Now the lithium ion batteries are widely used in electric vehicles (EV). The battery modeling and state estimation are of great importance. The rigorous physics-based electrochemical model is too ...complicated for on-line simulation in vehicle. In this work, the simplification of the physics-based model for application on real vehicle is proposed. An improved single particle (SP) model is introduced with high precision and the same level of computations as the original single particle model. A simplified pseudo-two-dimensional (SP2D) model is developed. The distribution of the pore wall flux is analyzed and an approximate method is developed to find the solution. The developed models are compared with rigorous electrochemical model and original SP models. The results demonstrate that the models introduced in this work could simulate the battery efficiently without too much loss of accuracy. A state of charge (SOC) estimation algorithm using the Luenberger observer with the SP2D model is proposed and shows high precision. This SOC estimation method could be used in the BMS in real vehicle.
Now the lithium ion batteries are widely used in electric vehicles (EV). The cycle life is among the most important characteristics of the power battery in EV. In this report, the battery cycle life ...experiment is designed according to the actual working condition in EV. Five different commercial lithium ion cells are cycled alternatively under 45 °C and 5 °C and the test results are compared. Based on the cycle life experiment results and the identified battery aging mechanism, the battery cycle life models are built and fitted by the genetic algorithm. The capacity loss follows a power law relation with the cycle times and an Arrhenius law relation with the temperature. For automotive application, to save the cost and the testing time, a battery SOH (state of health) estimation method combined the on-line model based capacity estimation and regular calibration is proposed.
•A dynamic cycle life experiment is designed according to the EV application and five different cells are tested.•Capacity loss is simulated using a semi-empirical model based on the experiment results and identified aging mechanism.•An on-board battery capacity loss estimation method is proposed.
This paper deals with the lateral motion control of four-wheel-independent-drive electric vehicles (4WID-EVs) subject to onboard network-induced time delays. It is well known that the in-vehicle ...network and x-by-wire technologies have considerable advantages over the traditional point-to-point communication. However, on the other hand, these technologies would also induce the probability of time-varying delays, which would degrade control performance or even deteriorate the system. To enjoy the advantages and deal with in-vehicle network delays, an H ∞ -based delay-tolerant linear quadratic regulator (LQR) control method is proposed in this paper. The problem is described in the form of an augmented discrete-time model with uncertain elements determined by the delays. Delay uncertainties are expressed in the form of a polytope using Taylor series expansion. To achieve a good steady-state response, a generalized proportional-integral control approach is adopted. The feedback gains can be obtained by solving a sequence of linear matrix inequalities (LMIs). Cosimulations with Simulink and CarSim demonstrate the effectiveness of the proposed controller. Comparison with a conventional LQR controller is also carried out to illustrate the strength of explicitly dealing with in-vehicle network delays.
Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide ...application in the area of consumer electronics. However, lithium-ion batteries for vehicles have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, uniformity and cost, imposes limitations on the wide application of lithium-ion batteries in the vehicle. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, in the hope of providing some inspirations to the design and research of the battery management system.
► This paper briefly reviews key technology of battery management system in EV. ► The composition of battery management system is analyzed. ► The battery state estimation methods are summarized and compared. ► The battery uniformity theory and equalization methods are reviewed. ► The battery fault diagnosis methods are discussed.
•Different control strategies compared for battery/supercapacitor hybrid energy systems.•A dynamic battery degradation model is adopted to evaluate the battery capacity loss.•Fuzzy-logic and ...rule-based controllers perform better along certain driving cycles.•The comparison result is validated by the dynamic programing result.•The system life cycle cost is dramatically reduced when supercapacitors are adopted.
This paper deals with the real-time energy management strategies for a hybrid energy storage system (HESS), including a battery and a supercapacitor (SC), for an electric city bus. The most attractive advantage deriving from HESSs is the possibility of reducing the battery current stress to extend its lifetime. To quantitatively compare the effects of different control strategies on reducing battery degradation, a dynamic degradation model for the LiFePO4 battery is proposed and validated in this paper. The battery size is optimized according to the requested minimal mileage, while the size of SC is optimized based on the power demand profile of the typical China Bus Driving Cycle (CBDC). Based on the optimized HESS, a novel fuzzy logic controller (FLC) and a novel model predictive controller (MPC) are proposed and compared with the existing rule-based controller (RBC) and filtration based controller (FBC), after all the controllers are tuned to their best performance along the CBDC. It turns out that FLC and RBC achieve the best performance among the four controllers, which is validated by the DP-based result. Furthermore, about 50% of the HESS life cycle cost is reduced in comparison with the battery-only configuration. In addition, the controllers are also compared along the New European Driving Cycle (NEDC), which represents another normalized driving cycle. The results show that the RBC, MPC, and FLC achieve a similar performance, and they reduce about 23% of the HESS life cycle cost when compared to the battery-only configuration. The RBC and FLC are regarded as the best choices in practical applications due to their remarkable performance and easy implementation.
