D. Anseán

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

Lithium-ion battery degradation indicators via incremental capacity analysis

Lithium ion battery (LIB) degradation originates from complex mechanisms, usually interacting simultaneously, and in various degrees of intensity. Due to its complexity, to date, identifying battery aging mechanisms remains challenging. To resolve such issue, various techniques have been developed, including in-situ incremental capacity (IC) and peak area (PA) analysis. The use of these techniques has been proved to be valuable for identifying LIB degradation, both qualitatively and quantitatively. In addition, due to their in-situ and non-destructive nature, the implementation of these techniques is feasible for onboard, battery management systems (BMS). However, the understanding and direct applicability of IC and PA techniques is not straightforward, as it requires the understanding of electrochemical and material science principles. Unfortunately, BMS design teams rarely include battery scientists, and are mainly composed of electrical engineers. Aiming to bridge gaps in knowledge between electrical engineering and battery science, here we present a set of direct look-up tables generated from IC analysis, that provides a simple tool for the evaluation of LIB degradation modes. We begin with a brief overview of the basics of IC and PA techniques and their relation to battery degradation modes, to later present the look-up tables, and conclude with various real-life examples of cell degradation, to illustrate the use of the look-up tables. This study exemplifies the use of look-up tables for BMS applications, providing a simple, fast and accurate real-time estimation of LIB degradation modes.

Electric Vehicle Li-Ion Battery Evaluation Based on Internal Resistance Analysis

Internal resistance (IR) is considered one of the most important parameters of a battery, as it is used to evaluate the batterys power performance, energy efficiency, aging mechanisms or equivalent circuit modeling. In addition, in electric vehicle (EV) applications, the IR provides essential information related with regenerative braking capabilities, dynamic charge and discharge efficiencies, or physical degradation of the battery. This work aims to provide the insight details of the IR of a battery under several testing conditions and methods, to present its practical implications on EVs. The experimental tests are carried out on lithium iron phosphate (LFP) batteries ranging from 16 Ah to 100 Ah, suitable for its use in EVs. We study the IR dependency with batterys capacity, SOC and the charge/discharge rate; also, the convenience of using a certain IR measurement method is evaluated. Furthermore, the main results are put into context for practical EV applications, to enhance the design of battery management systems (BMS) in relation with the systems energy efficiency.

High power LiFePO 4 cell evaluation: Fast charge, depth of discharge and fast discharge dependency

High power lithium iron phosphate (LFP) batteries suitable for Electric Vehicles are tested in this work. An extended cycle-life testing is carried out, consisting in various types of experiments: standard cycling, optimized fast charge with high constant current discharge (4 C) and simulating driving dynamic stress protocols (DST). The fast charge/DST discharge tests are carried out with depth of discharge (DOD) dependency (100% DOD and partial 50% DOD discharge). A complete analysis of the cycling results is developed, showing an overall good performance of the tested batteries. In all of experiments, long term U.S. Advanced Battery Consortium goals are met: fast charging, cycle life and specific energy. Only the long term specific energy goal is not achieved, which is a drawback intrinsic in this technology. The results provide useful information for battery selection, BMS designs and other applications in EV industry.

Health Assessment of Automotive Batteries Through Computational Intelligence-Based Soft Sensors: An Empirical Study

An empirical comparison of different intelligent soft sensors for obtaining the state of health of automotive rechargeable batteries is presented. Data streamed from on-vehicle sensors of current, voltage and temperature is processed through a selection of model-based observers of the state of health, including data-driven statistical models, first principle-based models, fuzzy observers and recurrent neural networks with different topologies. It is concluded that certain types of recurrent neural networks can outperform well established first-principle models and provide the supervisor with a prompt reading of the State of Health. The algorithms have been validated with automotive Li-FePO\(_4\) cells.

Effect of aging on C/LFP battery impedance: Operating conditions to which the impedance has minimal variations

This work uses the electrochemical impedance spectroscopy to study how the battery impedance changes with aging. Tests performed with a fresh cell and another one aged at different states of charge and temperature were compared. The obtained results can be useful to determine the best possible use for a second life of the battery.

Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the electrodes of the battery. The proposed soft sensor is able to exploit the information contained in operational records of the vehicle better than the alternatives, this being particularly true when the charge or discharge currents are between moderate and high. The accuracy of the neural model has been compared to different alternatives, including data-driven statistical models, first principle-based models, fuzzy observers and other recurrent neural networks with different topologies. It is concluded that monotonic echo state networks can outperform well established first-principle models. The algorithms have been validated with automotive Li-FePO4 cells.

Multiphase resonant converter with output current multiplier for battery charger applications

This paper presents the analysis and design of a multiphase resonant converter suitable for high-current low-voltage battery charger applications. In order to reduce the conduction losses, the inverter stage of the converter is obtained from the parallel connection of N class D LCp resonant inverters. In the same way, the output stage is based on a current multiplier, obtained through the parallel connection of two current-doubler rectifiers. The regulation of the charging current is implemented at constant frequency, modifying the phase displacement of the drive signals. The battery charger is designed for a high-performance Absorbent Glass Mat (AGM) battery; at present, widely used in micro-hybrid vehicles.

DC internal resistance during charge: Analysis and study on LiFePO 4 batteries

DC internal resistance (IR) is considered one of the most important parameters of a battery, as it is used to evaluate the batterys power performance, energy efficiency, aging mechanisms or equivalent circuit modeling. In electric vehicle (EV) applications, the IR during charge gives also essential information related with regenerative braking and dynamic charge efficiency. In this work, we tested four lithium iron phosphate batteries (LFP) ranging from 16 Ah to 100 Ah, suitable for its use in EVs. We carried out the analysis using three different IR methods, and performed the tests at three charging rates (nominal, mid and high) through several states of charge (SOC). In this paper, we study the IR dependency with batterys capacity, SOC and the charging rate; also, the convenience of using a certain IR method is analyzed. Furthermore, the main results are put into context for practical EV applications, to enhance the design of battery management systems (BMS) in relation with the systems energy efficiency.