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Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions. • Introduce state-dependent mechanism into ECMs, proposing the SD-QLPVM. • Enhance modeling capacity and eliminate the dependency on SOC-OCV curves. • A close-loop and real-time approach for SOC estimation is designed based on the PF. • Verified across various temperature levels and drive cycles. [ABSTRACT FROM AUTHOR]

This review article explores the potential and significance of digital twins (DTs) in advancing battery technology, specifically in electric vehicles (EVs). Due to their ability to accurately predict, diagnose, and enhance energy systems, DTs offer a transformative solution for addressing environmental concerns and improving energy storage capabilities. Moreover, DTs hold promise in facilitating vehicle-to-grid (V2G) integration and testing autonomous driving systems, while robust cybersecurity measures will be essential to protect sensitive data. The applications of DTs in estimating state of charge (SOC) and state of health (SOH) parameters demonstrate their effectiveness in optimizing battery performance. In addition to exploring the application of artificial intelligence (AI) and machine learning models for improving battery performance while highlighting real-world case examples, the study also highlights how digital twin (DT) technology can enhance battery design, increase longevity, and ensure safety. Furthermore, this study discusses the challenges of implementing DTs for battery packs, and the potential applications of this technology in future directions are discussed. In conclusion, DTs are set to revolutionize the future of electric transportation, promoting sustainability and efficiency in the EV ecosystem. • Explores how digital twin technology can advance battery technology in electric vehicles (EVs). • The paper also highlights applications in V2G integration and autonomous driving systems, while emphasizing cybersecurity. • Overall, digital twins offer promising solutions for sustainable and efficient EVs. [ABSTRACT FROM AUTHOR]

The design and optimization of the battery pack in an electric vehicle (EV) is essential for continued integration of EVs into the global market. Reconfigurable battery packs are of significant interest lately as they allow for damaged cells to be removed from the circuit, limiting their impact on the entire pack. This paper provides a simulation framework that models a battery pack and examines the effect of replacing damaged cells with new ones. The cells within the battery pack vary stochastically and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the state of health of the pack can be consistently maintained above a certain threshold value selected by the user. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework as well as the results from this paper can be utilized to better optimize lithium ion battery pack design in EVs and make long term deployment of EVs more economically feasible. [ABSTRACT FROM AUTHOR]

A key challenge hindering the mass adoption of Lithium-ion and other next-gen chemistries in advanced battery applications such as hybrid/electric vehicles (xEVs) has been management of their functional performance for more effective battery utilization and control over their life. Contemporary battery management systems (BMS) reliant on monitoring external parameters such as voltage and current to ensure safe battery operation with the required performance usually result in overdesign and inefficient use of capacity. More informative embedded sensors are desirable for internal cell state monitoring, which could provide accurate state-of-charge (SOC) and state-of-health (SOH) estimates and early failure indicators. Here we present a promising new embedded sensing option developed by our team for cell monitoring, fiber-optic sensors. High-performance large-format pouch cells with embedded fiber-optic sensors were fabricated. The first of this two-part paper focuses on the embedding method details and performance of these cells. The seal integrity, capacity retention, cycle life, compatibility with existing module designs, and mass-volume cost estimates indicate their suitability for xEV and other advanced battery applications. The second part of the paper focuses on the internal strain and temperature signals obtained from these sensors under various conditions and their utility for high-accuracy cell state estimation algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Lithium iron phosphate (LFP) and lithium manganese oxide (LMO) are competitive and complementary to each other as cathode materials for lithium-ion batteries, especially for use in electric vehicles. A multi scale multi-dimensional physic-based model is proposed in this paper to study the thermal behaviour of the two lithium-ion chemistries. The model consists of two sub models, a one dimensional (1D) electrochemical sub model and a two dimensional (2D) thermo-electric sub model, which are coupled and solved concurrently. The 1D model predicts the heat generation rate (Qh) and voltage (V) of the battery cell through different load cycles. The 2D model of the battery cell accounts for temperature distribution and current distribution across the surface of the battery cell. The two cells are examined experimentally through 90 h load cycles including high/low charge/discharge rates. The experimental results are compared with the model results and they are in good agreement. The presented results in this paper verify the cells temperature behaviour at different operating conditions which will lead to the design of a cost effective thermal management system for the battery pack. [Copyright &y& Elsevier]

Abstract: Because of the inevitable inconsistency during manufacture and use of battery cells, cell variations in battery packs have significant impacts on battery pack capacities, durability and safety for electric vehicles (EVs). To reduce cell variations and increase pack capacity, cell equalization is essentially required. In the series of two papers, we discover that dissipative cell equalization (DCE) using dissipative resistances is a feasible on-line equalization method for battery packs in EVs. We subsequently propose on-line equalization algorithms for lithium-ion battery packs based on charging cell voltage curves (CCVCs). The objective of these algorithms is to maximize pack capacities by conditioning CCVCs. As the first paper of the series, we first briefly review equalization topologies and algorithms. We discover cell remaining charging capacity (RCC) can be on-line estimated and further propose DCE algorithm based on remaining charging capacity estimation (RCCE). We establish a pack model with 8 cells in series and simulate 4 scenes with different cell variations. RCCE–DCE algorithm is proved to be effective by comparing pack capacities with/without RCCE–DCE algorithm. The equalization capability and over-equalization prevention are further examined, and the result shows that RCCE–DCE algorithm is suitable for on-line equalization in EVs. [Copyright &y& Elsevier]

