Open Access
31-12-2024 | Electric, Fuel Cell, and Hybrid Vehicle, Fuels and Lubricants, Heat Transfer, Fluid and Thermal Engineering, Vision and Sensors
Comprehensive Analysis of Battery Thermal Management and Energy Consumption in an Electric Vehicle: Impact of Driving Modes and Ambient Temperatures
Authors:
Inji Park, Charyung Kim, Hyunwoo Lee, Cha-Lee Myung, Kyoungdoug Min
This study provides an in-depth analysis of how battery thermal management and energy consumption in an electric vehicle are influenced by different driving modes and ambient temperatures. It highlights the important role of the battery thermal management system (BMS) in ensuring efficient battery operation, particularly under extreme temperature conditions. At − 15 °C, energy efficiency dropped by 67% in city driving and 42% on the highway. This was mainly due to the heating system using more energy. Meanwhile, at 35 °C, energy consumption rose by 24% in city driving and 12% in highway driving, primarily due to the air conditioning system. The thermal management system helped regulate the battery’s temperature, reducing performance loss in both low and high temperature conditions. In cold environments, the heating system used 51% of the total energy in city driving and 30% in highway driving. This shows that ambient temperatures greatly affect energy use. These results stress the need to optimize both the BMS and HVAC (Heating, Ventilation and Air conditioning) systems. This will improve energy efficiency and ensure consistent performance in various driving conditions.
Notes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abbreviations
BEV
Battery electric vehicle
BMS
Battery thermal management system
CAN
Controller area network
COP
Coefficient of performance
DC
Direct current
EC
Energy consumption
Edc
DC discharge energy
ECdc
DC discharge energy consumption
HWFET
Highway fuel economy test
HVAC
Heating, ventilation, and air conditioning
IQR
Interquartile range
PTC
Positive temperature coefficient
SOC
State of charge
SOH
State of health
UDDS
Urban dynamometer driving schedule
1 Introduction
As global demand for sustainable transport grows, battery electric vehicles (BEVs) are seen as a way to reduce pollution and greenhouse gas emissions (GHGs). Their adoption is accelerating rapidly, with BEVs having zero emissions during use phase, thus playing a pivotal role in combating climate change. The widespread integration of BEVs supports global initiatives aimed at reducing dependence on internal combustion engine vehicles (ICEVs) and lowering GHGs within the automotive industry (Cui et al. 2022; Shen et al. 2023).
Environmental regulations and government policies worldwide are propelling the electric vehicle revolution. The European Union (EU) passed legislation banning the sale of ICEVs, excluding e-fuel, by 2035 (European Parliament and Council, 2023). Simultaneously, nations like the United States, China, Japan, and south Korea, are actively implementing incentive and subsidy policies to promote the adoption of electric vehicles (IEA, 2021). South Korea’s ambitious plan targets a 37.8% CO2 reduction in transportation by 2030, aiming for 4.2 million electric vehicles and 1.23 million charging stations. These initiatives, coupled with rapid technological progress, are transforming the automotive landscape. As countries race to meet climate goals, the electric vehicle market continues its swift ascent, reshaping global transportation frameworks (Government of Korea, 2021, 2023).
Advertisement
The energy consumption efficiency of BEVs is a crucial factor that determines vehicle performance and driving range, directly affecting user satisfaction and the success of commercialization. As electric vehicle use grows, optimizing energy efficiency is vital. It is affected by factors like the driving environment, driving cycles, and battery performance. Research by Szumska et al. (2021), Fan et al. (2021) and Lee et al. (2024a, 2024b) shows that BEVs’ energy efficiency depends on several factors, including vehicle speed, driving patterns, vehicle weight, HVAC system use, ambient temperatures, battery state of charge (SOC), and battery sate of health (SOH). It is critical to know these factors as they optimize performance and extend electric range. This matters to both vehicle manufacturers and consumers.
Specifically, the energy efficiency of BEVs is significantly affected by the environment, especially ambient temperature. It directly impacts driving performance and efficiency. In low-temperature conditions, the internal resistance of the battery increases, worsening energy efficiency and significantly reducing electric driving range (Iora et al. 2019; Jaguemont et al. 2016; Zhao et al. 2022). For example, Zhao et al. (2023) observed a remarkable decrease in energy efficiency in low temperature, where energy consumption at − 7 °C was 40.03 kWh/100 km, compared to 25.81 kWh/100 km at 23 °C, leading to a 36% reduction in driving range. Two main factors contribute to this. Firstly, cold condition reduces the efficiency of charging and discharging. Also, heating the battery increases its energy demand. Similarly, Jose and Chidambaram (2022) found that PTC (Positive Temperature Coefficient) heaters can use 30–50% of a vehicle’s stored energy in low-temperature conditions. This reduces the driving range by up to 40% compared to operation at room temperature.
Meanwhile, Li et al. (2024) reported that driving range can also decrease in high-temperature environments, with an average 29.64% reduction at 35 ± 3 °C compared to 23 ± 3 °C. The increased energy consumption in high temperatures is primarily attributed to the additional load from the air conditioning system and other low-voltage accessories, which can account for up to 60% of the increase in energy use. Also, according to a study on the real-world performance of battery electric passenger cars in China, in “hot” conditions (30–35 °C), the range was generally reduced by up to 15%, primarily due to air conditioning use (Jin et al. 2023).
