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SOC (State of charge) plays an important role in vehicle energy management, utilization of battery pack capacity, battery protection. Model based SOC estimation algorithm is widely regarded as an efficient computing method, but battery model accuracy and measuring noise variance will greatly affect the estimation result. This paper proposed an adaptive mixed estimation algorithm. In the algorithm, the recursive least squares algorithm was used to identify the battery parameters online with a second-order equivalent circuit model, and an adaptive unscented Kalman method was applied to estimate battery SOC. In order to verify the effect of the proposed algorithm, the experimental data of a lithium battery pack was applied to build a simulation model. The results show that the proposed joint algorithm has higher estimation accuracy and minimum root mean square error than other three algorithms.

A control strategy for searching the optimal working interval based on multi-parameter logic threshold is proposed for the power system of distributed hybrid electric vehicles. The battery state of charge working area and boundary velocity threshold are combined with the optimal engine working curve. Offline simulation of 0-32 km/h acceleration performance is conducted. To further verify the validity of generating C code, a hardware-in-the-loop (HIL) test platform based on MATLAB/RTW/xPC target is built. Real-time simulation and real-time performance comparison test are performed. Test results show that the designed multi-parameter logic threshold control strategy achieves reasonable allocation of energy and improves the dynamic performance of vehicles. The xPC HIL simulation test system is feasible and provides a fast test verification method for vehicle control strategy development.

Battery state estimation is one of the most important decision parameters for lithium battery energy management. It plays an important role in improving battery energy utilization, ensuring battery safety and enhancing system reliability. This paper is proposed to provide a dynamic correction of SOC in the full working condition, including static condition and dynamic condition. Based on the Coulomb-counting method, the current SOC value of the battery is calculated. Under the static conditions, the open circuit voltage of the battery is used to directly collect the initial SOC. Under the dynamic working conditions, the open circuit voltage of the battery is estimated by the sliding mode observer. Based on the deviation between the calculated and estimated values of the open circuit voltage, the current coefficient of the Coulomb-counting method is dynamically corrected by PI strategy.