Abstract: Systems integration issues, such as electrical and thermal design and management of full battery packs – often containing hundreds of cells – have been rarely explored in the academic literature. In this paper we discuss the design and construction of a 9kWh battery pack for a motorsports application. The pack contained 504 lithium cells arranged into 2 sidepods, each containing 3 modules, with each module in a 12P7S configuration. This paper focuses particularly on testing the full battery pack and diagnosing subsequent problems related to cells being connected in parallel. We demonstrate how a full vehicle test can be used to identify malfunctioning strings of cells for further investigation. After individual cell testing it was concluded that a single high inter-cell contact resistance was causing currents to flow unevenly within the pack, leading to cells being unequally worked. This is supported by a Matlab/Simulink model of one battery module, including contact resistances. Over time the unequal current flowing through cells can lead to significant differences in cells’ state of charge and open circuit voltages, large currents flowing between cells even when the load is disconnected, cells discharging and aging more quickly than others, and jeopardise capacity and lifetime of the pack. [Copyright &y& Elsevier]

Abstract: The SUBAT-project evaluates the opportunity to keep nickel–cadmium traction batteries for electric vehicles on the exemption list of European Directive 2000/53 on End-of-Life Vehicles. The aim of the SUBAT-project is to deliver a complete assessment of commercially available and forthcoming battery technologies for battery-electric, hybrid or fuel cell vehicles. This assessment includes a technical, an economical and an environmental study of the different battery technologies, including the nickel–cadmium technology. In a general perspective, the impacts of the different battery technologies should be analysed individually to allow the comparison of the different chemistries (lead–acid, nickel–cadmium, nickel-metal hydride, lithium-ion, sodium–nickel chloride, …) and to enable the definition of the most environmentally friendly battery technology for electrically propelled vehicles. The project officially ran from 2004-01-01 to 2005-03-31. This paper summarizes the outcome of the project at the time of the submission of the paper, i.e. January 2005. [Copyright &y& Elsevier]

With the rapid development of lithium-ion battery technology in the electric vehicle (EV) industry, the lifetime of the battery cell increases substantially; however, the reliability of the battery pack is still inadequate. Because of the complexity of the battery pack, a reliability design method for a lithium-ion battery pack considering the thermal disequilibrium is proposed in this paper based on cell redundancy. Based on this method, a three-dimensional electric-thermal-flow-coupled model, a stochastic degradation model of cells under field dynamic conditions and a multi-state system reliability model of a battery pack are established. The relationships between the multi-physics coupling model, the degradation model and the system reliability model are first constructed to analyze the reliability of the battery pack and followed by analysis examples with different redundancy strategies. By comparing the reliability of battery packs of different redundant cell numbers and configurations, several conclusions for the redundancy strategy are obtained. More notably, the reliability does not monotonically increase with the number of redundant cells for the thermal disequilibrium effects. In this work, the reliability of a 6 × 5 parallel-series configuration is the optimal system structure. In addition, the effect of the cell arrangement and cooling conditions are investigated. [ABSTRACT FROM AUTHOR]

The global issues of energy crisis and air pollution have offered a great opportunity to develop electric vehicles. However, so far, cycle life of power battery, environment adaptability, driving range and charging time seems far to compare with the level of traditional vehicles with internal combustion engine. Effective battery thermal management (BTM) is absolutely essential to relieve this situation. This paper reviews the existing literature from two levels that are cell level and battery module level. For single battery, specific attention is paid to three important processes which are heat generation, heat transport, and heat dissipation. For large format cell, multi-scale multi-dimensional coupled models have been developed. This will facilitate the investigation on factors, such as local irreversible heat generation, thermal resistance, current distribution, etc., that account for intrinsic temperature gradients existing in cell. For battery module based on air and liquid cooling, series, series-parallel and parallel cooling configurations are discussed. Liquid cooling strategies, especially direct liquid cooling strategies, are reviewed and they may advance the battery thermal management system to a new generation. [ABSTRACT FROM AUTHOR]

A thermal management system is necessary to control the operating temperature of the lithium ion batteries in battery packs for electrical and hybrid electrical vehicles. This paper proposes a new battery thermal management system based on one type of phase change material for the battery packs in hybrid electrical vehicles and develops a three dimensional electrochemical thermal model. The temperature distributions of the batteries are investigated under various operating conditions for comparative evaluations. The proposed system boils liquid propane to remove the heat generated by the batteries, and the propane vapor is used to cool the part of the battery that is not covered with liquid propane. The effect on the thermal behavior of the battery pack of the height of the liquid propane inside the battery pack, relative to the height of the battery, is analyzed. The results show that the propane based thermal management system provides good cooling control of the temperature of the batteries under high and continuous charge and discharge cycles at 7.5C. [ABSTRACT FROM AUTHOR]

To assess the economic benefits of battery reuse, the consistency and aging characteristics of a retired LiFePO 4 battery pack are studied in this paper. The consistency of battery modules is analyzed from the perspective of the capacity and the internal resistance. Test results indicate that battery module parameter dispersion increases along with battery aging. However, battery modules with better capacity consistency doesn't ensure better resistance consistency. Then the aging characteristics of the battery pack are analyzed and the main results are as follow: (1) Weibull and normal distribution are feasible to fit the capacity and resistance distribution of battery modules respectively; (2) SOC imbalance is the dominating factor in the capacity fading process of the battery pack; (3) By employing the incremental capacity (IC) and IC peak area analysis, a consistency evaluation method representing the aging mechanism variations of the battery modules is proposed and then an accurate battery screening strategy is put forward. This study not only provides data support for evaluating economic benefits of retired batteries but also presents a method to recognize the battery aging variations, which is helpful for rapid evaluation and screening of retired batteries for 2nd use. [ABSTRACT FROM AUTHOR]

