• Energy Research

Abstract In this paper, an innovative liquid cooling plate (LCP) embedded with phase change material (PCM) is designed for electric vehicle (EV) battery thermal management. The proposed cooling plate is named “hybrid cooling plate” as it takes advantage of both active (liquid) and passive (PCM) cooling methods. The hybrid LCP is 36% lighter than a volumetrically equivalent traditional aluminum LCP, and in addition to the cooling capability, it provides a heating solution to slow the temperature loss of the batteries during the cold stop. The thermal behavior of the hybrid LCP for two different scenarios including the cooling performance under a real driving cycle, and the cold stop temperature performance are investigated and compared with a traditional aluminum LCP using a computational fluid dynamics (CFD) model. An experimental test bench is developed to test a prototype of the hybrid LCP and verify the CFD model. The cooling performance results indicate that the use of hybrid LCP could reduce the energy consumption of the pump required for circulating the coolant up to 30% in comparison with an aluminum LCP. Moreover, the novel designed LCP improves the temperature uniformity effectively. It was also found that the hybrid LCP could significantly delay the temperature drop at the cold stop situation of the EV and therefore, reduce the energy needed for the active heating of the batteries after short-term parking. These imply that the hybrid liquid cooling plate concept could be a promising thermal management solution for EVs.

Abstract Nowadays, the most used thermal management system (TMS) is air-cooling, but due to weight and volume limitations, more promising cooling methods such as PCMs with high latent heat seems to be attractive. However, their disadvantages, like low thermal conductivity, should be managed to have an efficient TMS. Hence, aluminum-foil (PCM-Al) is added to the PCM in this work to enhance the PCM conductivity, and accurate experiments are accomplished to verify the simulation results. In this paper, a phase change material (PCM) with aluminum mesh grid foil is proposed to enhance cooling and temperature uniformity of a high-power dual-cell lithium capacitor (LiC) module. The optimization objective is keeping the temperature of the system below the safe range while maximizing the uniformity and minimizing the cost of the system. The effect of forced-convection (zero-PCM strategy), pure PCM, PCM-Al, PCM thickness, and influence of dual PCMs by different phase change temperatures on the thermal behavior of the module are studied numerically. The results indicate that the PCM-Al technique shows better thermal performance while reducing the maximum module temperature by 20%, and 13% in comparison with forced-convection and pure PCM, respectively. Moreover, 7 mm thickness is quite optimal, considering the cost, weight, and volume.

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells’ internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively.

In order to achieve safe and reliable performance in battery-motive applications, a thermal management control is applied to dissipate the heat and preserve the cells temperature at the optimal range. Among the most efficient solutions are the liquid-based cooling designs, which typically use a water/glycol cooling medium. Most recently dielectric coolants are applied directly to the cells to decrease thermal resistance. In this paper, we evaluate with numerical models and experimental assessments the performance of various liquid fluids that are used for battery packs.

The renewable energy sources (RESs) integration into the grid system aims to solve the problem of power shortage and satisfy the increasing demand with the production of surplus energy. However, the intermittent nature of these RESs (solar and wind) is a challenge to integrate with the grid system without the deployment of mitigating solutions. The re-use of first life-end-of-life (FL_EoL) electric vehicle batteries known as second life batteries (SLBs) is therefore proposed as a reliable solution to resolve this problem, satisfying the technoeconomic requirements of stationary applications. Though various studies performed on the technical viability evaluation of SLBs, most of them have not considered the field data of existing stationary plants and were found to be limited with simulation-based aging results. On the other hand, few of the studies have performed the second life aging test with limited cycles considering the end of test conditions rather than using the cells reached to their end of first lifecriteria. Therefore, in this paper, the prolonged cycling aging of SLBs is conducted (both cell-level and module-level aging), focusing on aging characteristics of SLBs by using a real-life renewable power smoothing profile extracted from an existing photovoltaic grid-connected system (PVGCS) installed in Ethiopia. Prior to the aging characterization of SLBs, assessment of the selected stationary application requirement, optimal sizing of the storage battery and cycling profile definition are performed using advanced moving average ramp rate controller (MARRC) algorithm. The proposed method of MARRC is used to determine the optimal SLBs capacity by analyzing the relationship between ramp-rate, initial battery capacity and window sizes of moving average control method in terms of time series. The algorithm addresses the main objectives of reducing unnecessary battery cycling, mitigating ramp-rate violations and meeting minimum storage requirements for the second-life application. From the result of the battery sizing study, the desired optimal battery capacity and the permissible ramp-rate limit below 10 %/minute is achieved. Moreover, the aging characterization and lifetime model validation results with a root mean square error (RMSE) of 1.5 % shows that, the technical performance of the SLBs is found to be promising with an efficiency of >90 % interpreted in terms of the service year that the SLBs can provide. Therefore, SLBs can be regarded as a viable solution for integrating RESs with the grid system for the power variability smoothing application.

Abstract Over the last few decades, lithium-ion (Li-ion) batteries have attracted significant attention due to their high energy density, low maintenance, and the variety of shapes, chemistries and performances available. Meanwhile, reliability and safety assessment of Li-ion batteries has become an important issue for original equipment manufacturers, in particular for future electric vehicles’ performance. Evaluation of reliability and safety plays an important role to assess overall Li-ion battery behavior over its lifespan. This paper presents the role, mechanism and outcome of the different failures for evaluating reliability and safety of Li-ion batteries in electric vehicles. Additionally, the contribution of Li-ion battery degradation in five main failure modes and capacity and power fade for providing reliability assessment models as solutions of existing challenges have been investigated.

A lithium-ion battery cell’s electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

In this work, a static electrical equivalent circuit (EEC) is proposed based on the charge and discharge performance of lithium-ion battery cells (LiBs), which can be obtained either directly from datasheet, or by a simple constant current charge/discharge cycle. The battery cell behavior is described by a non-linear mathematical representation with only dependency on the current rate (C-rate) and the state of charge (SoC) of the battery cells. Its coefficients are optimized using a single objective genetic algorithm and the methodology is tested on five different chemistries and various shaped commercial LiB cells. The results have shown a good agreement between the simulated and the experimental values. A simple and reasonably accurate EEC is proposed in this work, which can be part of the performance evaluation criterion of electric drive of the vehicles as well as investigating a potential energy storage system.