A study by National Big Data Alliance of New Energy Vehicles (NDANEV) in China, analyzed 140,000 electric vehicles. It found big gaps between certified and actual driving ranges in extreme temperatures. At sub-zero temperatures (≤ − 7 °C and 0 °C), real-world ranges dropped by 30–50% and 20–40%, respectively, from certification values. At 35 °C, ranges fell by 15%. This shows that extreme temperatures harm BEV efficiency and electric range (ICCT, 2023). Lee et al. (2024a, 2024b) confirmed these findings with real-world tests. They used the City-Highway-US06 mode and the Konkuk University Route. Their results showed the best energy efficiency for lithium-ion battery BEVs at 20–30 °C. However, at − 15 °C, energy use rose by up to 35.4%. This was due to less effective regenerative braking and higher power draw from the battery and motor. Also, HVAC use raised energy consumption by 5.4% in summer and 12.0% in winter. This shows how ambient temperature affects BEV efficiency.
Advertisement
In terms of electric vehicle system, extensive research is being conducted on battery types, energy density, the BMS, motor efficiency, regenerative braking, and heat pump control technologies to improve energy consumption efficiency as well as electric driving range (Al-Wreikat et al. 2022; Bae et al. 2024; Kropiwnicki and Furmanek, 2019; Vima et al. 2021). Additionally, studies have shown that implementing heat pump systems to recover waste heat from external air, the battery, motor, and electrical components can improve energy efficiency and extend driving range (Dan et al. 2023; Kang et al. 2023; Wray and Ebrahimi, 2022).
As ambient temperature significantly impacts BEV efficiency, real-world driving conditions must to be assessed to optimize performance. This study reconfigures test driving modes to match real-world Korean patterns. It uses high-res data from a CAN (Controller Area Network) over 5 Hz. It examines how ambient temperature affects battery performance and thermal management systems. The study also does an independent energy flow analysis. It focuses on energy use for driving, HVAC systems, and regenerative braking. These findings provide strategies to improve BEV energy efficiency. They also offer new insights into performance in different environments.
2 Methodology
2.1 Test Vehicle
In this study, a 2023 model electric vehicle was selected to analyze the factors affecting energy consumption efficiency in electric vehicles. The vehicle has a dual motor four-wheel drive system. The front motor delivers a maximum output of 158 kW at 6622 rpm, while the rear motor up to 208 kW at 6000 rpm, with a maximum torque of 36.0 kg·m. The vehicle is powered by a high-voltage battery rated at 360 V, with a capacity of 236 Ah. Also, the vehicle has an advanced thermal management system. It includes a heat pump system optimizing energy use, especially in cold weather to improve cabin heating and battery temperature control. Specifications of the test vehicle are presented in Table 1.
Table 1
Specifications of test vehicle
Specifications
Details
Model year
2023
Drive system
Dual motor four-wheel drive (4WD)
Maximum output
Front motor 158 kW/6622 rpm
Rear motor 208 kW/6000 rpm
Maximum torque
36.0 kg·m/6000 rpm
Battery capacity
360 V/236 Ah
Curb weight
2000 kg
Thermal management strategy
Heat pump system
2.2 Test Equipment
To analyze energy consumption efficiency under various ambient temperature conditions, a range of specialized equipment was used. Key equipment included a chassis dynamometer, environmental chamber, power analyzer, current sensor, and battery charger, with schematic diagram of equipment setup illustrated in Fig. 1 and detailed specifications provided in Table 2.
Fig. 1
Schematic diagram of equipment setting
Table 2
Specifications of test equipment
(a) Chassis dynamometer
Specifications
Details
Roller diameter
1219 mm (48 inch) ± 0.254 mm
Inertia weight
500 ~ 3500 kg
Air blower capacity
26,000 m3/h
Max. axle load
≥ 2500 kg
Max. speed
≥ 250 km/h
(b) Environmental chamber
Specifications
Details
Temperature range
− 40 ~ 60 ℃
Humidity range
0 ~ 100% RH
Cooling range
1.61 ℃/min (from 40 to − 30 ℃)
Heating range
3.14 ℃/min (from − 30 to 40 ℃)
(c) Power analyzer
Specifications
Details
High-frequency measurement
4 Channels
Frequency range 0.5 Hz ~ 5 kHz
Measurement range
Voltage 15 ~ 1500 V
Current 0.1 A ~ 20 kA
Effective power measurement range
0.0150 W ~ 39.600 MW
Frequency band
DC 0.5 Hz ~ 200 kHz
(D) Current Sensor
Specifications
Details
Rated current
AC/DC 500 A
Frequency band
DC ~ 200 kHz
Accuracy
± 0.3% of reading ± 0.02% of full scale
Output voltage
4 mV/A (2 V/500 A)
×
City and highway driving conditions were simulated using a 48-inch single-roll chassis dynamometer (Table 2a), capable of handling inertial weights from 500 to 3500 kg, with a maximum speed of 250 km/h. The environmental chamber (Table 2b) allowed for precise control of temperatures from − 40 to 60 °C and humidity levels from 0 to 100% relative humidity. A power analyzer (Table 2c) was used to measure battery energy discharge, handling voltages between 15 and 1500 V and currents up to 20 kA. Additionally, current sensors (Table 2d) measured current flow, rated at 500 A, ensuring accurate readings.
2.3 Vehicle Test Conditions and Data Acquisition
2.3.1 Driving Modes
This study reconfigured the standard MCT (Multi Cycle Test) mode aiming to better reflect the actual driving patterns and conditions of Korean drivers. The UDDS (Urban Dynamometer Driving Schedule) mode was simplified by including a 10-min parking time between two cycles. This simulates real-life scenarios, such as short stops during errands or heavy traffic while commuting. Also, to better simulate highway driving, the highway mode (HWFET, Highway Fuel Economy Test) was adjusted to include two continuous cycles without parking, effectively reflecting uninterrupted highway driving. The driving modes used in this study were composed of UDDS1—Parking—UDDS2—Parking—HWFET1—HWFET2, with a total driving distance of 57.0 km. This configuration closely aligns with actual Korean driving conditions. According to the research by Park et al. (2024), the average daily driving distance of Korean electric vehicle drivers was 60.1 km, with a median of 54.5 km. Therefore, this driving mode provides a more realistic and representative test for evaluating energy consumption efficiency.
Detailed information about the driving modes is presented in Table 3 and Fig. 2.
Table 3
Step description of driving modes
Driving modes
Step description
Time (sec.)
Distance (km)
Speed (km/h)
UDDS1
Cold start phase during city driving
1370
12.1
Ave. 61.5
Max. 91.2
Parking period
600
–
–
UDDS2
City driving stabilization phase
1370
12.1
Ave. 61.5
Max. 91.2
Parking period
600
–
–
HWFET1
Initial highway driving phase
765
16.4
Ave. 78.2
Max. 96.6
Short Pause
15
–
–
HWFET2
Highway driving stabilization phase
765
16.4
Ave. 78.2
Max. 96.6
Total
5485
57.0
–
Fig. 2
Configuration of driving modes
×
2.3.2 Test Conditions
The tests were conducted under 5 distinct ambient temperature conditions: − 15 °C, − 7 °C, 0 °C, 25 °C, and 35 °C. These specific temperature points were carefully chosen to assess the impact of both cold and hot environments on the performance of BEVs. The tests were carried out in a controlled environment, ensuring a constant temperature during each test.
To simulate real-world driving conditions, the vehicle’s road load and equivalent inertia weight were input into the chassis dynamometer. In sub-zero conditions, the coast down time representing the vehicle’s rolling resistance was reduced by 10% to reflect the increased resistance encountered in low temperatures. For high temperature conditions of 35 °C, an 850 W/m2 solar load was applied (SAE J1634, 2021). This allowed for a more precise simulation of the challenges faced by electric vehicles in both cold and hot environments, providing valuable insights into their performance under varying temperature conditions.
Prior to testing, the vehicle was soaked in an environmental chamber for 12 to 36 h at various temperature settings, with the HVAC set to automatic mode at 22 °C except under the 25 °C condition where the HVAC system was not used (U.S. Environmental Protection Agency, 2024). To eliminate any influence from initial battery conditions, the state of charge (SOC) was set to 100% at the beginning of each test. In this study, energy consumption efficiency was assessed through ECdc (km/kWh) values, which represent the DC discharge energy consumption measured during both City (ECdc_City) and highway (ECdc_Highway) driving cycles. Throughout the driving cycles, including idle periods, direct current (DC) voltage, current, and power were continuously measured and accumulated to determine the total amount of electrical energy discharged during testing (MOLIT 2022). These measured values were then applied to Eq. (1) to calculate energy consumption efficiency for both urban and highway driving modes in accordance with SAE J1634 (2021) standards. In this equation, Edc[Phase] (kWh) refers to the DC discharge energy of a cycle, representing the total electrical energy discharged from the battery during a specific phase. Equation (2) accounts for City driving energy consumption, applying weightings to reflect the higher energy use during the cold start phase (UDDS1), which requires additional power for preheating, and the stabilization in the second phase (UDDS2), assigning a lower weighting of 0.43 to UDDS1 compared to 0.57 for UDDS2 (MOLIT, 2022). ECdc_Highway (Eq. (3)) represents energy consumption over two highway driving phases, averaging efficiency across sustained high speeds for balanced energy use.
During the tests, a CAN (Controller Area Network) data acquisition system was used. It ran at over 5 Hz to collect real-time data from the vehicle’s onboard systems. It measures battery temperature, battery coolant flowrate, SOC, power consumption, the energy from regenerative braking system, and other relevant parameters. These metrices are vital for assessing how each test condition affects energy efficiency. A data collection rate over 5 Hz was specifically chosen to accurately capture transient phenomena, such as sudden changes in power demand or temperature fluctuations. This high-frequency data allowed a detailed analysis of vehicle system interactions under different test conditions.
2.4 Data Analysis
2.4.1 Energy Flow Analysis
This study analyzed energy flow to find the key components that affect BEV efficiency and performance. It also assessed each component’s contribution to overall energy use. The energy flow analysis breaks down vehicle energy use. It divides it into driving, accessory, and regenerative energy, and then evaluates the impact of each on total energy use. Wei et al. (2020) stressed the need for energy flow analysis as it can optimize vehicle efficiency by finding and reducing energy losses in its components. For the energy flow analysis in this study, energy consumption from the front and rear motors, HVAC system, and DC–DC converter, along with the regenerative energy, were measured using a current integrator. The components used for the energy flow analysis and their details are:
(1)
Driving Energy Consumption: Driving energy refers to the power delivered from the battery to the motors, which is used to propel the vehicle. In electric vehicles, driving energy accounts for the largest portion of total energy consumption.