Conventional cooling approaches that target either a singular tab or outer surface of common format cylindrical lithium-ion battery cells suffer from a high cell thermal resistance. Under an aggressive duty cycle, this resistance can result in the formation of large in-cell temperature gradients and high hot spot temperatures, which are known to accelerate ageing and further reduce performance. In this paper, a novel approach to internal thermal management of cylindrical battery cells to lower the thermal resistance for heat transport through the inside of the cell is investigated. The effectiveness of the proposed method is analysed for two common cylindrical formats when subject to highly aggressive electrical loading conditions representative of a high performance electric vehicle (EV) and hybrid electric vehicle (HEV). A mathematical model that captures the dominant thermal properties of the cylindrical cell is created and validated using experimental data. Results from the extensive simulation study indicate that the internal cooling strategy can reduce the cell thermal resistance by up to 67.8 ± 1.4% relative to single tab cooling, and can emulate the performance of a more complex pack-level double tab cooling approach whilst targeting cooling at a single tab. [ABSTRACT FROM AUTHOR]

A key challenge hindering the mass adoption of Lithium-ion and other next-gen chemistries in advanced battery applications such as hybrid/electric vehicles (xEVs) has been management of their functional performance for more effective battery utilization and control over their life. Contemporary battery management systems (BMS) reliant on monitoring external parameters such as voltage and current to ensure safe battery operation with the required performance usually result in overdesign and inefficient use of capacity. More informative embedded sensors are desirable for internal cell state monitoring, which could provide accurate state-of-charge (SOC) and state-of-health (SOH) estimates and early failure indicators. Here we present a promising new embedded sensing option developed by our team for cell monitoring, fiber-optic (FO) sensors. High-performance large-format pouch cells with embedded FO sensors were fabricated. This second part of the paper focuses on the internal signals obtained from these FO sensors. The details of the method to isolate intercalation strain and temperature signals are discussed. Data collected under various xEV operational conditions are presented. An algorithm employing dynamic time warping and Kalman filtering was used to estimate state-of-charge with high accuracy from these internal FO signals. Their utility for high-accuracy, predictive state-of-health estimation is also explored. [ABSTRACT FROM AUTHOR]

In recent decades, as a result of diminishing fossil fuel reserves and rising social concern, electric power systems as one of the largest sectors for environmental emissions, have had no choice but to search for alternatives to alleviate the environmental problems due to conventional generation system. Plug-in electric vehicles (PEVs) can cause difficulties in the electrical grid and system operation. To circumvent this issue, an efficacious stochastic optimization model is developed in this paper to enable the control entity to manage generation and storage assets by controlling the charging behavior of PEVs. To achieve the lowest total cost, a new strategy for reducing reliability costs is proposed. In this regard, vehicle-to-grid (V2G) tool is used to reduce the overall system cost. The presented energy management model for the MG in the grid-connected mode takes into consideration the uncertainty of output power of the wind turbine (WT) and the photovoltaic (PV), as well as the PEVs' charging and discharging. In this study, an innovative and effective optimization algorithm known as the modified fluid search optimization algorithm (MFSO) is utilized to optimize the MG operation problem. The results show that the suggested model incorporating electric vehicles (EVs) can enable MG operation at the minimum cost and reliability. As can be observed, the amount of time required by the presented method to solve the first case problem is 6.64s, which is significantly less than the amount of time required by the other methods. In a similar vein, the MFSO method is noticeably quicker than both GA and PSO when applied to case studies 2 and 3. [Display omitted] • Proposing economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles. • Presenting modified fluid search optimization to solve the proposed model. • Reporting superior solutions in Short-Term Scheduling of Micro-grid. • Proposing optimal size of Fuel cell with electricity price. [ABSTRACT FROM AUTHOR]

Battery cells and modules are critical to the reliability of the power supply system of an EV. Therefore, their reliability is mainly concern before. However, EV's power supply system is a complex system. Besides battery cells and modules, it also includes many other components, the failure of which can cause a breakdown of the system as well. So, the reliability of the battery should be evaluated from the perspective of the entire power supply system rather than only considering the reliability of the battery cells and modules. This motivates the research of this paper. In the present study, the failure rates of all major components are predicted first. Then, based on the prediction results, the reliability of the entire battery system is assessed. From the assessment results, it is found that the reliability of the entire battery system is indeed much lower than those of individual components including battery modules. Moreover, as compared to the degradation of individual components, the entire system degrades more rapidly over time. Additionally, it is interestingly found that the Battery Management System (BMS) controller is not as reliable as the battery modules in the battery system considered in this paper. • The reliability of EV's entire power supply system is assessed. • BMS controller is most vulnerable at subsystem level. • Battery cell and SMCs are most unreliable at component level. • The entire system degrades more rapidly than individual power supply components do. [ABSTRACT FROM AUTHOR]

This paper provides a methodology to assess the average surface temperature of battery cells under realistic transient scenarios. Computational fluid dynamics modelling of battery cooling is conducted for the cases exposed to the ramps of internal heat generation inferred from the standard driving cycles. The results are then post-processed to determine the effectiveness of air and water as the coolant fluids. A quantitative measure of the maximum overshoot, dimensionless settling (DST), heating (DHT), and cooling (DCT) time is subsequently presented. It is shown that, compared to water, air produces a considerably delayed response to temporal changes in the internal heat generation and is slower at reaching the new steady state condition. Cooling battery cells by using water almost always ensures remaining within the safe operating range. Nonetheless, regardless of the coolant type, the long period ramps tend to produce smaller values of DST. The primary origin of the delay is the slow heat conduction within the battery cells. In addition, it is shown that water responds to changes in the internal heat generation far quicker during the heating and the cooling phases. The study highlights the importance of transient analyses for characterising the thermal behaviour of battery packs. • A novel measure of battery heat release due to internal heat generation is presented. • Transient cases with sharp gradients cause large delays in system response. • Air cases always produce an overshoot and are far slower at reaching steady state. • Battery cells remain within the safe operating range in almost all water cases. • Water cases react far quicker to changes in the internal heat generation. [ABSTRACT FROM AUTHOR]

Environmental pollution and oil shortage issues accelerate the revival and growth of the electric vehicle (EV) industry. However, the performance and lifespan of power batteries or fuel cells (FCs) and motors of EVs are significantly affected by their operating temperature. Additionally, the cooling and heating systems in EV cabins consume a large amount of electricity, which can easily increase mileage anxiety. Thermal management solutions are therefore essential for EVs. This paper comprehensively reviews the recent development of separate thermal management solutions to respectively maintain batteries, FCs, motors and cabins of EVs within acceptable temperature ranges. The state-of-art integrated thermal management solutions which couple multiple thermal management systems are also discussed in depth. The benefits and drawbacks of each thermal management solution have been critically commented. The challenges faced by EV thermal management solutions and the development trends to address these challenges have been present. • The effects of temperature on EVs and the mechanisms of these effects are summarized. • The pros and cons of various BTM, FCTM, MTM and CTM solutions are discussed. • The configurations, advantages and limitations of different ITMSs are reviewed. • Prospects of thermal management solutions for EVs in the future are present. [ABSTRACT FROM AUTHOR]

The risk of accelerated electric vehicle battery degradation is commonly cited as a concern inhibiting the implementation of vehicle-to-grid (V2G) technology. However, little quantitative evidence exists in prior literature to refute or substantiate these concerns for different grid services that vehicles may offer. In this paper, a methodology is proposed to quantify electric vehicle (EV) battery degradation from driving only vs. driving and several vehicle-grid services, based on a semi-empirical lithium-ion battery capacity fade model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the time-varying battery temperature and working parameters including current, internal resistance and state-of-charge (SOC), while an EV is driving and offering various grid services. We use the proposed method to simulate the battery degradation impacts from multiple vehicle-grid services including peak load shaving, frequency regulation and net load shaping. The degradation impact of these grid services is compared against baseline cases for driving and uncontrolled charging only, for several different cases of vehicle itineraries, driving distances, and climate conditions. Over the lifetime of a vehicle, our results show that battery wear is indeed increased when vehicles offer V2G grid services. However, the increased wear from V2G is inconsequential compared with naturally occurring battery wear (i.e. from driving and calendar ageing) when V2G services are offered only on days of the greatest grid need (20 days/year in our study). In the case of frequency regulation and peak load shaving V2G grid services offered 2 hours each day, battery wear remains minimal even if this grid service is offered every day over the vehicle lifetime. Our results suggest that an attractive tradeoff exists where vehicles can offer grid services on the highest value days for the grid with minimal impact on vehicle battery life. [ABSTRACT FROM AUTHOR]

An Equivalent Circuit Model (ECM) of a lithium ion (Li-ion) battery is an empirical, linear dynamic model and the bandwidth of the input current signal and level of non-linearity in the voltage response are important for the model’s validity. An ECM is, however, generally parametrised with a pulse current signal, which is low in signal bandwidth (Part 1) and any non-linear dependence of the voltage on the current due to transport limitations is ignored. This paper presents a general modelling methodology which utilises the higher bandwidth and number of signal levels of a pulse-multisine signal to estimate the battery dynamics and non-linear characteristics without the need of a 3D look-up table for the model parameters. In the proposed methodology a non-parametric estimate of the battery dynamics and non-linear characteristics are first obtained which assists in the model order selection, and to assess the level of non-linearity. The new model structure, termed as the Non-linear ECM (NL-ECM), gives a lower Root Mean Square (RMS) and peak error when compared to an ECM estimated using a pulse data set. [ABSTRACT FROM AUTHOR]

Concerns have been expressed regarding the displacement of harmful emissions from internal combustion engine vehicle (ICEV) tailpipes to the sources of electricity generation used to charge electric vehicles (EV). The degree to which EVs can reduce the environmental impacts of road transportation is dependent on the fuel mix of electricity generation within a country and on the real-time charging behaviours of EV users since the fuel mix of electricity generation frequently changes. This paper analyses the varying environmental impacts of EVs based on contrasting user behaviours and compares them to the environmental impacts of ICEVs. Individual EV user profiles are developed which comprise real data emerging from charge events undertaken in Ireland, and information is gathered on the actual CO 2 intensity of the electrical grid in real-time, allowing for the carbon intensity of every individual charge event to be determined using time and date information. The results show that the environmental impacts of EVs are highly influenced by the charging behaviours of individual users, and night-time charging was found to produce the largest environmental impact. EVs are shown to be a more environmentally friendly choice of vehicle than ICEVs under a wide majority of scenarios. [ABSTRACT FROM AUTHOR]

With the growing number of electric vehicle (EV) applications, the function of the battery management system (BMS) becomes more sophisticated. The accuracy of remaining energy estimation is critical for energy optimization and management in EVs. Therefore the state-of-energy (SoE) is defined to indicate the remaining available energy of the batteries. Considering that there are inevitable accumulated errors caused by current and voltage integral method, an adaptive SoE estimator is first established in this paper. In order to establish a reasonable battery equivalent model, based on the experimental data of the LiFePO 4 battery, a data-driven model is established to describe the relationship between the open-circuit voltage (OCV) and the SoE. What is more, the forgetting factor recursive least-square (RLS) method is used for parameter identification to get accurate model parameters. Finally, in order to analyze the robustness and the accuracy of the proposed approach, different types of dynamic current profiles are conducted on the lithium-ion batteries and the performances are calculated and compared. The results indicate that the proposed approach has robust and accurate SoE estimation results under dynamic working conditions. [ABSTRACT FROM AUTHOR]

