Battery Thermal Management Strategy Research Articles

Battery thermal management strategy utilizing a secondary heat pump in electric vehicle under cold-start conditions

When an energy storage system (ESS) operates in cold conditions, the power and capacity of the battery critically fade. Therefore, an appropriate battery thermal management strategy (BTMS) is essential to prevent severe driving range loss at low ambient temperatures. However, none of existing studies considered the effect of BTMS on the driving range from a systematic perspective. Thus, we evaluated the trade-off between the performance enhancement by ESS heating and the additional energy consumption for ESS heating. We suggested and analyzed three BTMSs combined with a secondary heat pump: self-heating, active heating, and heat recovery. Active heating of the battery augmented the driving range of the EV by up to 18.8% over the self-heating strategy when the battery was used to full depletion, whereas the heat recovery strategy reduced the state-of-charge (SOC) decrease and thus increased EV driving range in nondepleted conditions. Furthermore, battery preheating with the heat pump achieved a temperature rise of 20 °C within an hour, consuming 38.4% less battery power than with conventional electric heater preheating. We expect this study contributes to the range extension of EV by suggesting the optimal BTMS depending on the driving conditions.

Robust predictive thermal management strategy for lithium-ion battery based on thermoelectric cooler

An effective battery thermal management (BTM) strategy is very important for electric vehicles (EVs). In this paper, a robust predictive BTM strategy based on thermoelectric cooler (TEC) is proposed to adjust the battery temperature within an appropriate range and reduce energy consumption. Firstly, a modeling method of TEC based on thermal resistance is presented, which considers the influence of heat sink and fan on modeling effect. Next, a distributed battery thermal model is developed by using the difference method, which takes the tab thermal model as the first kind of boundary condition of the cell core. Then, a thermal management model is developed by combining the TEC model and distributed battery thermal model, and a neural network (NN) observer is proposed to compensate the model uncertainty. Finally, a nonlinear model predictive control (NMPC) method is proposed to optimize the cooling process. Genetic algorithm (GA) optimization is used to solve the nonlinear programming problem in NMPC method. Additional stability analysis shows the convergence of the proposed observer. Experiments and verifications suggest that the proposed method can accurately control the battery working near the target temperatures under four different cycles, and the errors are less than 0.5 °C.

Effect analysis on performance enhancement of a novel and environmental evaporative cooling system for lithium-ion battery applications

Lithium-ion (Li-ion) batteries play an essential role in our daily lives and are considered the main power source in electric vehicles. The process of charging and discharging the battery continuously drives to a significant amount of heat generation which results in temperature differences, non-uniformity, and thermal runaway. An effective battery thermal management strategy (BTMS) is required to maintain battery temperature in the optimal range and thus ensure high performance, safety, and longevity of lithium-ion batteries. This study proposes a unique battery thermal management depends on using the jute as a plant-based, cheap, environmental, economic, renewable and lightweight fiber. Thorough experiments are carried out on a 50 Ah prismatic high-energy battery cell with an integrated evaporative cooling system and its effects on battery thermal behavior are studied. The maximum temperature, temperature difference, and temperature uniformity on the cell surface are compared under different conditions, which include natural convection cooling, forced air cooling, and evaporative cooling. The results confirm that using the jute for the proposed BTMS improves the efficiency of air cooling with a better temperature uniformity as well as reduced equipment and weight. This article represents the first attempt to analyze the performance of jute fiber as a cooling medium in BTMS. Thus, it contributes to improving the battery thermal management system air-based.

Thermal management system with nanofluids for hybrid electric aircraft battery

Electric aircraft should be used more intensively due to the increase in energy consumption and the decrease in reserves of fossil fuels. Batteries are also an important factor in the development of electric aircraft. One of the most important parameters for the effective operation of batteries is the evaluation of heat dissipation. The even distribution or reduction of the heat generated during the operation of lithium-ion batteries extends the life of the batteries and ensures the safety of electric aircraft. In order to improve heat transfer dissipation in batteries in hybrid electric aircraft, it is necessary to increase the thermal conductivity of the coolant. For this reason, it has been suggested that solid particles with high thermal conductivity should be added to the coolant. In the present study, different battery thermal management strategies are suggested. To research the effect of nanofluids for thermal management in lithium-ion batteries, the study included two different base fluids (water and ethylene glycol) and three different volumetric fractions (1%, 2%, and 5%) of nanoparticle (Fe2O3) were added to these fluids. While the study was evaluated at four different discharge rates (1.0, 1.5, 2.0, and 2.5), the inlet velocities and temperature of the fluid were also changed. The cooling performances for 15 batteries module were examined and the simulation results showed that the nanofluid-based coolant could keep the temperature of the module at the desired value. As a result of the analysis, water was found to be the best nanofluid coolant parameter because it had a higher thermal conductivity than ethylene glycol. When the C value of the battery module was increased, that is, when the module was charged faster, its temperature increased. It was seen that the module was cooled better when the volume fraction of nanofluids and the speed of the refrigerant were increased.

