Development and Evaluation of Passive Balancing System Model for Lithium-Ion Battery Pack in Electric Vehicles Using Numerical Simulation

Co-estimation of state of charge and capacity for battery packs in real electric vehicles with few representative cells and physics-informed machine learning

Safety and reliability are the two key challenges for large-scale electrification of road transport sector. Current Li-ion battery packs are prone to failure due to reasons such as continuous transmission of mechanical vibrations, exposure to high impact forces and, thermal runaway. Robust mechanical design and battery packaging can provide greater degree of protection against all of these. This chapter discusses design elements like thermal barrier and gas exhaust mechanism that can be integrated into battery packaging to mitigate the high safety risks associated with failure of an electric vehicle (EV) battery pack. Several patented mechanical design solutions, developed with an aim to increase crashworthiness and vibration isolation in EV battery pack, are discussed. Lastly, mechanical design of the battery pack of the first fully electric bus designed and developed in Australia is presented. This case study showcases the benefits of adopting modularity in the design of EVs. In addition, it highlights the importance of packaging space for EVs, particularly in low-floor electric buses, as weight distribution becomes a challenge in these applications.

The rapid adoption of electric vehicles (EVs) represents a critical step towards reducing greenhouse gas emissions and achieving sustainable transportation solutions. One of the key components in an EV is its battery, which plays a pivotal role in determining the vehicle's performance, range, and overall efficiency. Ensuring the health and longevity of EV batteries is essential for promoting EV adoption and optimizing their operational capabilities. This project presents an IoT-based Battery Monitoring System (BMS) designed to address the challenges associated with monitoring and managing the batteries in electric vehicles. The system leverages the power of Internet of Things (IoT) technology to continuously gather real-time data from the EV's battery pack. The IoT-based BMS consists of various components, including battery sensors, microcontrollers, wireless communication modules, and cloud-based data analytics. Battery sensors collect data on parameters such as voltage, current, temperature, and state of charge (SoC) from individual cells within the battery pack. In conclusion, this project presents an innovative IoT-based Battery Monitoring System that has the potential to revolutionize the way we manage and maintain electric vehicle batteries. Key Words: Battery pack, Electric vehicle Voltage, State of charge, Vehicle performance.

Mathematical modeling is an essential tool for the design, construction, and operation of battery cells and systems. There is a large number of battery models with various complexities and length scales of consideration. At the highest level there are system models which usually do not have a spatial dimension. The smallest component is typically a battery cell. Often they are real-time capable and can be used in HIL (hardware-in-the-loop) simulations. At the next level (length scales ~cm to ~mm) electrothermal models are used. They are capable of predicting the temperature distribution in battery cells, modules and packs and are typically based on empirical current/voltage relationships. For the length scales ~mm to ~mm electrochemical transport models are applied. In these models reaction layers are spatially resolved and basic physical transport mechanisms, e.g. the diffusion of lithium ions, are calculated. Finally, for the smallest scales (~mm to ~nm) molecular models exist which are used in material science.This work focuses on the comparison of the electrothermal and electrochemical models implemented in the multiphysics software package FIRE ®developed by AVL List GmbH [1]. Whereas electrothermal models often are three-dimensional, electrochemical models usually contain only one spatial direction (e.g. normal to the separator). In the present work, both models are used in three dimensions allowing for a detailed comparison of their results in temporal evolutions as well as spatial distributions. The most important characteristics of both models are listed in Table 1. The pros and cons are obvious: The electrochemical model provides a higher accuracy but also requires a higher calculation time. The electrothermal model needs a larger number of fitting parameters whereas the electrochemical model needs a larger number of material parameters but, hence, also allows for the investigation of different material properties of the reaction layers.This work is divided into the following three main parts:Theoretical background of both modelsModel fitting to experimental dataModel application to a realistic case with experimental validation For the second and third part a high energy lithium-ion battery of the pouch type, typically used in electric vehicles, is considered. In the second part, both the electrothermal and the electrochemical model are adjusted independently from each other to experimental discharge curves for different constant current loads and temperatures – see Figure 1 for the electrothermal model. After that, the calculation results obtained with both models are compared to each other: The temporal evolution of averaged quantities (e.g. cell voltage) as well as the spatial distribution of quantities (e.g. temperature or state of charge – see Figure 2) at fixed time steps are shown. Moreover, a deep insight into the battery is given with the electrochemical model via a visualization of results in the electrodes (e.g. lithium concentration). In the third part, finally, the models are tested for a case where the battery is loaded with a realistic current profile – see Figure 3. Voltage response and temperature evolution are being compared to experimental data. Moreover, the results of both models are being compared to each other once more.With the results shown in this work the strengths and weaknesses of electrothermal and electrochemical models are pointed out clearly. Conclusions to adequate application fields for both models can be drawn from the results. Future work includes the application of the electrothermal and electrochemical model to battery modules and stacks as well as an investigation of degradation phenomena in batteries with the electrochemical model.[1] FIRE ® v2013, Electrification / Hybridization Manual, AVL List GmbH, 2013.[2] M. Doyle and J. Newman, J. Electrochem. Soc. 143, 1890-1903, 1996.

