Electric Vehicle Battery Pack Research Articles

Faulty Characteristics and Identification of Increased Connecting and Internal Resistance in Parallel-Connected Lithium-Ion Battery Pack for Electric Vehicles

This paper investigates the faulty characteristics and develops an identification method to distinguish connecting and increased internal resistance faults in the parallel-connected lithium-ion battery pack. On one hand, the experiments under faulty conditions are conducted. A novel method for collecting cell voltage is adopted to reflect the difference in the tab voltage. The incremental capacity is analyzed to clarify the reason for the capacity loss of the battery pack. The current and temperature are also discussed to investigate the characteristics. On the other hand, an identification method is proposed based on the experiments. The parallel-connected battery unit is regarded as a large battery. The parameters of the large battery are estimated by recursive least squares. It is judged whether the large battery has a loose connection or internal resistance increases by the estimated resistance and its standard deviation. Then, combine with the tab voltage and temperature, the specific faulty cell is identified. The fault is considered as the second level. When the temperature difference among cells exceeds 5°C, the fault deteriorates to a first level. The estimated resistance, the tab voltage, and the temperature are used as faulty indicators, which jointly determine the specific fault type and position.

Minimum-time optimal control for battery electric vehicles with four wheel-independent drives considering electrical overloading

This paper analyses the performance benefit due to electrical overloading of battery pack and electric motors in battery electric vehicles with four wheel-independent drives. The performance enhancement is quantified by relative lap time improvements which are computed using minimum-time optimal control. Additionally, a concurrent optimisation is performed which provides the optimal gear ratios for the front and rear reduction gears as well as the optimal performance distribution between the front and rear motors under the specification of a fixed total motor performance. We characterise the motor performance by the maximum torque, the maximum speed and the overload capability of the electric machines. The essential dynamics are represented using a nonlinear two-track vehicle model and a Thevenin battery model. We propose a simplified modelling approach for electrical overloading using power loss maps and energy reservoirs. This way, we keep the number of additional states low which is advantageous for optimal control problems. Direct optimal control and Hermite-Simpson collocation is used to solve the optimisation problem numerically.

Overview of Optical Digital Measuring Challenges and Technologies in Laser Welded Components in EV Battery Module Design and Manufacturing

Ensuring the precision and repeatability of component assembly in the production of electric vehicle (EV) battery modules requires fast and accurate measuring methods. The durability of EV battery packs depends on the quality of welded connections, therefore exact positioning of the module components is critical for ensuring safety in exploitation. Laser welding is a non-contact process capable of welding dissimilar materials with high precision, for that reason it has become the preferred joining method in battery production. In high volume manufacturing, one of the main production challenges is reducing the time required for assessment of dimensional and geometrical accuracy prior to joining. This paper reviews the challenges of EV battery design and manufacturing and discusses commercially available scanner-based measurement systems suitable for fabrication of battery pack components. Versatility of novel metrological systems creates new opportunities for increasing the production speed, quality and safety of EV battery modules.

A novel matrix-vector-based framework for modeling and simulation of electric vehicle battery packs

Abstract Facing the growing electrification of the transport sector, there is an increasing need for models capturing the entire vehicle battery. The performance of the model is crucial for the development of energy storage systems but also for real-time applications. The challenge of these so-called control-oriented models is to precisely map the individual cell behavior and the interactions with the neighboring cells while keeping the computational effort low. In this work, a novel framework for the electro-thermal modeling of large-scale battery packs is presented. Both the individual cell behavior and the interactions between neighboring cells as well as cyclic aging are taken into account. For the parameterization, two cell types are characterized over a temperature range from 15∘C to 45∘C and over the entire state of charge range. The model is validated based on three fault cases in the battery system, which are mapped correctly with the model framework. From the results, fault-specific findings are derived for model-based and date-driven fault diagnosis. A battery system consisting of 500 cells is simulated to evaluate the model performance. The simulation of this battery pack is real-time capable up to a frequency of 100Hz.

