Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

Abstract

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early stages of diagnostic design. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests.With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented. Keywords-Lithium-ion battery; Fault detection and isolation; Structural analysis; Statistical inference residualevaluation. IntroductionWith the development of Electric Vehicles (EVs) in recent years, the lithium-ion batteries, as the energy storage device, are gaining more and more attentions due to its inherent benefits of high energy and power density, low self-discharge rate and long lifespan [1]. To guarantee the battery safety, performance, reliability and life, a welldesigned battery management system (BMS) is required to perform the functions such as thermal management to ensure the batteries work at optimal average temperature and reduced gradient, State-of-Charge (SoC) and State-of-Health (SoH) estimations, as well as over-current, over-/under-voltage protections [2]- [6]. These critical functions are mainly dependent on the embedded current, voltage and temperature sensor measurements.

Advanced Fault Diagnosis for Lithium-Ion Battery Systems

“…The parity space method c an be used to verify this relationship by analyzing the input and output measurements of the battery system [50 ], [51]. Structural analysis theory finds and utilizes the structural over-determined part of system dynamic equations [52], and then achieves the structura l detectability and isolability analysis of faults [53]- [58]. A comparison of the above model-based methods is illustrated in Table 5.…”

“…After analyzing and evaluating the residuals, the magnitude, type, and location of faults can be dete rmined. Typical battery models for sensor fault diagnosis include the EM [160]- [162], ECM [163]- [167], lumped-parameter thermal models [168], [169], and two-state lumped parameter thermal models [52]. Lombardi et al [13] tested the electrical relationship between the current sensor and voltage sensor measurements based on Kirchhoff's law to generate residuals, and achieve the FDI of voltage and current sensors according to the battery pack structure and the residual set associated with each sensor.…”

“…Finally, the residuals are generated by checking the analytical redundancy relationship in each test. Structural analysis theory [20 ], [52], [169] can effectively reduce the workload in selecting residual generators. However, this type of analysis is easily affected by noise and model uncertainty.…”

“…Strategies combining multiple model-based methods can compensate for the inherent flaws in a single method. For example, Liu et al [52] constructed two diagnostic tests based on the structural analysis theory. Then, the residuals were generated based on EKF in each diagnostic test.…”

Battery Management System Hardware Concepts: An Overview

“…This is also illustrated in Figure 7: enabling the balancing circuit (Section 3) leads to a voltage change at the adjacent cells, whose magnitude is much larger than normal and can easily be detected by the AFE. Other types of faults, for example defective sensors, can be detected by appropriate diagnostic algorithms [75]. Knowledge of the electrical behaviour of batteries can also be used to check sensor signals for validity.…”