Dynamic thermal characteristics of heat pipe via segmented thermal resistance model for electric vehicle battery cooling

Highlights

• A “segmented” thermal resistance model of a heat pipe is proposed.

• Accuracy of “segmented” model is verified by comparing with “non-segmented” model.

• Ultra-thin micro heat pipe(UMHP) is compact and effective for EV battery cooling.

• The cooling effect of an UMHP pack with natural/forced convection is evaluated.

• The thermal performance of an UMHP pack with different arrangements is compared.

Abstract

Heat pipe cooling for battery thermal management systems (BTMSs) in electric vehicles (EVs) is growing due to its advantages of high cooling efficiency, compact structure and flexible geometry. Considering the transient conduction, phase change and uncertain thermal conditions in a heat pipe, it is challenging to obtain the dynamic thermal characteristics accurately in such complex heat and mass transfer process. In this paper, a “segmented” thermal resistance model of a heat pipe is proposed based on thermal circuit method. The equivalent conductivities of different segments, viz. the evaporator and condenser of pipe, are used to determine their own thermal parameters and conditions integrated into the thermal model of battery for a complete three-dimensional (3D) computational fluid dynamics (CFD) simulation. The proposed “segmented” model shows more precise than the “non-segmented” model by the comparison of simulated and experimental temperature distribution and variation of an ultra-thin micro heat pipe (UMHP) battery pack, and has less calculation error to obtain dynamic thermal behavior for exact thermal design, management and control of heat pipe BTMSs. Using the “segmented” model, the cooling effect of the UMHP pack with different natural/forced convection and arrangements is predicted, and the results correspond well to the tests.

Introduction

The issues of the serious energy shortage and environment pollution all over the world have induced the growing opportunity of some alternative energy to fossil fuel as the power for clean vehicles recently [1], [2]. For instance, hydrogen could act as a fuel for internal combustion engines (ICEs) and for fuel cells used in fuel cell electric vehicles (FCEVs), or the battery as a power source for electric vehicles (EVs) is attractive [3], [4], [5], [6]. Among them, the battery is significantly concerned by designers, manufacturers and EV-users about its performance characteristics to satisfy the requirements of an EV, such as good dynamic charging/discharging performance for long driving range, fast acceleration, adequate idle-stop, motor assisting and regenerative braking, and good adaptability to thermal environment from extreme hot summer to cold winter and reliable running safety [7], [8]. Therefore, reliable, efficient and safe operation, good cycle lifespan, and superior State of Charge (SOC) and State of Health (SOH) of the battery system especially under highly dynamic driving conditions, have become the key factors for the determination of power, reliability, safety, cost and broad application of EVs [9], [10].

Among various power batteries, Lithium-ion (Li-ion) batteries are taking an increasing market share for EVs' application because of their outstanding characteristics in higher energy density, longer life-time and lower self-discharge rate [11]. Generally, to achieve sufficient voltages and capacities to power an EV, even hundreds to thousands of cells must be connected in series/parallel to form a large-scale battery module/pack usually working at high frequency charge/discharge rate, which will cause large heat generation with various chemical and electrochemical reactions. However, the performance of Li-ion cells is sensitive to extremely high or low temperature and uneven temperature distribution caused by such reactions. According to the Arrhenius law, the reaction rate increases exponentially with the rising cell temperature; the higher the temperature, the faster the rate of aging [12], [13]. As a result, hotter cells degrade more quickly than colder ones, and shorten the lifetime of entire battery pack. Additionally, overheating and non-uniform temperature gradient commonly caused by high currents or internal short-circuiting without rapid and sufficient cooling will trigger the thermal runaway (combustion or even explosion) and failure of cells [14]. Chiu et al. [15] expressed that under 45 °C ambient temperature, after 1322 cycles with 0.5C charge/discharge, the capacity of a 26650 Li-ion cell (2.3 Ah) decayed by 17%; while at 60 °C, it faded by almost 20% through only 754 cycles. Additionally, at low temperature, electric polarization of the graphite electrode caused by the low diffusivity of Li-ions will cause the loss of power and lifetime of cells [16]. A common phenomenon is that the thermal performance of Li-ion cells deteriorate drastically below −10 °C [17]. Consequently, for Li-ion batteries, the optimal operating temperature range is 25 °C–40 °C, and the maximum temperature difference is less than 5 °C [18], [19].

