Conference PaperPDF Available

LiFePO4 optimal operation temperature range analysis for EV/HEV

Authors:

Abstract and Figures

The LiFePO4 batteries are widely used in Electric Vehicle(EV)/Hybrid Electric Vehicle(HEV) because of the high energy and power density. However, high environment temperature could accelerate the aging of batteries, while low temperature could reduce output power capability. Therefore, optimal working temperature for batteries should be determined to maintain good performance in all kinds of tough conditions. In this paper, the optimal working temperature range for batteries is analyzed. The capacity loss model is applied to determine the upper limit. The lower limit is calculated taking available capacity and output power loss into consideration. Simulation and experimental results show that the working temperature range between 10℃ and 40℃ could ensure the performance and available capacity.
Content may be subject to copyright.
K. Li et al. (Eds.): LSMS/ICSEE 2014, Part III, CCIS 463, pp. 476–485, 2014.
© Springer-Verlag Berlin Heidelberg 2014
LiFePO4 Optimal Operation Temperature Range
Analysis for EV/HEV
Jinlei Sun, Peng Yang , Rengui Lu, Guo Wei, and Chunbo Zhu
Harbin Institute of Technology Harbin 150001 China
{sunjinlei,lurengui}@hit.edu.cn,
{yangforyang,wg_weiguo}@sina.com,
zhuchunbo@gmail.com
Abstract. The LiFePO4 batteries are widely used in Electric Vehicle(EV)/Hybrid
Electric Vehicle(HEV) because of the high energy and power density. However,
high environment temperature could accelerate the aging of batteries, while low
temperature could reduce output power capability. Therefore, optimal working
temperature for batteries should be determined to maintain good performance in all
kinds of tough conditions. In this paper, the optimal working temperature range
for batteries is analyzed. The capacity loss model is applied to determine the upper
limit. The lower limit is calculated taking available capacity and output power loss
into consideration. Simulation and experimental results show that the working
temperature range between 10 and 40 could ensure the performance and
available capacity.
Keywords: Electric Vehicle, Hybrid Electric Vehicle, LiFePO4, optimal
working temperature.
1 Introduction
With the problems of energy crisis and environment becoming increasingly prominent,
Electric Vehicles(EVs)/Hybrid Electric Vehicles(HEVs) have attracted more and more
attention[1]. Lithium-ion batteries are becoming the best choice for solving these
problems owing to the characteristics of high energy and power density[2]. But the
drawbacks such as cost, safety and lifetime are the bottlenecks for EVs/HEVs taking the
place of traditional vehicles. The performance of power LiFePO4 tends to be greatly
affected by temperature, high temperature may accelerated aging and lead to thermal run
away[3]. It is reported that he slow charge transfer at the electrode/electrolyte interface
leads to the poor performance at low temperature[2]. At extreme low temperature the cell
capacity fades greatly comparing to the nominal capacity under room temperature [4].
Wide range working temperature has great influence on the performance and safety for
EVs/HEVs. The traditional fuel vehicles have been developed over 200 years and have
been able to withstand the harsh environment, while the EVs/HEVs must solve the
problem of battery pack thermal management to get satisfied performance at an extreme
cold or hot temperature. The optimal operation temperature range is available to provide
references for TMS and to prevent undesirable performance fade caused by environment.
LiFePO4 Optimal Operation Temperature Range Analysis for EV/HEV 477
The goal of battery thermal management is to maintain the battery within optimal
temperature range. For example, the aging and resistance rise caused by high
temperature and the available capacity and power fade caused by low temperature[5].
The battery thermal management methods mentioned in the literatures include: the
forced air cooling[6], liquid-based thermal management system [7,8],PCM based
thermal management system [9,10] and Thermo Electric Cooler(TEC) based
heating/cooling. [11,12] The forced air cooling is the traditional method for cooling,
the air flows across the surface of battery pack to take the heat away, this method has
been used in the Toyota Prius HEV application[13]. The liquid-based thermal
management takes the heat away directly or indirectly by liquid such as water, glycol,
oil, acetone or even refrigerants. Thanh-Ha Tran designed a flat heat pipe cooling
system, which could reduce the thermal resistance by 30% comparing with the natural
air cooling[7]. Zhonghao Rao[14] developed a thermal management system whose
maximum temperature could be controlled below 50 when the heat generation rate
was lower than 50 W and the maximum temperature difference is below 5. The
phase change materials(PCM) are developed rapidly recent years, PCM absorb heat
released by battery and make the temperature decrease rapidly, the heat is stored in
the form of PCM. The heat releases to the battery when in extreme cold environment.