Owing to the dynamic randomness of traffic flow, driving patterns stochastically change from time to time and person to person, which inevitably triggers significant variability in the energy efficiency of electric vehicle (EV) powertrains. For optimizing the expected energy efficiency of the EV powertrain in probable driving operations while reducing the variability in energy efficiency, this paper engineers an integrated stochastic optimization (SO) method for the design scheme and control strategy of EV powertrains. To evaluate the candidate design scheme and identify the superior design scheme in random driving operations, an instantaneous optimal control and a Monte Carlo simulation-aided iterative searching process are developed and utilized as critical components of the SO. According to simulation validation, by optimizing the energy consumption in extreme operations, the SO improves the expectation of the energy efficiency of the EV powertrain by 26.4% and reduces the variability in energy efficiency by 90.4%. Moreover, the proposed SO has no side-effect on the energy consumption in typical/frequent driving operations. Even operated in the driving cycle from which the deterministic optimization (DO) result is obtained, the increase in energy consumption of the SO result is less than 5% compared with the energy consumption of the DO result. • A commuting-pattern-oriented representation of random driving conditions is developed. • An efficient instantaneous optimal control method is engineered. • Integrated stochastic optimization of powertrain design and control is engineered. • The stochastic optimization improves the expected energy economy by 12%. • The stochastic optimization reduces variability in the energy economy by 90.4%. [ABSTRACT FROM AUTHOR]

In order to predict the battery remaining discharge energy in electric vehicles, an accurate onboard battery model is needed for the terminal voltage and state of charge (SOC) estimation in the whole SOC range. However, the commonly-used equivalent circuit model (ECM) provides limited accuracy in low-SOC area, which hinders the full use of battery remaining energy. To improve the low-SOC-area performance, this paper presents an extended equivalent circuit model (EECM) based on single-particle electrochemical model. In EECM, the solid-phase diffusion process is represented by the SOC difference within the electrode particle, and the terminal voltage is determined by the surface SOC (SOC surf ) representing the lithium concentration at the particle surface. Based on a large-format lithium-ion battery, the voltage estimation performance of ECM and EECM is compared in the entire SOC range (0–100%) under different load profiles, and the genetic algorithm is implemented in model parameterization. Results imply that the EECM could reduce the voltage error by more than 50% in low-SOC area. The SOC estimation accuracy is then discussed employing the extended Kalman filter, and the EECM also exhibits significant advantage. As a result, the EECM is very potential for real-time applications to enhance the voltage and SOC estimation precision especially for low-SOC cases. [ABSTRACT FROM AUTHOR]

In this paper, a new passive thermal management system for electric vehicles is developed. A latent heat thermal energy storage with nanoparticles is designed and optimized. A genetic algorithm method is employed to minimize the length of the heat exchanger tubes. The results show that even the optimum length of a shell and tube heat exchanger becomes too large to be employed in a vehicle. This is mainly due to the very low thermal conductivity of phase change material (PCM) which fills the shell side of the heat exchanger. A carbon nanotube (CNT) and PCM mixture is then studied where the probability of nanotubes in a series configuration is defined as a deterministic design parameter. Various heat transfer rates, ranging from 300 W to 600 W, are utilized to optimize battery cooling options in the heat exchanger. The optimization results show that smaller tube diameters minimize the heat exchanger length. Furthermore, finned tubes lead to a higher heat exchanger length due to more heat transfer resistance. By increasing the CNT concentration, the optimum length of the heat exchanger decreases and makes the improved thermal management system a more efficient and competitive with air and liquid thermal management systems. [ABSTRACT FROM AUTHOR]

Extended battery system lifetime and reduced costs are essential to the success of electric vehicles. An effective thermal management strategy is one method of enhancing system lifetime increasing vehicle range. Vehicle-typical space restrictions favor the minimization of battery thermal management system (BTMS) size and weight, making their production and subsequent vehicle integration extremely difficult and complex. Due to these space requirements, a cooling plate as part of a water–glycerol cooling circuit is commonly implemented. This paper presents a computational fluid dynamics (CFD) model and multi-objective analysis technique for determining the thermal effect of coolant flow rate and inlet temperature in a cooling plate—at a range of vehicle operating conditions—on a battery system, thereby providing a dynamic input for one-dimensional models. Traditionally, one-dimensional vehicular thermal management system models assume a static heat input from components such as a battery system: as a result, the components are designed for a set coolant input (flow rate and inlet temperature). Such a design method is insufficient for dynamic thermal management models and control strategies, thereby compromising system efficiency. The presented approach allows for optimal BMTS design and integration in the vehicular coolant circuit. [ABSTRACT FROM AUTHOR]

Abstract: Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but have a limited utility due to factors including driver range anxiety and access to charging infrastructure. In this paper we apply NREL's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V) to examine the sensitivity of BEV utility to range anxiety and different charging infrastructure scenarios, including variable time schedules, power levels, and locations (home, work, and public installations). We find that the effects of range anxiety can be significant, but are reduced with access to additional charging infrastructure. We also find that (1) increasing home charging power above that provided by a common 15 A, 120 V circuit offers little added utility, (2) workplace charging offers significant utility benefits to select high mileage commuters, and (3) broadly available public charging can bring many lower mileage drivers to near-100% utility while strongly increasing the achieved miles of high mileage drivers. [Copyright &y& Elsevier]