(2)
Accessory Energy Consumption: This energy powers operating convenience and essential auxiliary systems in the vehicle, aside from driving. Key energy consumers include devices such as the heat pump, PTC heater, air conditioning, and DC–DC converter. Accessory systems are critical to energy management as they significantly affect energy efficiency, especially under various temperature and driving conditions. The heat pump system contributes to heating and cooling by absorbing heat from outside air, the battery, and drivetrain, while the PTC heater consumes a large amount of electricity for cabin heating and battery management under cold conditions (Wray et al. 2022). The DC–DC converter, which converts high-voltage battery power into lower voltage to supply vehicle electronics, also generates energy losses, which were analyzed as part of the accessory energy consumption.
(3)
Regenerative Energy: This refers to the amount of energy recovered by the regenerative braking system. It converts the vehicle’s kinetic energy into electrical energy during deceleration and stores it in the battery. This system improves overall vehicle energy efficiency by contributing to battery recharging.
2.4.2 Statistical Analysis
Spearman’s rank correlation coefficient was used to analyze the relationship between factors influencing battery thermal management under different ambient temperature conditions during city and highway driving cycles. Spearman’s correlation, which is based on rank order of data, is particularly useful for datasets with non-normal distributions or outliers, making it an appropriate method for analyzing data such as coolant flow rates and temperature in electric vehicles. The formula for Spearman’s rank correlation coefficient (ρs) measures the strength and direction of a monotonic relationship between two variables (Eq. (4)).
The significance of Spearman’s correlation is generally determined by the p-value, where a p-value below 0.05 is typically considered statistically significant, indicating that the observed relationship is unlikely to occur by chance. According to Cohen’s (1988) guidelines for interpreting effect sizes, a correlation coefficient between ± 0.1 and ± 0.3 indicates a small effect size (weak correlation), ± 0.3 to ± 0.5 indicates a medium effect size (moderate correlation), and values above ± 0.5 indicate a large effect size (strong correlation). A medium effect size suggests a meaningful association, though not a perfect relationship.
3 Test Results and Discussion
3.1 Impact of Ambient Temperature on Battery Performance and Thermal Management
This section presents time-series graphs, shown in Fig. 3 depicting the behavior of key variables related to battery thermal management and performance under various ambient temperatures of − 15 °C, − 7 °C, 0 °C, 25 °C and 35 °C. These variables include the maximum discharge power (kW), maximum regenerative braking power (kW), SOC (%), battery coolant flow rate (LPM), coolant temperature at the battery inlet (°C), and the maximum and minimum battery cell temperatures (°C). Driving modes for city and highway driving were illustrated using vehicle speed data (km/h), with shaded areas indicating 10-min parking phases.
Fig. 3
Time-series analysis of battery thermal and power behavior under different temperature conditions
×
3.1.1 Analysis of Maximum Discharge and Regenerative Braking Power
Understanding the behavior of maximum discharge and regenerative braking power is essential for understanding battery thermal management performance and energy efficiency under different ambient temperature conditions. At − 15 °C, the maximum discharge power was limited to about 350 kW and the regenerative braking power dropped below 30 kW. Regenerative braking began about two-thirds through the first highway driving segment at − 15 °C. And for the − 7 °C condition, it started in the second city driving phase. This shows that both discharge power and regenerative braking power drop significantly in cold conditions, which can negatively impact overall energy efficiency by limiting energy recovery and increasing energy consumption during operation. Steinstraeter et al. (2021) found that the regenerative braking capability of electric vehicles is significantly diminished in low temperatures to protect the battery, reducing energy efficiency. This limitation can lead to a range reduction of up to 21.7%. In cold conditions, energy use for heating can cut the range of BEVs by about 50%, which can greatly affect their driving range.
Conversely, at 35 °C, the maximum discharge power during the driving phase was about 450 kW. This was similar to the normal temperature results. Therefore, the battery performance does not decline much in high temperature condition. A similar finding was reported by Szumska and Jurecki (2021). At 40 °C, the NEDC (New European Driving Cycle) depth of discharge was observed to be quite similar to that at 20 °C, indicating that energy consumption at high temperatures did not notably increase battery discharge compared to moderate temperatures. Also, maximum regenerative braking power increased to 80 kW, which was the highest observed across all ambient temperature conditions. This suggests that regenerative braking efficiency is enhanced, allowing for smooth energy recovery.
To further analyze the effect of ambient temperature on discharge and regenerative braking power, a boxplot analysis was conducted (Fig. 4). This method was chosen because it effectively identifies variations and outliers in performance across different temperature conditions. The interquartile range (IQR), which represents the range between the first quartile (Q1) and third quartile (Q3) of data, was used to measure variability. At − 7 °C and − 15 °C, the IQR values for discharge power was approximately 173.2 kW and 138.4 kW, respectively, indicating the highest levels of variability under these cold temperature conditions (Table 4). Despite this high variability, relatively few outliers were observed, suggesting stable and predictable performance within the main distribution range. In contrast, the IQR values decreased significantly with increasing temperature conditions, 6.8 kW at 0 °C, 5.2 kW at 35 °C and 0.8 kW at 25 °C, indicating relatively consistent performance within the interquartile range. However, multiple outliers were observed at these temperatures, representing occasional instances where discharge power deviated significantly from the typical range of observed values.
