurements of fuel consumption by that car were also taken. Emission of exhaust pollution and fuel consumption of the this car were also taken in the RDE road test.

scholarly journals Environmental Assessment of the Vehicle Operation Process

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 76
Author(s):  
Małgorzata Mrozik ◽  
Agnieszka Merkisz-Guranowska
Keyword(s):  
Life Cycle ◽  
Fuel Consumption ◽  
Environmental Assessment ◽  
Combustion Engine ◽  
Environmental Safety ◽  
Point Of View ◽  
Pollutant Emissions ◽  
So2 Emissions ◽  
Passenger Cars ◽  
Passenger Car

The environmental safety of a car is currently one of the most important indicators of vehicle competitiveness and quality in the consumer market. Currently, assessment of the ecological properties of vehicles is based on various criteria. In the case of combustion-powered cars, most attention is usually paid to the values characterizing their use, and in terms of environmental assessment, pollutant emissions, and operational fuel consumption are key factors. The current article considers the possibility of using the life cycle assessment (LCA) method to analyze the ecological properties of a passenger car during its operation. A simplified LCA method for vehicles, which, in strictly defined cases, can be used for the analysis of environmental impact and assessment of the energy analysis related to its operation, is presented. For this purpose, a vehicle life cycle model is developed. Data on the operation of 33 passenger cars from different manufacturers with similar operational characteristics, coming from different production periods, are analyzed in detail. The vehicle use model takes into account the environmental load due to fuel consumption and pollutant emissions from the internal combustion engine, as well as processes related to the maintenance of the car. The obtained results show that, from the point of view of a car’s impact on the environment throughout its life cycle, the phase of its operation plays the most important role. For the annual operation period, the results of the analysis lead to the conclusion that, in the assessment of energy inputs and related emissions throughout the life cycle of a passenger car, the mileage of the car, which is determined by both the periodicity of replacement of elements and materials subject to normal wear and the length of the adopted period, is of key importance. For the tested vehicles, both the energy input resulting from fuel consumption as well as CO2 and SO2 emissions constitute about 94% to 96% of the total input during the annual operation of the vehicle.

scholarly journals CO2 control by means of the increased penetration of diesel passenger cars in Finland

2013 ◽  
Vol 10 (2) ◽  
pp. 174-182
Keyword(s):  
European Union ◽  
Fuel Consumption ◽  
Co2 Emissions ◽  
The Other ◽  
Transport Sector ◽  
The European Union ◽  
Passenger Cars ◽  
Passenger Car

One efficient way to control the CO2 emissions from the transport sector is the replacement of gasoline passenger cars by Diesel ones, which emit less CO2. This can be more effective in Finland, where the Diesel penetration is only 13.6%, which is very low compared to the other member countries of the European Union. The benefit in CO2 emitted from the new passenger cars is studied in the case of an increased Diesel penetration in this country, after several scenarios using the current and estimated future passenger car registrations and the fuel consumption. The results show that, in the case of new passenger cars, a CO2 benefit of more than 2.6% can be achieved, if a Diesel penetration higher than 30% occurs in the case of the current fleet. If the penetration reaches 50%, this benefit reaches 5.9%. Future total CO2 emissions from transport sector will increase significantly and can be partially controlled by the introduction of Diesel passenger cars or the replacement of heavy passenger cars by lighter ones.

scholarly journals Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

2021 ◽  
Vol 11 (3) ◽  
pp. 34
Author(s):  
Yuyang Li ◽  
Yuxin Gao ◽  
Minghe Shao ◽  
Joseph T. Tonecha ◽  
Yawen Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Memory Storage ◽  
Wireless Sensor ◽  
Energy Capacity ◽  
Embedded Platform ◽  
Sensing System ◽  
Image Sensing ◽  
Space Limitation ◽  
Battery Energy

Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.

Objective evaluation of the rumbling sound in passenger cars based on an artificial neural network

2005 ◽  
Vol 219 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Byung-Soo Kim ◽  
Dong-Chul Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Passenger Cars ◽  
Passenger Car ◽  
Artificial Neural ◽  
The Relationship

A rumbling sound is one of the most important sound qualities in a passenger car. In previous work, a method for objectively evaluating the rumbling sound was developed based on the principal rumble component. In the present paper, the rumbling sound was found to relate effectively not only to the principal rumble component but also to the loudness and roughness. The last two subjective parameters are sound metrics in psychoacoustics. The principal rumble component, roughness, and loudness were used as the sound metrics for the development of the rumbling index to evaluate the rumbling sound objectively. The relationship between the rumbling index and these sound metrics is identified by an artificial neural network. Interior sounds of 14 passenger cars were measured, and 21 passengers subjectively evaluated the rumbling sound qualities of these interior sounds. Through this research, it was found that the results of these evaluations and the output of a neural network have a high correlation. The rumbling index has been successfully applied to the objective evaluation of the rumbling sound quality of mass-produced passenger cars.

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

2021 ◽  
pp. 095440702110674
Author(s):  
Bingjiao Liu ◽  
Qin Shi ◽  
Zejia He ◽  
Yujiang Wei ◽  
Duoyang Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Driving Cycle ◽  
Electric System ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Hybrid Electric

This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

scholarly journals Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1311
Author(s):  
Shuai Chen ◽  
Chengpeng Jiang ◽  
Jinglin Li ◽  
Jinwei Xiang ◽  
Wendong Xiao
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Energy Storage ◽  
The United States ◽  
Electricity Price ◽  
Utilization Efficiency ◽  
Battery Energy Storage ◽  
Industrial Parks ◽  
Battery Energy ◽  
The Cost

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

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