When lithium-ion batteries age with cycling, the battery capacity decreases and the resistance increases. The aging mechanism of different types of lithium-ion batteries differs. The loss of lithium ...inventory, loss of active material, and the increase in resistance may result in battery aging. Generally, analysis of the battery aging mechanism requires dismantling of batteries and using methods such as X-ray diffraction and scanning electron microscopy. These methods may permanently damage the battery. Therefore, the methods are inappropriate for the battery management system (BMS) in an electric vehicle. The constant current charging curves while charging the battery could be used to get the incremental capacity and differential voltage curves for identifying the aging mechanism; the battery state-of-health can then be estimated. This method can be potentially used in the BMS for online diagnostic and prognostic services. The genetic algorithm could be used to quantitatively analyze the battery aging offline. And the membership function could be used for onboard aging mechanism identification.
•Aging mechanisms of five different battery types are analyzed by incremental capacity curve.•Aging mechanisms of LFP batteries are analyzed by charging voltage curve reproduction.•On-line identification of aging mechanism and SOH estimation of LFP batteries are discussed.
•A non-linear model regarding fuel economy and system durability of FCEV.•A two-step algorithm for a quasi-optimal solution to a multi-objective problem.•Optimal parameters for DP algorithm ...considering accuracy and calculating time.•Influences of FC power and battery capacity on system performance.
A typical topology of a proton electrolyte membrane (PEM) fuel cell electric vehicle contains at least two power sources, a fuel cell system (FCS) and a lithium battery package. The FCS provides stationary power, and the battery delivers dynamic power. In this paper, we report on the multi-objective optimization problem of powertrain parameters for a pre-defined driving cycle regarding fuel economy and system durability. We introduce the dynamic model for the FCEV. We take into consideration equations not only for fuel economy but also for system durability. In addition, we define a multi-objective optimization problem, and find a quasi-optimal solution using a two-loop framework. In the inside loop, for each group of powertrain parameters, a global optimal energy management strategy based on dynamic programming (DP) is exploited. We optimize coefficients for the DP algorithm to reduce calculating time as well as to maintain accuracy. For the outside loop, we compare the results of all the groups with each other, and choose the Pareto optimal solution based on a compromise of fuel economy and system durability. Simulation results show that for a “China city bus typical cycle,” a battery capacity of 150Ah and an FCS maximal net output power of 40kW are optimal for the fuel economy and system durability of a fuel cell city bus.
•A new battery/supercapacitor energy storage system is proposed in this paper.•A novel dynamic battery capacity fade model is employed in system optimization.•The system cost and the battery capacity ...loss are simultaneously minimized.•The battery degradation is reduced rapidly with the initial increase in SC usage.•Candidates appear in the inflection area can be regarded as the optimal solutions.
This paper proposes a semi-active battery/supercapacitor (SC) hybrid energy storage system (HESS) for use in electric drive vehicles. A much smaller unidirectional dc/dc converter is adopted in the proposed HESS to integrate the SC and battery, thereby increasing the HESS efficiency and reducing the system cost. We have also included a quantitative battery capacity fade model, in addition to the theoretical HESS model proposed in this paper. For the proposed HESS, we have examined the sizing optimization of the HESS parameters for an electric city bus, including the parallel and series number of the battery cell and the SC module. Considering the constraint of requirement on minimal mileage, the optimization goal is to simultaneously minimize (i) the total cost of the HESS and (ii) the capacity loss of a LiFePO4 battery over a typical China Bus Driving Cycle. The simulation result shows that these two objectives are conflicting, and trades them off using a non-dominated sorting genetic algorithm II. Finally, the Pareto front including optimal HESS parameter groups has been obtained, which indicates that the battery capacity loss can be reduced rapidly when the SC cost increases within the range from 10 to 40 thousand RMB.
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 (SOCsurf) 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.
•An extended equivalent circuit battery model (EECM) with enhanced accuracy in low state-of-charge (SOC) area is introduced.•The EECM uses the surface SOC (SOCsurf) to reflect the solid-phase diffusion process.•The low-SOC-area voltage estimation error could be reduced by more than 50% through the EECM.•An improved SOC estimation accuracy in low-SOC area is resulted by the EECM.
•An energy prediction (EP) method is introduced for battery ERDE determination.•EP determines ERDE through coupled prediction of future states, parameters, and output.•The PAEP combines parameter ...adaptation and prediction to update model parameters.•The PAEP provides improved ERDE accuracy compared with DC and other EP methods.
In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (ERDE) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available ERDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the ERDE directly to the current state of charge (SOC). To enhance the ERDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the ERDE prediction horizon, and the ERDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different ERDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the ERDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online ERDE prediction. The correlation of SOC estimation and ERDE calculation is then discussed to illustrate the importance of an accurate ERDE method in real-world applications.
