Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms

2019

Energies

The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

Section: Km H S Mmentioning

confidence: 99%

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Liu

2019

Energies

“…From the various model predictive control algorithms, the dynamic matrix control model predictive control (DMC MPC) is an effective algorithm among them due to its characteristics of strong robustness, fast tracking speed, high precision for tracking control, avoiding the parameter identification for the transfer function model, and solving the problem of delay process effectively. A new method that linearizes the RC equivalent circuit model and predicts available battery power according to original Dynamic Matrix Control algorithm is proposed [19]. An application of dynamic matrix control (DMC) to a drum-type boiler-turbine system is proposed [20].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

Sheng

et al. 2020

Sensors

This paper develops an improved model predictive controller based on the online obtaining of softness factor and fusion velocity for automatic train operation to enhance the tracking control performance. Specifically, the softness factor of the improved model predictive control algorithm is not a constant, conversely, an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic is adopted, and an improved whale optimization algorithm has been proposed to solve the adjustable parameters; meanwhile, the system output value for automatic train operation is not sampled by a normal speed sensor, on the contrary, an improved online velocity sampled method for the system output value based on a fusion velocity model and an intelligent digital torque sensor is applied. In addition, the two improved strategies proposed take the real-time storage and calculation capacities of the core chip of the controller into account. Therefore, the proposed improved strategies (I) have good performance in tracking precision, (II) are simple and easily conducted, and (III) can ensure the accomplishing of computational tasks in real-time. Finally, to verify the effectiveness of the improved model predictive controller, the Matlab/simulink simulation and hardware-in-the-loop simulation (HILS) are adopted for automatic train operation tracking control, and the tracking control simulation results indicate that the improved model predictive controller has better tracking control effectiveness compared with the existing traditional improved model predictive controller.

“…Alexander Farmann and Dirk Uwe Sauer wrote a detailed summary of the SOF estimation strategy, and the SOF estimation strategy has so far been considered as mainly including methods based on the model and one based on the feature variables [1]. At present, SOF research has been primarily concentrated on two methods: one is to use the equivalent circuit and battery mechanism for battery modeling, and then obtain the variables directly related to the battery SOF, such as voltage, capacity, and other related parameters, to calculate SOF [2][3][4][5][6][7][8][9][10], but the calibration process is often expensive and has a long cycle; the other is to analyze the variables closely related to SOF, which is used in the estimation equation of SOF adopting data statistical methods, such as the neural network and Kalman filter, to acquire the estimation equation of SOF and then conduct the estimation of SOF -however, this needs a large amount of data and a lot of preparatory work. The fuzzy inference system, based on fuzzy mathematics, is an advanced intelligent system which uses fuzzy rules to describe knowledge and experience and make decisions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

Yang

Gan

et al. 2019

WEVJ

Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy.