This paper has presented a comparative study of the temperature and velocity distributions within the mini-channel cold plates placed on a prismatic lithium-ion battery cell using experimental and numerical techniques. The study was conducted for water cooling methods at 1C and 2C discharge rates and different operating temperatures of 5°C, 15°C, and 25°C. A total of nineteen thermocouples were used for this experimental work, and were purposefully placed at different locations. Ten T-type thermocouples were placed along the principal surface of the battery, and four K-type thermocouples were used to measure water inlet and outlet temperature. Computationally, the k-ε model in ANSYS Fluent was used to simulate the flow in a mini-channel cold plate, and the data was validated with the experimental data for temperature profiles. The present results show that increased discharge rates and increased operating temperature results in increased temperature of the cold plates. Furthermore, the sensors nearest the electrodes (anode and cathode) measured the higher temperatures than the sensors located at the center of the battery surface.

Understanding the rate of heat generation in a lithium-ion cell is critical for the safety and performance behaviour. This paper presents in situ measurements of the heat generation rate for a prismatic Lithiumion battery at 1C, 2C, 3C and 4C discharge rates and 5°C, 15°C, 25°C, and 35°C boundary conditions (BCs). For this work, an aluminum-laminated battery consisting of LiFePO 4 cathode material with 20 Ah capacity was adopted to investigate the variation of the rate of heat generation as a function of the discharge capacity. Ten thermocouples and three heat flux sensors were applied to the battery surface at distributed locations. The results of this study show that the highest rate of heat generation was found to be 91W for 4C discharge rate and 5 °C BC while the minimum value was 13W measured at 1C discharge rate and 35 °C BC. It was also found that the increase in discharge rate and thus the discharge current caused consistent increase in the heat generation rate for equal depth of discharge points. The model is later developed using the neural network approach and validated. The heat generation rate predicted by the simulation demonstrates an identical behavior with experimental results.

Summary This paper presents a degradation testing of a lithium‐ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium‐ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium‐ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3‐month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge‐discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

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Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.