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Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman filter is proposed to further improve the accuracy and stability of estimation. Firstly, the transformer-based estimator is introduced to provide an accurate pre-estimation of vehicle mass, with the nonlinear dynamics among vehicle states being learned. Secondly, on the basis of comparing the real-time input and training data of neural network, the weight adjustment module is designed to present an adaptive law. Finally, the adaptive extended Kalman filter is proposed to meet the demand of accuracy and stability, where the pre-estimation of transformer-based estimator is integrated with the adaptive law. Dataset is collected by conducting heavy-duty vehicle simulation. The mean absolute percentage error, mean absolute error, root mean square error and convergence rate averaged over simulation tests are 0.90%, 256.47 kg, 357.01 kg and 184 steps, respectively. The results show the outperformance of the proposed method in terms of accuracy and stability.

Lithium-ion batteries are an ideal power supplier for electric vehicles (EVs) due to their high-power density and wide operating voltage, but their performance decays to 80% before retirement from EVs. Nevertheless, they still have a particular use value after decommissioning, so recycling the retired power battery in cascade can be considered. Therefore, accurate estimation of battery state-of-charge (SoC) and state-of-health (SoH) is crucial for extending the service life and echelon utilization of power lithium-ion battery packs. This paper proposes a comprehensive co-estimation scheme of battery SoC/SoH for the second-use of lithium-ion power batteries in EVs under different cycles using an adaptive extended Kalman filter (AEKF). First, according to the collected battery test data at different aging cycle levels, the external battery characteristics are analyzed, and then a cycle-dependent equivalent circuit model (cECM) is built up. Next, the parameter estimation of this battery model is performed via a recursive least square (RLS) algorithm. Meanwhile, the variations in internal battery parameters of the cycle numbers are fitted and synthesized. Moreover, validation of the estimated parameters is further carried out. Based on this enhanced battery model, the AEKF algorithm is utilized to fulfill battery SoC/SoH estimation simultaneously. The estimated results of SoC/SoH are obtained for a LiCoO2 cell in the case of CCC (constant current condition) under different cycle times. The results show that this proposed co-estimation scheme can predict battery SoC and SoH well, wherein the peak values of the SoC errors are less than 2.2%, and the peak values of SoH, calculated by the estimated capacity and internal resistance, are less than 1.7% and 2.2%, respectively. Hence, this has important guiding significance for realizing the cascade utilization of lithium-ion power batteries.

Lithium-ion power batteries are widely used in the electric vehicle (EV) industry due to their high working voltage, high energy density, long cycle life, low self-discharge rate, and environmental protection. A multi-algorithm fusion method is proposed in this paper to estimate the battery state of charge (SOC), establishing the Thevenin model and collecting the terminal voltage residuals when the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF), and H infinite filter (HIF) estimate the SOC separately. The residuals are fused by Bayesian probability and the weight is obtained, and then the SOC estimated value of the fusion algorithm is obtained from the weight. A comparative analysis of the estimation accuracy of a single algorithm and a fusion algorithm under two different working conditions is made. Experimental results show that the fusion algorithm is more robust in the whole process of SOC estimation, and its estimation accuracy is better than the EKF algorithm. The estimation result for the fusion algorithm under a Dynamic Stress Test (DST) is better than that under a Hybrid Pulse Power Characterization (HPPC) test. With the emergence of cloud batteries, the fusion algorithm is expected to realize real vehicle online application.

This paper presents an algorithm for actuator fault diagnosis of nonlinear systems. The method is derived under classical uniform complete observability, controllability, and persistent excitation condition. To this end, the fault is modeled as a constant or a piecewise constant parameter vector. The diagnosis algorithm is based on the Extended Kalman Filter (EKF) with an explicit update law for the actuator fault estimation. From a practical point of view, the proposed algorithm can be used for general nonlinearity. To illustrate the effectiveness of the diagnosis algorithm, we present two numerical examples using the models of an autonomous car and a gantry crane. This paper presents an algorithm for actuator fault diagnosis of nonlinear systems. The method is derived under classical uniform complete observability, controllability, and persistent excitation condition. To this end, the fault is modeled as a constant or a piecewise constant parameter vector. The diagnosis algorithm is based on the Extended Kalman Filter (EKF) with an explicit update law for the actuator fault estimation. From a practical point of view, the proposed algorithm can be used for general nonlinearity. To illustrate the effectiveness of the diagnosis algorithm, we present two numerical examples using the models of an autonomous car and a gantry crane.

This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests.

According to characteristic of interceptor terminal flying, the current stochastic model based AEKF algorithm was combined into the stochastic sliding model variable structure guidance law to design the interceptor terminal guidance law. Firstly, on the basis of the passive radar's angle measurement information, the current stochastic model based AEKF algorithm was used to obtain the relative-distance and relative-velocity. Secondly, the influence of system state noise and observation noise were taken into account, designed a stochastic sliding model variable structure guidance law, which had the merit of robustness and angle of sight convergence. The simulation results indicate that the current stochastic model based AEKF algorithm combined to stochastic sliding model variable structure guidance law has high estimation precision and improves the guidance precision and minishs the miss distance. It turns out to be much value in engineering applications.

The traditional speed prediction generally utilizes the Global Position System (GPS) and video images, and thus, the prediction precision mainly depends on environmental factors (i.e., weather, ionosphere, troposphere, air, and electromagnetic waves). We study the Radio Frequency Identification (RFID) data-driven vehicle speed prediction and proposed an improved extended kalman filter (i.e., the adaptive extended kalman filter, AEKF) algorithm. Firstly, the on-board RFID reader equipped in the vehicle reads the information (i.e., current speed and time) from the tag deployed on the road. Secondly, the received information is transmitted to the on-board information processing unit, and it is demodulated and decoded into available information. Finally, based on the vehicle state space model, the AEKF algorithm is proposed to predict vehicle speed and improve the prediction results, so that the vehicle speed gradually approaches the actual vehicle speed. The simulation results show that compared with the conventional extended kalman filter (EKF) algorithm, our proposed AEKF algorithm improves the dynamic performance of the filtering and better suppresses the filtering divergence process. Moreover, the AEKF algorithm also improves the precision of the Mean Square Error (MSE) and Mean Absolute Error (MAE) by 57.4% and 32.4%, respectively.

Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a fast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.