A method of cell-to-cell variation evaluation for battery packs in electric vehicles with charging cloud data

Abstract During the use of electric vehicles (EVs), especially with the decay of battery, the cell-to-cell variations in Lithium-ion batteries increase. The cell-to-cell variations of power battery may lead to battery failure and cause safety problems. With the fast development of cloud data, EV data can be monitored on the cloud to evaluate the safety and cell-to-cell variations of EVs. Although the data are sampled with a high frequency in EVs, the recording frequency of the cloud data is relatively low. Thus, the charging data are more valuable and suitable to evaluate cell-to-cell variations of EVs. In this paper, a method based on charging cloud data is proposed to evaluate the cell-to-cell variations of lithium-ion batteries. 5 indicators, including variations of the voltage, temperature, internal resistance, capacity and electric quantity, are analyzed and evaluated by the original signals. Cell capacities and the electric quantities are achieved with the estimation of the remaining charging/discharging capacities by the charging voltage curves transformation. To comprehensively score the cell-to-cell variations of the battery pack, a weighted score mechanism is proposed. The weight factors are decided by the analytic hierarchy process based on the judgment matrix. Finally, experiments are carried out, and 3 packs are evaluated using the proposed scoring system. The results show the proposed method can effectively evaluate the cell-to-cell variations of battery packs.

Reducing Emissions and Costs with Vehicle-to-Grid

Determining when and how to charge electric vehicles to minimize costs and potential greenhouse gas emissions is a complex optimization problem. Recently in Applied Energy, Brinkel et. al. developed a multi-objective smart charging method that can reduce costs and cut carbon emissions without upgrading existing grid infrastructure in model case scenarios.

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

Effects of ambient temperature on the health characteristics of vehicular Li-ion batteries by electrochemical impedance spectroscopy

To widely commercialize electric vehicles (EVs), more efforts to improve their life span seem profoundly inevitable. Thermal conditions have profound the nonlinear effects on the degradation rate, performance, and safety level of an EV battery pack. In the current study, both cycle life and calendar life of a commercial LiFePO4-based cell are investigated experimentally via electrochemical impedance spectroscopy (EIS) for different operating temperatures. This is the first time that the effects of low temperatures such as − 20 °C and − 5 °C on the life of LiFePO4-based cells are investigated via EIS, which is the crucial case during the cold start of EVs in cold climate regions of the world. The results show a considerable increase of solvent resistance for the cell which experienced calendar aging test at 55 °C; this demonstrates a drastic thickening of the SEI for this storage temperature. Moreover, anode cracking is clearly observed at such a high temperature. The results of the cycle aging tests also show that the increase in the solvent resistance is negligible for all other three operating temperatures. Nevertheless, the increasing of SEI thickness at low-temperature condition (− 5 °C) is noticeable. The role of temperature on the degradation level of LiFePO4-based cells has also been discovered via scanning electron microscopy.

A novel space-constrained vehicle suspension mechanism synthesized by a systematic design process employing topology optimization

Demands on larger motors and battery packs in electric vehicles cause a suspension layout issue not appearing in gas-powered vehicles. Motivated by this need, our research aims to design a new-concept suspension applicable to electric vehicles, where given space-related constraints are satisfied without scarifying their kinematic performance. Here, we propose a three-phase design process for the synthesis of rear suspensions of an electric vehicle: concept topology design, kinematic feature identification, and detailed design. For the concept design to determine the mechanism topology, we employ the topology optimization method developed for mechanism synthesis subjected to a reduced suspension design space as well as a tighter condition on the camber rate—known as yielding better vehicle’s dynamic performance. The next phase is to extract the underlying kinematic features of the synthesized suspension obtained by the topology optimization method as it may be difficult to directly figure out how the synthesized mechanism functions kinematically. For the extraction, we propose a connectivity-mapping technique followed by the wrench calculation. This phase is followed by the final detailed design to meet the specific requirements imposed on the target suspensions. The new suspensions designed by the proposed three-phase design approach will be shown to successfully resolve the suspension layout issue typically encountered in electric vehicles.

Intelligent optimization methodology of battery pack for electric vehicles: A multidisciplinary perspective

Past studies focused on proposing new materials for batteries components, state of health (SOH) prediction, thermal design, equivalent circuit modeling, and so on. Those studies have been implemented on individual basis on a single battery or battery pack. However, there is hardly any research found that encompasses all the multidisciplinary aspects (such as materials, SOH, intelligent configuration [assembly], thermal design, mechanical safety, and recycling of materials and pack) simultaneously for the battery pack design of electric vehicles. This research article proposes a synthetic methodology for an advanced design of battery pack and its components by incorporating optimal scenario of materials selection for battery electrodes, SOH estimation, configurations (assembly) of cells, thermal (air and liquid cooling) design, battery pack casing mechanical safety, and recycling aspects of battery and battery pack. The problem is divided into the several parts and methodology for each is proposed. Cumulative advantages of the methodology with six future critical directions are discussed in the end.

