Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

https://www.mdpi.com/1996-1073/8/6/5916/pdf

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

The estimation of state of charge (SOC) is a crucial evaluation index in a battery management system (BMS). The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF) based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST) and New European Driving Cycle (NEDC). Making a comparison with extended Kalman filter (EKF) and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness.

/pdf/a-new-method-for-state-of-charge-estimation-of-lithium-ion-49l28rb81n.pdf

A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter

In this paper, we study the interacting multiple model (IMM) estimator for networked control systems with packet loss but without packet acknowledgment (ACK). The ACK is a signal sent by the actuator to inform the estimator of whether control packets are lost or not. A system with ACK is usually called a transmission control protocol (TCP) like system; otherwise, it is called a user datagram protocol (UDP) like system. We show that the stability of the IMM estimator for UDP-like systems is determined by the observation packet arrival rate (p.a.r.) and is independent of the control p.a.r. and control inputs. The IMM estimator is stable if the observation p.a.r. is greater than a critical value. We show that this critical value is the same as the critical value for the stability of the optimal estimator for its corresponding TCP-like system. If control inputs eventually tend to zero, the error covariance of the IMM estimator converges to that of the optimal estimator for TCP-like systems. We characterize the impact of the control/observation p.a.r. and the control input on estimation performance. Finally, we prove that the average estimation performance of the IMM estimator approximates that of the optimal estimator within a finite bound, and is superior to that of the linear minimum mean square error estimator.

/pdf/interacting-multiple-model-estimator-for-networked-control-2ra3wexs1x.pdf

Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

Underwater acoustic sensor networks (UASNs) play an important role in the ocean's protection. They can realize real-time data collection, monitoring, exploration, and many other underwater applications by connecting and coordinating seafloor sensors and underwater vehicles. To achieve these application objectives, such as fishes tracking in biological monitoring field and submarines tracking in military field, target tracking is one of the key techniques. This paper presents a centralized fusion algorithm based on the interacting multiple models and the adaptive Kalman filter (IMMCFAKF) for target tracking in UASNs. Specifically, by introducing an adaptive forgetting factor into the optimal centralized fusion Kalman filter algorithm, the optimal centralized fusion adaptive Kalman filter (CFAKF) algorithm is obtained first. Then, combining the superiorities of both the optimal CFAKF algorithm and the conventional IMM algorithm, the optimal IMMCFAKF is achieved. The numerical simulations are provided to demonstrate the effectiveness of the proposed optimal IMMCFAKF algorithm.

Centralized Fusion Based on Interacting Multiple Model and Adaptive Kalman Filter for Target Tracking in Underwater Acoustic Sensor Networks

Autonomous vehicles navigation in urban area suppose to deal with static and dynamic objects. To ensure safe autonomous navigation, the detection accuracy of a moving object is very critical to avoid false alarm while tracking the direction of moving object for collision avoidance. This paper presents the experimental results of the proposed Detection and Tracking Moving Object (DATMO) algorithm for a single moving object. Experiments were conducted in outdoor environment using a vehicle equipped with a laser range finder to provide the input data to the DATMO algorithm while the Kalman filter with the integration of constant velocity (CV) model was used for tracking. The results from the experiments showed that the DATMO algorithm developed successfully tracks a single moving object in an outdoor environment.

/pdf/model-based-detection-and-tracking-of-single-moving-object-kv86ro3r3g.pdf

Model-Based Detection and Tracking of Single Moving Object Using Laser Range Finder

In this paper we present an new approach based on two filters α β and α β γ using Interacting Multiples Models (IMM) design instead of a Kalman filter second and third order for the tracking a single maneuver target. The comparison between the proposed filter and the IMM improves the computing time amount about 60% while having a high accuracy.

/pdf/a-new-imm-algorithm-using-fixed-coefficients-filters-fastimm-2w3hy8g7n6.pdf

A new IMM algorithm using fixed coefficients filters (fastIMM)

State estimation is a major problem in many fields, such as target tracking. For a linear Gaussian dynamic system, the KF provides the optimal state estimate, in the minimum mean square error sense. In general, however, real-world systems are governed by the presence of non-Gaussian noise and/or nonlinear systems. In this paper, the problem of state estimation in the case of a linear system affected by a non-Gaussian measurement noise is addressed. Based on the theoretical framework of the Gaussian sum filters (GSF), we propose a novel static version of this filter that uses the well known αβ filter. The simulation results show that the proposed filter has acceptable performances in terms of RMSE and a reduced computational load, compared to the classical GSF.

A simplified αβ based Gaussian sum filter

Pseudo steady state filters for target tracking with polar measurements

In a recent paper, a new discrete-time Bayesian filter, named the cubature Kalman filter (CKF), was derived. To reduce the complexity of the filter, we propose in this paper to combine the CKF with the linear Kalman filter, when either the process equation or the measurement equation is linear. The resulting filter is referred to as the Reduced CKF (RCKF). It is here applied to the problem of tracking in Cartesian coordinates a moving object whose state can be modeled by a linear dynamic equation, but whose measurement equation is non linear, due to the fact that the measurements represent position measurements in polar coordinates. The simulations results show that, in terms of root Mean Square Error (RMSE), the RCKF and CKF have the same performance, but the processing time of the RCKF is lower than that of the CKF.

Reduced cubature Kalman filtering applied to target tracking

Two new algorithms are presented in this paper to solve the bearing only tracking problem, by using the recently proposed algorithms for nonlinear filtering, which are the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF). These filters are used to solve the high intrinsic nonlinearity in the modified polar coordinates dynamic model. The resulting filters, named the Modified Polar Unscented Kalman Filter (MPUKF) and the Modified Polar Cubature Kalman Filter (MPCKF) are applied to the two dimensional bearing-only tracking problem. The estimation performance of these nonlinear filters is assessed by means of the root mean square error (RMSE). The simulation results show that these new filters are more efficient than other similar filters, such as the Modified Polar Extended Kalman Filter (MPEKF). Compared to this filter, the MPUKF and the MPCKF are, in particular, non-sensitive to the initial range and more stable, by using a dedicated initialization procedure, proposed in this paper.

Meche Abdelkrim

Papers

A new IMM algorithm using fixed coefficients filters (fastIMM)

A simplified αβ based Gaussian sum filter

Pseudo steady state filters for target tracking with polar measurements

Reduced cubature Kalman filtering applied to target tracking

Enhanced Modified Polar Coordinates Filter Applied to Bearing Only Tracking