通讯机构：
[Caixia Huang] H;Hunan Provincial Key Laboratory of Vehicle Power and Transmission System (Hunan Institute of Engineering), Xiangtan 411104, China
摘要：
State-of-charge (SOC) estimation is an important aspect for modern battery management systems. Extended Kalman filter (EKF) has been extensively used in battery SOC estimation. However, EKF cannot obtain accurate estimation results when the model parameters have strong uncertainty or/and the accurate initial value of noise covariance matrix is unknown. To overcome these defects, the parameters of Lithium-ion battery model on the basis of the second-order resistor–capacitor (RC) equivalent model are identified, and then an improved adaptive EKF (IAEKF) of SOC estimation method for Lithium-ion battery pack is proposed for enhancing estimation accurate and robustness. In IAEKF, the statistical characteristics of measurement noise is adaptively corrected using a forgetting factor, namely, Sage–Husa EKF (SHEKF), and the error covariance matrix is adaptively corrected in accordance with the innovation, in which the calculation of the actual innovation covariance matrix adopts the variable sliding window length. Results of numerical simulation and experiment show that the proposed SOC estimation method can accurately estimate SOC under complex driven condition and has strong robustness to the uncertainty of model parameters and the initial value of the noise covariance matrix.
关键词：
active balance;charge and discharge;extended Kalman filter;lithium-ion battery pack;state of charge estimation
摘要：
Differences in the environment and parameters of lithium-ion battery (LiB) cells may lead the residual capacity between the battery cells to be inconsistent, and the battery cells may be damaged due to overcharging or overdischarging. In this study, an active balancing method for charging and discharging of LiB pack based on average state of charge (SOC) is proposed. Two different active balancing strategies are developed according to the different charging and discharging states of LiB pack. When the LiB pack is charging, charging balance strategy is performed, wherein the battery cells whose SOC is higher than the average SOC of the LiB pack are balanced to increase the charging capacity of the entire LiB pack. When the LiB pack is discharging or static standing, discharging balance strategy is performed, wherein the batter cells whose SOC is lower than the average SOC of the LiB pack are balanced to increase the discharging capacity of the entire LiB pack. The experimental results show that the proposed active balancing method can reduce the inconsistency of residual energy between the battery cells and improve the charging and discharging capacity of the LiB pack.
关键词：
Braking-in-turn;braking performance;lateral stability;optimal slip ratio control;torque allocation control
摘要：
The active safety control of vehicles during braking-in-turn maneuver involves longitudinal and lateral dynamic control. The lateral stability and braking performance of vehicles can be ensured by properly coordinating the longitudinal and lateral forces of tires. In this study, a control system with a three-layer structure is used to achieve the above-mentioned purpose. The expected yaw rate and sideslip angle are adopted to calculate the direct yaw moment to guarantee the lateral stability of vehicles in the motion tracking layer. Considering the minimization of tire workload usage and braking force deviation as optimization objectives, torque allocation control is achieved for the direct yaw moment with lateral stability and the upper bound of longitudinal force (UBLF) of tires as constraint in the torque allocation layer. In the braking hydraulic pressure control layer, the hydraulic pressure in the wheel cylinder is adjusted according to the expected braking force of the wheel. This study proposes a method for determining the UBLF based on the optimal slip ratio (UBLF_OSR), which cannot only avoid obtaining the lateral force of tires but also directly restrict the distribution of tire force. The control system performance is analyzed on the basis of MATLAB/AMESim co-simulation. Results show that the proposed collaborative control strategy of lateral stability and braking performance ensures the lateral stability and braking performance during braking-in-turn maneuver.
