Unballanced performance of parallel connected large format lithium ion batteries for electric vehicle application

The integration of cells that exhibit differing electrical characteristics, such as variations in energy capacity and internal resistance can degrade the overall performance of the energy storage system (ESS) when those cells are aggregated into single battery pack. When cells are connected electrically in parallel, such variations can lead to significant individual differences in battery load current, state of charge (SOC) and heat generation. Further, if consideration is given to small variations in cell interconnection resistance, the detrimental effect on load imbalance is amplified. Given that cell resistance is known to be a function of both SOC and temperature, the impact of the imbalance is compounded as the performance of cells further diverge under load. During extended periods of excitation, variations in cell depth of discharge (DOD) and the occurrence of temperature gradients across the parallel connection will accelerate cell ageing and, if unmanaged, may present safety concerns such as the onset of thermal runaway. In this paper the impact of varied SOC and temperature on the overall performance of the ESS with parallel connected cells has been investigated. The results highlight that 8% variation in the initial SOC can result in a current difference of 62% among the cells, while a temperature variation of 8℃ results in a current deviation of 14%. Moreover, it was found that the interconnection resistance can significantly increase the inhomogeneity

Analysis of parallel connected lithium-ion cells imbalanced performance based on electrothermal modelling environment

Typical battery management systems (BMS) do not provide the opportunity of monitoring each parallel string cell individually, rather considering cells operate at common voltage and current levels. In reality, manufacturing induced characteristics imbalances, pack thermal gradients and interconnection resistances impose uneven states among cells. As such, this paper intends to take advantage of common Equivalent Circuit Modelling (ECM) approaches to investigate the incurring interrelationships among 15 cells connected in parallel. The module is discharged under real-world drive cycle conditions in different scenarios, aligning model parameters variation to literature based evidence to accurately replicate their impact on imbalanced performance. The simulation results indicate a 1% ratio between interconnection resistance and internal resistance has the largest influence on cell-to-cell load current imbalance and thermal gradient among the investigated factors. Second, manufacturing related 25% internal resistance variability and poor cooling system design induced thermal gradient caused non-negligible imbalances in cells’ performance. A 9% cell-to-cell variation of capacity, contrarily, emerged as having a limited impact on pack behaviour, although constant current loads could imply different conclusions. Last, between investigated Z and Ladder pack configurations, the first choice guaranteed a more uniform performance. This work draws attention to the fact that the presence of imbalances not captured by the BMS can have a detrimental impact on the performance of different pack areas. Hence, the developed Activate® ECM pack model will be coupled in future work with HyperStudy® optimization tool to perform sensitivity analysis. This is expected to improve and accelerate the pack design process ensuring diagnostics and prevention of protracting imbalances, lowering pack safety and degradation concerns

Quantifying cell-to-cell variations of a parallel battery module for different pack configurations

Cell-to-cell variations can originate from manufacturing inconsistency or poor design of the battery pack/thermal management system. The potential impact of such variations may limit the energy capacity of the pack, which for electric vehicle applications leads to reduced range, increased degradation along with state of health dispersion within a pack. The latter is known to reduce the accessible energy and the overcharging/discharging of some of the cells within a system, which may cause safety concerns. This study investigates the short-term impact of such effects, which is highly important for designing of an energy storage system. A generic pack model comprising individual cell models is developed in Simscape and validated for a 1s-15p module architecture. The results highlight that a number of cells and interconnection resistance values between the cells are the dominant factors for cell-to-cell variation. A Z shape module architecture show a significant advantage over a ladder configuration due to the reduced impact of interconnection resistance on differential current flow within the module. Current imbalance is significantly higher for a ladder system and its magnitude is not dependent on the module current. Capacity variation does not have a significant impact on the system. By increasing the capacity variation from 9% to 40% the current inhomogeneity increases from 4% to 13%, whilst 25% resistance variation leads to 22% current dispersion. Further, a linear relationship is observed between the current inhomogeneity and thermal gradient

