“Infoergy” is not a typo, but rather the energization of information, which is an abbreviation for “information-energy”. Vehicle energy-saving is progressively evolving toward intelligent energy management systems that integrate information interaction; however, there remains a lack of quantitative description of the coupling relationship between information quantity and energy consumption. This hinders the full utilization of information to uncover energy-saving potential in energy management decisions and prevents an objective assessment of information's contribution to enhancing system energy efficiency. Therefore, this study proposes for the first time an information-energy equation that describes the intrinsic connection between information and energy in vehicle energy management technology. Aiming at the comprehensive information representation of multi-source time-varying information within the context of vehicle energy management, this equation defines vehicle velocity as the observable random variable of working condition information to quantify its uncertainty. An energy-saving potential evaluation system is then established. Then, by utilizing information metrics and energy-saving indicators to construct a nonlinear mapping model, it reveals the impact of working condition complexity on energy consumption from the perspective of information. The validation results demonstrate that the proposed infoergy equation exhibits excellent fitting accuracy and generalization capability. It effectively reproduces energy consumption trends and possesses good physical interpretability. At the application level, compared to other methods, the new paradigm of information-energy coupling mechanisms demonstrates better energy-saving effects. It provides a novel theoretical foundation and precise quantitative tools for unlocking energy-saving potential in intelligent energy management, laying the groundwork for deep energy savings in intelligent transportation systems.
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Sui et al. (2026) studied this question.
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