Abstract: Employing electric vehicles as short-term energy storage could improve power system stability and at the same time create a new income source for vehicle owners. In this paper, the economic viability of this concept referred to as Vehicle-to-Grid is investigated. For this purpose, a price-responsive charging and dispatching strategy built upon temporally resolved electricity market data is presented. This concept allows vehicle owners to maximize returns by restricting market participation to profitable time periods. As a case study, this strategy is then applied using the example of Singapore. It is shown that an annual loss of S$ 1000 resulting from a non-price-responsive strategy as employed in previous works can be turned into a S$ 130 profit by applying the price-responsive approach. In addition to this scenario, realistic mobility patterns which restrict the temporal availability of vehicles are considered. In this case, profits in the range of S$ 21–S$ 121 are achievable. Returns in this order of magnitude are not expected to make Vehicle-to-Grid a viable business case, sensitivity analyses, however, show that improved technical parameters could increase profitability. It is further assumed that employing the price-responsive strategy to other national markets may yield significantly greater returns. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate an adaptive energy management and power splitting system for a fuel cell hybrid electric vehicle. The battery pack is the main power source whereas the fuel cell is considered as a range extender that cannot sustain alone the vehicle traction power. In addition, the fuel cell contributes to reduce the battery pack degradation by limiting its depth-of-discharge (DoD). This energy management system is based on a two layer architecture in which the upper layer computes the anticipated end-of-trip DoD using online mass estimation. The lower layer is designed to split the driver power demand by minimizing a cost function which includes the hydrogen/electricity cost ratio. Therefore, the best trade-off between reducing battery pack degradation and using cost effective energy is provided. Furthermore, the system allows the fuel cell to operate at its maximum efficiency. Comparative study results indicate that using online mass estimation improves the overall fuel consumption efficiency whilst contributing at the same time to DoD reduction. [Copyright &y& Elsevier]

Abstract: Depending on the actual battery temperature, electrical power demands in general have a varying impact on the life span of a battery. As electrical energy provided by the battery is needed to temper it, the question arises at which temperature which amount of energy optimally should be utilized for tempering. Therefore, the objective function that has to be optimized contains both the goal to maximize life expectancy and to minimize the amount of energy used for obtaining the first goal. In this paper, Pontryagin's maximum principle is used to derive a causal control strategy from such an objective function. The derivation of the causal strategy includes the determination of major factors that rule the optimal solution calculated with the maximum principle. The optimization is calculated offline on a desktop computer for all possible vehicle parameters and major factors. For the practical implementation in the vehicle, it is sufficient to have the values of the major factors determined only roughly in advance and the offline calculation results available. This feature sidesteps the drawback of several optimization strategies that require the exact knowledge of the future power demand. The resulting strategy's application is not limited to batteries in electric vehicles. [Copyright &y& Elsevier]

Abstract: In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban. [Copyright &y& Elsevier]

Abstract: This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models. [Copyright &y& Elsevier]

Lithium plating is one of the severest problems of lithium-ion batteries at low temperature and high charge rates, which induces severe capacity fading and may lead to safety problems. Therefore, reliable and non-destructive detection methods of lithium plating are needed for safety and reliable operation of lithium-ion batteries. In electric vehicles, lithium-ion batteries are operated under dynamic discharging conditions which exceeds the applicable scope of the existing methods. In this paper, two lithium plating detection methods suitable for offline and online use are proposed. The detection of lithium plating based on the high voltage plateau in the discharge profile at plating conditions is further extended to the dynamic discharging conditions. The offline method is applicable to data platform and has further potential to be combined with big data analysis. The online method can be operated in real-time during discharging and therefore can be used in the battery management system. Three kinds of large-format commercial batteries are used to conduct experiments to verify the effectiveness of the proposed methods. Characteristic parameters which have positive correlation with the amount of lithium plating are proposed. • Non-destructive methods to detect lithium plating under dynamic discharging process. • Two methods based on the RLS and VMD algorithms for on-line and off-line use. • Low temperature tests with three types of large format batteries to validate the methods. • Characteristic parameters to indicate the amount of lithium plating qualitatively. [ABSTRACT FROM AUTHOR]

Abstract: Reduction of the charging time for batteries is a crucial factor in the promotion of consumer interest in the commercialization of electric vehicles (EVs). Fast charging methods for EVs are therefore important to create and understand; however, fast charging is not tolerated by all lithium-ion chemistries because it typically affects the battery functionality and accelerates its aging mechanisms. A fast charging technique is proposed in this paper, and the results of extensive testing on a high power lithium iron phosphate cell subjected to the method are reported. The evaluation characterized the cell's capacity fade, cycle life, and energy efficiency with respect to the U.S. Advanced Battery Consortium (USABC) goals. The proposed charging algorithm permits a full recharging of the cell (0–100% SOC) in approximately 20 min, even after 4500 cycles are reached. The cycling scheme is characterized by high current discharges that reach specific powers of 400 W kg−1. The results show that the proposed fast charging technique does not introduce any significant degradation effects on the cell and is energy efficient; they also show that performance is lost due to capacity fade rather than an increase in internal resistance. [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of estimating the state of charge (SOC) of lithium-ion batteries. Based on the analysis of the impedance spectra obtained by electrochemical impedance spectroscopy (EIS), a simplified battery impedance model is derived with the constant phase element (CPE). To interpret the impedance model, a fractional order calculus (FOC) method is introduced to model the CPE in the impedance model. A new identification method is proposed to identify the impedance model, considering both accuracy and simplicity. The fractional Kalman filter is introduced to estimate the SOC of the lithium-ion battery based on the impedance model. Finally, a battery test bench is established, and the proposed method is verified by a scaled down system on the urban dynamometer driving schedule (UDDS) drive cycle test. [Copyright &y& Elsevier]