Fig. 4
Boxplot analysis of battery thermal and power behavior under different temperature conditions
Table 4
Descriptive statistics of battery performance under different temperature conditions
Ambient Temp
Parameters
− 15 ℃
− 7 ℃
0 ℃
25 ℃
35 ℃
Max. battery cell temp. median (℃)
2.0
4.5
13.0
31.0
40.0
Max. battery cell temp. IQR (℃)
7.5
5.5
2.5
0.5
6.0
Min. battery cell temp. median (℃)
1.5
4.5
12.5
30.5
39.5
Min. battery cell temp. IQR (℃)
8.0
5.0
2.0
0.5
5.5
Coolant temp. battery inlet median (℃)
4.4
6.5
12.0
30.0
39.1
Coolant temp. battery inlet IQR (℃)
6.1
4.1
3.0
1.4
1.5
Coolant flowrate median (LPM)
6.2
6.1
6.1
6.0
5.2
Coolant flowrate IQR (LPM)
0.7
0.2
1.0
1.3
3.9
Max. discharge power median (kW)
184.0
334.1
379.0
432.7
445.1
Max. discharge power IQR (kW)
138.4
173.2
6.8
0.8
5.2
Max. regen. power median (kW)
0.0
24.2
18.1
38.9
56.6
Max. regen. power IQR (kW)
0.0
30.6
18.3
28.2
41.2
SOC median (%)
89.2
92.0
95.9
96.8
95.9
SOC IQR (%)
7.20
4.60
3.3
2.6
3.5
SOC slope (%/s)
− 0.0035
− 0.0024
− 0.0017
− 0.0014
− 0.0017
×
Regarding regenerative braking power, performance was significantly limited under cold conditions (Fig. 4 and Table 4). Under extreme cold conditions, at − 15 °C, both the median and the IQR values were 0 kW, indicating negligible energy recovery, while at − 7 °C, the median improved slightly to 24.2 kW with the IQR of 30.6 kWh, reflecting limited and inconsistent performance. In contrast, warmer ambient conditions demonstrated significantly better regenerative braking performance. At 35 °C, the median values reached 56.6 kW, the highest among all conditions, with the IQR of 41.2 kW, indicating both improved efficiency and greater variability. Similarly, at 25 °C, the median was 38.9 kW with a smaller IQR of 28.2 kW, suggesting more stable but less efficient performance compared to 35 °C. These findings emphasize the strong influence of temperature on regenerative braking performance, highlighting the need for precise thermal management to optimize energy recovery.
3.1.2 Analysis of SOC Changes
The SOC is one of the most critical indicators for evaluating battery performance, and its accuracy is essential for effective battery management strategies. SOC plays a crucial role in determining battery efficiency, driving range, and overall energy performance (Szumska and Jurecki, 2021). In this study, changes in SOC were significantly influenced by ambient temperature. The smallest SOC reductions occurred at 25 °C and 35 °C, while the largest were observed at − 7 °C and − 15 °C. During the city driving cycle, the SOC dropped to its smallest value of 3.8% at 25 °C and to 5.1% at 35 °C. However, at − 7 °C and − 15 °C, SOC reductions increased sharply to 8.9% and 12.7%. During the highway driving cycle, SOC decreased more gradually in sub-zero temperatures compared to city driving. Conversely, at temperatures above 0 °C, highway driving led to greater SOC reductions, with 25 °C showing a 5.3% reduction, 35 °C showing 5.9%, and 0 °C showing 6.0%, as illustrated in Fig. 3.
The characteristics of SOC decline across various ambient temperatures can also be confirmed through an analysis of SOC reduction slopes, which reflect the degree of battery efficiency deterioration (Fig. 3 and Table 4). It is derived from the linear regression of SOC values over time, expressed in percent per second (%/s). Steeper slopes indicate faster energy depletion, while gentler slopes reflect more efficient battery usage. The steepest SOC reduction slope was observed at − 15 °C (− 0.0035%/s) and − 7 °C (− 0.0024%/s), while it was more gradual at 0 °C and 35 °C (− 0.0017%/s) and 25 °C (− 0.0014%/s). The sharp SOC reduction in cold temperatures is closely linked to increased power consumption, such as from PTC heaters, due to the higher internal resistance of the battery (Wray and Ebrahimi, 2022). At 35 °C, although the SOC reduction slope was slightly higher than at 25 °C, it remained lower than in sub-zero conditions. In high temperatures, the increased chemical reaction rate within the battery reduces internal resistance, resulting in a similar SOC reduction slope to normal conditions during city driving. However, during highway driving, the accumulation of heat leads to greater energy consumption by the cooling system, accelerating SOC reduction. A similar trend was observed in a study by Zhao et al. (2023), where SOC declined the fastest at − 7 °C, followed by 35 °C. This is due to increased power demands from air conditioning and PTC systems in extreme temperatures. Additionally, battery output power varied significantly across different temperatures, with − 7 °C and 35 °C requiring higher output power compared to 23 °C, further contributing to SOC reduction.