The above issues including thermal safety, stability and uniformity have been crucially considered for a well-designed and high-efficient battery thermal management system (BTMS) to ensure the batteries working under their desired temperature range and uniform temperature distribution. Thus, the BTMSs should provide reliable thermal regulation to keep the batteries best performing, and also satisfy the requirements for EVs, such as compactness, lightweight, convenient maintenance, electrical insulation, low power consumption and low cost.

Up to now, many researches [20], [21] have reported various types of BTMSs for EVs, in which the selection of cooling mediums, the thermal design of cooling systems and the assessment of cooling effect became focused. In general, air, liquid and phase change materials (PCMs) as coolants were more considered; occasionally two or more mediums were combined to improve the cooling effect. Among the above methods, air cooling can be passive/active, parallel/series or natural/forced, and most widely used due to its low cost, availability and easy installation (e.g. Toyota Prius and RAV-4) [22], [23]. In fact, even using air forced cooling still cause the non-uniform temperature distribution especially for a large-scale battery pack in EVs [24]. Liquid cooling (e.g. GM Volt and Tesla Model S) usually uses water, glycol or insulated oil as the common coolants, has higher heat transfer coefficient and offers greater cooling capacity than air cooling [25], [26]. However, some additional equipment such as pumps, tanks, heat exchangers and valves have to be installed in EVs to cause more occupied space, weight and requirements for leakage protection and complicated maintenance. Furthermore, the relatively high pressure drop across the liquid-cooled heat exchangers will lead to significant increased energy consumption and cost of the system [27]. PCMs (e.g. paraffin wax or capric acid) have high thermal energy storage capacity because of the large latent heat in the phase change process with small temperature variations [28]. Therefore, the PCM cooling systems can maintain uniform temperature distribution among the cells closing to the melting point of PCMs [29]. But suffering from the inherent limitation of low thermal conductivity, PCMs have insufficient long term thermal stability even if adding copper/aluminum foam or carbon fibre [30]. Furthermore, the high cost and the possible liquid leakage caused by the volume expansion after melting have limited the PCM systems widely applied for EVs.

The weakness of the above BTMS cooling systems makes it very difficult to meet the thermal requirements for power batteries working at various complex driving conditions. Thus, in demanding for high efficient and low-energy consumption BTMSs, heat pipes have been intensively concerned by researches [31], [32]. They make use of phase change heat transfer, evaporate at a heat source (evaporator) and condense at a heat sink (condenser). Furthermore, they possess excellent characteristics such as high thermal conductivity, compact structure, flexible geometry, bidirectional heat transfer characteristics, long service life and convenient maintenance. Therefore, heat pipes have been widely used for cooling and thermal management of various electrical and electronic equipments [33], [34], [35], [36].

