Predictive Energy Management of Hybrid Electric Vehicles via Multi-Layer Control

Razi, Maryam; Murgovski, Nikolce; McKelvey, Tomas; Wik, Torsten

doi:10.1109/tvt.2021.3081346

Cited by 25 publications

(6 citation statements)

References 43 publications

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“…This type of OCPs are generally difficult to be solved by existing optimization solvers because of the huge exploration spaces caused by control decisions at many time steps. A general solution to these OCPs is DDP, which is applicable for complex nonlinear and non-convex OCPs [39]. However, due to the "curse of dimensionality", DDP can hardly be directly implemented online.…”

Section: Adp-based Ems Designmentioning

confidence: 99%

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Liu

Tan

Zhu

et al. 2024

IEEE Trans. Intell. Veh.

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Section: Adp-based Ems Designmentioning

confidence: 99%

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Liu

Tan

Zhu

et al. 2024

IEEE Trans. Intell. Veh.

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“…Implementation of powertrain control, including energy and thermal management systems in an integrated multi-level framework, can facilitate the application of complex optimisation-based strategies onto real-time vehicle controllers by calculating lower control layers at the required smaller time steps while higher supervisory calculations are run at larger time steps or even on external resources (cloud), thereby minimising the overall onboard computational efforts [203,204]. Such a multi-levelled framework also supports separate development, modification and testing of individual layers and smoother translation of the developed strategies onto changing vehicle architectures, varying component sizes and powertrain specifications, further assisting in the fast industrialisation of such technologies.…”

Section: Energy Management Strategies and Multi-level Controlmentioning

confidence: 99%

“…MPC can easily accommodate multi-input multi-output (MIMO) control problems based on defined weights for tracking the different objectives [253,254]. MPC has seen applications in the decision layer (multi-objective power split or cooling system heat evacuation commands) as well as supervisor and external layers (calculating SoC, temperature set points, vehicle speed objectives) [204,255] in the context of the above multi-level framework (Figure 14). Ferrara et al have simulated and compared the behaviour of different energy management power split strategies such as ECMS, MPC and rule-based methods for HD FCEV trucks on real-world derived mission profiles and payloads for combined cost function minimisation comprising of H 2 fuel consumption and FCS ageing [197].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

“…When applying DP to actual powertrain management problems, drive cycle or route prediction uncertainty can be covered by introducing probability-based stochastic variables, making it Stochastic DP [231]. DP has seen some application in control and decision layers (discreet sub-problems such as gear shifting) [203,204] but is primarily seen in the supervisor layer (SoC, temperature reference, vehicle speed set-points) (Figure 14).…”

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

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A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

et al. 2022

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Long-haul heavy-duty vehicles, including trucks and coaches, contribute to a substantial portion of the modern-day European carbon footprint and pose a major challenge in emissions reduction due to their energy-intensive usage. Depending on the hydrogen fuel source, the use of fuel cell electric vehicles (FCEV) for long-haul applications has shown significant potential in reducing road freight CO2 emissions until the possible maturity of future long-distance battery-electric mobility. Fuel cell heavy-duty (HD) propulsion presents some specific characteristics, advantages and operating constraints, along with the notable possibility of gains in powertrain efficiency and usability through improved system design and intelligent onboard energy and thermal management. This paper provides an overview of the FCEV powertrain topology suited for long-haul HD applications, their operating limitations, cooling requirements, waste heat recovery techniques, state-of-the-art in powertrain control, energy and thermal management strategies and over-the-air route data based predictive powertrain management including V2X connectivity. A case study simulation analysis of an HD 40-tonne FCEV truck is also presented, focusing on the comparison of powertrain losses and energy expenditures in different subsystems while running on VECTO Regional delivery and Longhaul cycles. The importance of hydrogen fuel production pathways, onboard storage approaches, refuelling and safety standards, and fleet management is also discussed. Through a comprehensive review of the H2 fuel cell powertrain technology, intelligent energy management, thermal management requirements and strategies, and challenges in hydrogen production, storage and refuelling, this article aims at helping stakeholders in the promotion and integration of H2 FCEV technology towards road freight decarbonisation.

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“…2 The optimal energy distribution and the dynamic torque coordination between the engine and the electric machine (EM) are critical control issues. 3,4 Many minimizing fuel consumption control strategies were developed to dynamically coordinate the torque management scheme of the vehicle controller by researching the working modes combined with the distribution between the engine and the EM in the energy optimal fuel economy, for example, multi-objective hierarchical optimal strategy, 5 linear-quadratic tracking (LQT) strategy, 6 approximate dynamic programming (DP) strategy, 7 and model predictive control (MPC) strategy. 8,9 In addition to the above energy management strategies, many studies have been carried out for the mode transition process via torque-coordinated control between the engine and EM to improve smoothness and efficiency in the face of uncertain parameters and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive control for hybrid electric vehicles using sliding mode control

Ding,

Jiao,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The complicated transient coupling driving performance of the electromechanical coupling driving system for hybrid electric vehicles can be guaranteed by reasonably distributing the power between the engine and the electric machine. However, the disturbances resulting from system parameter perturbation, model uncertainty, unknown road condition, and uncertain preceding vehicle acceleration would degrade the control performance. To this issue, this article investigates a robust adaptive control scheme based on sliding mode control techniques to handle the complicated transient coupling driving problem for the electromechanical coupling driving process. In the robust adaptive control method, the coordinated control mechanism is designed using the electric machine to compensate for the response deviation of engine torque. Meanwhile, the double-loop adaptive terminal sliding mode speed-tracking control scheme and the adaptive dynamic sliding mode torque tracking control method are designed for the engine and the electric machine, respectively. The proposed control scheme ensures accurate tracking of the desired trajectories of the engine and electric machine in the presence of parameter perturbation and the unknown driving condition. Both simulation and actual vehicle experimental results demonstrate the effectiveness and practicality of the proposed control strategy.

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Predictive Energy Management of Hybrid Electric Vehicles via Multi-Layer Control

Cited by 25 publications

References 43 publications

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

Robust adaptive control for hybrid electric vehicles using sliding mode control

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