3.1.3 Cooling Strategies and Battery Cell Temperature Distribution
In this study, analyzing cooling strategies and battery cell temperature distribution is crucial for understanding how the BMS maintains optimal battery performance across different ambient temperatures. Efficient cooling is necessary to prevent overheating in high temperatures and to manage the increased energy consumption required for heating in colder conditions (Wang et al. 2016). These insights are essential for improving battery longevity and vehicle performance under varying environmental conditions.
At 35 °C, the coolant flow rate increased to 20 LPM to prevent battery overheating, demonstrating the active response of the BMS in high temperatures, as shown in Fig. 3. Although battery performance sustained in 35 °C ambient temperature, efficient cooling is required to maintain optimal battery temperature. Similarly, an increase in coolant flow rate was observed at low temperature conditions of –15 °C and − 7 °C, indicating that the BMS activates thermal management even in cold environments (Jaguemont et al. 2016), as seen in Fig. 3. In contrast, at 25 °C, the coolant flow rate remained stable. This suggests that battery performance is optimized at this temperature.
A further analysis of coolant flow rates, illustrated in Fig. 4, revealed significant variations across different temperature conditions. At 35 °C, the IQR for coolant flow rate was 3.9 LPM, reflecting the BMS dynamically adjusting the coolant flow rate. As the battery temperature rose, higher flow rates were seen, showing the system’s active response to managing temperature and preventing overheating. At low temperature condition, a similar pattern emerged, with − 7 °C having the most outliers. This shows the BMS can respond to sudden changes in cold environments. In contrast, at − 15 °C, there were fewer outliers and a smaller IQR. This suggests that the cooling strategy was applied less frequently, likely due to the lower demand for active cooling in extreme cold conditions.
Following the analysis of coolant flow rates, it is also important to examine the behavior of coolant temperature, as it plays a key role in managing battery performances. At ambient temperatures below 35 °C, the coolant temperature rose sharply, peaking at 50 °C before dropping (Fig. 3). This is due to the BMS actively circulating coolant to lower the temperature when the battery’s temperature rises rapidly. In high-temperature environments, a cooling strategy is crucial to prevent battery overheating. Similarly, the study by Fan et al. (2021) shows that the BMS controls airflow and cooling power. It keeps battery temperatures within a specific range, especially in heat. This idea matches our observations. The BMS adjusts coolant flow to prevent overheating at critical battery temperatures. We analyzed the IQR of coolant temperature in different conditions to better understand the BMS’s response to temperature changes. Figure 4 shows that the highest number of outliers was observed under 35 °C conditions, suggesting variability in the coolant temperature. This could indicate the BMS’s response to temperature fluctuations, potentially adjusting coolant flow to manage thermal conditions. Similarly, notable outliers were found at − 7 °C, suggesting that the thermal management system may be reacting to battery temperature changes, even in cold environments. It helps to maintain optimal battery performance.
3.1.4 Uniformity of Battery Cell Temperatures
We conducted an IQR analysis of the maximum and minimum battery cell temperatures under different ambient conditions. It assessed the temperature uniformity among the cells. Figure 3 shows that at − 15 °C, the IQR values for the max. and min. battery cell temperatures were 7.5 °C and 8.0 °C, respectively. This indicates the largest temperature differences between cells, suggesting a high likelihood of thermal imbalance. According to Xu et al. (2023), it is critical to maintain thermal uniformity within a battery pack. Deviations beyond 5 °C can cause performance loss, aging, or even thermal runaway. At 25 °C, the IQR values for both the max and min battery cell temperatures were 0.5 °C, the smallest among all ambient temperatures. This showed that temperature uniformity was well-maintained. Thus, it was an ideal environment for battery performance. At 35 °C, the IQR values for the max. and min. battery cell temperatures were 6.0 °C and 5.5 °C, respectively. These were the second highest among the conditions.
According to Xu et al. (2023), maintaining thermal uniformity within a battery pack is critical for preventing performance degradation and ensuring safety, as deviations beyond 5 °C can lead to accelerated aging or even thermal runaway risks. In contrast, at 25 °C, the IQR values for both the maximum and minimum battery cell temperatures were 0.5 °C, the smallest among all ambient temperature conditions, indicating that temperature uniformity was well-maintained, making this an ideal environment for battery performance. Under the high-temperature condition of 35 °C, the IQR values for the maximum and minimum battery cell temperatures were 6.0 °C and 5.5 °C, respectively, the second highest among the conditions. This means that, in high-temperature environments, cells generate heat unevenly, making thermal management more challenging.
3.2 Correlation Analysis of Battery Thermal Management Factors Based on Ambient Temperature
It is vital to analyze the link between battery thermal management variables and ambient temperature. Such an analysis helps in understanding how the battery system responds to different environment conditions, as illustrated in Fig. 5. Since the internal temperature of the battery and its thermal management requirements vary depending on the ambient environment, examining these relationships can provide insights into how efficiently the system maintains optimal battery performance across different temperatures (Li et al. 2023; Spitthoff et al. 2021).
