Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.

Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.

Optimal Integrated Emission Management through Variable Engine Calibration

In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.

Design of Real-Time Control Based on DP and ECMS for PHEVs

A real-time control is proposed for plug-in-hybrid electric vehicles (PHEVs) based on dynamic programming (DP) and equivalent fuel consumption minimization strategy (ECMS) in this study. Firstly, the resulting controls of mode selection and series mode are stored in tables through offline simulation of DP, and the parallel HEV mode uses ECMS-based real-time algorithm to reduce the application of maps and avoid manual adjustment of parameters. Secondly, the feedback energy management system (FMES) is built based on feedback from SoC, which takes into account the charge and discharge reaction (CDR) of the battery, and in order to make full use of the energy stored in the battery, the reference SoC is introduced. Finally, a comparative simulation on the proposed real-time controller is conducted against DP, the results show that the controller has a good performance, and the fuel consumption value of the real-time controller is close to the value using DP. The engine operating conditions are concentrated in the low fuel consumption area of the engine, and when the driving distance is known, the SoC can follow the reference SoC well to make full use of the energy stored in the battery.

Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration

There are many different mathematical models that can be used to describe relations between energy machines in the power-split hybrid drive system. Usually, they are created based on simulations or measurements in bench (laboratory) conditions. In that sense, however, these are the idealized conditions. It is not known how the internal combustion engine and electrical machines work in real road conditions, especially during acceleration. This motivated the authors to set the goal of solving this research problem. The solution was to implement and develop the model predictive control (MPC) method for driving modes (electric, normal) of a hybrid electric vehicle equipped with a power-split drive system. According to the adopted mathematical model, after determining the type of model and its structure, the measurements were performed. There were carried out as road tests in two driving modes of the hybrid electric vehicle: electric and normal. The measurements focused on the internal combustion engine and electrical machines parameters (torque, rotational speed and power), state of charge of electrochemical accumulator system and equivalent fuel consumption (expressed as a cost function). The operating parameters of the internal combustion engine and electric machines during hybrid electric vehicle acceleration assume the maximum values in the entire range (corresponding to the set vehicle speeds). The process of the hybrid electric vehicle acceleration from 0 to 47 km/h in the electric mode lasted for 12 s and was transferred into the equivalent fuel consumption value of 5.03 g. The acceleration of the hybrid electric vehicle from 0 to 47 km/h in the normal mode lasted 4.5 s and was transferred to the value of 4.23 g. The hybrid electric vehicle acceleration from 0 to 90 km/h in the normal mode lasted 11 s and corresponded to the cost function value of 26.43 g. The presented results show how the fundamental importance of the hybrid electric vehicle acceleration process with a fully depressed gas pedal is (in these conditions the selected driving mode is a little importance).

Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler

An appropriate energy management strategy is essential to enhance the performance of hybrid electric vehicles. A novel modified equivalent fuel-consumption minimization strategy (ECMS) is developed considering the engine operating point deviation from the optimum operating line. This paper focuses on an all-inclusive evaluation of this modified ECMS with other state-of-art energy management strategies concerning battery ageing, engine switching along with fuel economy and charge sustenance. The simulation-based results of a hybrid two-wheeler concept are analysed, which shows that the modified ECMS offers the highest benefit compared to rule-based controllers concerning fuel economy and reduction in engine switching events. However, the improvement in fuel economy using modified ECMS has significant negative potential effects on critical battery parameters influencing battery ageing. The results are analysed and found consistent for two different drive cycles and three different powertrain component configurations. The results show a significant reduction in fuel consumption of up to 21.18% and a reduction in engine switching events of up to 55% with modified ECMS when compared with rule-based strategies. However, there is a significant increase in battery temperature by 31% and battery throughput by 378%, which plays a major role in accelerating battery ageing. This paper emphasizes the need to consider battery-ageing parameters along with other control objectives for a robust assessment of energy management strategies. This study helps in laying down a foundation for future improvements in energy management development and it also aids in establishing a basis for comparing energy management controllers.

ANALYSIS OF EQUIVALENT FUEL SAVINGS OF ELECTRIC VEHICLES

The development of electric vehicles in the near future is outlined, their general classification and problems of their use are given. The most common energy elements used to power electric traction electric motors are analyzed, their advantages and disadvantages are described. The analysis shows the most economical electric cars in 2018 and describes their traction and speed characteristics. The peculiarities of methodology for determining fuel economy for hybrid vehicles (PHEV - plugin hybrid electric vehicle) and for vehicles running on alternative fuel type (NGV-natural gas vehicle; FCV-fuel cell vehicle) are revealed and the possibility of its improvement is revealed. Methodological bases of estimation of fuel economy of electric vehicles are developed. This will allow potential buyers, owners or economists of the trucking companies to objectively estimate the equivalent fuel consumption and successfully choose one or the other brand of electric vehicle. An algorithm for determining the equivalent fuel economy of electric vehicles was developed and described taking into account the energy price policy for different countries of the world.It is concluded that lithiumion batteries have become the most widespread, as the feeding elements of electric vehicles. It is found that the equivalent fuel consumption is the most objective and informative, from the user's point of view, characterizing the use of electric vehicles compared to indicating the amount of energy (kWh) required to overcome 100 miles of travel. Using the proposed method, the equivalent fuel economy of these electric vehicles is calculated, the results are plotted against. It is established that for Ukraine, considering the cost of energy carriers, the use of electric vehicles is the most costeffective compared to other countries.