Remaining available energy prediction for lithium-ion batteries considering electrothermal effect and energy conversion efficiency

Accurate remaining available energy (ERAE) prediction of lithium-ion batteries is still a challenging issue for electric vehicles, which is crucial for the prediction of remaining driving range. An approach for battery ERAE prediction is proposed considering the electrothermal effect and energy-conversion-efficiency. Firstly, a novel definition of battery State-of-Energy (SOE) is proposed based on the first law of thermodynamics to reflect the battery remaining chemical energy (ERCE) state. Secondly, due to the strong nonlinear characteristics of the battery, an adaptive Square-Root Unscented Kalman Filter is adopted to accurately estimate the battery model parameters and SOE. Finally, in order to extract ERAE from ERCE, the energy-conversion-efficiency (ECE) of the battery is studied. Since the prediction of battery SOE and ECE both depend on the future load, a Markov model is established to realize the future load prediction. To validate the proposed method, two different kinds of lithium-ion batteries are tested under dynamic conditions. The results indicate that the new method have high accuracy and good robustness. Even with 20% initial SOE error, the predicted battery SOE could quickly converge to the actual value in less than 1 min. The estimation error of battery SOE and ERAE can both be controlled within 2% under dynamic conditions.