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Summary An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium‐ion battery's efficiency and safety. The equivalent circuit model‐based methods and data‐driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model‐based methods and data‐driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters—fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered. Finally, a suggested method is promising for online state estimation of battery working at different temperatures and initial working state. The results indicate that the maximum state of charge estimation errors of the fully connected deep network with drop methods is 0.56% for the fully charged battery. Simultaneously, with an uncertain initial state of charge, the extended Kalman filter shows the lowest maximum state of charge estimation errors (1.4%). For the state of health estimation, the optimized method uses extended Kalman filters to do the monitor first; after 5 testing points, if the state of health drops to lower than 0.95, extended Kalman filters—fully connected deep network with drop methods is suggested. And finally, estimation errors for this method decreased from 30% to 2%.

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Methane (CH4) is an important greenhouse gas. There is increasing attention to CH4 abatement strategies because of its contribution to short-term warming and strong benefits of decreasing CH4 emissions. China greenhouse gas inventory methods are used to predict CH4 emissions from the energy industry and to assess the potentials of CH4 abatement policies and techniques by 2050. The NDC scenario results show using oil and gas as transitional clean energy sources instead of coal will increase CH4 emissions from oil and gas industries at least 70%, but CH4 emissions from the coal industry will decrease 45%, meaning total CH4 emissions from the energy industry will continually decrease at least 30% in 2030 compared with 2020. Energy-related CH4 emissions might peak around 2025, ahead of CO2 emission peaking. CH4 emissions will then decrease slightly and decrease markedly after 2030. Emissions in 2050 are expected to be 32% lower than emissions in 2020. In an extreme scenario, emissions may be 90% lower in 2050 than in 2020. It is suggested that the verification system for the energy industry’s CH4 emission accounting at the national level be improved and CH4 control targets in line with national emission targets and the “14th Five-Year Plan” development stage be formulated.