The blower and pump are no longer needed in the PCM system. Selman and Al-Hallaj
did some research on the PCM and take the PCM to battery thermal management
system for the first time. In [15],they established 2D model for comparing four
thermal methods: (1) natural convection cooling; (2) presence of aluminum foam heat
transfer matrix;(3) use of phase change material (PCM); and (4) combination of
aluminum foam and PCM. They came to the conclusion that the use of aluminum
foam with PCM causes a significant temperature drop of about 50% compared to the
first case of no thermal management. In [16] the PCM and air-based methods are
compared and the advantages of heat pipe under extreme cold temperature were
highlighted. Chakib Alaoui worked on the TEC heater/cooler based on Peltier effect
for several years. The TEC based heater/cooler controls the temperature of cabin and
battery pack and took the place of vehicle air conditioning [12]. In [11], the TEC was
placed on the surface of each cell for the 24 series connected battery pack. The
Coefficient of Performance (COP) under the condition of US06 was as high as 1.2
and the energy consumption is only 4% of the fully charged pack.
Although there were many methods for battery thermal management, the
temperature control target is not uniform. Ref[17] argues that the highest battery
operating temperature should below 40 and the maximum temperature differences
is within 5. The FreedomCAR Battery Test Manual [18] defines the working
temperature range to be between -30 and 52. The wide range of working
temperature could not ensure the performance of battery pack. Thus, there should be a
specific optimal working temperature range for battery pack considering the power
and capacity characterizes.
In this paper, the experiments are taken first to test the temperature characterizes of
battery. Then the results of HPPC and cold cranking tests are analyzed. The cell
capacity loss model is used to analyze the aging of battery under high temperature the
power fade is analyzed according to capacity loss and power capability. Finally, the
optimal operation temperature range is determined.
478 J. Sun et al.
2 Experiment Design
2.1 Measurement Equipment
The commercial LiFePO4 used in the temperature characteristic experiment is
5Ah/3.2V (Voltage range 2.5V-3.65V). The test platform contains Arbin BT2000
battery tester(Output current range 0-100A, Voltage range 0-18V, Accuracy 0.02%-
0.05%FSR) and Testsky temperature control box (Temperature range -40-200,
Accuracy ±0.5). Fig.1 shows the devices for experiment.
Fig. 1. The devices for battery test and temperature control
2.2 HPPC Tests at Different Temperatures
The cell samples are placed in different temperature environments (-20,-10, 0,
10, 20, 30, 40, 50, 60) for 5 hours respectively. Each cell was fully
charged by constant current and constant voltage (CCCV) under different
temperature. The charge current was 9.37A and the discharge current was 12.5A . The
HPPC test was taken every 10% SOC intervals with 1C rate discharge current. The
HPPC test profile is shown in Fig. 2. In Fig. 2 the dotted line represents the current,
the solid line represents the voltage. The experiment is stopped as soon as the call
voltage reaching the cutoff voltage.
2.3 Cold Cranking Tests
According to the FreedomCAR Battery Test Manual [18], the pack should be replaced
when capacity fades to 80% of the rated capacity. In order to further study the output
power performance, a power pulse start test is taken according to the FreedomCAR
Battery Test Manual [18]. The pulse profile is shown in Fig.3.The tests are conducted
under different SOCs and temperatures, the maximum output power is measured
every 10% SOC internals. The steps are as follows:
1) Charge the cell to fully charged (Constant current and then constant voltage)
2) Discharge to the target SOC
3) Rest for 5 hours under the target temperature
LiFePO4 Optimal Operation Temperature Range Analysis for EV/HEV 479
4) Take 3 power pulse tests at constant power, each pulse lasts 2 seconds and rest for
10 seconds. As is shown in Fig.7
5) If the discharge cutoff voltage is met, return to step1) and decrease the power value
in step 4)
6) If the steps are finish, repeat step1 and increase the power value in step 4) until the
maximum power is found.
