The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

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Industrial Applications of the Kalman Filter: A Review

Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter

With high energy/power density, flexible and lightweight design, low self-discharge rates and long cycle life, lithium-ion (Li+) batteries have experienced a surging growth since being commercialized in the early 1990s [1]. They are dominant today in the consumer electronics sector. Due to continually declining manufacturing costs, they are also rapidly penetrating sectors such as the power grid, renewable energy, automotive, and aerospace, where largescale energy storage is needed. Looking into the future, the role of Li+ batteries will be further strengthened as a key energy-storage technology to support the progression of the world into the green energy era. However, their vulnerability to overcharge, overdischarge, and overheating can easily expose them to performance degradation, shortened cycle life, and even fire and explosion, thus raising many concerns about their deployment. These challenges have been driving a massive solution-seeking effort in various relevant research fields. Associated with this trend is the control-theory-enabled advancement of battery-management system (BMS) technologies.

Revisiting the State-of-Charge Estimation for Lithium-Ion Batteries: A Methodical Investigation of the Extended Kalman Filter Approach

Extended complex Kalman filter for sensorless control of an induction motor

The paper presents a new algorithm for a 2-dimensionnal direction of arrival estimation. Based on the extended Kalman filter (EKF), we analyse a recursive procedure for 2-dimensional directions of arrival (DOA) estimation and we will employ the two-L-shape arrays. A new space-variable model which we call a spatial state equation is presented using array element locations and incident angles. In this paper we briefly recapitulate the most important features of the extended Kalman filter (EKF). The performance of the proposed approach is examined by a simulation study with three signals model. The simulation results show a good estimate performance.

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Direction of Arrival Estimation Method Using a 2-L Shape Arrays Antenna

A general two-stage extended Kalman filter (GTSEKF), which extends the linear general two-stage Kalman filter to nonlinear systems, is further proposed. A new nonlinear two-stage transformation is introduced to facilitate achieving this extension. As in the linear one, the GTSEKF is derived mainly by applying the nonlinear two-stage transformation to the well-known extended Kalman filter (EKF), and is shown to be equivalent to the EKF with a decoupled computing structure. A nonlinear filter for estimating constant parameters in dynamic systems is presented to illustrate one application of the proposed GTSEKF. A literature example is also given to demonstrate the correctness and usefulness of the proposed results.

Chien-Shu Hsieh

Papers

General two-stage extended Kalman filters