Driving cycle tracking device big data storing and management

<span>Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing.</span>

Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data

This paper deals with fuel consumption prediction based on vehicle velocity, acceleration, and road slope time series inputs. Several data-driven models are considered for this purpose, including linear regression models and neural network-based ones. The emphasis is on accounting for the road slope impact when forming the model inputs, in order to improve the prediction accuracy. A particular focus is devoted to conversion of length-varying driving cycles into fixed dimension inputs suitable for neural networks. The proposed prediction algorithms are parameterized and tested based on GPS- and CAN-based tracking data recorded on a number of city buses during their regular operation. The test results demonstrate that a proposed neural network-based approach provides a favorable prediction accuracy and reasonable execution speed, thus making it suitable for various applications such as vehicle routing optimization, synthetic driving cycle validation, transport planning and similar.

Investigation of the Contribution of Deceleration Fuel Cut-off and Start/Stop Technologies to Fuel Economy by Considering New European Driving Cycle

Greenhouse gas (GHG) emissions released into the atmosphere cause climate change and air pollution. One of the main causes of GHG emissions is the transportation sector. The use of fossil fuels in internal combustion engine vehicles leads to the release of these harmful gases. For this reason, since 1992, several standards have been introduced to limit emissions from vehicles. Technologies such as reducing engine sizes, advanced compression-ignition or start/stop, and fuel cut-off have been developed to reduce fuel consumption and emissions. In this study, the contribution of deceleration fuel cut-off and start/stop technologies to fuel economy has been examined considering the New European Driving Cycle. Therefore, the fuel consumption values were calculated by creating a longitudinal vehicle model for a light commercial vehicle with a diesel engine. At the end of the study, by using the two strategies together, fuel economies of 17.5% in the urban driving cycle, 3.7% in the extra-urban cycle, and 10% in total were achieved. CO2 emissions decreased in parallel with fuel consumption, by 10.1% in total.

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

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.

USE OF NEURAL NETWORKS IN ADAPTIVE ELECTRIC CAR CONTROL

To overcome the lack of information about the parameters of the driving cycle of the electric car, neural networks are used, which provide adaptive control that allows you to adapt. electric car to external operating conditions, as well as to compensate for inaccuracies in mathematical models. Use of iterative optimization of parameters allows to adjust optimum work of power plant of the electric car (PEC) in the course of its movement. This method allows you to use a single approach to study different processes, regardless of the parametric features of electric vehicles. To accelerate adaptation, the neurocontroller and neural network model are trained using a reference control model, which is either an optimal strategy or a strategy based on logical rules of choice, obtained by methodical programming for a given driving cycle. Based on the results of the research, an adaptation algorithm is proposed. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The purpose of training the neuro-fuzzy controller is the formation of such control effects of the power plant, which would reduce the quadratic value of the assessment of the quality of management.

Experimental Validation of Hydrogen Fuel−Cell and Battery−Based Hybrid Drive without DC−−DC for Light Scooter under Two Typical Driving Cycles

Faced with key obstacles, such as the short driving range, long charging time, and limited volume allowance of battery−−powered electric light scooters in Asian cities, the aim of this study is to present a passive fuel cell/battery hybrid system without DC−−DC to ensure a compact volume and low cost. A novel topology structure of the passive fuel cell/battery power system for the electric light scooter is proposed, and the passive power system runs only on hydrogen. The power performance and efficiency of the passive power system are evaluated by a self−developed test bench before installation into the scooters. The results of this study reveal that the characteristics of stable power output, quick response, and the average efficiency are as high as 88% during the Shanghainese urban driving cycle and 89.5% during the Chinese standard driving cycle. The results present the possibility that this passive fuel cell/battery hybrid powertrain system without DC−DC is practical for commercial scooters.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

Methodology for rationing the fuel consumption of the PAZ-320412 bus

The article analyzes suburban bus transportation with specific routes in the Republic of Sakha (Yakutia). For the experimental study, the route No. 101, «Yakutsk – Tabaga» with a total length of 31 km was chosen. The schedule of buses of Municipal Unitary Enterprise «Yakut Passenger Transport Company (YAPAK)» on the suburban route is shown. The basic technical data of the bus PAZ-320412 was studied. In accordance with international regulations for the buses, the determination of fuel consumption and specific emissions of normalized toxic components is carried out using a riding cycle on running drums. For the calculation of fuel consumption, the technique of modeling of indicators of work of the engine which provide change of traction and speed characteristics of the car according to the set driving cycle was used. Finally, the results of the calculated fuel consumption for the NEDC driving cycle are compared with experimental data. As a comparison of the calculated and theoretical fuel consumption data with practical data, the Cummins engine type Cummins ISF 3.8 is considered. This internal combustion engine is installed on a PAZ-320412 bus. Experimental data on the fuel consumption of this bus per 100 km. showed 48 nm3, and theoretical calculations of bus fuel consumption per 100 km. by the proposed method showed 42 nm3. Therefore, to assess the traction and speed properties of the bus, the proposed combined method can be used which allows one to obtain calculation of fuel consumption which is closer to the experimental data on a driving cycle. Using the source data of the vehicle, effective engine performance indicators are evaluated. A calculation method is proposed for modeling a test, and experimental driving cycle of automobile transport with a total mass of more than five tons is suggested.