A BAYESIAN NETWORK APPROACH TO BATTERY AGING IN ELECTRIC VEHICLE TRANSPORTATION AND GRID INTEGRATION

Nowadays, batteries in electric vehicles (EVs) are facing a variety of tasks in their connection to the power grid in addition to the main task, driving. All of these tasks play a very significant role in the battery aging, but they are highly variable due to the change in the driver behavior, grid connection availability and weather conditions. The effect of these external factors in the battery degradation have been studied in literature by mostly deterministic and some stochastic approaches, but limited to specific cases. In this dissertation, first, a large-scale deterministic approach is implemented to evaluate the effect of variations in the EV battery daily tasks. To do so, a software tool named REV-Cycle is developed to simulate the EV powertrain and studied the effect of driving behavior, recharging facilities and timings, grid services and temperature/weather change effects, one by one. However, there are two main problems observed in the deterministic aging evaluation: First, the battery capacity fade factors such as temperature, cycling current, state of charge (SOC) … are dependent to the external variables such as location, vehicle owner’s behavior and availability of the grid connection. Therefore, it is not possible to accurately evaluate the battery degradation with a deterministic model, while its inputs are stochastic. Second, the battery aging factors’ dependency is hierarchical and it is not easy to follow and implement this hierarchy with deterministic models. Therefore, using a hierarchical probabilistic framework is proposed that can better represent the problem and realized that the Bayesian statistics with Markov Chain Monte Carlo (MCMC) can provide the problem solving structure needed for this purpose. A comprehensive hierarchical probabilistic model of the battery capacity fade is proposed using Hierarchical Bayesian Networks (HBN). The model considers all uncertainties of the process including vehicle acceleration and velocity, grid connection for charging and utility services, temperatures and all unseen intermediate variables such as battery power, auxiliary power, efficiencies, etc. and estimates the capacity fade as a probability distribution. Metropolis-Hastings MCMC algorithm is applied to generate the posterior distributions. This modeling approach shows promising result in different case studies and provides more informative evaluation of the battery capacity fade

Current Distribution and Anode Potential Modelling in Battery Modules with a Real-World Busbar System

The performance of a lithium-ion battery pack is not only related to the behavior of the individual cells within the pack, but also presents a strong interdependency with the temperature distributions, interconnect resistance between cells, and the cell’s physical location within the complete battery pack. This paper develops representative busbar circuits with different fidelities to simulate the behavior of cells within a battery module and analyses the influence of cell-to-cell heat transfer and interconnect resistance on the distribution of cell current and anode potential in a battery module. This work investigates multi-physics interactions within the battery module, including cells, interconnect resistances, and temperature distributions, while analyzing the lithium plating problem at the module level. Specifically, the cell model used in this study is a validated thermally coupled single-particle model with electrolyte, and the battery module uses a commercially representative busbar design to include 30-cells in parallel. The effects of parameter changes within the battery pack on individual cells are simulated and analyzed. The study highlights that some cells in the battery module would present a higher risk of lithium plating during fast-charge conditions as they experience a lower anode potential during the charge events

LITHIUM-ION BATTERY DEGRADATION EVALUATION THROUGH BAYESIAN NETWORK METHOD FOR RESIDENTIAL ENERGY STORAGE SYSTEMS

Batteries continue to infiltrate in innovative applications with the technological advancements led by Li-ion chemistry in the past decade. Residential energy storage is one such example, made possible by increasing efficiency and decreasing the cost of solar PV. Residential energy storage, charged by rooftop solar PV is tied to the grid, provides household loads. This multi-operation role has a significant effect on battery degradation. These contributing factors especially solar irradiation and weather conditions are highly variable and can only be explained with probabilistic analysis. However, the effect of such external factors on battery degradation is approached in recent literature with mostly deterministic and some limited stochastic processes. Thus, a probabilistic degradation analysis of Li-ion batteries in residential energy storage is required to evaluate aging and relate to the external causal factors. The literature review revealed modified Arrhenius degradation model for Li-ion battery cells. Though originating from an empirical deterministic method, the modified Arrhenius equation relates battery degradation with all the major properties, i.e. state of charge, C-rate, temperature, and total amp-hour throughput. These battery properties are correlated with external factors while evaluation of capacity fade of residential Li-ion battery using a proposed detailed hierarchical Bayesian Network (BN), a hierarchical probabilistic framework suitable to analyze battery degradation stochastically. The BN is developed considering all the uncertainties of the process including, solar irradiance, grid services, weather conditions, and EV schedule. It also includes hidden intermediate variables such as battery power and power generated by solar PV. Markov Chain Monte-Carlo analysis with Metropolis-Hastings algorithm is used to estimate capacity fade along with several other interesting posterior probability distributions from the BN. Various informative and promising results were obtained from multiple case scenarios that were developed to explore the effect of the aforementioned external factors on the battery. Furthermore, the methodologies involved to perform several characterizations and aging test that is essential to evaluate the estimation proposed by the hierarchical BN is explored. These experiments were conducted with conventional and low-cost hardware-in-the-loop systems that were developed and utilized to quantify the quality of estimation of degradation

Battery Management Systems of Electric and Hybrid Electric Vehicles

The topics of interest in this book include significant challenges in the BMS design of EV/HEV. The equivalent models developed for several types of integrated Li-ion batteries consider the environmental temperature and ageing effects. Different current profiles for testing the robustness of the Kalman filter type estimators of the battery state of charge are used in this book. Additionally, the BMS can integrate a real-time model-based sensor Fault Detection and Isolation (FDI) scheme for a Li-ion cell undergoing degradation, which uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters. This book will fully meet the demands of a large community of readers and specialists working in the field due to its attractiveness and scientific content with a great openness to the side of practical applicability. This covers various interesting aspects, especially related to the characterization of commercial batteries, diagnosis and optimization of their performance, experimental testing and statistical analysis, thermal modelling, and implementation of the most suitable Kalman filter type estimators of high accuracy to estimate the state of charg