Abstract: Because of the diversiform driving conditions and the cell variations, it is difficult to accurately determine battery pack capacities in electric vehicles (EVs) by model prediction or direct measurement. This paper studies the charging cell voltage curves (CCVC) for the estimation of the LiFePO4 battery pack capacities in EVs. We propose the uniform CCVC hypothesis and estimate cell capacities by overlapping CCVCs using CCVC transformation. CCVCs of two LiFePO4 cells with large capacity difference are used to verify the hypothesis. We further develop an equivalent simplified approach using voltage-capacity rate curve (VCRC) and implement Genetic Algorithm (GA) to find the optimum transformation parameter for overlapping VCRCs. A small battery pack with four LiFePO4 cells in series is employed to verify the method and the result shows that the estimation errors of both pack capacity and cell capacities are less than 1%. With the proposed method, the battery pack capacity can be precisely estimated which could be used for the driving range prediction. Meanwhile, the estimated cell capacities in battery packs will significantly support the study of cell degradation and cell variations in vehicle driving conditions. [Copyright &y& Elsevier]

Abstract: Fault diagnosis for inconsistent cells in a battery pack is important for electric vehicle (EV) safety. Discovering the fault and identifying its cause on-site are extremely challenging. The literature has rarely explored such issues. This paper presents a battery pack system in a demonstrated EV with 96 cells in series and discovers the battery power fade fault during the demonstration. The preliminary analysis, after collecting data and preprocessing the typical data periods, shows that the internal or contact resistance increase causes the fault. To further identify the fault cause, we introduce an equivalent circuit model (ECM) and identify the cell resistances using the total least squares algorithm. A diagnostic method is proposed, using the identified cell resistances from 3 months demonstration data. Calculating the Shannon entropy clearly identifies the cause of the power fade fault. Appropriate measures are taken to solve the fault, and the latent safety issue is eliminated. The proposed diagnostic method can be used on-site for EV diagnosis without laboratory testing methods. [Copyright &y& Elsevier]

Abstract: Ever increasing acceptance of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs cannot have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It is beneficial if the performance gain can be evaluated online prior to replacing certain battery module regarding to actual driving. In this paper, the research objectives are two-fold. First, we propose an on-line battery module degradation diagnostic scheme using the intrinsic signals of battery module equalization. Without need for additional sensors or offline tests, this scheme is cost-effective for constructing and updating the battery pack “health map” in real time during the vehicle operation. Secondly, based on the derived battery health map, the Worthiness of Replacement (WOR) for certain modules/cells is proposed to evaluate the performance of the battery pack for customer specified trip. Such evaluation index provides a quantitative measure for module replacement and battery pack swapping Simulation study is performed to validate the proposed ideas using an actual driving cycle recorded for a commuting trip. [Copyright &y& Elsevier]

Abstract: Vehicle-to-grid (V2G) describes a system in which plug-in electric vehicles (PEV), which includes all electric vehicles and plug-in hybrid electric vehicles, utilize power by plugging into an electric power source and stored in rechargeable battery packs. PEVs significantly increase the load on the grid, much more than you would see in a typical household. The objective of this paper is to demonstrate the use of intelligent solutions for monitoring and controlling the electrical grid when connected to and recharging PEV batteries. In order to achieve this aim, the study examines the distribution of electricity in the power grid of a large-scale city so that PEVs can tap into the system using smart grid electricity. The electricity grid for the large-scale city is modelled, and it can be shown that the vehicle electrification can play a major role in helping to stabilize voltage and load. This developed grid model includes 33 buses, 10 generators, 3 reactors, 6 capacitors, and 33 consumer centers. In addition, the grid model proposes 10 parking servicing 150,000 vehicles per day. The smart grid model uses intelligent controllers. Two intelligent controllers including (i) fuzzy load controllers and (ii) fuzzy voltage controllers have been used in this study to optimize the grid stability of load and voltage. The results show that the smart grid model can respond to any load disturbance in less time, with increased efficiency and improved reliability compared to the traditional grid. In conclusion it is emphasized that smart grid electricity should contribute to PEVs accessing renewable energy. Although the V2G will play a major role in the future portfolio of vehicle technologies, but does not make much sense if the carbon content of the electricity generated by the grid will not be reduced. Thus, the recourse to renewable energy and other alternatives is crucial. The energy is stored in electrochemical power sources (such as battery, fuel cells, supercapacitors, photoelectrochemical) when generated and then delivered to the grid during peak demand times. [Copyright &y& Elsevier]

Abstract: The high cost of lithium ion batteries is a major impediment to the increased market share of plug-in hybrid electric vehicles (PHEVs) and full electric vehicles (EVs). The reuse of PHEV/EV propulsion batteries in second use applications following the end of their automotive service life may have the potential to offset the high initial cost of these batteries today. Accurately assessing the value of such a strategy is exceedingly complex and entails many uncertainties. This paper takes a first step toward such an assessment by estimating the impact of battery second use on the initial cost of PHEV/EV batteries to automotive consumers and exploring the potential for grid-based energy storage applications to serve as a market for used PHEV/EV batteries. It is found that although battery second use is not expected to significantly affect today''s PHEV/EV prices, it has the potential to become a common component of future automotive battery life cycles and potentially to transform markets in need of cost-effective energy storage. Based on these findings, the authors advise further investigation focused on forecasting long-term battery degradation and analyzing second-use applications in more detail. [Copyright &y& Elsevier]