Fig. 2. The HPPC test profile
Fig. 3. The constant power start pulse profile
2.4 Results and Discussion
The results of the HPPC tests at different temperatures are shown in Fig.4-Fig.6 and
that for cold cranking are shown in Fig.6.
Fig.4 shows that the discharge capacity of the same cell under different
temperature conditions. It could be seen that the cell capacities are nearly the same at
the temperature between 40 and 60, while the cell capacity decreases obviously
480 J. Sun et al.
with the decrease of temperature, especially below 0. The cell capacity is 80% at
0 and could hardly discharge at -20.
Fig. 4. The cell capacity profile at different temperatures
Fig. 5. The cell ohmic resistance and polarization resistance at different temperatures and SOCs
The discharge capacity fades with the decrease of temperature. The ohmic
resistance and polarization resistance (Ro and Rp) under different temperatures are
indentified according to the method mentioned in FreedomCAR Battery Test Manual
[18]. Just as shown in Fig.4. The ohmic resistance changes a little at different SOCs at
the same temperature. The ohmic resistance increases with the drop of temperature.
The polarization resistance decreases with the drop of temperature, but it changes
greatly at different SOC under the same temperature. Due to the discharge capacity is
almost zero, the data at -20 is not universal.
Fig.6 shows the maximum charging and discharging power at different
temperatures and SOCs. The maximum charging and discharging power at target
SOC is defined as the product of maximum charging/discharging voltage during pulse
and the current. The charging power increases with the drop of temperature, while the
LiFePO4 Optimal Operation Temperature Range Analysis for EV/HEV 481
discharging power fades with the decrease of temperature. At the same temperature
the charging and discharging power in the full SOC range are nearly the same.
Fig. 6. The maximum charge and discharge power at different temperatures and SOCs
3 The Optimal Operation Temperature Range
As is analyzed in Section 2.3, the target cell capacities at a temperatures higher than
40 are nearly the same, while the discharge capacities begin to fade below 0.
Many researches claim that high temperature accelerates the aging[19] and the
performance fades during low temperature [20].In this section, the operation
temperature range is determined considering the current output power capability and
long term lifetime.
3.1 The Determination of Operation Temperature Range Upper Limit
John Wang established the capacity loss model taking DOD, temperature, discharge
rate into consideration in his research[19].
0.55
31700 370.3
exp( )( )
rate
loss h
C
QB A
R
T
−+×
=⋅ (1)
Where is the percentage of capacity loss, B represents the pre-exponential
factor, the Ah-throughput, which is expressed as Ah = (cycle
number)×(DOD)×(full cell capacity), and z is the power law factor, R is the gas
constant. T is the absolute temperature.
Yuejiu Zheng[21] further developed the model and have confirmed the parameter B.
1/3
15
10000( )
rate
BC
=
(2)
loss
Q
h
A
482 J. Sun et al.
The 1C rate discharge capacity loss is calculated according to equals (1) and (2)
0.55
31329.7
24662 exp( )( )
loss h
QA
R
T
=⋅ (3)
The aging experiment takes considerable time and work. To explain the
temperature influence on aging, we take the 1C discharge rate with 80% DOD
capacity loss model to simulate and analyze. The simulations under the conditions of
10 to 60 (10 internals) are taken. The results are plotted every 50 points, as is
shown in Fig.7.
Fig. 7. The capacity loss simulation at different temperatures
When the cycle number comes up to 2000, the capacity losses below 40 are
lower than 20%. The 2000 times cycle is enough for the lifetime of both the battery
and vehicle. Additionally, the maximum average temperature in summer is 40, the
maximum capability for thermal management system is to make the temperatures in
and out of the EV/HEV nearly the same. To sum up, 40 is determined to be the
upper limit of operation temperature range to maintain the performance and prevent
accurate aging caused by high temperature.
3.2 The Determination of Operation Temperature Range Lower Limit
The low temperature affects the charge transfer at the electrode/electrolyte interface,
which leads to the significant plating on the negative electrode during charging. It
irreversibly causes the capacity loss. Low temperature affects the driving distance and
output power performance for EVs/HEVs.
