A Fuzzy Logic Global Power Management Strategy for Hybrid Electric Vehicles Based on a Permanent Magnet Electric Variable Transmission

Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO). To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD) is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.

Section: Predictive Control Modelmentioning

confidence: 99%

“…Transportation consumes a large proportion of energy, and the application of electric vehicles (EVs) has become a global strategy for saving energy and using sustainable energy [1][2][3]. In order to improve the energy efficiency further, regenerative braking and mechanical braking cooperative systems are widely used in EVs.…”

Section: Introductionmentioning

confidence: 99%

A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

Guo

Sun

2013

“…The dashed arrow lines in the figure indicate the directions of power flows. To guarantee a quick and adequately precise simulation, a quasi-static modeling methodology [21] was used to model the power request of the hybrid powertrain. Table 1 lists the vehicle parameters used in the model.…”

Section: Hybrid Powertrain Modelingmentioning

confidence: 99%

Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Zou

Liu

et al. 2015

This paper presents a reinforcement learning (RL)-based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming-based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

“…Many control strategies, such as fuzzy control, logic threshold control, dynamic programming and artificial neural network control, have been developed and successfully applied in HEVs [2][3][4][5][6][7][8][9][10]. Comparative studies have shown that the flexibility of the control strategies is very important for the improvement of the control effects [2,3]. This paper attempts to achieve an identification of vehicle driving patterns to improve the control flexibility, and eventually improve the fuel economy of HEVs.…”

Section: Introductionmentioning

confidence: 99%

“…Since the powertrain control strategy plays a very important role in improving the performance of HEVs, the study of new control theories that can be applied to the power trains of HEVs is very significant [1,2]. Many control strategies, such as fuzzy control, logic threshold control, dynamic programming and artificial neural network control, have been developed and successfully applied in HEVs [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

Sun

Zhang

2012

Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250-300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.