Abstract: Electric vehicles are commonly seen as one of the alternatives to reduce the oil dependency and the greenhouse gas emissions in the transport sector. The aim of this paper is to evaluate the impact of different electric vehicle charging strategies on the national grid including the storage utilization of electric vehicles (V2G-vehicle to grid). Furthermore, an economic analysis of electric vehicle utilization is performed and the results are compared with the conventional diesel vehicle. To accomplish this aim the availability of passenger cars in Germany to be plugged into the grid showed to be high at any time over the day (>89%), which is advantageous for the V2G concept. The impact of the different electric vehicle charging strategies is investigated by employing three scenarios. The first scenario (unmanaged charging) shows that 1 mil. electric vehicles only impacts slightly on the daily peak electricity demand. In the second scenario (Grid stabilizing storage use) a maximum reductions of grid fluctuations of 16% can be achieved with the use of 1 mil. electric vehicles as storage. The last scenario (profit maximization by power trading) the maximum daily revenues from V2G activities are calculated to be 0.68 EUR2009. [ABSTRACT FROM AUTHOR]

Abstract: Imperial Racing Green is an undergraduate teaching project at Imperial College London. Undergraduate engineers have designed, built and raced hydrogen fuel cell hybrid vehicles in the Formula Zero and Formula Student race series. Imperial Racing Green has collaborated with its fuel cell partners to develop a 13kW automotive polymer electrolyte membrane fuel cell (PEMFC) system. A team of undergraduate engineers were given a relatively modest budget and less than 8 months to design and assemble an operational high-power PEMFC system. The fuel cell system was designed to provide the average power required by the team''s 2011 Formula Student entry. This paper presents the team''s experience of developing and testing an automotive fuel cell system for a race application and plans for its future development and integration onto the vehicle. [Copyright &y& Elsevier]

Abstract: This paper presents a control principle for utilizing PEM fuel cell as main power source and supercapacitors as auxiliary power source for electric vehicle applications. The strategy is based on dc link voltage regulation, and fuel cell is simply operating in almost steady state conditions in order to minimize the mechanical stresses of fuel cell and to ensure a good synchronization between fuel flow and fuel cell current. Supercapacitors are functioning during transient energy delivery or transient energy recovery. To authenticate control algorithms, the system structure is realized by analogical current loops and digital voltage loops (dSPACE). The experimental results with a 500W PEM fuel cell point out the fuel cell starvation problem when operating with dynamic load, and also confirm that the supercapacitor can improve system performance for hybrid power sources. [Copyright &y& Elsevier]

Abstract: A state-of-the-art Chinese fuel cell city bus, using a hybrid power system is proposed. This comprises a proton exchange membrane fuel cell and Ni/MH batteries to combine the high energy density of fuel cells with the high power density of batteries. A dc/dc converter is placed between the fuel cell and the battery to control the electric power flow. This paper presents a novel control strategy for efficiency of the fuel cell operation and optimization of the hybrid power system for the bus. The control strategy is able to regulate the output current of the fuel cell and the charging current or voltage for the battery while limiting the discharge current of the battery. It can achieve a higher efficiency, longer fuel cell lifetime, and higher drive performance. The hybrid fuel cell power system and the proposed control strategy were verified by using dynamometer and road test experiments. The experimental results demonstrated that the control strategy has great flexibility and generality, and also validated that the peak power capacity of the active hybrid power source and the vehicle drive performance can be significantly enhanced. [Copyright &y& Elsevier]

The objective of this paper is to create a method to size battery subsystems for an electric vehicle to optimize battery performance. Optimization of performance includes minimizing corrosion by operating at a constant current density. These subsystems will allow for easy mechanical recharging. A proper choice of battery subsystem will allow for longer battery life, greater range and performance. For longer life, the current density and reaction rate should be nearly constant. The control method requires control of power by controlling electrolyte flow in battery sub modules. As power is increased more sub modules come on line and more electrolyte is needed. Solenoid valves open in a sequence to provide the required power. Corrosion is limited because there is no electrolyte in the modules not being used. [Copyright &y& Elsevier]

Depending on the level of electrification ([PH]EV, [M]HEV) and cell format (pouch, prismatic, cylindrical), the number of individual cells in a vehicle's battery pack can span several orders of magnitude (from tens to thousands). In this paper, we develop a novel analytical framework to investigate the impact of cell-level manufacturing variability on pack performance. Statistical distributions of pack energy and pack power are derived for any NsMp pack configurations and any level of cell-to-cell variability. These distributions are used to develop vehicle-dependent cell-level manufacturing requirements. The degree to which the series direction negatively affects pack statistics, and the degree to which the parallel direction improves pack statistics, is first quantified under a random sampling scenario. PHEV packs (96s1p) are found to have the highest pack-level statistical penalty, while EV packs made of small-format cylindrical cells (96s74p) incur virtually no statistical penalty. Pack-level statistics for Ns1p packs are shown to greatly benefit from the clustering of low-performing cells. Cell binning strategies for NsMp packs present both advantages and disadvantages. The impacts of cell aging and pack-level voltage limits are further discussed. The results of this study apply even in the presence of cell balancing and under partial SOC usage. [Display omitted] • A novel analytical model is developed to relate cell variability and pack statistics. • The model is valid for any level of cell variability and any pack configuration. • Pack distribution of energy and power is derived for different vehicle segments. • Cell binning strategies to optimize pack-level statistics are presented. • The effect of cell balancing, cell aging and pack-level voltage limits are discussed. [ABSTRACT FROM AUTHOR]