Generic stochastic particle filter algorithm for predictive energy optimization of a Plug-in Hybrid Electric Vehicle extended by a battery temperature control and implemented on a Hardware-in-the-Loop system

Aubeck, Franz; Lenz, Martin; Mertes, Simon; Zylka, Kevin; Pischinger, Stefan

doi:10.1016/j.conengprac.2021.105002

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…The system framework is shown in Figure 5,and it consists of three main components: the front vehicle velocity prediction model, the filter, and the controller. Eq (17), Eq (18), Eq (19), Eq (20) Eq( 25 The front vehicle velocity prediction model, described in Section 2.1, uses a queue to store historical data and follows the FIFO (first-in, first-out) principle. When a new value arrives, it deletes the earliest value in the queue.…”

Section: Mpc Controller Algorithmmentioning

confidence: 99%

“…Some recent studies have considered control systems with noise. Aubeck F et al addressed the plug-in hybrid vehicle energy management problem using a generalized stochastic particle filtering algorithm for filtering, followed by a two-level MPC for coordinated vehicle fuel use and charging management [19]. Another example is [20], where Yan D et al combined a Kalman-consistent filter and a fixed-time disturbance observer with a multi-constraint MPC strategy to control the formation flight of unmanned aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Optimal Adaptive Control Based on Model Predictive Control

Hao

2023

Sensors

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Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final simulation results show that the proposed algorithm has a stronger energy-saving capability compared to previous studies and always maintains an appropriate relative distance and relative speed to the vehicle in front, verifying the effectiveness of the proposed algorithm.

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Section: Mpc Controller Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Optimal Adaptive Control Based on Model Predictive Control

Hao

2023

Sensors

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“…An AC asynchronous dynamometer emulates vehicle and road loads. An observer-based approach to PHEV control is tested using HIL in Aubeck et al 83…”

Section: Applicationsmentioning

confidence: 99%

“…An AC asynchronous dynamometer emulates vehicle and road loads. An observer-based approach to PHEV control is tested using HIL in Aubeck et al 83 Some systems, typically developed for regulatory bodies or development and test organisations, are designed for both model flexibility and an ability to accommodate a wide range of architectures. One example is the HyVETS system at SwRI which has an engine, an AC IM on either side of a DC dynamometer, 84 and a bi-directional power supply which can be activated to mimic the battery.…”

Section: Powertrainmentioning

confidence: 99%

A review of component-in-the-loop: Cyber-physical experiments for rapid system development and integration

Fagcang

Stobart

Steffen

2022

Advances in Mechanical Engineering

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To meet rising demands in performance and emissions compliance, companies are driven to develop systems of ever-increasing complexity. In-the-loop methods use a hybrid approach combining a physical subsystem with a virtual subsystem in real-time that can make product development processes faster and cheaper, enabling physical subsystems to be tested in real-world conditions while they are immersed in virtual environment simulation. These techniques evolved from being used solely in controller development with only the embedded controller placed as hardware-in-the-loop (HIL), to being a more holistic technique for system synthesis, capable of both component- and system-level studies. This paper delves into the development of the latter form, henceforth referred to as ‘component-in-the-loop’ or CIL. It provides a literature review of the growing uses of the technique in automotive development, giving a general implementation guidance in system architecture, methodology and control system structure. Furthermore, it suggests the need to clarify terminologies related to in-the-loop methods for a more efficient taxonomy. Definitions are proposed where deemed appropriate. In the age of search engines, this should facilitate knowledge transfer within and across disciplines. Finally, it highlights the key challenges in constructing such systems while discussing attempted solutions. CIL has reached a state of maturity where it is ready for wider adoption and continued technological progress will only push its potential further.

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