In this study, an experimental heat pump dryer was designed. The specific moisture extraction rate and moisture extraction rate were used as performance indicators to explore the influence of environmental factors and the style of the hot air cycle on heat pump drying. The average temperature and humidity in Nanjing’s summer, winter, and throughout the whole year were taken as the experimental ambient temperature and humidity. Garlic slices 3 mm thick, with an initial moisture content of 66.714% w.b., were dried until the end moisture content was 10% w.b. Experimental results and thermal analysis showed that the open and semi-open heat pump dryers were greatly affected by ambient temperature and humidity. The closed heat pump drying system was greatly affected by the bypass air rate.

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State of charge (SOC) estimation of deep-discharging Li-ion batteries under complicated working conditions at different temperatures is still challenging. Nowadays, the depth of discharge (DOD) of batteries in electric vehicles (EVs) is generally low, resulting in the insufficient use of battery energy. This paper proposes a SOC estimation method using a novel partial adaptive forgetting factors recursive least square (PAFFRLS), which adjusts the forgetting factors based on the own physical properties of each parameter in equivalent circuit models (ECMs) to accommodate to greatly changing under deepdischarging range and high dynamic working conditions. The gain matrix in the proposed method is split to update independently according to each parameter, which solves the issue of mutual influence between parameters vary with different rates. In addition, four typical test profiles, including DST, UDDS, US06, and EUDC, are employed to simulate different working conditions of EVs. Eventually, numerous simulations and experiments results at different temperatures are employed to verify the validity of the proposed method. All average errors of the SOC estimation under four different kinds of working conditions are less than 1.3% as well as all peak errors are less than 5%. All peak errors are less than 3% while DOD is larger than 90%, which illustrates the effectiveness of the proposed method in the case of deep-discharging and provides better guidance to the design of battery management system (BMS) in EVs.INDEX TERMS Deep-discharging Li-ion battery, battery equivalent circuit analysis, state of charge estimation, partial adaptive forgetting factors, parameter identification.

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