A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

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.