Characterization and modeling of a hybrid electric vehicle lithium-ion battery pack at low temperatures

Although lithium-ion batteries have penetrated hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), they suffer from significant power capability losses and reduced energy at low temperatures. To evaluate those losses and to make an efficient design, good models are required for system simulation. Subzero battery operation involves nonclassical thermal behavior. Consequently, simple electrical models are not sufficient to predict bad performance or damage to systems involving batteries at subzero temperatures. This paper presents the development of an electrical and thermal model of an HEV lithium-ion battery pack. This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries over the selected operating range of currents and battery capacities. In addition, a thermal modeling method has been developed for this model so that it can predict the battery core and crust temperature by including the effect of internal resistance. First, various discharge tests on one cell are carried out, and then, cell's parameters and thermal characteristics are obtained. The single-cell model proposed is shown to be accurate by analyzing the simulation data and test results. Next, real working conditions tests are performed, and simulation calculations on one cell are presented. In the end, the simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV. © 2015 IEEE

Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells

Cold start of proton exchange membrane fuel cells (PEMFCs) at sub-zero temperatures is perceived as one of the obstacles in their commercialization way in automotive application. This paper proposes a novel internal-based adaptive strategy for the cold start of PEMFC to control its operating current in real time in a way to maximize the generated heat flux and electrical power in a short time span. In this respect, firstly, an online parameter identification method is integrated into a semi-empirical model to cope with the PEMFC performances drifts during cold start. Subsequently, an optimization algorithm is launched to find the best operating points from the updated model. Finally, the determined operating point, which is the current corresponding to the maximum power, is applied to PEMFC to achieve a rapid cold start. It should be noted that the utilization of adaptive filters has escaped the attention of previous PEMFC cold start studies. The ultimate results of the proposed strategy are experimentally validated and compared to the most commonly used cold start strategies based on Potentiostatic and Galvanostatic modes. The experimental outcomes of the comparative study indicate the striking superior performance of the proposed strategy in terms of heating time and energy requirement. © 2018 Elsevier Lt

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

This paper studies the impact of two significant aspects, namely fuel cell (FC) degradation and thermal management, over the performance of an optimal and a rule-based energy management strategy (EMS) in a fuel cell hybrid electric vehicle (FCHEV). To do so, firstly, a vehicle's model is developed in simulation environment for a low-speed FCHEV composed of a FC stack and a battery pack. Subsequently, deterministic dynamic programming (DP), as an optimal strategy, and bounded load following strategy (BLFS), as a common rule-based strategy, are utilized to minimize the hydrogen consumption while respecting the operating constraints of the power sources. The performance of the EMSs is assessed at different scenarios. The first objective is to clarify the effect of FC stack degradation on the performance of the vehicle. In this regard, each EMS determines the required current from the FC stack for two FCs with different levels of degradation. The second objective is to evaluate the thermal management contribution to improving the performance of the new FC compared to the considered cases in scenario one. In this respect, each strategy deals with determining two control variables (FC current and cooling fan duty cycle). The results of this study indicate that negligence of adapting to the PEMFC health state, as the PEMFC gets aged, can increase the hydrogen consumption up to 24.8% in DP and 12.1% in BLFS. Moreover, the integration of temperature dimension into the EMS can diminish the hydrogen consumption by 4.1% and 5.3% in DP and BLFS respectively. © 2020 Elsevier Lt

Lithium-ion battery aging experiments at subzero temperatures and model development for capacity fade estimation

Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at higherature operations. Even vehicle maneuvers at low temperatures (T<0°C)contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered lowerature degradation. This is due to the lack of studies on aging of Li-ion batteries at sub-zero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP04 Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests (-20°C and 25°C). The calendar test was carried out on one single battery at -20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C. © 1967-2012 IEEE

Real-time backstepping control for fuel cell vehicle using supercapacitors

A key issue of real-time applications is ensuring the operation by taking into account the stability constraints. For multisource vehicles, stability is impacted by the multisource interactions. Backstepping control ensures stable control for most classes of nonlinear systems. Nevertheless, no backstepping control in real time has been yet proposed for multisource vehicles. The objective of this paper is to apply the backstepping control to a multisource vehicle with fuel cell and supercapacitors for real-time implementation. A distribution criterion is used to allocate energy between sources. Experimental results demonstrate that the developed backstepping control can be implemented in real-time conditions. The supercapacitors can thus help the fuel cell to meet the requirements of the load with a guarantee of system stability. © 1967-2012 IEEE

Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature

Comparative analysis of two online identification algorithms in a fuel cell system

Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well-known recursive algorithms are compared for online estimation of a multi-input semi-empirical FC model parameters. In this respect, firstly, a semi-empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Integrating model predictive control with federated reinforcement learning for decentralized energy management of fuel cell vehicles

Abtract The optimization-based energy management strategy (EMS) enables expertise to improve the performance of fuel cell vehicles (FCVs). Ongoing efforts are mostly focused on optimizing a centralized EMS using a variety of high-computing technologies without offering appropriate scalability and modularity for the onboard powertrain components. In real-time applications, the time-accomplishment capability of EMSs is crucial; hence, decentralized EMSs with low-cost components and limited processing capability are necessary. Local units handle the computation load on a modular platform. In addition, the decentralized system’s plug-and-play functionality minimizes the total cost. This paper presents a decentralized model predictive control (D-MPC) based on the consensus-based alternating direction method of multipliers (C-ADMM) that explicitly considers the coordination of the dynamic reactions of powertrain components and future driving profiles. In addition, a decentralized learning method is proposed to seek the optimal policy for the moving horizon dimensions in the D-MPC using the federated reinforcement learning (FRL) algorithm in order to improve processing time. Due to the deployment of a fully modular system in the proposed learning technique, agents are restricted from sharing their trajectories. Using a highly dynamic module-to-module communication layer in a fully decentralized arrangement, the powertrain components utilize the multi-step method to attain the global optimum. The performance of the proposed framework is evaluated with regards to its precision, convergence speed, and scalability. The results of numerical simulation and implementation demonstrated that the proposed method is superior to the centralized and fixed-horizon MPC approaches