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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.

It is important to verify the requirements of battery management strategy (BMS), which is directly related to the safety of the electric vehicles. A model-in-loop test framework was established to realize the automated test based on the application scenario of BMS, including battery simulation module, battery management strategy module, test sequence module and function assessment module. The inputs of the automated test were designed in the test sequence module. The function requirements of state estimation, balance control, security protection, thermal management and fault diagnosis were designed in the function assessment module. The test results show that the proposed model-in-loop test can reach 100% of function requirements, and 87% of execution coverage. Additional automated test cases can be added to the proposed model-in-loop test, and it will be an effective method to the requirements verification of battery management strategy.

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.