关键词：
Hybrid electric vehicle;Predictive energy management;Battery aging;Temperature-aware method
摘要：
Lithium-ion battery degradation is one of the most important issues in hybrid electric vehicles. In order to minimize degradation and its equivalent battery life cost, the degradation of Li-ion batteries needs to be considered in energy management strategies. The existing methods mainly consider the battery aging in energy management strategies while ignoring its thermal dynamics. This paper proposes a battery aging-and temperature-aware predictive energy management strategy for parallel hybrid electric vehicles. This method is developed based on model predictive control (MPC) and evaluated in the scenario of urban bus transportation. First, due to the stochastic nature of speed transitions under actual driving conditions, a stochastic speed predictor is built based on the Markov chain model. Then, an optimal control framework for energy management strategy (EMS) is developed based on MPC, and the battery electrical-thermal-aging dynamics are considered in this control framework. The optimization is performed on the receding horizon using Pontryagin's minimization principle (PMP). The newly developed PMP-based MPC method is compared with the rule-based method and the global optimization method. The comparisons show that the PMP method is superior to dynamic programming when the battery electrical-thermal-aging dynamics are considered in the EMS development, and also show that the battery temperature-aware EMS can lower the total energy consumption.
期刊：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING,2019年234(2-3):669-680 ISSN：0954-4070
通讯作者：
Wenguang Wu
作者机构：
Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-Performance Mechanical Equipment, Changsha University of Science & Technology, Changsha, China;[Pan, Da; Wu, Wenguang] College of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha, China;[Huang, Caixia] College of Information, Mechanical and Electrical Engineering, Hunan International Economics University, Changsha, China;[Zhang, Zhiyong] Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-Performance Mechanical Equipment, Changsha University of Science & Technology, Changsha, China<&wdkj&>College of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha, China
通讯机构：
[Wenguang Wu] C;College of Automobile and Mechanical Engineering, Changsha University of Science & Technology, Changsha, China
关键词：
Operational transfer path analysis;vibration source identification;contribution analysis;heavy commercial vehicle;cab mount system
摘要：
Operational transfer path analysis is applied in this study to identify the vibration source and its critical transfer path. A simple analytical five-degrees-of-freedom mechanical isolation system is first taken as an example to illustrate the analysis flow and to validate the accuracy of operational transfer path analysis. The acceleration amplitude spectrum of the receiver is used to prove the accuracy, and the path contribution of each path is used to identify the critical path. Operational transfer path analysis is then applied to the cab mount system of a heavy commercial vehicle to identify the vibration source and its critical transfer path. The vibration energy propagation capabilities from the four cab mounts to the driver’s seat are analyzed by operational transfer path analysis with the path contribution analysis, and the maximum vibration source is identified by the path operation contribution analysis. The analysis and evaluation method of the operational transfer path analysis introduced in this study can provide a research foundation and reference for vibration or noise source identification in mechanical systems.
关键词：
Electric vehicle;power performance;motor modeling;brushless DC motor;parameter determination
摘要：
The analysis and control of powertrain systems of electric vehicle, which is an important type of new energy vehicle, have been the focus of extensive research, but determining the motor modeling parameters remains a problem. A method of parameter determination for brushless DC motor modeling based on vehicle power performance was developed in this study. The power and torque of the driving motor of an electric vehicle were obtained by using the dynamic equation of the electric vehicle to satisfy the requirements of power performance. The ranges of the back electromotive force coefficient and the winding inductance were derived from the voltage and dynamic equations of brushless DC motor, which were deduced from the expected power and torque of the motor. The modeling parameters were then determined on the basis of the influence of power source voltage, back electromotive force coefficient, winding inductance, and winding resistance on vehicle power performance. A hardware-in-loop simulation of vehicle power performance was performed to verify the effectiveness of the proposed method. Results indicate that the maximum vehicle velocity is 172 km/h, and the acceleration time of 100 km/h is 13 s, which reveal that the motor modeling parameters obtained with the method satisfy relevant requirements.
摘要：
Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in-wheel-motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady-state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single-chip microcomputer verifies the strong real-time performance and easy-to-implement characteristics of the proposed algorithm.
通讯机构：
Changsha University of Science and Technology, Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha, China
