Battery Aging Model Research Articles

Modeling of a Permanent Magnet Synchronous Motor of an E-Scooter for Simulation with Battery Aging Model

For the growing market of electric two-wheelers (e-scooters) especially in China, the paper presents an implementation of a time efficient powertrain model. The model consists of an electric motor, power electronics and a Li-ion battery with aging prediction. Depending on the intended use of the models, two different accuracy levels requiring different computational power and computation time are introduced. As motor model a Permanent Magnet Synchronous Motor design is used. An empirical aging model for a Lithium Manganese Oxide cell is presented, which is based on aging measurements. Together, the models provide a possibility to evaluate the long-term cell aging behavior under realistic drive cycles taking motor effects into account.

Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging

A physics-based Li-ion battery (LIB) aging model accounting for both lithium plating and solid electrolyte interphase (SEI) growth is presented, and is applied to study the aging behavior of a cell undergoing prolonged cycling at moderate operating conditions. Cell aging is found to be linear in the early stage of cycling but highly nonlinear in the end with rapid capacity drop and resistance rise. The linear aging stage is found to be dominated by SEI growth, while the transition from linear to nonlinear aging is attributed to the sharp rise of lithium plating rate. Lithium plating starts to occur in a narrow portion of the anode near the separator after a certain number of cycles. The onset of lithium plating is attributed to the drop of anode porosity associated with SEI growth, which aggravates the local electrolyte potential gradient in the anode. The presence of lithium metal accelerates the porosity reduction, further promoting lithium plating. This positive feedback leads to exponential increase of lithium plating rate in the late stage of cycling, as well as local pore clogging near the anode/separator interface which in turn leads to a sharp resistance rise.

On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system

Renewable energies are a key pillar of power sector decarbonisation. Due to the variability and uncertainty they add however, there is an increased need for energy storage. This adds additional infrastructure costs to a degree that is unviable: for an optimal case of 15 GW of storage by 2030, the cost of storage is circa: £1000/kW. A promising solution to this problem is to use the batteries contained within electric vehicles (EVs) equipped with bi-directional charging systems to facilitate ancillary services such as frequency regulation and load balancing through vehicle to grid (V2G) technologies. Some authors have however dismissed V2G as economically unviable claiming the cost of battery degradation is larger than arbitrage. To thoroughly address the viability of V2G technologies, in this work we develop a comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial C6/LiNiCoAlO2 batteries. The comprehensive model accounts for all established modes of degradation including calendar age, capacity throughput, temperature, state of charge, depth of discharge and current rate. The model is validated using six operationally diverse real-world usage cycles and shows an average maximum transient error of 4.6% in capacity loss estimates and 5.1% in resistance rise estimates for over a year of cycling. This validated, comprehensive battery ageing model has been integrated into a smart grid algorithm that is designed to minimise battery degradation. We show that an EV connected to this smart-grid system can accommodate the demand of the power network with an increased share of clean renewable energy, but more profoundly that the smart grid is able to extend the life of the EV battery beyond the case in which there is no V2G. Extensive simulation results indicate that if a daily drive cycle consumes between 21% and 38% state of charge, then discharging 40%–8% of the batteries state of charge to the grid can reduce capacity fade by approximately 6% and power fade by 3% over a three month period. The smart-grid optimisation was used to investigate a case study of the electricity demand for a representative University office building. Results suggest that the smart-grid formulation is able to reduce the EVs' battery pack capacity fade by up to 9.1% and power fade by up to 12.1%.

Model-based Economic Assessment of Stationary Battery Systems Providing Primary Control Reserve

Rapidly decreasing battery prices and high capacity prices on the German primary control reserve (PCR) market promote the attractiveness of battery energy storage systems (BESS) for primary control provision. In order to assess the economic feasibility in this application field, two case studies based on a 2 MWh BESS are performed. By coupling a PCR simulation model with a battery aging model, battery lifetimes are estimated. Costs and revenues occurring during the lifetime are calculated using the net present value approach. The results indicate that a BESS with a power-to-energy ratio of 1:2 is not economically feasible under the current framework. A BESS with a power-to-energy ratio of 1:1 will break even after approximately nine years of operation. Decreasing battery system prices are likely to increase the price pressure on the PCR market leading to decreasing revenues for PCR supply. Battery aging results suggest similar aging behavior for both systems presented due to the prevalence of shallow DoD cycles and the resulting predominance of calendar aging.