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Research progress and future prospects of battery thermal management system based on heat pipe technology

The lithium-ion battery, a key technology for electric vehicles, is an electrochemical power source with complex ion flow and heat transfer processes. Temperature is one of the main parameters affecting the performance of these battery systems, as high temperatures may accelerate the degradation rate of a battery cell and shorten its lifespan. Low temperatures can also reduce the battery efficiency and affect its discharge capacity, and subsequently, its life cycle. In addition, uneven temperature distribution within a battery pack could exacerbate the inconsistency between cells and cause life cycle decay. A suitable working temperature window for the lithium-ion battery is usually between 25°C to 40°C, and the temperature difference among the cells should be maintained below 5°C to ensure the cells performance and durability. Therefore, battery thermal management (BTM) is required to keep the battery temperature within the desirable operating range and maintain temperature uniformity. This paper provides a review of two aspects: The significance of BTM and current BTM strategies, and the research status of heat pipe-based BTM systems. Firstly, the thermal characteristics of the lithium-ion power battery are introduced, and the significance of BTM are expounded. Then, the advantages of heat pipe technology are introduced, and the research status of BTM based on heat pipes is evaluated in detail. In this study, the heat transferred in a heat pipe-based thermal management system was divided into three processes: The heat generation process, which is determined by the operating condition; the heat transfer process of a heat pipe, which is related to its structural design and its arrangement in the system; and the heat dissipation strategy on the condensation section. With respect to heat generation, researchers have studied the effect of operating conditions on the heat generation characteristics of the system. Results show that the temperature is closely related to the dynamic operating conditions. Further research should be combined with the actual vehicle operating conditions to formulate effective real-time control strategies to achieve a high efficiency and low energy consumption BTM system. Researchers have evaluated various factors that affect heat transfer performance. This study concludes that both the internal structure of heat pipe and its arrangement in the battery pack should be considered in the design of the BTM system to achieve optimum heat transfer performance. Future studies should focus on the analysis and optimization of flat plate heat pipes, which have good application prospects in BTM systems. Concerning heat dissipation enhancement, air cooling, direct liquid cooling, and indirect liquid cooling are the most common strategies for heat pipe cooling. However, most designs aim to reduce the temperature rise and temperature difference, and the system parameters are seldom taken into consideration. Further investigation into the heat dissipation enhancement of heat pipes should focus on the multi-objective optimization of the system including, synthesizing the thermal and electrical characteristics, improving energy consumption, and making the system lightweight. In addition, the heat pipe is also a highly efficient heat transfer element for battery heating. Current research has verified its heating rate and heating efficiency. Notably, the heating strategies are now in the pilot testing stage and have not been used in battery pack productions. One of the key elements for future research may involve the heating characteristics of a BTM based on heat pipes in different operating environments. Another aspect could be researching the heating strategy in low temperature environments.

Robust Predictive Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles Based on Thermoelectric Parameter Uncertainty

The connected and automated hybrid electric vehicles (CAHEVs) have the potential to improve safety and mitigate traffic congestions. A crucial problem of the CAHEVs is that the lithium-ion batteries are highly temperature sensitive, whose output power is severely limited at low temperatures. Moreover, the cells may be premature aging at high operating temperatures and even result in an explosion, seriously threatening the human safety. Consequently, a practical and energy-efficient battery thermal management (BTM) strategy is required with the minimum possible cooling/heating energy consumption. To achieve the multiple objectives, a finite-set based model predictive control (FSMPC) strategy is presented for the BTM in CAHEVs. Since the thermoelectric model of the BTM system is highly nonlinear and time variant, an extended state observer is implemented for accurate system state estimation and prediction so that the parameter uncertainties inherent in the system can be compensated. The hardware-in-the-loop validation of the proposed strategy is conducted on the urban dynamometer drive schedule-based on a Toyota Prius plug-in HEV model. The results demonstrate that the proposed BTM strategy can maintain the battery pack in the CAHEV operating at the optimum temperature and save 30% BTM energy compared to the conventional method even under 50% parameter uncertainty.

A Real-Time Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles (CAHEVs) Based on Iterative Dynamic Programming

Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.

Exploring the efficacy of nanofluids for lithium-ion battery thermal management

Thermal implications related to heat generation and potential temperature excursions during operation in lithium-ion batteries are of critical importance for electric vehicle safety, performance and life. Concurrently, appropriate thermal management strategies for lithium-ion batteries are crucial to maintain cell temperatures within a desired range. Different battery thermal management strategies have been proposed, each with various advantages and disadvantages depending on the applications. This work proposes the use of nanofluids, colloidal suspensions of nanoparticles in a base fluid, as a heat transfer fluid for active thermal management. To analyze the efficacy of nanofluids for thermal management in lithium-ion batteries, different nanofluids and their effect on the temperature distribution within typical battery modules are investigated for two different flow configurations. In particular, the study is focused on battery performance, heat dissipation capability under high discharge rates and ambient temperatures, and design considerations relevant to electric vehicle applications. This study underscores the potential of this innovative thermal management technique toward effective thermal safety without performance penalty of lithium-ion batteries for vehicle electrification.