The design of interaction strategy between the human driver and Automated Driving System (ADS) brings great challenges to driving automation. A servo-level human-machine shared control flexible strategy (SHSC-FS) is proposed to solve the problem of how to reasonably allocate Driving Control Authority (DCA—the proportional distribution of outputs from the driver and ADS when they complete the same dynamic driving task at the same time together). The strategy can realize DCA allocation by evaluating whether the driver’s long-term ability and status can cope with the real-time environmental risk from different directions around. Through the verification test and the analysis of the natural vehicle trajectory dataset, it is found that the regionalized environmental risk faced by human drivers during driving obeys normal distribution. The real-time driving abilities (which is defined as driving status here) of human drivers also obey normal distribution. In addition, the verification tests show that SHSC-FS proposed tends to give higher DCA to the drivers with strong driving ability when they are facing low environmental risk from regions where they are sensitive and good at coping; When dealing with the high environmental risk from regions where drivers are insensitive and not good at dealing with, DCA given to the drivers with weak driving ability is lower.

Aiming at the estimation timescale selection problem in the multi-state joint estimation for state of charge (SOC) and capacity at cell level of series battery pack, an adaptive multi-timescale dynamic time-varying strategy (AMts-DtvS) is proposed. This strategy includes a triggered update strategy for the mean capacity of the battery pack, a time-varying polling update strategy for the differential SOC, and a triggered polling update strategy for the differential capacity. This strategy can adaptively adjust the timescale of multi-state joint estimation throughout the entire lifecycle of the battery pack based on the operating conditions, the degree of consistency deterioration and the change rate of capacity, achieving the goal of balancing complexity and estimation accuracy. Based on AMts-DtvS, an adaptive multi-timescale H infinity filter (AMts-HIF) algorithm is formed to achieve joint estimation for cell SOC and cell capacity of the battery pack. In the experimental section, based on 4 different datasets, the proposed AMts-HIF is compared with 3 different fixed timescale series battery pack state estimation algorithms. Through comparative verification, it can be concluded that the proposed AMts-HIF based on AMts-DtvS can obtain comparable estimation accuracy with less computational complexity in the discharge natural temperature risk scenario, Li (NiCoMn)O ₂ battery natural aging scenario, and LiFePO ₄ battery natural aging scenario. In scenarios where capacity drop/consistency deterioration due to faults/low temperatures, etc, it is possible to obtain higher accuracy with comparable complexity.

In order to solve the problem that the unsatisfactory accuracy of SOH estimation method, which seeks the relationship between battery life and external characteristics through experiments, is restricted by battery consistency in a large number of battery applications, this paper proposes an SOH estimation framework which can automatically correct the errors caused by the battery consistency problem online. The SOH framework realizes the automatic online fast correction of SOH estimation error through the designed closed‐loop feedback framework. Another advantage of this framework is that it can achieve accurate estimation for the batteries state of health (SOH) during the irregular charging and discharging process of electric vehicles. And in this framework, a new equivalent circuit based on the autoregressive (AR) model is proposed to reduce the complexity of the battery method while ensuring the accuracy of the estimation, which has better robustness in practical applications. Finally, it is proved that the online estimation of lithium‐ion batteries SOH during discharge proposed in this paper has better practicability and higher estimation accuracy by comparing with the traditional SOH method of external feature relationship.