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.
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