Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

[1]  Jianqiu Li,et al.  The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis , 2015 .

[2]  Simona Onori,et al.  Nonlinear parameter estimation for capacity fade in Lithium-ion cells based on a reduced-order electrochemical model , 2012, 2012 American Control Conference (ACC).

[3]  Simona Onori,et al.  Optimal energy management of HEVs with consideration of battery aging , 2014, 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific).

[4]  Simona Onori,et al.  Energy Management Strategy for HEVs Including Battery Life Optimization , 2015, IEEE Transactions on Transportation Electrification.

[5]  Yang Yunyun.  Energy Management Strategy of Plug-in Hybrid Electric SUV , 2012 .

[6]  Simona Onori,et al.  Capacity and power fade cycle-life model for plug-in hybrid electric vehicle lithium-ion battery cells containing blended spinel and layered-oxide positive electrodes , 2015 .

[7]  Yann Chamaillard,et al.  Optimal Energy Management Strategy including Battery Health through Thermal Management for Hybrid Vehicles , 2013 .

[8]  Xiaohong Jiao,et al.  A modified energy management strategy based on SDP policy iteration for commuter hybrid electric vehicles , 2016, 2016 35th Chinese Control Conference (CCC).

[9]  Eric Wood,et al.  Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive Cycles , 2012 .

[10]  Sun Zechang,et al.  A new SOH prediction concept for the power lithium-ion battery used on HEVs , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[11]  Giorgio Rizzoni,et al.  Energy management strategy including battery life optimization for a HEV with a CVT , 2016, 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific).

[12]  Lide M. Rodriguez-Martinez,et al.  Cycle ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictions , 2014 .