Real-time battery thermal management for electric vehicles

Kim, Eugene; Shin, Kang G.; Lee, Jin-Kyu

doi:10.1109/iccps.2014.6843712

Cited by 41 publications

(16 citation statements)

References 22 publications

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“…From Eqs. (8) and (9) and the definition of z, the relationship between maximum z and maximum braking torque of the motor is obtained as follows:…”

Section: Active Control Of Charge Currentmentioning

confidence: 99%

“…Besides, the heat accumulation triggers faster vicious circle of temperature rise in the defective battery modules [6], which finally leads to the collapse of the entire battery pack [7]. As a result, the battery life cycle also degrades quickly under these circumstances [8]. Secondly, uneven temperature distribution causes different resistances between cells in a battery pack [9].…”

Section: Introductionmentioning

confidence: 98%

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Effect of energy-regenerative braking on electric vehicle battery thermal management and control method based on simulation investigation

Huang

Qin

Peng

2015

Energy Conversion and Management

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“…From Eqs. (8) and (9) and the definition of z, the relationship between maximum z and maximum braking torque of the motor is obtained as follows:…”

Section: Active Control Of Charge Currentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Effect of energy-regenerative braking on electric vehicle battery thermal management and control method based on simulation investigation

Huang

Qin

Peng

2015

Energy Conversion and Management

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“…With the wide adoption of battery systems such as electric vehicles and aircraft [2,6,30,39], significant research effort has been devoted to improving the system performance through effective system monitoring [25], advanced design of battery management systems [13,21,31,35,36], optimal battery discharge scheduling [11,12,14,23,33,42], etc.…”

Section: Related Workmentioning

confidence: 99%

Gü

Zhu

et al. 2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

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Unbalanced battery cells are known to significantly degrade the performance and reliability of a large-scale battery system. In this paper, we exploit emerging reconfigurable battery packs to mitigate the cell imbalance via the joint consideration of system reconfigurability and State-of-Health (So-H) of cells. Via empirical measurements and validation, we observe that a significantly larger amount of capacity can be delivered when cells with similar SoH levels are connected in series during discharging, which in turn extends the system operation time. Based on this observation, we propose two SoH-aware reconfiguration algorithms focusing on fully and partially reconfigurable battery packs, and prove their (near) optimality. We evaluate the proposed SoH-aware reconfiguration algorithms using both experiments and simulations. The algorithms are shown to deliver about 10-30% more capacity than SoH-oblivious configuration approaches.

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“…• multiple inputs and multiple outputs • high nonlinear behavior • to fulfill multiple and often contradictory goals At present, these complex battery TM systems are commonly controlled with finite-state-machines and PID controllers which use a set of rules learned from experience [11], [12]. However despite these methods are valid for vehicle operation within the current specifications, they are normally oversized and far away from optimum, particularly in the case of hybrid and electric vehicles where few experience is available.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

Lopez-Sanz

Ocampo‐Martínez

Alvarez-Florez³

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems.This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubspermissions@ieee.orgThe authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101).J. Lopez-Sanz and G. sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2• C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Index Terms-nonlinear model predictive control (NMPC), thermal management, plug-in hybrid electric vehicles (PHEV), Li-ion battery cooling.

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Real-time battery thermal management for electric vehicles

Cited by 41 publications

References 22 publications

Effect of energy-regenerative braking on electric vehicle battery thermal management and control method based on simulation investigation

Effect of energy-regenerative braking on electric vehicle battery thermal management and control method based on simulation investigation

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Nonlinear Model Predictive Control for Thermal Management in Plug-in Hybrid Electric Vehicles

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