Battery powered Electric Vehicles are starting to play a significant role in today's automotive industry. There are many types of batteries found in the construction of today's Electric Vehicles, being hard to decide which one fulfils best all the most important characteristics, from different viewpoints, such as energy storage efficiency, constructive characteristics, cost price, safety and utilization life. This study presents the autonomy of an Electric Vehicle that utilizes four different types of batteries: Lithium Ion (Li-Ion), Molten Salt (Na-NiCl2), Nickel Metal Hydride (Ni-MH) and Lithium Sulphur (Li-S), all of them having the same electric energy storage capacity. The novelty of this scientific work is the implementation of four different types of batteries for Electric Vehicles on the same model to evaluate the vehicle's autonomy and the efficiency of these battery types on a driving cycle, in real time, digitized by computer simulation.

https://iopscience.iop.org/article/10.1088/1757-899X/252/1/012058/pdf

Comparison of Different Battery Types for Electric Vehicles

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles

Variations in cell properties are unavoidable and can be caused by manufacturing tolerances and usage conditions. As a result of this, cells connected in series may have different voltages and states of charge that limit the energy and power capability of the complete battery pack. Methods of removing this energy imbalance have been extensively reported within literature. However, there has been little discussion around the effect that such variation has when cells are connected electrically in parallel. This work aims to explore the impact of connecting cells, with varied properties, in parallel and the issues regarding energy imbalance and battery management that may arise. This has been achieved through analysing experimental data and a validated model. The main results from this study highlight that significant differences in current flow can occur between cells within a parallel stack that will affect how the cells age and the temperature distribution within the battery assembly.

Dataset to support article: 'Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system'

Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin's minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts.

/pdf/a-real-time-nonlinear-model-predictive-controller-for-yaw-2j819u4z8g.pdf

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

This article proposes a computationally efficient path-following control strategy of autonomous electric vehicles (AEVs) with yaw motion stabilization. First, the nonlinear control-oriented model, including path-following model, single-track vehicle model, and magic formula tire model, is constructed. To handle the stability constraints with ease, the nonlinear model predictive control (NMPC) technique is applied for path-following issue. Here, NMPC control problem is reasonably established with the constraints of vehicle sideslip angle, yaw rate, steering angle, lateral position error, and Lyapunov stability. To mitigate the online calculation burden, the continuation/generalized minimal residual (C/GMRES) algorithm is adopted. The dead-zone penalty functions are employed for handling the inequality constraints and holding the smoothness of solution. Moreover, the varying predictive duration is utilized in this article to gain the good initial solution fast by numerical algorithm. Finally, the simulation validations are carried out, which yields that the proposed strategy can achieve desirable path following and vehicle stability efficacy, while greatly reducing the computational burden compared with the NMPC controllers by active set algorithm or interior point algorithm.

/pdf/a-computationally-efficient-path-following-control-strategy-8rsqedl3yz.pdf

A Computationally Efficient Path-Following Control Strategy of Autonomous Electric Vehicles With Yaw Motion Stabilization

In this paper, a hierarchical energy management strategy is proposed to achieve optimal energy distribution in plug-in hybrid electric vehicles by dividing the energy management algorithm into two layers. Between two control layers, a novel velocity-prediction method based on wavelet transformation and a radial basis function neural network is introduced to realize accurate vehicle speed prediction. To simplify the problem, a quadratic optimization method is employed to find the optimal state-of-charge (SOC) trajectory in the upper layer and the calculation time can be minimized to be within 400 ms. Model predictive control is established simultaneously in the bottom layer to achieve fast, and local energy management based on the predicted velocity and the planned SOC trajectory. Simulation results show that the proposed method can increase the accuracy of the velocity prediction and improve the fuel economy with a fast calculation speed.

https://ieeexplore.ieee.org/ielaam/6287639/8274985/8387740-aam.pdf

A Hierarchical Energy Management Strategy for Power-Split Plug-in Hybrid Electric Vehicles Considering Velocity Prediction

A supervisory control strategy, including dynamic control supervisor, handling-stability controller, energy efficiency controller, and coordinated torque allocator, is proposed for distributed drive electric vehicles to coordinate vehicle handling, lateral stability, and energy economy performance. In the dynamic control supervisor, first, the phase plane analysis is implemented to accurately define the vehicle stability boundary so that the lookup table of bounds can be established for online applications. Subsequently, based on the feedback drive conditions and vehicle states, the identified boundary is dynamically quantified by the designed varying weight factor (VWF) in real time. In the handling-stability controller, a unified yaw rate reference of VWF is developed to simultaneously guarantee vehicle maneuverability and lateral stabilization. Then, a novel integral triple-step method is proposed to calculate the proper direct yaw moment for the desired vehicle motion. In the energy efficiency controller, the interaxle torque distribution map is optimized for optimal vehicle energy economy. In the coordinated torque allocator, a torque increment allocation problem is formulated and optimized to realize the desired forces, meanwhile, based on VWF to minimize energy consumption and tire workload usage. The validations of the proposed strategy are conducted under various maneuvers, yielding comprehensive improvements in terms of vehicle handling, lateral stability, and energy performance.

Ningyuan Guo

Papers

Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

A Computationally Efficient Path-Following Control Strategy of Autonomous Electric Vehicles With Yaw Motion Stabilization

A Hierarchical Energy Management Strategy for Power-Split Plug-in Hybrid Electric Vehicles Considering Velocity Prediction

A Supervisory Control Strategy of Distributed Drive Electric Vehicles for Coordinating Handling, Lateral Stability, and Energy Efficiency