Yuan Zou

Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles

Abstract Accurate battery State of Charge (SOC) estimation is of great significance for safe and efficient energy utilization for electric vehicles. This paper presents a comparison between a novel robust extended Kalman filter (REKF) and a standard extended Kalman filter (EKF) for Li-ion battery SOC indication. The REKF-based method is formulated to explicitly compensate for the battery modeling uncertainty and linearization error often involved in EKF, as well as to provide robustness against the battery system noise to some extent. Evaluation results indicate that both filters have a good average performance, given appropriate noise covariances, owing to a small average modeling error. However, in contrast, the REKF-based SOC estimation method possesses slightly smaller root-mean-square (RMS) error. In the worst case, the robustness characteristics of the REKF result in an obviously smaller error bound (around by 1%). Additionally, the REKF-based approach shows superior robustness against the noise statistics, leading to a better tolerance to inappropriate tuning of the process and measurement noise covariances.

Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting

A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.

Optimal sizing and control strategy design for heavy hybrid electric truck

This paper presents a parametric study focused on sizing of the power-train components and optimization of the power split between the engine and electric motor for minimizing fuel consumption. A scalable vehicle model based on a bi-level optimization framework is built to promote this study. The iterative plant-controller combined optimization methodology is adopted to optimize the key plant parameters and control strategy simultaneously. The design parameters can be altered within the design space randomly in the outer loop. Dynamic programming is applied to find the optimal control in the inner loop with a prescribed cycle. The results are analyzed, and the relationship between the key parameters and fuel cost illustrates that both optimal sizing and control can be achieved.

Modeling and Simulation Technology for Ground Vehicle Hybrid Propulsion System

The structure and control of the ground-vehicle hybrid powertrain systems become more complex and synthesized on a daily basis. The complexity appears not only in the flexible and variable powertrain system, the working mode and the working space, but in the coupling among the processes of the different subjects and different phenomena, more importantly in the heavy dependence on the control. It has developed into a typical electromechanical synthesized system. Developing the modeling and simulation technology adapted to the hybrid powertrain system is necessary. It must incorporate the integrated development principle mathematically to improve the system design and decrease the development period and cost. Modeling and simulation meaningful to the design and development of a hybrid powertrain system is a general technology, involving professional software. This book addresses modeling and simulation technology as one element because both are significant.

The Nonlinear Programming Optimal Control of a Hybrid Drive System

The optimal control problem of a vehicle’s hybrid driving system can be converted to nonlinear optimization problems to solve. In this case, the drive system’s parameters and controls are regarded as the optimized variables within the scope of the time domain to address. Because of the existence of a large-scale nonlinear optimization solver, the above method is becoming increasingly viable. The current application consists of the pseudo-spectral method and convex optimization method. A pseudo-spectral method is a form of nonlinear programming methodology for general optimal control problem and the convex optimization is a type of algorithm for convex optimization problems.

Architecture of the Ground Vehicle Hybrid Drive System

The mobility and maneuvering is the core function of the ground vehicle in priority. The architecture of the hybrid drive system affects the mobility and maneuvering of the ground vehicle significantly. On the one hand, the architecture of the hybrid drive system determines the basic structure and the technical characteristics of the drive system, forms the constraints in the system level, under which the system is designed and regulated. On the other hand, the architecture of the hybrid drive system is closely coupled with the selection of components and parameters of the powertrain system. For vehicles with the same mobility and maneuvering performance requirement, the different architecture requests the different components and control to match with each other. This chapter starts from the basic structure and classification of the hybrid drive system, analyzes the configuration characteristics of the hybrid drive system for wheeled and tracked ground vehicles.

The Modeling and Identification of Lithium-Ion Battery System

Power battery often serves as energy storage in electrified vehicle. Battery performance directly affects fuel economy and mobility of electric vehicles. A battery cell usually consists of positive and negative electrodes, an electrolyte, and a separating plate (insulating porous material). The electrode materials determine the type and fundamental performance of the battery: electrolyte for ionic conduction; separator for electrically isolating the positive and negative electrodes to avoid short circuit.

Optimal Control and System Optimization of Ground Vehicle Hybrid Drive System

Optimal control and system optimization are two crucial problems in the design of ground vehicle hybrid drive system, coupled and interacted closely with each other. The system integration and optimization mainly defines the architecture and component property. The confirmation of a properly matching optimized drive system configuration and controller is always challenging, which forms the prerequisite and basis of the consequent system control design. Optimal control mainly deals with the efficiently regulation of the power flow within the multiple power sources and transmission system to meet the requirement of vehicle operation. At the same time, the optimal control must ensure that the components operates within the permitted range, the optimal comprehensive performance index and adaptability. This chapter focuses on the design and technical realization of optimal control for the ground vehicle hybrid drive system, and the role the optimal control plays in the system optimization will be discussed.