With the rapid evolution of electric vehicles (EVs), ensuring efficient thermal management for battery packs is a critical challenge for enhancing their performance, safety, and longevity. This manuscript explores the use of Computational Fluid Dynamics (CFD) to model and analyze thermal behavior in EV battery packs. The study identifies the key factors influencing thermal performance, including cooling methods, battery cell arrangement, and material properties. By employing CFD simulations, this research evaluates the effectiveness of different thermal management strategies such as passive and active cooling systems. The results highlight the significance of optimal cooling system design in preventing overheating and ensuring uniform temperature distribution within the battery pack. The findings provide valuable insights for the development of more efficient, reliable, and safe battery systems for electric vehicles. With the rapid adoption of electric vehicles (EVs), the efficiency, safety, and longevity of their battery systems are critically dependent on effective thermal management. The thermal management of EV battery packs is essential to optimize performance, prevent overheating, and enhance the lifespan of lithium-ion cells. This manuscript focuses on the use of Computational Fluid Dynamics (CFD) to simulate and analyze the thermal performance within electric vehicle battery packs under different operating conditions. By investigating multiple cooling strategies, including passive and active cooling systems, the research aims to identify the most effective methods for maintaining temperature stability. The study emphasizes the importance of uniform temperature distribution across the cells, especially under high charge and discharge cycles. Simulation results show that while passive cooling offers a simplified approach, active cooling systems, particularly liquid-cooling, deliver better results in preventing overheating and thermal gradients within the battery pack. Additionally, phase change materials (PCMs) are explored as a promising solution for mitigating temperature fluctuations. The findings of this research offer valuable insights for improving the thermal management of EV batteries, contributing to the development of safer, more reliable, and energy-efficient electric vehicles.

In grid energy storage systems based on batteries, the interface converter topology plays a significant role in the optimized operation of the system. Among different types of multi-level converters, cascaded H-bridge converter (CHBC) provides the advantage of utilizing isolated battery pack (BP) with a lower number of series-connected cells and can perform state of charge (SoC) balancing by controlling the power flow to/from each BP. However, the rate of SoC balancing is limited due to low balancing current when the amount of power exchange is low. This paper proposes a new power control method using hybrid modulation strategy with extended operating region (HMSEOR) which enables the CHBC to perform fast SoC balancing at constant current irrespective of the amount of power exchange between the battery system and the grid. The HMSEOR allows power flow from a BP with a higher SoC to a BP with a lower SoC without compromising power exchange with the grid. The performance of the proposed SoC balancing method is validated through real-time hardware-in-the-loop (HIL) simulation and compared with the conventional SoC balancing at unity power factor operation.

Influence of orientation on ageing of large-size pouch lithium-ion batteries during electric vehicle life

It is difficult for existing methods to solve the real-time accuracy problem of battery module-level state of charge (SoC) and the impact of single battery inconsistency at the same time under dynamic operating conditions. The integration of data-driven technology and traditional algorithms is insufficient, resulting in limited error compensation effect. In view of the accuracy of the SoC estimation of electric vehicle (EV) battery packs under dynamic driving conditions, this paper proposes a hybrid SoC estimation method for battery management system (BMS) based on cloud master-slave architecture. The hybrid framework combines direct measurement methods (Coulomb counting method, open circuit voltage method), state estimation algorithms (extended Kalman filtering, traceless Kalman filtering), and data-driven technologies (neural networks, NARMA L-2 models), and verifies its effectiveness through hardware-in-the-loop experiments. The research results show that under dynamic operating conditions, the hybrid Coulomb counting+neural network (CC+NN) method has the fastest error convergence speed and is better than other methods. In addition, the proposed cloud master-slave BMS architecture significantly improves system reliability through real-time cross-verification of the SoC data of the advanced algorithms of the on-board BMS (slave device) and the master device. The experiment is based on the FTP-75 driving cycle and verifies the high efficiency of this method in practical applications.The final analysis shows that the CC+NN combination exhibits optimal error suppression ability in complex scenarios, and provides a high-precision solution for electric vehicle battery management.

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, it is necessary for early detection of soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC by coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in detecting a soft SC fault quickly and estimate soft SC resistance accurately.