The maximum output power test results at different SOCs and temperatures are
shown in Fig.8. It shows that at room temperature, the maximum output power is
38W with almost no change within the whole SOC range. With the decrease of
temperature, the maximum output power fades gradually at the same temperature and
different SOCs. For example, at -10 and 100% SOC the maximum output power is
LiFePO4 Optimal Operation Temperature Range Analysis for EV/HEV 483
the same as that at room temperature. However, the power differences between 10%
and 100% are 10W. When the temperature comes to 10, the output power is similar
to that of room temperature and the power differences are little with different SOC.
Thus, the lower limit of operation temperature range is determined to be 10.
Fig. 8. The maximum output power at different temperature and SOC
4 Conclusion
In this paper, we proposed the optimal temperature operation range for batteries in
EV/HEV. We first take the HPPC and cold cranking tests under different
temperatures to obtain the temperature characteristics of LiFePO4. And then the
upper limit is determined according to the aging model. The lower limit is
determined considering discharge capacity loss and output power fades. Finally,
the optimal operation temperature range is proved to be 10 to 40 according to
the experimental results. The range provides a temperature control target for pack
thermal management. Working in the proposed temperature range is good for
maintaining vehicle in good performance and reducing energy loss during heating
or cooling.
Acknowledgments. This research was supported by the National High Technology
Research and Development Program of China (2012AA111003) in part and the
NSFC-EPSRC Collaborative Research Initiative in Smart Grids and the Integration of
Electric Vehicles (51361130153) and Science and Technology Project of State Grid
Corporation of China and the Fundamental Research Funds for the Central
Universities (Grant No.HIT.IBRSEM.201306).
484 J. Sun et al.
References
1. Lu, L., Han, X., Li, J., Hua, J., Ouyang, M.: A review on the key issues for lithium-ion
battery management in electric vehicles. Power Sources 226, 272–288 (2013)
2. Bandhauer, T.M., Garimella, S., Fuller, T.F.: A Critical Review of Thermal Issues in
Lithium-Ion Batteries. Electrochem Soc. 158(3), R1–25 (2011)
3. Zhang, X.: Thermal analysis of a cylindrical lithium-ion battery. Electrochim Acta 56(3),
1246–1255 (2011)
4. Tippmann, S., Walper, D., Balboa, L., Spier, B., Bessler, W.G.: Low-temperature charging
of lithium-ion cells part I: Electrochemical modeling and experimental investigation of
degradation behavior. Power Sources 252(0), 305–316 (2014)
5. Rao, Z.H., Wang, S.F.: A review of power battery thermal energy management.
Renewable & Sustainable Energy Reviews 15(9), 4554–4571 (2011)
6. Yang, Y., Hu, X., Qing, D., Chen, F.: Arrhenius Equation-Based Cell-Health Assessment:
Application to Thermal Energy Management Design of a HEV NiMH Battery Pack.
Energies 6(5), 2709–2725 (2013)
7. Tran, T., Harmand, S., Desmet, B., Filangi, S.: Experimental investigation on the
feasibility of heat pipe cooling for HEV/EV lithium-ion battery. Appl. Therm. Eng. 63(2),
551–558 (2014)
8. Park, Y., Jun, S., Kim, S., Lee, D.: Design optimization of a loop heat pipe to cool a
lithium ion battery onboard a military aircraft. Journal of Mechanical Science and
Technology 24(2), 609–618 (2010)
9. Fleming, E., Wen, S., Shi, L., Da Silva, A.K.: Thermodynamic model of a thermal storage
air conditioning system with dynamic behavior. Appl Energ. 112(SI), 160–169 (2013)
10. Zhang, X., Kong, X., Li, G., Li, J.: Thermodynamic assessment of active cooling/heating
methods for lithium-ion batteries of electric vehicles in extreme conditions. Energy 64,
1092–1111 (2014)
11. Alaoui, C.: Solid-State Thermal Management for Lithium-Ion EV Batteries. IEEE T. Veh.
Technol. 62(1), 98–107 (2013)
12. Alaoui, C., Salameh, Z.M.: A novel thermal management for electric and hybrid vehicles.
IEEE T. Veh. Technol. 54(2), 468–476 (2005)
13. Zolot, M., Pesaran, A.A., Mihalic, M.: Thermal evaluation of Toyota prius battery pack.
In: 2002 Future Car Congress, Arlington, VA, United states (2002)
14. Rao, Z., Wang, S., Wu, M., Lin, Z., Li, F.: Experimental investigation on thermal
management of electric vehicle battery with heat pipe. Energ. Convers Manage. 65, 92–97
(2013)
15. Khateeb, S.A., Amiruddin, S., Farid, M., Selman, J.R., Al-Hallaj, S.: Thermal management
of Li-ion battery with phase change material for electric scooters: experimental validation.