Adaptive surrogate model-based optimization framework applied to battery pack design

The efficiency represented by the computational costs for solving complex engineering optimization problems can be improved based on surrogate models. Adaptive sampling methods can effectively reduce the number of samples and enhance the accuracy of the surrogate model. However, from the perspective of optimization, most adaptive sampling methods ignore the inevitable connection between the optimization process and the establishment of a surrogate model. The efficiency and accuracy of existing adaptive sampling methods are poor. In this paper, a novel adaptive sampling method is proposed based on the complex method (CM). In the proposed method, the samples are used not only to establish the surrogate model but also to form complex shapes to guide the optimization search. An iterative process is added to collect new samples to improve the accuracy of the surrogate model. The proposed method is compared with three classic adaptive methods with several benchmark functions. The comparison results indicate that the proposed method can obtain a global optimum with low computational burden and a small sample. Finally, the optimum design of an electric vehicle battery pack is presented to illustrate the feasibility and validity of the proposed method.

Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles

With the continuous improvement in battery life requirements, the modeling, analysis and management of battery pack life become an important topic in the design of electric vehicles. A more realistic and generic modeling method coupling the electrochemical, thermal, SEI formation model of cells, fluid dynamics and the series-parallel circuit model is proposed to accurately describe the coupling relationships and quantify the inconsistencies, including temperature and current. A life model including capacity fade and reliability is established to evaluate the life of lithium-ion battery pack system (LIBPs). And a model implementation method is established to raise the computational accuracy and efficiency, then, it is validated from the perspective of electrochemical, degradation, thermal performance and coupling effect using experiments. Moreover, the multiphysical behavior and life of LIBPs in different situations are analyzed and predicted, followed by the analysis of coupling effect, inconsistency, reliability and economy. The results show the degradation rate of LIBPs first decreases and then accelerates in the whole life. Furthermore, adding series cells is uneconomical to extend the life of LIBPs. Finally, a SoH balanced management method of changing the airflow direction is proposed to extend the life of LIBPs, and the optimal strategy for different SoH inconsistency are obtained.

Resistive Force Calculation and Battery Pack Configuration using Simulink Model

This paper provides a step by step guide for calculation of powertrain unit including the configuration of battery pack of a two-wheeler electric vehicle. Based on the design and desired performance of the vehicle, the total resistance force, torque and power is calculated and designed in MATLAB and Simulink, which is essential for selection of electric motor. Knowing the voltage and capacity of an individual cell the configuration of battery pack is calculated and depicted in SOLIDWORKS Computer Aided Design model. A website is developed which is competent to perform the necessary calculations and display the output of the desired performance parameters.

Li-ion power battery temperature control by a battery thermal management and vehicle cabin air conditioning integrated system

Efficient and effective thermal management of Li-ion battery pack for electric vehicle application is vital for the safety and extended-life of this energy storage system. In this paper, the thermal management system of a battery module is presented as an integral part of the electric vehicle air conditioning system. The refrigerant is bifurcated from the main system through a pipe and expansion valve and guided to the battery module compartment consisting of 64 18650-type Li-ion battery arranged in the form of 8 × 8 array. Several experimental strategies such as expansion valve throttle level, thermostat sensitivity and proportional-integral-derivative (PID) control algorithm are investigated. The results indicate that a larger openness of the throttling valve avoids superheating of the refrigerant and maintains the maximum temperature difference in the battery module around 2 °C. Additionally, thermostat sensitivity and PID control algorithm is found to be able to offer effective thermal management for an electric vehicle battery pack.

Innovative Scaled Hardware Simulator for Designing and Testing an EV’s Battery Storage System Incorporated with an Adaptive ANN Model