Lithium battery aging model based on Dakin’s degradation approach

Abstract This paper proposes and validates a calendar and power cycling aging model for two different lithium battery technologies. The model development is based on previous SIMCAL and SIMSTOCK project data. In these previous projects, the effect of the battery state of charge, temperature and current magnitude on aging was studied on a large panel of different battery chemistries. In this work, data are analyzed using Dakin’s degradation approach. In fact, the logarithms of battery capacity fade and the increase in resistance evolves linearly over aging. The slopes identified from straight lines correspond to battery aging rates. Thus, a battery aging rate expression function of aging factors was deduced and found to be governed by Eyring’s law. The proposed model simulates the capacity fade and resistance increase as functions of the influencing aging factors. Its expansion using Taylor series was consistent with semi-empirical models based on the square root of time, which are widely studied in the literature. Finally, the influence of the current magnitude and temperature on aging was simulated. Interestingly, the aging rate highly increases with decreasing and increasing temperature for the ranges of −5 °C–25 °C and 25 °C–60 °C, respectively.

A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries

In this paper, a semi-empirical Lithium-iron phosphate-graphite battery aging model is identified over data mimicking actual cycling conditions that a hybrid electric vehicle battery encounters under real driving scenarios. The aging model is then used to construct the severity factor map, used to characterize relative aging of the battery under different operating conditions. This is used as a battery degradation criterion within a multi-objective optimization problem where battery aging minimization is to be achieved along with fuel consumption minimization. The method proposed is general and can be applied to other battery chemistry as well as different vehicular applications. Finally, simulations conducted using a hybrid electric vehicle simulator show how the two modeling tools developed in this paper, i.e., the severity factor map and the aging model, can be effectively used in a multi-objective optimization problem to predict and control battery degradation.

Battery Life Extending Charging Strategy for Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles

This paper presents an optimal control based charging strategy for plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs). This work proposes a method to minimize battery capacity degradation incurred during charging by optimizing current profile. A semi-empirical battery aging model is adopted to quantify the capacity loss; a generic control-oriented vehicle cabin thermal model is developed to describe the battery surroundings taking into account solar radiation. Optimal control solution offered by Pontryagin’s Minimum Principle (PMP) is presented. Simulation-based results show that the benefit of this strategy in terms of decreasing battery aging is significant, when compared with the existing strategies, such as the widely accepted constant current constant voltage (CC-CV) protocol.

Model Parametrization and Adaptation Based on the Invariance of Support Vectors With Applications to Battery State-of-Health Monitoring

Support vector regression (SVR) algorithms have been applied to the identification of many nonlinear dynamic systems due to their excellent approximation and generalization capability. However, the standard SVR algorithm involves an iterative optimization process, which is often computationally expensive and inefficient. For applications such as the battery state-of-health (SOH) monitoring, where the identification algorithm needs to be applied repeatedly for multiple cells because of the variation in model dynamics (due to battery aging and cell-to-cell difference), the computational burden could pose difficulties for real-time or onboard implementation. In this paper, the battery $V{-}Q$ curve identification problem for SOH monitoring is studied. Based on experimental battery aging data, we develop a model parametrization and adaptation framework utilizing the simple structure of SVR representation with determined support vectors (SVs) so that the model parameters can be estimated in real time. Through mathematical analysis and simulations using a mechanistic battery aging model, it is shown that the SVs of the battery models stay invariant, even when the batteries age or vary. The invariance of the SVs is verified using experimental aging data. Consequently, the resulting model for the battery $V{-}Q$ curve can be directly incorporated into the battery management system (BMS) and adapted online for SOH monitoring. Moreover, the general characteristics of the data that could maintain the SVR invariance are identified. The proposed automated model parametrization process (via an optimization algorithm) can be extended to nonlinear dynamic systems with the given properties.

Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries—Part II: Aged Battery Model and State of Charge Estimation

Recently, extensive research has been conducted in the field of battery management systems due to increased interest in vehicles electrification. Parameters, such as battery state of charge (SOC) and state of health, are of critical importance to ensure safety, reliability, and prolong battery life. This paper includes the following contributions: 1) tracking reduced-order electrochemical battery model parameters variations as battery ages, using noninvasive genetic algorithm optimization technique; 2) the development of a battery aging model capable of capturing battery degradation by varying the effective electrode volume; and 3) estimation of the battery critical SOC using a new estimation strategy known as the smooth variable structure filter based on reduced-order electrochemical model. The proposed filter is used for SOC estimation and demonstrates strong robustness to modeling uncertainties, which is relatively high in case of reduced-order electrochemical models. Batteries used in this research are lithium-iron phosphate cells widely used in automotive applications. Extensive testing using real-world driving cycles is used for estimation strategy application and for conducting the aging test. Limitations of the proposed strategy are also highlighted.

Power split strategies for hybrid energy storage systems for vehicular applications

This paper deals with the control system development for a hybrid energy storage system, consisting of a battery and a supercapacitor, for a through-the-road-parallel hybrid electric vehicle. One of the main advantages deriving from the coupling of a battery and a supercapacitor is the possibility of reducing battery ageing, in addition to energy efficiency improvements when the system operates in critical climate conditions. At the moment, no specific controller has been proposed with the aim of directly reducing battery wear. This paper presents a novel model predictive controller and a dynamic programming algorithm including a simplified battery ageing model in their formulations. The simulation results of the model predictive controller and dynamic programming algorithm are compared with the results deriving from a rule-based strategy. The rule-based controller achieves a 67% reduction of the root mean square values of battery current along a selection of driving cycles in comparison with the same vehicle equipped with battery only. In the same conditions the battery peak current is reduced by 38%. The model predictive controller and the dynamic programming algorithm further reduce the root mean square value by 6% and 10% respectively, whilst the peak values are additionally decreased by 17% and 45%.

Simulation of SLI Lead-Acid Batteries for SoC, Aging and Cranking Capability Prediction in Automotive Applications

SLI lead-acid batteries are still the most commonly used technology in automotive applications around the world. Despite its relatively low gravimetric and volumetric energy density in comparison with other battery solutions it is still installed in the newest micro-hybrid and conventional cars due to its low cost. To facilitate the design of multi-physical systems as complex as modern automobiles, it is critical to have a precise battery aging model that incorporates various operation conditions. This paper presents an aging model that provides crucial information about state of health of SLI battery during lifetime for any conditions like climate, driving style, charging strategy or different battery management approaches. Described model is based both on empirical and physico-chemical solutions and can be calibrated to simulate any SLI lead-acid battery without design limitations.

Simulation of SLI Lead-Acid Batteries for SoC, Aging and Cranking Capability Prediction in Automotive Applications

SLI lead-acid batteries are still the most commonly used technology in automotive applications around the world. Despite its relatively low gravimetric and volumetric energy density in comparison with other battery solutions it is still installed in the newest micro-hybrid and conventional cars due to its low cost. To facilitate the design of multi-physical systems as complex as modern automobiles, it is critical to have a precise battery aging model that incorporates various operation conditions. This paper presents a comprehensive battery model which consists of electrical, thermal and aging part that provides crucial information about the current state of SLI battery during lifetime for any conditions like climate, driving style, charging strategy or different battery management approaches. The described model is based on empirical, physico-chemical and equivalent circuit solutions and can be calibrated to simulate any SLI lead-acid battery without design limitations.

A battery ageing model used in stand alone PV systems

A battery ageing model used in stand alone PV systems

A battery ageing model used in stand alone PV systems

The authors present a new model for the ageing of a lead-acid battery which is based on the initial model of Shepherd. The proposed model allows to predict temporal variations of the Shepherd coefficients and to control the deterioration of the battery parameters and performances. The model validation has been realised by the recursive least square (RLS) algorithm by using long-term measurements under several solicitations. This study will improve the storage section of stand-alone photovoltaic systems and reduce overloads and deep discharges.