Recently, heat pipe cooling has been garnering more attention in BTMSs. Rao et al. [37], [38] investigated the cooling performance of an oscillating heat pipe (OHP) BTMS by experiments, and concluded that the maximum temperature of battery could be controlled below 50 °C when the heat generation was lower than 50 W. Wang et al. [39] provided an experimental characterization of a heat pipe BTMS coupled with liquid cooling, and pointed out that the battery temperature could be kept below 40 °C if the heat generation was less than 10 W/cell, and reduced down to 70 °C under uncommon thermal conditions (e.g. 20–40 W/cell). Tran et al. [40], [41] indicated that adding heat pipe to a battery module would reduce the thermal resistance of a common heat sink by 30% under natural convection and 20% under low air velocity cooling. Also, they compared the measured temperature of battery under adiabatic and forced convections with the simulated data from an AMESim model, in which the temperature within each cell was assumed being homogeneous. Greco et al. [42] proposed a simplified one-dimensional (1D) transient computational model of battery using the thermal circuit method in conjunction with the thermal model of heat pipe to analyze the thermal behavior of a heat pipe BTMS. They predicted the maximum temperature of 27.6 °C by heat pipes compared with 51.5 °C by forced convection, which is in agreement with both the analytical solution and the corresponding three-dimensional (3D) computational fluid dynamics (CFD) results. But there are no details about CFD modeling and relevant experimental validation. Ye et al. [43] confirmed an optimized BTMS with heat pipe cold plates (HPCPs) to be feasible on different cooling strategies even during 8C charging by experiments. Also, a simple numerical model was conducted to predict the thermal behavior of the heat pipe BTMS, in which the effective heat transfer coefficient at the surfaces in contact with HPCPs, was estimated from steady-state experimental data. Considering the space and weight constrains of EVs, a heat pipe BTMS needs to be more compact, lighter and of easier installation. Among various heat pipes, micro heat pipes (MHPs) and miniature heat pipes (mHPs) are small scale devices with hydraulic diameter on the order of 10–500 μm and 2∼4 mm respectively [44], [45]. Zhao et al. [46] demonstrated an experimental study on excellent cooling effect of two different ultra-thin aluminum heat pipe BTMSs with wet cooling by comparing with other four cooling methods (i.e. in ambient, by horizontal/vertical fans and thermostat bath).

The above relevant investigations revealed the superior aspects of thermal performance of heat pipe BTMSs compared to other cooling systems mainly based on experiment methods. As known, some different heat transfer phenomena occur within the complicated constitution of a heat pipe, such as thermal conduction, phase change, fluid dynamics and the unpredictable thermal boundary conditions. This makes it much difficult to obtain complete and accurate numerical temperature distribution and variation in the heat pipes' heat and mass transfer process in addition to the batteries' transient and high frequency charging/discharging. The computational thermal model of heat pipe BTMSs becomes very complex in CFD simulation including thermal parameters acquirement and thermal boundaries determination. Meanwhile, the compactness and miniaturization of the cooling device for meeting the requirements in optimal geometrical structure and layout for EVs also make it even harder. Thus, our work was motivated by the need to investigate and develop more accurate computational methods to analyze in depth the dynamic thermal characteristics especially in complex heat transfer in and between heat pipes and batteries for optimizing a heat pipe BTMS.

A thermal resistance (R) network model using thermal circuit method was developed by Faghri [47] and Zuo et al. [48] to describe the thermal behavior of a heat pipe, where the equivalent thermal conductivity (k ∝ (1/R)) is calculated as the thermal parameter for CFD simulation. In this situation, the pipe is usually simply treated as one conductor (here denoted as “non-segmented” model). In this case, a single parameter (i.e., total equivalent thermal conductivity) is used to reflect the overall thermal performance of the heat pipe. As a result, the variation in heat transfer between evaporator and condenser of the pipe caused by different phase changes is ignored. This will result in dynamic calculation error which affects the exact thermal design, management and control for BTMSs. In this paper, a “segmented” thermal resistance model of a heat pipe was proposed based on the thermal circuit method. The equivalent conductivities of different segments (the evaporator and condenser of pipe), are used to determine their own thermal parameters and conditions integrated into the thermal model of battery for a complete 3D CFD simulation to predict temperature distribution and variation of heat pipe BTMSs. The main contributions of this study are the development of such a computational model and its application to thermal management of a battery pack with heat pipe cooling, and thus to serve as a guide to thermal design and control of heat pipe BTMSs.

The remained of this paper is organized as follows: Section 2 describes a Li-ion battery pack with ultra-thin micro heat pipe (UMHP) cooling for EVs; Section 3 proposes a “segmented” thermal model and details the comparison with the “non-segmented” model; the 3D transient thermal model of battery cell is established in Section 4; experimental investigation on the thermal performance of the UMHP pack is carried out in Section 5; then the temperature predictions from the “segmented” model are validated by comparing with the “non-segmented” model, and the cooling effect of the UMHP pack with different natural/forced convection and arrangements are discussed in Section 6; finally, some conclusions are drawn in Section 7.