Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

Investigating electrical contact resistance losses in lithium-ion battery assemblies for hybrid and electric vehicles

Lithium-ion (Li-ion) batteries are favored in hybrid-electric vehicles and electric vehicles for their outstanding power characteristics. In this paper the energy loss due to electrical contact resistance (ECR) at the interface of electrodes and current-collector bars in Li-ion battery assemblies is investigated for the first time. ECR is a direct result of contact surface imperfections, i.e., roughness and out-of-flatness, and acts as an ohmic resistance at the electrode-collector joints. A custom-designed testbed is developed to conduct a systematic experimental study. ECR is measured at separable bolted electrode connections of a sample Li-ion battery, and a straightforward analysis to evaluate the relevant energy loss is presented. Through the experiments, it is observed that ECR is an important issue in energy management of Li-ion batteries. Effects of surface imperfection, contact pressure, joint type, collector bar material, and interfacial materials on ECR are highlighted. The obtained data show that in the considered Li-ion battery, the energy loss due to ECR can be as high as 20% of the total energy flow in and out of the battery under normal operating conditions. However, ECR loss can be reduced to 6% when proper joint pressure and/or surface treatment are used. A poor connection at the electrode-collector interface can lead to a significant battery energy loss as heat generated at the interface. Consequently, a heat flow can be initiated from the electrodes towards the internal battery structure, which results in a considerable temperature increase and onset of thermal runaway. At sever conditions, heat generation due to ECR might cause serious safety issues, sparks, and even melting of the electrodes.     

Power management system for a fuel cell/battery hybrid vehicle incorporating fuel cell and battery degradation

Optimization of fuel cell/battery hybrid vehicle systems has primarily focused on reducing fuel consumption. However, it is also necessary to focus on fuel cell and battery durability as inadequate lifespan is still a major barrier to the commercialization of fuel cell vehicles. Here, we introduce a power management strategy which concurrently accounts for fuel consumption as well as fuel cell and battery degradation. Fuel cell degradation is quantified using a simplified electrochemical model which provides an analytical solution for the decay of the electrochemical surface area (ECSA) in the fuel cell by accounting for the performance loss due to transient power load, start/stop cycles, idling and high power load. The results show that the performance loss based on remaining ECSA matches well with test data in the literature. A validated empirical model is used to relate Lithium-ion battery capacity decay to C-rate. Simulations are then conducted using a typical bus drive cycle to optimize the fuel cell/battery hybrid system. We demonstrate that including these degradation models in the objective function can effectively extend the lifetime of the fuel cell at the expense of higher battery capacity decay resulting in a lower average running cost over the lifetime of the vehicle.     

II. A combined model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles

We combine a detailed battery model with a simple vehicle model to examine the battery size and capacity usage of a Li_xC₆/Li_y+0.16Mn_1.84O₄ cell (with a normal and artificially flat equilibrium potential) and a Li_4+3xTi₅O₁₂/Li_yFePO₄ cell. The features of cell chemistry we are concerned with are the magnitude and shape of the cell equilibrium potential and internal resistance. Our key findings include that a battery for a hybrid electric vehicle application has a capacity usage from 15 to 25% (for a minimum separator area size), and as one moves from a HEV battery to a plug-in hybrid electric vehicle battery there is a change in the slope of the separator area vs. equivalent-electric range curve due to the shape of the pulse-power capability. We also find that defining the resistance using the HPPC protocol has limitations because in general the pulse resistance depends on the applied current and pulse duration. Our detailed, combined model also shows that the benefits of a flat-potential system may be limited because of the relative positions of a flat and sloped equilibrium potential, and the lack of a driving force for the relaxation of solid-phase concentration gradients throughout the electrode. That latter effect is shown to be more significant for electrodes with a non-uniform current distribution.     