Power Sources 142(1-2), 345–353 (2005)
16. Kizilel, R., Sabbah, R., Selman, J.R., Al-Hallaj, S.: An alternative cooling system to
enhance the safety of Li-ion battery packs. Power Sources 194(2), 1105–1112 (2009)
17. Park, C., Kaviany, M.: Evaporation-combustion affected by in-cylinder, reciprocating
porous regenerator. Journal of Heat Transfer 124(1), 184–194 (2002)
18. ID, D. FreedomCAR Battery Test Manual For Power-Assist Hybrid Electric Vehicles
(2003)
19. Wang, J., Liu, P., Hicks-Garner, J., Sherman, E., Soukiazian, S., Verbrugge, M., et al.:
Cycle-life model for graphite-LiFePO4 cells. Power Sources 196(8), 3942–3948 (2011)
LiFePO4 Optimal Operation Temperature Range Analysis for EV/HEV 485
20. Yi, J., Kim, U.S., Shin, C.B., Han, T., Park, S.: Modeling the temperature dependence of
the discharge behavior of a lithium-ion battery in low environmental temperature. Power
Sources 244(SI), 143–148 (2013)
21. Zheng, Y., Ouyang, M., Lu, L., Li, J., Han, X., Xu, L.: On-line equalization for lithium-ion
battery packs based on charging cell voltages: Part 1. Equalization based on remaining
charging capacity estimation. Power Sources 247, 676–686 (2014)
... The design of different systems for the thermal management of batteries has been studied in [15] and [16]. References [17] and [18] have presented methods to estimate battery SOC, and 3 [19] the optimal range of battery temperature has been investigated. In [20], the battery's lifetime depends only on its current. ...
... The speed of the heat exchange at the entrance of the channels, which is a function of the temperature of the air entering and exiting the channel, is obtained by equation (17). Q u,i =ρ air C p,air q p,air (θ out,i -θ in,i ) ...
... Using relations (14) to (17) and removing out,i from these relations, equations (18) to (23) are obtained to calculate the heat dissipation rate (Qd). ...
Preprint
Full-text available
This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.
... The design of different systems for the thermal management of batteries has been studied in [15] and [16]. References [17] and [18] have presented methods to estimate battery SOC, and [19] the optimal range of battery temperature has been investigated. In [20], the battery's lifetime depends only on its current. ...
... According to equations (6) to (17), the equation of state for battery temperature will be according to relation (18). ...
Preprint
Full-text available
This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.
... Manajemen efisiensi pengisian baterai dan masa umur baterai akan berkurang jika tidak memperhitungkan suhu pengoprasian baterai mencapai 40ºC. Biasanya baterai lithium liion dan lifepo4 beroperasi pada kisaran suhu optimal 10ºC-40ºC (Pancha, et al., 2015;He & Ma, 2016;Sun, et al., 2014). Namun saya membatasi operasi kerja baterai pada kisaran suhu 30ºC agar suhu sistem penggerak tidak melebihi suhu panas pada kontroler 30ºC dan motor bldc 45ºC. ...
... Baterai Lithium LiFePO4 (Panchal, et al., 2015;He, F et al., 2016;Sun, J, et al., 2014)  ...
... In this section, all the Simulink model design and the controller setup will be discussed and further explained. Besides, the desired temperature in the temperature control system design is set to 27 °C or 300 K [23]. ...
... For different battery types, the temperature rating will vary from À40 C to 60 C (Siddique et al., 2018). Nevertheless, to obtain optimal output from the battery, the required operating temperature should vary in the range of 10 Ce40 C (Sun et al., 2014). During this range of temperature, the nominal battery capacity is 100%. ...
