Predictive Thermal Management for an Electric Vehicle Powertrain

Schaut, Stefan; Arnold, Eckhard; Sawodny, Oliver

doi:10.1109/tiv.2021.3131944

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…The use of active aerodynamics by controlling the size of frontal cooling openings of the vehicle depending on actual cooling requirements and speed can also be used to reduce drag coefficient, decrease propulsion energy consumption and thus improve overall efficiency [327]. Schaut et al have developed a predictive thermal management strategy for motor temperature control using nonlinear MPC and prior route information [328]. To improve the robustness of this closed-loop predictive cooling strategy, uncertainties in driving were covered by a scenario-based stochastic MPC.…”

Section: Intelligent Thermal Management Strategiesmentioning

confidence: 99%

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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Section: Intelligent Thermal Management Strategiesmentioning

confidence: 99%

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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“…To this end, some additional results on optimization of EVs heat pump-based thermal management system can be noted [26][27][28][29][30]. One can distinguish primarily the use of NMPC techniques for heat pump-based thermal management systems of EVs [31][32][33][34][35]. These results are complemented by model-based and model-free control methods for the thermal management system of EVs and the improved exploitation of the heat from the vehicles' batteries [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control of heat pumps in electric vehicles

Rigatos,

Siano,

Cuccurullo

et al. 2024

Preprint

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Heat pumps can be used for thermal management of electric vehicles (EVs) in a low-cost and environmental friendly manner. Heat pumps can exploit the heat that is diffused by the vehicle’s batteries so as to perform temperature control of the passengers cabin. A heat pump-based thermal management system of EVs has the dual objective of (a) stabilizing temperature in the passengers’ cabin at the desirable levels so as to ensure the passengers’ comfort and (b) stabilizing temperature at the batteries’ side so as to avoid degradation of the batteries power storage capacity and of their regenerative charging capability. So far nonlinear model predictive control (NMPC) methods have been mainly applied for the nonlinear optimal control problem of heat-pumps in EVs. In the present article a novel nonlinear optimal control method is proposed for solving the nonlinear optimal control problem of heat pumps. This method is computationally simple, has clear implementation stages and is of proven global stability. The dynamic model of the heat pump undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Linearization takes place at each sampling instance, around a temporary operating point which is defined by the present value of the heat pump’s state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the heat pump an H-infinity stabilizing controller is designed. The feedback gains of the controller are computed through the solution of an algebraic Riccati equation, at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. Moreover, to enable state estimation-based control, the H-infinity Kalman Filter is used as a robust observer. The method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

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“…One of the primary areas where DT technology can revolutionize electric motor applications lies in thermal management [15]- [17]. Temperature, particularly within critical components such as rotor magnets, windings, and bearings, significantly influences motor performance, safety, and lifespan.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimental Validation of a Multiphysics Twin of a High Voltage EV Motor

Torchio,

Conte,

Martin

et al. 2024

Preprint

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The integration of electric motors into various industrial and automotive applications emphasizes the critical necessity for reliable performance and operational efficiency. The advent of advanced digital technologies offers opportunities for predictive maintenance strategies. Digital Twins (DTs), mathematical models simulating a system’s physical behavior in real-time, present a transformative approach to enhance real-time monitoring of critical quantities, which is imperative to improve operational efficiency and minimize downtime. In this paper, we explore the feasibility and efficacy of deploying real-time physics-based DTs for condition monitoring in electric motor applications. Particularly, we focus on employing on-the-edge DTs, implemented on low-power onboard microprocessors, ensuring continuous communication with the physical asset for reliable real-time monitoring. The study applies DT technology to a high-voltage high-density Electric Vehicle (EV) motor, assessing its predictive capabilities in a real-world scenario. Results showcase the potential of DTs in revolutionizing condition monitoring, thereby meeting the evolving operational and maintenance requirements of contemporary electric motor systems.

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Predictive Thermal Management for an Electric Vehicle Powertrain

Cited by 12 publications

References 38 publications

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

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

Nonlinear optimal control of heat pumps in electric vehicles

Design and Experimental Validation of a Multiphysics Twin of a High Voltage EV Motor

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