It is a known fact that if all systems and devices that generate a substantial amount of pollution are transformed (if possible) into electrical devices (i.e., electrified) and all the electrical energy that powers those devices is generated from a renewable energy source, the result would be a significant decrease of CO2 contributors. With transportation making up 60% of the world’s carbon emission, this chapter is concentrated on the electrification process of vehicles by usage of electrical devices, thus transforming them into electrical vehicles. Although the electric vehicle has become the representative of the renewable energy revolution, it has its own flaws. The electric vehicle’s power train consists of electric motors, AC/DC inverters, DC/DC converters, braking choppers, transmission, but there is one system that is considered the core of the electric vehicle, the battery package. The electric vehicle’s battery package (i.e., battery pack) is the main source of power for all the onboard systems. It contains the energy to power the electrical propulsion system, main systems (inverter; VCU, or vehicle control unit; servo system; cooling system; lights; etc.), media system (infotainment), and secondary systems (HVAC, or heating, ventilation, and air-conditioning; cabin lights; charger; etc.). This underscores the battery package’s importance and defines the need for it to be as efficient, reliable, safe, and environment-friendly as possible. The battery cell with the efficiency of 90% is the building block of the battery pack, and it comes not only in different shapes and sizes (cylindrical, pouch, prismatic) but also in different chemical compositions (LiMn2O4, LiNiMnCoO2, etc.). The lithium-ion battery cell is the most popular battery cell because when compared to other battery technologies, it has the most energy density per kilogram. The conventional way of building a battery pack is done with a serial–parallel combination of battery cells. The amount of battery cells in series depends on the required battery pack voltage, where in parallel it depends on the required battery pack capacity. With all these in mind, the lithium-ion battery cell needs to operate within certain conditions (temperature range, current discharge rate, voltage/current charging). If these conditions are not met, the battery cell’s health will degrade, and this can lead to thermal runaway. The battery management system (i.e., BMS), combined with the appropriate sensors, is used for monitoring, control, and diagnosis of the battery pack. The main goal of the BMS is the battery pack’s safe, uninterrupted, and highly optimized electrical energy supply. The BMS contains various safety functions (interlocks, thermal runaway prevention functions, and overcurrent functions), monitoring functions (state of health estimation, current, voltage, and temperature analysis), and optimization functions (battery cell balancing). With the introduction of supercapacitors into the architecture, the battery cell degradation rate is decreased. With the property of high current charge/discharge rate, the supercapacitor’s role is to take in any high-current surge that the battery cell may experience during regenerative braking or ramping up of the electric motor. Along with regenerative braking, auxiliary sources of energy (alternators, generators, range extenders, solar panels, etc.) are used to charge the battery pack while in operation, which results in wider usability range and a reduction in charging frequency of the electric vehicle and also extends its drive range.

In order for successful second-life implementation of Electric Vehicle (EV) battery packs, the viability of the intended second-life use must be ascertained based on a cost-benefit analysis and technical appraisal of the estimated condition of the available battery packs. This paper discusses the issues in measuring State-of-Health (SoH) and other battery condition metrics of a battery pack. Measurements on real-life battery packs sent for recycling are taken that demonstrate a typical 85% SoH; slightly higher than predicted by Original Equipment Manufacturers (OEM). A model is introduced that can simulate the energy demand in a home/dwelling being met by a number of sources including mains (utility) power, photovoltaic generation (PV), and second-life battery storage. The model is applied to three scenarios using second-life battery storage, to create energy costs savings through time-shifting of energy using on-peak/off-peak electricity tariffs. For each scenario a cost-benefit analysis is produced, indicating that whilst energy costs savings can be achieved, excessive usage of the battery pack can cause the payback period of the capital investment to be longer than the predicted second-lifetime of the battery pack. However, the final scenario demonstrates that combining the battery pack with local generation, such as PV, yields cost savings that are significant at 75%, and the payback period is within the estimated lifetime of the battery pack.

This research paper explores the integration of Phase Change Materials (PCMs) into Electric Vehicle (EV) battery packs for enhanced thermal management. Through a comprehensive study involving design, simulation, and analysis using tools like ANSYS, the effectiveness of PCM integration in managing temperature profiles and heat dissipation within EV battery packs is evaluated. The research highlights the advantages of PCM-based thermal management over traditional air and water cooling methods, emphasizing improvements in battery performance, lifespan, and overall safety. The findings contribute valuable insights to advancing EV technology and sustainability, paving the way for more efficient and reliable electric mobility solutions.

Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Design of the Electric Vehicle (EV) battery pack involves different requirements related to the driving range, acceleration, fast-charging, lifetime, weight, volume, etc. Therefore, sizing of the EV battery pack necessitates a multi-objective optimization study to achieve the right trade-off considering the aforementioned factors. This “trade-off” can vary depending on the type and size of the EV, as well as use cases. In this regard, a nice solution is to use a hybridized battery pack consisting of both High-Energy (HE) and High-Power (HP) battery cells, which will help to meet a wider range of customer requirements. Hybridization decouples energy and power and thus increases design flexibility to achieve a better trade-off for a wider range of EV applications. This paper proposes an effective framework for optimal sizing of such hybridized battery packs for a typical EV, namely the Mitsubishi MiEV. Lithium-ion cells with Nickel Manganese Cobalt Oxide (NMC) and Lithium Titanate Oxide (LTO) chemistry are chosen as HE and HP cells, respectively. A detailed analysis is fulfilled to determine the best hybridization topology, e.g. voltage profile, interface with DC-link, etc. The genetic algorithm is used to solve the multi-objective optimization problem considering two different driving cycles, namely the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP). The results favor the usefulness of the hybrid battery pack to simultaneously achieve lifetime and charge power requirements compared to mono battery systems. The hybrid pack offers >+40,000 km improvement in the achievable driving when an end-of-life criterion of 70 % for the cell capacity is considered. Remarkably, the optimized hybrid pack can also be fast charged up to 70 % within 6 min.