Article
Deeply decarbonizing electricity production will likely require low-carbon sources that meet energy demand throughout days, years, and decades. Renewable energy sources (RES) are possible low-carbon options, but the intermittency of RES can limit their potential as viable options. Energy storage can be a solution to this problem by storing excess power from RES and providing power to the load when output power of RES is insufficient. To date, some researchers have investigated the effect of energy storage on power system operations and their environmental impacts. However, the effects of battery thermal management (BTM) controller on the decarbonization of power grid are not sufficiently covered. Thus, this paper presents a comprehensive review on the benefits of thermal management control strategies for battery energy storage in the effort towards decarbonizing the power sector. In this regard, the impacts of BTM controller and optimized controller approaches in terms of cooling, heating, operation, insulation, and the pros and cons of each are reviewed and discussed. Additionally, the impact of batteries, BTM controller, and renewable energy integration towards grid decarbonization are discussed. The advantages and disadvantages of various types of batteries concerning power density, normal temperature range, life-cycle, size, environmental impact, and toxic emission are also highlighted. This study also focuses on different issues and challenges related to the BTM as well as recommendations for future research. The review demonstrates that the existing BTM control techniques not only improve the batteries efficiency, but also reduce its environmental impacts. However, further enhancement of BTM control using optimization methods is needed to reduce greenhouse gas emissions. Besides, as the majority of current approaches are restricted to simulation analysis, experimental validation becomes necessary. In summary, this review will hopefully strengthen the efforts toward the development of clean power systems with less environmental impact toward grid decarbonization.
Article
In a world where great efforts are being made to reduce carbon emissions and pollution, electric vehicles are becoming more appealing. Vehicles based on internal combustion engines are slowly being phased out and electric or hybrid vehicles are replacing them. Batteries being the primary power source of these vehicles, an effective battery thermal management system is required to ensure safe operation conditions and durability in all environments. The application of 1-tetradecanol as a Phase Change Material (PCM) in a Battery Thermal Management System (BTMS) is studied, for its use in an electric vehicle that uses Lithium Iron Phosphate (LiFePO4) battery. In the present work, a computational model is developed using a professional modelling software, ANSYS Fluent to investigate the thermal performance of 1-Tetradecanol PCM during its melting process within a cylindrical enclosure. The thermal behaviour of the system under the influence of proposed BTMS is studied. However, pure PCM based passive thermal management systems are not perfect due to their inherent low conductivity, which may be improved by incorporating conductivity enhancing materials. It was evident from the results that, it has the potential to be a better alternative to conventional battery thermal management systems and can be a lightweight and cost effective PCM passive thermal management system to limit the temperature of EV batteries to acceptable operating ranges.
Article
Full-text available
This paper presents a model-based cell-health-conscious thermal energy management method. An Arrhenius equation-based mathematical model is firstly identified to quantify the effect of temperature on the cell lifetime of a Nickel Metal Hydride (NiMH) battery pack. The cell aging datasets collected under multiple ambient temperatures are applied to extract the Arrhenius equation parameters. The model is then used as an assessment criterion and guidance for the thermal management design of battery packs. The feasibility and applicability of a pack structure with its cooling system, is then evaluated, and its design problems are studied by a computational fluid dynamics (CFD) analysis. The performance and eligibility of the design method is validated by both CFD simulations and experiments.
Article
Full-text available
Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for vehicles have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, uniformity and cost, imposes limitations on the wide application of lithium-ion batteries in the vehicle. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, in the hope of providing some inspirations to the design and research of the battery management system.
Article
A thermodynamic model was developed to predict transient behavior of a thermal storage system, using phase change materials (PCMs), for a novel electric vehicle climate conditioning application. The main objectives of the paper are to consider the system's dynamic behavior, such as a dynamic air flow rate into the vehicle's cabin, and to characterize the transient heat transfer process between the thermal storage unit and the vehicle's cabin, while still maintaining accurate solution to the complex phase change heat transfer. The system studied consists of a heat transfer fluid circulating between either of the on-board hot and cold thermal storage units, which we refer to as thermal batteries, and a liquid-air heat exchanger that provides heat exchange with the incoming air to the vehicle cabin. Each thermal battery is a shell-and-tube configuration where a heat transfer fluid flows through parallel tubes, which are surrounded by PCM within a larger shell. The system model incorporates computationally inexpensive semianalytic solution to the conjugated laminar forced convection and phase change problem within the battery and accounts for airside heat exchange using the Number of Transfer Units (NTUs) method for the liquid-air heat exchanger. Using this approach, we are able to obtain an accurate solution to the complex heat transfer problem within the battery while also incorporating the impact of the airside heat transfer on the overall system performance. The implemented model was benchmarked against a numerical study for a melting process and against full system experimental data for solidification using paraffin wax as the PCM. Through modeling, we demonstrate the importance of capturing the airside heat exchange impact on system performance, and we investigate system response to dynamic operating conditions, e.g., air recirculation.
