Analysis of wavelet controller for robustness in electronic differential of electric vehicles: an investigation and numerical developments

In road transportation systems, differential plays an important role in preventing the vehicle from slipping on curved tracks. In practice, mechanical differentials are used, but they are bulky because of their increased weight. Moreover, they are not suitable for electric vehicles, especially those employing separate drives for both rear wheels. The electronic differential constitutes recent technological advances in electric vehicle design, enabling better stability and control of a vehicle on curved roads. This article articulates the modeling and simulation of an electronic differential employing a novel wavelet transform controller for two brushless DC motors ensuring drive in two right and left back driving wheels. Further, the proposed work uses a discrete wavelet transform controller to decompose the error between actual and command speed provided by the electronic differential based on throttle and steering angle as the input into frequency components. By scaling these frequency components by their respective gains, the obtained control signal is actually given as input to the motor. To verify the proposal, a set of designed strategies were carried out: a vehicle on a straight road, turning right and turning left. Numerical simulation test results of the controllers are presented and compared for robust performance and stability

An ADMM Algorithm for MPC-based Energy Management in Hybrid Electric Vehicles with Nonlinear Losses

In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for energy management in hybrid electric vehicles, and an Alternating Direction Method of Multipliers (ADMM) algorithm for its solution. We develop a new proof of convexity for the problem that allows the nonlinear dynamics to be modelled as a linear system, then demonstrate the performance of ADMM in comparison with Dynamic Programming (DP) through simulation. The results demonstrate up to two orders of magnitude improvement in solution time for comparable accuracy against DP

Research on Self-adaptive Online Vehicle Velocity Prediction Strategy Considering Traffic Information Fusion

In order to increase the prediction accuracy of the online vehicle velocity prediction (VVP) strategy, a self-adaptive velocity prediction algorithm fused with traffic information was presented for the multiple scenarios. Initially, traffic scenarios were established inside the co-simulation environment. In addition, the algorithm of a general regressive neural network (GRNN) paired with datasets of the ego-vehicle, the front vehicle, and traffic lights was used in traffic scenarios, which increasingly improved the prediction accuracy. To ameliorate the robustness of the algorithm, then the strategy was optimized by particle swarm optimization (PSO) and k-fold cross-validation to find the optimal parameters of the neural network in real-time, which constructed a self-adaptive online PSO-GRNN VVP strategy with multi-information fusion to adapt with different operating situations. The self-adaptive online PSO-GRNN VVP strategy was then deployed to a variety of simulated scenarios to test its efficacy under various operating situations. Finally, the simulation results reveal that in urban and highway scenarios, the prediction accuracy is separately increased by 27.8% and 54.5% when compared to the traditional GRNN VVP strategy with fixed parameters utilizing only the historical ego-vehicle velocity dataset.Comment: 9 pages, 7 figure

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163948/1/els2bf00154.pd

Analysis of wavelet controller for robustness in electronic differential of electric vehicles: an investigation and numerical developments

In road transportation systems, differential plays an important role in preventing the vehicle from slipping on curved tracks. In practice, mechanical differentials are used, but they are bulky because of their increased weight. Moreover, they are not suitable for electric vehicles, especially those employing separate drives for both rear wheels. The electronic differential constitutes recent technological advances in electric vehicle design, enabling better stability and control of a vehicle on curved roads. This article articulates the modeling and simulation of an electronic differential employing a novel wavelet transform controller for two brushless DC motors ensuring drive in two right and left back driving wheels. Further, the proposed work uses a discrete wavelet transform controller to decompose the error between actual and command speed provided by the electronic differential based on throttle and steering angle as the input into frequency components. By scaling these frequency components by their respective gains, the obtained control signal is actually given as input to the motor. To verify the proposal, a set of designed strategies were carried out: a vehicle on a straight road, turning right and turning left. Numerical simulation test results of the controllers are presented and compared for robust performance and stability

Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology

Fault Characteristics and Control Strategies of Multiterminal High Voltage Direct Current Transmission Based on Modular Multilevel Converter

The modular multilevel converter (MMC) is an emerging voltage source converter topology suitable for multiterminal high voltage direct current transmission based on modular multilevel converter (MMC-MTDC). This paper presents fault characteristics of MMC-MTDC including submodule fault, DC line fault, and fault ride-through of wind farm integration. Meanwhile, the corresponding protection strategies are proposed. The correctness and effectiveness of the control strategies are verified by establishing a three-terminal MMC-MTDC system under the PSCAD/EMTDC electromagnetic transient simulation environment