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.

Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon

Cooperative automated driving technology has emerged as a potential way to increase the efficiency of transportation systems and enhance traffic safety by allowing vehicles to exchange relevant information via wireless communication. Truck platooning utilizes this technology and achieves synchronized braking and acceleration, controlling two or more trucks simultaneously. This synchronized control makes driving with a very short inter-vehicle distance among trucks possible and reduces aerodynamic drag. This provides significant fuel consumption reduction, both in leading and trailing trucks, and achieves fuel-saving improvement. However, the static positioning sacrifices trucks in the front since they consume more fuel energy because of more air resistance than the rears. To solve this in-equivalent fuel consumption reduction benefit, this paper presents several heuristic algorithms to balance fuel consumption reduction and prolong the driving ranges by exploiting position changes among trucks in a platoon. Furthermore, the proposed algorithms try to reduce the number of position changes as much as possible to prevent any fuel waste caused by the unnecessary position change operations. In this manner, each truck in the platoon is likely to share a similar fuel consumption reduction with fewer position change counts, thus addressing the challenge of in-equivalent fuel savings distribution and obtaining optimal fuel efficiency. Our extensive simulation results show that the proposed algorithms can prolong the total distance by approximately 3% increased in two-truck platooning and even higher in six-trucks platooning of approximately 8%. Moreover, our proposed algorithms can decrease the position change count and ensure that trucks only switch to position arrangement once with no duplicate. Therefore, truck platooning obtains the maximum driving range with fewer position change counts, thus achieving efficient fuel saving.

Adaptive energy control strategy of PHEV based on the Pontryagin’s minimum principle algorithm

The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.

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.

Ambient Temperature Effects on Battery Electric Vehicle

The driving range of an electric vehicle depends on the vehicle weight, road load conditions, battery capacity, and battery performance. The battery rated capacity and its characteristics could be heavily affected by the ambient temperature. This paper investigates the effects of ambient temperature on the electric vehicle driving range, equivalent fuel economy, and performance. A production-type battery electric vehicle is modeled and simulated in the AVL-Cruise platform using semi-empirical data. The modeled vehicle battery pack consists of 20Ah Lithium-Nickel-Manganese-Cobalt-Oxide (LiNiMnCoO2) cells. The battery cell characteristics are experimentally measured to build the battery pack model. The simulated driving range and equivalent fuel economy are correlated with the published information as vehicle model validation. Series of simulations on driving cycles (UDDS, HWFET, US06, and WLTP) with across a broad range of ambient temperatures are conducted to investigate the quantified effects of ambient temperature on driving range, equivalent fuel economy, and vehicle performance. Simulation results show that driving range and fuel economy are much reduced to 70% at low ambient temperature. Driving range and fuel economy are almost not affected by high ambient temperature, such as 50 C, since this model does not include accessory load of thermal management. The vehicle performance is almost not affected by the ambient temperature.