Fig. 5
Correlation of battery coolant flowrate with thermal variables
×
This study employed Spearman’s rank correlation coefficient to assess key battery thermal management variables. These variables include coolant flow rate, coolant temperature at the battery inlet, and the maximum and minimum battery cell temperatures. The analysis was conducted across five distinct ambient temperature conditions to understand how these factors interact in varying environments and to demonstrate that battery thermal management dynamically adapts to temperature changes. The findings showed that all p-values were less than 0.05, indicating that the correlations identified through the Spearman method were statistically significant (Cohen, 1988).
At extreme cold temperature, at − 15 °C, positive correlations were observed between coolant inlet temperature, maximum and minimum battery cell temperatures, and coolant flow rate, with coefficients of ρs = 0.34 (p < 0.001), ρs = 0.37 (p < 0.001), and ρs = 0.37 (p < 0.001), respectively. This indicates that as coolant flow increases, these temperatures rise accordingly. Such behavior suggests that the system operates in heating mode, where the coolant is circulated through components like heat pumps to keep the battery warm in extremely cold environments (Jaguemont et al. 2016).
In contrast, at high ambient temperatures, the primary goal is to remove excess heat. At 35 °C, a strong negative correlation was observed between coolant flow rate and battery cell temperature, with ρs = − 0.59 (p < 0.001). This indicates that as coolant flow increases, battery temperature decreases significantly, demonstrating the effectiveness of this cooling method at high temperatures. The system achieves this by transferring heated coolant to external radiators or heat exchangers to prevent battery overheating (Wray and Ebrahimi, 2022).
At 0 °C, coolant flow strongly affects battery temperature. A strong negative correlation ρs = –0.60 (p < 0.001) was observed with coolant temperature, while positive correlations of ρs = 0.48 (p < 0.001) and ρs = 0.43 (p < 0.001) were seen with maximum and minimum battery cell temperatures, respectively. This suggests that the battery thermal management system operates dynamically, balancing heating and cooling requirements at this ambient temperature.
Meanwhile, at − 7 °C and 25 °C, the effect of coolant flow on temperatures was minimal, as indicated by weak Spearman correlation coefficients (ρs < ± 0.3). At − 7 °C, weak negative correlations were observed with coolant temperature of battery inlet (ρs = − 0.24, p < 0.001), maximum battery cell temperature (ρs = − 0.13, p = 6.36 × 10−155), and minimum battery cell temperature (ρs = − 0.14, p = 2.37 × 10−190). These results suggest that coolant flow had little influence on temperature regulation at this condition. Similarly, at 25 °C, weak positive correlations were observed with coolant temperature of battery inlet (ρs = 0.02, p = 0.002), maximum battery cell temperature (ρs = 0.06, p = 2.63 × 10−35), and minimum battery cell temperature (ρs = 0.11, p = 1.51 × 10−120). These results indicate limited interaction between coolant flow and temperature regulation. These findings demonstrate that, under both conditions, the thermal management system maintained battery temperatures with minimal reliance on coolant flow adjustments.
Based on the Spearman rank correlation analysis, the most active temperature regulation occurs at − 15 °C and 35 °C in extreme environment conditions. At − 15 °C, the thermal management system actively engages in heating to maintain battery functionality, while at 35 °C, it intensively cools the battery to prevent overheating. This analysis highlights these two conditions as requiring the most significant thermal management interventions, demonstrating the system’s ability to adapt dynamically to extreme ambient environments.
3.3 Energy Consumption Efficiency and Energy Flow Based on Driving Mode and Ambient Temperature
Urban and highway driving modes exhibit distinct energy consumption patterns across temperature ranges. Energy flow analysis reveals key differences, breaking down overall usage into driving, accessory, and regenerative components. This method illuminates how various systems impact efficiency in each mode. By identifying the main sources of energy loss, more effective thermal management strategies can be developed. This, in turn, leads to improved energy efficiency and better vehicle performance across a range of temperatures (Wei et al. 2020; Zhao et al. 2023).
3.3.1 City Driving Mode
The energy consumption efficiency and energy flow analysis results in city driving mode are illustrated in Fig. 6. In this mode, the highest energy consumption efficiency was observed at 25 °C, with a value of 8.084 km/kWh. Compared to the standardized condition of 25 °C, energy consumption efficiency decreased significantly at other ambient temperatures: at 35 °C, efficiency dropped to 6.163 km/kWh, a 23.8% reduction; at 0 °C, it decreased to 6.082 km/kWh, a 24.8% reduction; at − 7 °C, efficiency fell to 3.774 km/kWh, a 53.3% reduction; and at − 15 °C, efficiency was reduced to 2.667 km/kWh, representing a 67.0% reduction. This clearly indicates a trend of declining energy consumption efficiency as ambient temperature deviates from the optimal range.
Fig. 6
Energy flow analysis of energy consumption efficiency and component contributions relative to 25 °C in city driving mode
×
Under extreme cold conditions of − 15 °C and cold conditions of − 7 °C, accessories contributed to 51.0 percentage points and 40.5 percentage points of the total efficiency reduction relative to the 25 °C condition, respectively. This reflects the substantial energy required for heating the vehicle cabin and supplying heat to the battery and drivetrain through the use of PTC heaters. According to Wray and Ebrahimi (2022), in temperatures below − 10 °C, PTC heaters are activated when the heat pump alone cannot meet the heating demands. In such situations, the system switches to PTC heaters, which have a coefficient of performance (COP) = 1, meaning they convert electrical energy directly into heat without the efficiency advantages of a heat pump. This sharp increase in accessory energy consumption in cold environments is a major contributor to the overall efficiency decline.