I. A simplified model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles

We develop a simplified model to examine the effect of the shape and magnitude of the battery pulse-power capability on capacity usage and battery size. The simplified model expresses the capacity usage and a dimensionless battery area in terms of a dimensionless energy-to-power ratio and a parameter that characterizes the shape of the pulse-power capability. We also present dimensional results that show how the capacity usage depends on the equivalent-electric range and separator area, and how the battery area depends on the equivalent-electric range. Key results include the presence of a Langmuir-like relationship between the capacity usage and the dimensionless energy-to-power ratio, and a linear relationship between the dimensionless energy-to-power ratio and a dimensionless area, with a slope and offset that depend on the shape of the pulse-power capability. We also found that a flat pulse-power capability curve increases capacity usage and decreases battery size, and that two important parameters for battery design are (U − V_min)V_min/R, which reflects the maximum power capability, and Q〈V〉, which reflects the battery energy. The results and analysis contained herein are used to help interpret the results from a combined battery and vehicle model, presented in a companion paper.     

Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle

A prediction-based power management strategy is proposed for fuel cell/battery plug-in hybrid vehicles with the goal of improving overall system operating efficiency. The main feature of the proposed strategy is that, if the total amount of energy required to complete a particular drive cycle can be reliably predicted, then the energy stored in the onboard electrical storage system can be depleted in an optimal manner that permits the fuel cell to operate in its most efficient regime. The strategy has been implemented in a vehicle power-train simulator called LFM which was developed in MATLAB/SIMULINK software and its effectiveness was evaluated by comparing it with a conventional control strategy. The proposed strategy is shown to provide significant improvement in average fuel cell system efficiency while reducing hydrogen consumption. It has been demonstrated with the LFM simulation that the prediction-based power management strategy can maintain a stable power request to the fuel cell thereby improving fuel cell durability, and that the battery is depleted to the desired state-of-charge at the end of the drive cycle. A sensitivity analysis has also been conducted to study the effects of inaccurate predictions of the remaining portion of the drive cycle on hydrogen consumption and the final battery state-of-charge. Finally, the advantages of the proposed control strategy over the conventional strategy have been validated through implementation in the University of Delaware's fuel cell hybrid bus with operational data acquired from onboard sensors.     

Optimal energy management strategy of fuel-cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs

Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.     

Lithium battery thermal management system for hybrid vehicles

为研究动力电池组的温度特性以及维持其工作在最佳的温度范围内,以锂离子电池为研究对象,设计了一种新型混合动力汽车的电池热管理系统,利用空调系统和发动机排气系统来调控电池组的温度。建立了锂电池组的三维瞬态产热数值模型,以电池组的三维尺寸和进风口流速为输入参数,以降低电池组的最大温升和提高电池组的温度均匀性为输出参数,利用FLUENT仿真软件和DesignXplorer模块进行联合优化设计了电池组的结构。优化后的电池组的温升比优化前降低了5.39 K,电池组温差降低了6.41 K。分析了恒倍率放电以及对流换热系数对单体电池温升的影响,研究表明：放电倍率越大电池温升越快,放电结束后电池的温度越高,在对流换热系数小于30 W/（m²·K）时,散热效果明显。对电池组在不同条件下加热或者冷却进行了仿真分析,验证了该电池热管理系统的可行性。     

Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications

This paper proposes a perfect energy source supplied by a polymer electrolyte membrane fuel cell (PEMFC) as a main power source and storage devices: battery and supercapacitor, for modern distributed generation system, particularly for future fuel cell vehicle applications. The energy in hybrid system is balanced by the dc bus voltage regulation. A supercapacitor module, as a high dynamic and high power density device, functions for supplying energy to regulate a dc bus voltage. A battery module, as a high energy density device, operates for supplying energy to a supercapacitor bank to keep it charged. A FC, as a slowest dynamic source in this system, functions to supply energy to a battery bank in order to keep it charged. Therefore, there are three voltage control loops: dc bus voltage regulated by a supercapacitor bank, supercapacitor voltage regulated by a battery bank, and battery voltage regulated by a FC. To authenticate the proposed control algorithm, a hardware system in our laboratory is realized by analog circuits and numerical calculation by dSPACE. Experimental results with small-scale devices (a PEMFC: 500-W, 50-A; a battery bank: 68-Ah, 24-V; and a supercapacitor bank: 292-F, 30-V, 500-A) corroborate the excellent control principle during motor drive cycle.     