In this study, a scaled-down system, which can be used as a benchmark test for the battery storage designing of electric vehicles (EVs) is proposed. This model was based on the hardware simulator of the battery storage system (BSS) used from a single cell up to 4 cells in a series pack system, which simulates a practical battery pack. The developed simulator can charge and discharge any rechargeable battery, such as Li-Ion, Ni–MH or Pb battery. The scaling ratio of the simulator was evaluated by the ratio of the current or power of the battery pack specimen related to the specification. Also, this study proposes an innovative state of charge (SoC) estimation of the battery pack for EVs based on genuine results obtained through practical tests. This estimation was carried out by an adaptive artificial neural network (ANN) algorithm, using simple inputs. As well, this model can deduct the state of health (SoH) of the battery pack based on the power output level and waveform characteristics. The results of the ANN showed high generalization, a low error of SoC estimation at the level of 1.1%, with a calculation time less than 16.5 s. Regarding the hardware simulator, the similarity of the results and waveform accuracy of the scaled-down battery systems compared with the real battery pack was very acceptable with a maximum deviation of 2.1% in the worst scenario. The cells cycled with different depths of discharge (DoD) or C-rates, at different temperatures with different initial SoCs using any arbitrary current waveforms. Our conclusions will help battery manufacturers to test and evaluate the performance of the BSS in different applications, such as EVs, PV generation, and wind farm, with significant cost reduction. Also, the ANN algorithm can be used and embedded in EVs or in any other industrial application, as proposed in this paper. This study contributed to the real-time diagnosis of the BSS without interrupting the normal operation based on its features.

Battery Second-Life for Dedicated and Shared Energy Storage Systems Supporting EV Charging Stations

Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system.

Improvement of Thermal Performance of Electric Vehicle Battery Pack with Phase-change Material

Improvement of Thermal Performance of Electric Vehicle Battery Pack with Phase-change Material

Thermal management for prevention of failures of lithium ion battery packs in electric vehicles: A review and critical future aspects

AbstractThe major concerns with Lithium‐ion batteries failures are temperature rise and temperature non‐uniformity during adverse operating conditions like fast charging/discharging and extreme ambient conditions (extreme hot/cold weather). These problems lead to safety issues like thermal runaway of the battery pack. To negate these issues and to ensure better performance of the battery pack, battery thermal management system (BTMS) is adopted in EVs. The prominent BTMSs are air‐based BTMS, liquid‐based BTMS and phase change based BTMS. This paper collates various thermal management issues and numerous cooling methods developed to mitigate these problems and throws light on some of the research gaps on recovery and utilization of low‐grade heat generated in the battery pack.

Integration of electric vehicle user charging preferences into Vehicle-to-Grid aggregator controls

Vehicle-to-Grid (V2G) describes the concept of using the battery packs of parked electric vehicles (EVs) as distributed battery energy storage to support the power grid. The bi-directional power flow in a V2G network is managed by a so-called aggregator which has to consider the needs of both the power grid and connected EVs. ‘Range anxiety’ is a major factor holding back the EV market as many drivers consider range limitations as a major disadvantage of EVs over more traditional vehicles with internal combustion engines. Implementing V2G could potentially worsen this perception as connected EVs could actually lose charge over short periods when providing grid services. Enabling EV owners/users to state charging preferences via simple user interfaces might increase the acceptance of V2G and reduce range anxiety.The inclusion of user charging preferences puts further constraints on an aggregator and complicates charge/discharge scheduling. Taking such preferences into account and weighting them against (potentially conflicting) needs of the power grid is a major challenge for V2G scheduling and adds computational costs through increased complexity. However, depending on its application, the responsiveness of a V2G network is crucial and the scheduling process is hence time-sensitive. In this research, a method of capturing user charging preferences and accounting for the resulting constraints is presented. The impact of added complexity on the execution time of the scheduling process is addressed by resolving user inputs prior to the actual scheduling. The ability of each EV to either charge or discharge is scored taking into account user inputs and the current state of the battery pack. This scoring system informs the selection of suitable EVs before the scheduling process.

Optimal charging of an electric vehicle battery pack: A real-time sensitivity-based model predictive control approach

Lithium-ion battery packs are usually composed of hundreds of cells arranged in series and parallel connections. The proper functioning of these complex devices requires suitable Battery Management Systems (BMSs). Advanced BMSs rely on mathematical models to assure safety and high performance. While many approaches have been proposed for the management of single cells, the control of multiple cells has been less investigated and usually relies on simplified models such as equivalent circuit models. This paper addresses the management of a battery pack in which each cell is explicitly modelled as the Single Particle Model with electrolyte and thermal dynamics. A nonlinear Model Predictive Control (MPC) is presented for optimally charging the battery pack while taking voltage and temperature limits on each cell into account. Since the computational cost of nonlinear MPC grows significantly with the complexity of the underlying model, a sensitivity-based MPC (sMPC) is proposed, in which the model adopted is obtained by linearizing the dynamics along a nominal trajectory that is updated over time. The resulting sMPC optimizations are quadratic programs which can be solved in real-time even for large battery packs (e.g. fully electric motorbike with 156 cells) while achieving the same performance of the nonlinear MPC.