Regenerative energy decreased as temperatures fell below 0 °C, with recovery rates of 2.6 percentage points at 0 °C, 4.9 percentage points at − 7 °C, and 6.3 percentage points at − 15 °C, compared to the 25 °C condition. The efficiency of the regenerative braking system significantly deteriorated under extremely cold conditions, particularly at − 15 °C. Moreover, driving energy consumption increased to 9.8 percentage points at − 15 °C, and 7.9 percentage points at − 7 °C relative to the standard condition (25 °C). This indicates that more energy is required to overcome driving and rolling resistance in cold ambient temperatures.
At 35 °C, accessories accounted for 27.9 percentage points of the total 23.8% reduction in energy consumption efficiency compared to the 25 °C condition. This increased accessory energy consumption is due to the energy required for cooling the battery and the vehicle cabin (Wray and Ebrahimi, 2022). However, in high temperature environments, regenerative braking energy increased by 2.3 percentage points, and driving energy consumption decreased by 1.9 percentage points compared to the standard condition, indicating some improvement in driving efficiency under these conditions.
3.3.2 Highway Driving Mode
In highway driving mode, the energy consumption efficiency peaked at 25 °C, with a value of 8.311 km/kWh. This confirms that this temperature condition is the most efficient and optimized, aligned with city driving mode. In response to a change in ambient temperature, energy consumption efficiency decreased moderately on the highway compared to city driving. For instance, at 35 °C, the efficiency was 7.314 km/kWh, 12.0% lower than the peak. At 0 °C, it was 7.206 km/kWh, a 13.3% decrease. The decrease was more pronounced at lower temperatures, with efficiencies of 6.163 km/kWh at − 7 °C (25.9% lower) and 4.823 km/kWh at − 15 °C (42.0% lower). A key reason for this difference is that high-speed highway driving helps to stabilize the cabin, battery, and drivetrain temperatures after the initial urban driving phase, especially during cold starts. In highway mode, the vehicle systems are more stable, so efficiency drops less. These results are presented in Fig. 7.
Fig. 7
Energy flow analysis of energy consumption efficiency and component contributions relative to 25 °C in highway driving mode
×
Similar to the city driving mode, the decline in energy consumption efficiency in extremely cold and cold environments in the highway driving mode was primarily attributed to the increased use of accessories. For instance, under − 15 °C condition, accessories accounted for 29.9 percentage points of the total 42.0% reduction in energy consumption efficiency relative to the 25 °C condition, primarily due to increased heating demand. At − 7 °C, accessories contributed to 16.3 percentage points of efficiency reduction. On the other hand, at 0 °C, accessory energy consumption impact was the lowest at 9.5%, which can be interpreted as a reduction in the heating energy demand due to the relatively moderate sub-zero temperature. As the temperature rose to 35 °C, the increased use of air conditioning and vehicle cooling systems caused accessories for 14.7 percentage points of the efficiency reduction.
Furthermore, in highway mode, driving resistance is more significant due to the constant high speeds, leading to higher driving energy consumption across all temperatures compared to city mode. In contrast, regenerative braking is less effective in highway mode. There are fewer instances of acceleration and braking than in urban driving, where frequent stops and starts allows more energy recovery. Since there are fewer instances of acceleration and braking compared to urban driving, where frequent stops and starts enable more regenerative braking. Consequently, highway mode is more sensitive to ambient temperatures in terms of driving energy consumption and less sensitive in regenerative energy recovery than city mode.
4 Conclusion
This study examines how driving modes and ambient temperature conditions affect battery performance, thermal management, and energy consumption efficiency in an electric vehicle equipped with a heat pump system.
The results showed that both city and highway driving were significantly affected by extreme temperatures. In city driving at − 15 °C, energy consumption efficiency decreased by up to 67% and by 42% in highway driving. At 35 °C, city driving efficiency dropped by 24%, while highway efficiency decreased by 12%, largely due to increased cooling demands. In cold conditions, the operation of PTC heaters led to higher energy consumption, whereas in hot conditions, air conditioning systems caused a loss of efficiency.
The correlation analysis revealed that the BMS actively responded to extreme ambient temperatures. At − 15 °C, the BMS prioritized heating the battery through increased coolant flow, while at 35 °C, it focused on intensive cooling to prevent overheating. These conditions required the most significant thermal management adjustments, reflecting the system’s ability to maintain battery functionality under extreme environments. In contrast, at 25 °C, the BMS demonstrated reduced thermal management demands, suggesting conditions that required less intensive regulation compared to extreme conditions.
Overall, the findings emphasize the importance of optimizing both the BMS and HVAC systems in BEVs with heat pump systems, particularly those similar to the tested vehicle in this study. Such optimizations can enhance energy efficiency and driving range across diverse driving modes and ambient temperatures. Effective thermal management strategies will be essential for maintaining performance and minimizing energy consumption, especially in extreme temperature conditions.
Acknowledgements
This work is supported by the Korea Agency for infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, infrastructure and Transport (Grant RS-2023-00243220).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information.
Order your 30-days-trial for free and without any commitment.
Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik.
Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.