Recent progress in lithium-ion battery thermal management for a wide range of temperature and abuse conditions

Lithium-ion batteries are important power sources for electric vehicles and energy storage devices in recent decades. Operating temperature, reliability, safety, and life cycle of batteries are key issues in battery thermal management, and therefore, there is a need for an effective thermal-management system. This review summarises the latest research progress on lithium-ion battery thermal management under high temperature, sub-zero temperature, and abuse conditions. Heat generation mechanisms are characterised under both normal and abuse conditions. Different cooling methods, which include air cooling, liquid cooling, phase change cooling, heat pipe cooling, and their combinations are reviewed and discussed. Thereafter, features of different battery heating methods such as air/liquid heating, alternate current heating, and internal self-heating are discussed. An improvement in battery safety under abuse conditions is discussed from the perspective of battery material modification and thermal management design. The research progress in recent investigations is summarised, and the prospects are proposed.     

Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part I: Initial characterizations

Evaluating commercial Li-ion batteries presents some unique benefits. One of them is to use cells made from established fabrication process and form factor, such as those offered by the 18650 cylindrical configuration, to provide a common platform to investigate and understand performance deficiency and aging mechanism of target chemistry. Such an approach shall afford us to derive relevant information without influence from processing or form factor variability that may skew our understanding on cell-level issues. A series of 1.9 Ah 18650 lithium ion cells developed by a commercial source using a composite positive electrode comprising {LiMn_1/3Ni_1/3Co_1/3O₂ + LiMn₂O₄} is being used as a platform for the investigation of certain key issues, particularly path-dependent aging and degradation in future plug-in hybrid electric vehicle (PHEV) applications, under the US Department of Energy's Applied Battery Research (ABR) program. Here we report in Part I the initial characterizations of the cell performance and Part II some aspects of cell degradation in 2C cycle aging. The initial characterizations, including cell-to-cell variability, are essential for life cycle performance characterization in the second part of the report when cell-aging phenomena are discussed. Due to the composite nature of the positive electrode, the features (or signature) derived from the incremental capacity (IC) of the cell appear rather complex. In this work, the method to index the observed IC peaks is discussed. Being able to index the IC signature in details is critical for analyzing and identifying degradation mechanism later in the cycle aging study.     

Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.     

An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack

Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as “AEKFAh”, is proposed. This method uses the adaptive Kalman filtering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.     

Optimization of powerplant component size on board a fuel cell/battery hybrid bus for fuel economy and system durability

The size of the individual powerplant components on board a fuel cell/battery hybrid vehicle affects the power management strategy which determines both the fuel economy and the durability of the fuel cell and the battery, and thus the average lifetime cost of the vehicle. Cost is one of the major barriers to the commercialization of fuel cell vehicles, therefore it is important to study how the sizing configuration affects overall vehicle cost. In this paper, degradation models for the fuel cell and the battery on board a fuel cell/battery hybrid bus are incorporated into the power management system to extend their lifetimes. Different sizing configurations were studied and the results reveal that the optimal size with highest lifetime and lowest average cost is highly dependent on the drive cycle. The vehicle equipped with a small fuel cell stack serving as a range extender will fail earlier and consume more fuel under drive cycles with high average power demand resulting in higher overall cost. However, the same configuration gives optimal results under a standard bus cycle with lower average power demand. At the other end of the spectrum, a fuel cell-dominant bus does not guarantee longer lifetime since the fuel cell operates mostly under low-load conditions which correspond to higher potentials reducing lifetime. Such a configuration also incurs a higher initial capital cost of the fuel cell stack resulting in a high average cost. The best configuration is a battery-dominated system with moderately-sized fuel cell stack which achieves the longest lifetime combined with the lowest average running cost throughout the lifetime of the vehicle.     

Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results.     

Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

The proton exchange membrane fuel cell (PEMFC) stack is a key component in the fuel cell/battery hybrid vehicle. Thermal management and optimized control of the PEMFC under real driving cycle remains a challenging issue. This paper presents a new hybrid vehicle model, including simulations of diver behavior, vehicle dynamic, vehicle control unit, energy control unit, PEMFC stack, cooling system, battery, DC/DC converter, and motor. The stack model had been validated against experimental results. The aim is to model and analyze the characteristics of the 30 kW PEMFC stack regulated by its cooling system under actual driving conditions. Under actual driving cycles (0–65 kW/h), 33%–50% of the total energy becomes stack heat; the heat dissipation requirements of the PEMFC stack are high and increase at high speed and acceleration. A PID control is proposed; the cooling water flow rate is adjusted; the control succeeded in stabilizing the stack temperature at 350 K at actual driving conditions. Constant and relative lower inlet cooling water temperature (340 K) improves the regulation ability of the PID control. The hybrid vehicle model can provide a theoretical basis for the thermal management of the PEMFC stack in complex vehicle driving conditions.     