Article
Lithium-ion batteries are well-suited for fully electric and hybrid electric vehicles due to their high specific energy and energy density relative to other rechargeable cell chemistries. However, these batteries have not been widely deployed commercially in these vehicles yet due to safety, cost, and poor low temperature performance, which are all challenges related to battery thermal management. In this paper, a critical review of the available literature on the major thermal issues for lithium-ion batteries is presented. Specific attention is paid to the effects of temperature and thermal management on capacity/power fade, thermal runaway, and pack electrical imbalance and to the performance of lithium-ion cells at cold temperatures. Furthermore, insights gained from previous experimental and modeling investigations are elucidated. These include the need for more accurate heat generation measurements, improved modeling of the heat generation rate, and clarity in the relative magnitudes of the various thermal effects observed at high charge and discharge rates seen in electric vehicle applications. From an analysis of the literature, the requirements for lithium-ion thermal management systems for optimal performance in these applications are suggested, and it is clear that no existing thermal management strategy or technology meets all these requirements.
Article
Battery thermal management system (BTMS) is essential for electric-vehicle (EV) and hybrid-vehicle (HV) battery packs to operate effectively in all climates. Lithium-ion (Li-ion) batteries offer many advantages to the EV such as high power and high specific energy. However, temperature affects their performance, safety, and productive life. This paper is about the design and evaluation of a BTMS based on the Peltier effect heat pumps. The discharge efficiency of a 60-Ah prismatic Li-ion pouch cell was measured under different rates and different ambient temperature values. The obtained results were used to design a solid-state BTMS based on Peltier thermoelectric coolers (TECs). The proposed BTMS is then modeled and evaluated at constant current discharge in the laboratory. In addition, The BTMS was installed in an EV that was driven in the US06 cycle. The thermal response and the energy consumption of the proposed BTMS were satisfactory.
Article
In this paper, the use of flat heat pipe as an effective and low-energy device to mitigate the temperature of a battery module designed for a HEV application was investigated. For this purpose, nominal heat flux generated by a battery module was reproduced and applied to a flat heat pipe cooling system. The thermal performance of the flat heat pipe cooling system was compared with that of a conventional heat sink under various cooling conditions and under several inclined positions. The results show that adding heat pipe reduced the thermal resistance of a common heat sink of 30% under natural convection and 20% under low air velocity cooling. Consequently, the cell temperature was kept below 50 °C, which cannot be achieved using heat sink. According to the space allocated for the battery pack in the vehicle, flat heat pipe can be used in vertical or horizontal position. Furthermore, flat heat pipe works efficiently under different grade road conditions. The transient behaviour of the flat heat pipe was also studied under high frequency and large amplitude variable input power. The flat heat pipe was found to handle more efficiently instant increases of the heat flux than the conventional heat sink.
Article
Fast charge of Li-ion cells is one of the main challenges in automotive battery application. As a particular problem at low temperatures and high charging rates, lithium deposits as metal on the anode surface (so-called lithium plating) instead of intercalation. Electrochemical models help to understand internal processes and predict aging effects, which finally lead to optimized charging strategies. In this work, a 1D + 1D (pseudo-2D) electrochemical model is developed, applied over a wide range of temperature (T = −25 °C to 40 °C) and current (I = 0.1 C to 6 C), and coupled with a 0D thermal model. The model is parameterized with measurement data in frequency domain using electrochemical impedance spectroscopy (EIS) and validated with time-domain data. In experiments cells are charged under different operating conditions. Capacity fade is measured after a significant number of cycles and compared to the simulated anode potential. A qualitative correlation is found between the degradation in experiment and the anode potential dropping below 0 V vs. Li/Li+ at the separator-anode boundary in the simulation. Furthermore a semi-quantitative expression for degradation is introduced. The transformation of the model into an on-board applicable form is presented in the companion contribution (part II).