A thermal management system for the battery pack of a hybrid electric vehicle: modeling and control

Tao, Xinran; Wagner, John R.

doi:10.1177/0954407015582323

Cited by 44 publications

(16 citation statements)

References 20 publications

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“…The estimater model can be developed based on thermophysical properties, some measured control variables and the amount of current flow occurs in the battery. Xinran (William) Tao and Wagner 107 developed Kalman filter estimater for predicting the battery core temperature by adopting a lumped thermal model. In this approach, with the help of an estimater support, the real-time optimal controller is designed to determine the essential air temperature for effective cooling the battery.…”

Section: Roles Of Electronic Controller Unit In Li-ion Battery Thermal Managementmentioning

confidence: 99%

Critical review towards thermal management systems of lithium-ion batteries in electric vehicle with its electronic control unit and assessment tools

Kannan

Vignesh

Karthick

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Lithium-ion batteries are facing difficulties in an aspect of protection towards battery thermal safety issues which leads to performance degradation or thermal runaway. To negate these issues an effective battery thermal management system is absolute pre-requisite to safeguard the lithium-ion batteries. In this context to support the future endeavours and to improvise battery thermal management system (BTMS) design and its operation the article reveals on three aspects through the analysis of scientiﬁc literatures. First, this paper collates the present research progress and status of various battery management strategies employed to lithium-ion batteries. Further, to promote stable and efficient BTMS operation as an initiation the extensive attention is paid towards roles of BTMS electronic control unit and also presented the essential functionality need to consider for designing best BTMS control strategy. Finally, elucidates the various unconventional assessment tools can be employed to recognize the suitable thermal management technique and also for establish optimum BTMS operation based on requirements. From the experience of this article additionally delivers some of the research gaps identified and the essential areas need to focus for the development of superior lithium-ion BTMS technology. All the contents reveal in this article will hopefully assist to the design commercially suitable effective BTMS technology especially for electro-mobility application.

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Section: Roles Of Electronic Controller Unit In Li-ion Battery Thermal Managementmentioning

confidence: 99%

Critical review towards thermal management systems of lithium-ion batteries in electric vehicle with its electronic control unit and assessment tools

Kannan

Vignesh

Karthick

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…Lyu et al 13 performed an experimental investigation on electric vehicle BTMS with thermoelectric cooling (TEC) which realized a 43°C battery surface temperature drop. Tao et al 14,15 minimized power consumption using a nonlinear controller for a HEV’s BTMS. Zhu et al 16 proposed an iterative dynamic programming method to improve BTMS performance and energy-efficiency for connected and automated HEVs.…”

Section: Introductionmentioning

confidence: 99%

Development of a heat pipe–based battery thermal management system for hybrid electric vehicles

Huang

Naini

Miller

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Enhanced battery pack cooling remains an open thermal management challenge in hybrid electric vehicle applications. A robust cooling system should maintain the battery pack core temperature within a prescribed operating range to improve system performance, durability, and reliability while minimizing power consumption. This paper proposes a smart battery thermal management system utilizing heat pipes as a thermal bus to efficiently remove heat. The system couples a standard air conditioning system with traditional ambient air ventilation. The two loops can run independently or in tandem to achieve the desired control. A nonlinear model predictive controller was developed to maintain the battery core temperature within a designated range using the compressor and fan speeds as the control inputs. A mathematical battery thermal model was developed to estimate the core and surface temperatures. The system performance and power requirements were evaluated for various driving cycles and ambient conditions. Numerical results showed that the proposed cooling system can regulate the battery core temperature within the desired temperature range (maximum tracking error of 2.1°C) while compensating for ambient temperature conditions using a suitable cooling strategy. The simulation results showed the ability to remove up to 1135 kJ of heat. The simulation also presents the power consumed by system components under varying modes and ambient conditions.

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“…A lithium-ion battery has the advantages of a high nominal voltage, a high energy density and a long life and has been widely used in pure electric vehicles, hybrid vehicles and fuel cell vehicles. 3 In order to ensure a good performance and to prolong the service life of the power battery, it is necessary to acquire appropriate battery operating conditions to prevent thermal runaways, ageing and irreversible damage. A battery management system (BMS) can provide information on the battery state for vehicle control and energy management.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the existing estimation approach is more sensitive to the initial estimation value, and an inappropriate value can easily reduce the precision of estimation and may even cause the divergence of the filter. 3 Therefore, a good non-linear and anti-disturbance ability is essential for an effective and accurate SOC estimation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

Zhao

Kong

Wang

2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.

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A thermal management system for the battery pack of a hybrid electric vehicle: modeling and control

Cited by 44 publications

References 20 publications

Critical review towards thermal management systems of lithium-ion batteries in electric vehicle with its electronic control unit and assessment tools

Critical review towards thermal management systems of lithium-ion batteries in electric vehicle with its electronic control unit and assessment tools

Development of a heat pipe–based battery thermal management system for hybrid electric vehicles

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

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