Feasibility study and techno-economic analysis of an SOFC/battery hybrid system for vehicle applications

A feasibility study and techno-economic analysis for a hybrid power system intended for vehicular traction applications has been performed. The hybrid consists of an intermediate temperature solid oxide fuel cell (IT-SOFC) operating at 500–800 °C and a sodium–nickel chloride (ZEBRA) battery operating at 300 °C. Such a hybrid system has the benefits of extended range and fuel flexibility (due to the IT-SOFC), high power output and rapid response time (due to the battery). The above hybrid has been compared to a fuel cell-only, a battery-only and an ICE vehicle. It is shown that the capital cost associated with a fuel cell-only vehicle is still much higher than that of any other power source option and that a battery-only option would potentially encounter weight and volume limitations, particularly for long drive times. It is concluded that increasing drive time per day decreases substantially the payback time in relation to an ICE vehicle running on gasoline and thus that the hybrid vehicle is an economically attractive option for commercial vehicles with long drive times. In the case where the battery has reached volume production prices at £70 kWh⁻¹ and current fuel duty values remain unchanged then a payback time <2 years is obtained. For a light delivery van operating with 6 h drive time per day, a fuel cell system model predicted a gasoline equivalent fuel economy of 25.1 km L⁻¹, almost twice that of a gasoline fuelled ICE vehicle of the same size, and CO₂ emissions of 71.6 g km⁻¹, well below any new technology target set so far. It is therefore recommended that a SOFC/ZEBRA demonstration be built to further explore its viability.     

Modeling,control and power management of hybrid photovoltaic fuel cells with battery bank supplying electric vehicle

In this paper, modeling, control and power management (PM) of hybrid Photovoltaic Fuel cell/Battery bank system supplying electric vehicle is presented. The HPS is used to produce energy without interruption. It consists of a photovoltaic generator (PV), a proton exchange membrane fuel cell (PEMFC), and a battery bank supplying an electric vehicle of 3 kW. In our work, PV and PEMFC systems work in parallel via DC/DC converter and the battery bank is used to store the excess of energy. The mathematical model topology and it power management of HPS with battery bank system supplying electric vehicle (EV) are the significant contribution of this paper. Obtained results under Matlab/Simulink and some experimental ones are presented and discussed.     

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.     

Experimental investigation on the online fuzzy energy management of hybrid fuel cell/battery power system for UAVs

The hybrid powerplant combining a fuel cell and a battery has become one of the most promising alternative power systems for electric unmanned aerial vehicles (UAVs). To enhance the fuel efficiency and battery service life, highly effective and robust online energy management strategies are needed in real applications.In this work, an energy management system is designed to control the hybrid fuel cell and battery power system for electric UAVs. To reduce the weight, only one programmable direct-current to direct-current (dcdc) converter is used as the critical power split component to implement the power management strategy. The output voltage and current of the dcdc is controlled by an independent energy management controller. An executable process of online fuzzy energy management strategy is proposed and established. According to the demand power and battery state of charge, the online fuzzy energy management strategy produces the current command for the dcdc to directly control the output current of the fuel cell and to indirectly control the charge/discharge current of the battery based on the power balance principle.Another two online strategies, the passive control strategy and the state machine strategy, are also employed to compare with the proposed online fuzzy strategy in terms of the battery management and fuel efficiency. To evaluate and compare the feasibility of the online energy management strategies in application, experiments with three types of missions are carried out using the hybrid power system test-bench, which consists of a commercial fuel cell EOS600, a Lipo battery, a programmable dcdc converter, an energy management controller, and an electric load. The experimental investigation shows that the proposed online fuzzy strategy prefers to use the most power from the battery and consumes the least amount of hydrogen fuel compared with the other two online energy management strategies.